Policy Note Food Inflation in the Lao PDR: Trends, Drivers, and Impacts Policy Note Food Inflation in the Lao PDR: Trends, Drivers, and Impacts © 2024 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington, DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved. This work is a product of the staff of the World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. 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Special thanks are also due to Keomanivone Phimmahasay and Phetnidda Ouankhamchan for their valuable inputs for the study. The authors appreciate the Lao Statistics Bureau for providing the data used in this note, as well as the Ministry of Planning and Investment, the Ministry of Agriculture and Forestry, the Ministry of Industry and Commerce, and the Macroeconomic Research Institute for their invaluable insights, which have greatly enriched the content of this work. Cover design: Janet Pontin, Layout: Paul Bloxham, Photo: World Bank © Philippe Aramburu Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts iii Contents Key Messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Trends in Food Price Inflation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3. Drivers of Food Price Inflation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 4. Spatial Price Transmission: The Case of Rice, Pork, and Chicken . . . . . . . . . . . . . . . . . . . . . . 12 5. Welfare Implications of Food Price Inflation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 6. Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Annex 1: Price Transmission Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Annex 2: Welfare Cost of Exchange Rate Depreciation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 iv Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Key Messages Food price inflation in the Lao PDR has surged into double digits since mid-2022, outpacing global and regional trends. Exchange rate depreciation caused food inflation to accelerate more rapidly than core inflation. Rice exhibits greater volatility compared to overall food prices and other unprocessed foods. Food inflation increased at a faster pace in the northern region, which is characterized by provinces with a deficit in staple foods and a higher reliance on food imports. Domestic food prices are influenced by a combination of external and domestic factors. When a country extensively imports or exports food, its domestic food markets become integrated with international food markets. Domestic prices are often determined by international prices, the exchange rate, and transaction costs. For those food items that are not traded across borders, domestic prices are mainly shaped by domestic factors such as levels of production and consumption, although the exchange rate may still impact prices through fluctuating import prices of inputs. The depreciation of the Lao kip has significantly contributed to recent food inflation. Exchange rate depreciation results in higher prices for imported food and agricultural inputs. From 2000 to 2013, the exchange rate had minimal influence on domestic food prices. However, its impact has become increasingly evident since 2014 as Laos has integrated more deeply into global markets. The pass-through effect of the kip/dollar exchange rate appears to be stronger than that of the nominal effective exchange rate. Specifically, during the period from 2017 to 2023, a 1 percent depreciation in the kip/dollar parallel exchange rate led to a 1.1 percent rise in food prices, compared to 0.87 percent when using the nominal effective exchange rate. The impact of international food and oil prices in driving domestic food inflation has been negligible. The transmission of international prices for rice to local markets reflects growing market integration and rising trade values. From 2017 to 2023, a 1 percent increase in the international price of Thai glutinous rice and a 1 percent depreciation of the kip led to a 0.5 and 1.1 percent increase, respectively, in the Lao domestic price of second-quality glutinous rice. This has made rice prices not only vulnerable to weather-related shocks across the region but also to fluctuations in the exchange rate and global prices. No evidence of international to local price transmission is observed for pork and chicken, reflecting low amounts of trade between Laos and international markets. Instead, domestic factors likely play a predominant role in determining prices for these two staple meats. The degree and speed of international-to-local price transmission varies across provinces. Pass-through from changes in international prices for rice is stronger and faster in Savannakhet and Bokeo—provinces that tend to export or import rice. Conversely, there is no evidence of international-to-local price transmission in provinces like Sekong and Khammuan, where rice is mainly grown for subsistence rather than for sale abroad. While there is little pass-through in prices for chicken at the national level, the knock-on effects of rising international prices can be observed locally in Champassak, Oudomxay, Luang Prabang, Bokeo, and Savannakhet. Limited domestic market integration means that changes in prices for food commodities vary across Laos. When domestic food markets are integrated, food can move freely, and prices are transmitted across provinces. In Laos, due to information asymmetries, inefficient market structures, and high logistical costs and barriers, domestic food markets are not fully integrated, resulting in only partial price transmission. Consequently, food prices vary significantly across provinces, influenced by local factors. Despite sufficient rice production at the national level, consumers experience high price volatility and encounter higher prices in certain regions. Food inflation contributes to food insecurity and poverty, but its impacts on welfare vary across socioeconomic groups. Higher food prices have ambiguous effects: while they increase the cost of food consumption, they also raise agricultural profits and wages. Due to price increases and estimated changes in various income sources, an additional 11 percent of the urban population and 11 percent of the rural population may have fallen into poverty between 2018 and 2023. Urban households are expected to have Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts v been hit harder by inflation, as they tend to be net food buyers and rely heavily on wages and non-farm business incomes, which have struggled to keep pace with rising prices. However, rural households already had lower incomes, and although some have benefited from higher agricultural incomes, a large proportion of rural households remain at risk of falling into poverty. Policy recommendations: The findings of this report emphasize the need to move beyond short-term policy responses and adopt a comprehensive strategy focused on restoring macroeconomic stability, mitigating the impact of food inflation on vulnerable households, capitalizing on higher agricultural prices, and improving food market efficiency to enhance food price stability. Restoring macroeconomic stability. Given the significant impact of exchange rate depreciation on food inflation and poverty, policies to stabilize the currency are the most urgently needed. Measures to increase foreign currency reserves and reduce debt repayments—such as expediting debt renegotiations and mobilizing additional forex revenue—are essential to alleviate pressure on the exchange rate. Scaling up targeted income support measures. Price controls have proven inadequate in curbing price increases in Laos, especially for staples like rice and chicken, and could inadvertently lead to market distortions and shortages of goods when inflation stems from structural issues. This untargeted measure has prevented the government from helping those most in need. Income support measures are essential to assist those severely impacted by food inflation, particularly poor urban households. The Ministry of Agriculture and Forestry’s Helping Hand Programme has proven effective in mitigating the impact of food inflation on nutritional outcomes among such households and should be scaled up. Leveraging high agricultural prices to boost productivity and raise incomes for the poor. While high food prices can stimulate agricultural investment and commercialization, unlocking this potential requires strategic policy interventions to support poor farmers. For example, ensuring access to credit and agricultural inputs, providing targeted in-kind support and cash transfers, and offering training and technical assistance on farming techniques and commercial practices. This would enable farmers to expand production, invest in agricultural machinery, and capitalize on market opportunities in response to higher prices. Moreover, infrastructure development plays a crucial role in this process, with greater investments needed in irrigation systems, storage facilities, and roads to ensure access to and linkages between markets. Enhancing nationwide market integration and facilitating price transmission. Enhancing food market integration and price transmission—while enabling food to move freely in and out of the country and between regions based on price signals—would facilitate more efficient resource allocation, reducing food price volatility and spatial disparities. To achieve this, it is crucial to improve agricultural market monitoring to mitigate information asymmetries. Meanwhile, in the medium term, improving connective infrastructure to reduce logistical barriers and costs will further consolidate market integration. Using price signals is less distortive than quantity controls, which can have unintended negative consequences. Measures like the rice balancing scheme and export controls could unintentionally limit the benefits of higher agricultural prices by discouraging farmers from expanding production and improving farm productivity. Meanwhile, import restrictions may temporarily raise domestic prices, especially if local producers encounter high input and transportation costs, and if local production cannot quickly ramp up to offset the reduced imports. 1 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 1. Introduction Food price inflation in Laos has surged into double digits since mid-2022, surpassing global and regional trends. Although a global upswing in food prices commenced in late 2021, Laos experienced a notably sharper increase than its neighbors. Lao food inflation peaked in May 2023 at 53 percent year-on-year: the highest level seen in over two decades, and well above median global food inflation at 10 percent. Food price inflation remained elevated at 24 percent in March 2024, even as international commodity prices eased. Historically, while Lao food inflation tends to increase in tandem with non-food inflation, it accelerates more rapidly and surpasses core inflation during periods of heightened price growth. High food prices impact the welfare of both consumers and agricultural producers. Holding all other factors constant, increasing food prices diminish households' purchasing power. In Laos, this impact is particularly pronounced among low-income urban households, which dedicate a larger proportion of their expenditures to food purchases. Low-income households tend to allocate a larger share of their consumption to food, while rural households rely more on self-production than food purchases. Food constitutes over two-thirds of consumption for the poorest quintile of Lao households. Among these households, food expenditure accounts for only 20 percent of rural households' total consumption, compared to 40 percent among urban homes. The welfare of farmers is also significantly influenced by high food prices. Increased agricultural prices have the potential to improve their incomes, but higher input costs can erode profits. While high food prices can foster longer-term economic growth by stimulating agricultural investment and commercialization, these positive effects hinge on the implementation of appropriate policies and the development of infrastructure. This policy brief examines the causes and impact of food inflation in Laos, aiming to inform policy actions to address the issue. Our comprehensive analysis combines descriptive and econometric methods. In addition to estimating the potential impact of food inflation on household welfare and food insecurity, the analysis empirically examines the factors that contribute to food price inflation at the national level. Furthermore, it investigates the price formation and transmission mechanics of key commodities such as rice, pork, and chicken, both domestically and in international markets. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 2 2. Trends in Food Price Inflation Lao food inflation began to deviate from global and regional trends in mid-2022. Following extreme rainfall and flooding in 2018, domestic food prices started to rise in 2019, and remained high throughout 2020 due to severe drought and the COVID-19 pandemic. Yet food inflation in Laos remained comparable to that of regional peers, which also experienced extreme weather events (Figure 1, Figure 2). Inflation eased in 2021 before picking up again later in the year, following sharp increases to global food prices induced by the pandemic. However, food inflation accelerated dramatically to diverge from global and regional trends around mid-2022, peaking in May 2023 at 53 percent year-on-year. Since then, food inflation has fallen somewhat but remains in double digits. Figure 1: Lao and global food inflation (percent) Figure 2: Lao and regional food inflation (percent) 55 55 45 45 35 35 25 25 15 15 5 5 -5 -5 Jan-18 Jan-19 Jan-21 Jan-23 Jan-18 Jan-19 Jan-21 Jan-23 Jan-20 Jan-22 Jan-24 Jan-20 Jan-22 Jan-24 Jul-18 Jul-19 Jul-21 Jul-23 Jul-18 Jul-19 Jul-21 Jul-23 Jul-20 Jul-22 Jul-20 Jul-22 World (median) Laos Cambodia Myanmar Laos Thailand Vietnam Source: Lao Statistics Bureau, General Statistics Office of Vietnam, The Source: Lao Statistics Bureau, Food and Agriculture Organization Corporate Ministry of Commerce of Thailand, National Bank of Cambodia, Food and Statistical Database. Agriculture Organization Corporate Statistical Database. Note: Year-on-year food price inflation is calculated from the food Note: Year-on-year food price inflation is calculated from the food consumer price index in national currencies. consumer price index in national currencies. The divergence has been largely driven by the rapid depreciation of the kip. Unlike the increase in food prices during 2019–2020, which was primary driven by climatic shocks and supply disruptions, the recent deviation from global and regional trends has been significantly influenced by exchange rate depreciation, albeit with some lag (Figure 3). This is consistent with empirical evidence on the exchange rate pass-through to headline inflation in Laos. According to an IMF (2023) study covering December 2004 to December 2022, the exchange rate was the most significant contributor to headline inflation. Short-term exchange rate pass-through was estimated at around 25 percent, while the cumulative 12-month effect stood at approximately 50 percent. This suggests that roughly two-thirds of inflation can be attributed to exchange rate depreciation. Lao inflation was already susceptible to exchange rate shocks prior to the recent rapid depreciation. According to AMRO (2020), which analyzed the pass-through of the exchange rate into inflation in Laos between 2000 and 2019, the exchange rate has historically accounted for a significant proportion of the variation in the CPI and its sub-components (e.g., core, non-core, domestic, and import). Moreover, the study found that depreciation had a more pronounced and persistent impact on inflation than appreciation. 3 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Food inflation rose faster than core inflation in response to exchange rate depreciation. The food and non-alcoholic beverage consumer price index (food CPI) has lately increased at a much faster pace than the core consumer price index (core CPI)1. From August 2022 to October 2023, year-on-year monthly food price inflation averaged 41 percent, exceeding core inflation at 29 percent and headline inflation at 34 percent (Figure 4). Exchange rate pass-through into domestic food prices can occur via imports of final food products and intermediate inputs. Laos imports a diverse array of such goods from neighboring countries, with imported items amounting to 32 percent of the CPI basket. Significant exchange rate pass-through to food inflation is therefore to be expected, a topic further explored in Section 3. Figure 3: Year-on-year changes in food CPI and Figure 4: Headline, core, food, and real food exchange rate (percent) inflation (year-on-year, percent) 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 -10 -10 Jan-18 Jan-19 Jan-21 Jan-23 Jan-20 Jan-22 Jan-24 Jul-18 Jul-19 Jul-21 Jul-23 Jan-18 Jan-19 Jan-21 Jan-23 Jan-20 Jan-22 Jan-24 Jul-20 Jul-22 Jul-18 Jul-19 Jul-21 Jul-23 Jul-20 Jul-22 Food CPI Kip/$ parallel Kip/$ reference Food Headline Core Food (real terms) Source: Lao Statistics Bureau. Note: Real food inflation is defined as food inflation minus headline inflation. This indicator is reported by the World Bank’s Food Security Source: Lao Statistics Bureau, World Bank. Update to rank countries by food inflation in nominal and real terms. Rice prices exhibit greater volatility than overall food prices and other unprocessed foods. Rice inflation increased notably across 2019–20 as a result of extreme weather events and the pandemic. Furthermore, it rose at a significantly faster rate than food inflation due to rapid depreciation of the kip since May 2022. The annual inflation rate for rice peaked at 74 percent in March 2023 before declining to 21 percent a year later. The meat, fish, fruit, and vegetable price indices showed less volatility, closely tracking the exchange rate and overall food prices (Figure 5). The annual inflation rate of meat and fish rose to 46 percent in April 2023, before steadily declining to 18 percent in March 2024. During periods of high food inflation, food prices increased at a faster pace in the northern region. During the high food inflation period of 2019–2020 and from May 2022 onwards, food inflation climbed to slightly higher levels in the north (Figure 6). The region is characterized by provinces with a deficit in staple foods and a higher reliance on food imports. Despite growing demand for food due to increased economic activity, the north remains the area with the lowest food production in Lao (See Section 4). However, between January 2021 and April 2022, food prices in the south rose at a faster pace than in other regions. In such regions that are self-sufficient or boast a surplus in staple foods, food prices tend to respond more significantly to factors affecting domestic production, particularly movement control measures implemented during the pandemic in 2020–2021. 1 Food CPI is mostly driven by food prices: non-alcoholic beverages account for only 2 percent of the food CPI weight. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 4 Figure 5: Inflation by food group (year-on-year, Figure 6: Food inflation by region (year-on-year, percent) percent) 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 -10 -10 Jan-18 Jan-19 Jan-21 Jan-23 Jan-18 Jan-19 Jan-21 Jan-23 Jan-20 Jan-22 Jan-24 Jan-20 Jan-22 Jan-24 Jul-18 Jul-19 Jul-21 Jul-23 Jul-18 Jul-19 Jul-21 Jul-23 Jul-20 Jul-22 Jul-20 Jul-22 National Rice National North Meat and fish Fruit and vegetables Central South Source: Lao Statistics Bureau, World Bank. Source: Lao Statistics Bureau, World Bank. 5 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 3. Drivers of Food Price Inflation Previous literature has identified how prices are transmitted across markets through trade. Trade is associated with transaction costs incurred in moving goods between markets. When the price differential between two markets exceeds the transaction cost, trade occurs. The markets become integrated, and the difference in their prices equals the transaction cost of moving the goods between those markets (Meyer 2006). The equilibrium price in the smaller market is typically determined by the equilibrium price in the dominant market and transaction costs. For a small open economy, domestic food prices are influenced by a combination of external and domestic factors. If domestic factors (such as supply, demand, productivity, and input costs) result in a food price higher than the import parity price (international food price plus transaction costs), importing food becomes cheaper than domestic production. The country becomes a net importer, and domestic consumers face the import parity price. If domestic factors result in a food price lower than the export parity price (international food price minus transaction costs), exporting food is more profitable than selling it at domestic prices. The country becomes a net exporter, and domestic consumers face the export parity price. In both cases, trade occurs. The international and domestic food markets are integrated, and the domestic price is largely determined by the international price, transaction costs, and exchange rate. However, if domestic factors result in a food price falling between import and export parity prices, neither importing nor exporting food is profitable. In this case, trade is not induced, and the domestic price is primarily determined by domestic factors. When transaction costs (such as tariffs, non-tariff measures, and transportation costs) are high, the gap between import and export parity prices widens, making this scenario more likely. Country-specific characteristics determine the extent to which international prices are transmitted to domestic prices. These factors include openness to foreign trade, local market conditions, dependence on imported food and agricultural inputs, and exchange rate dynamics. When a country is a net food importer, increases in global food and oil prices and currency depreciations lead to higher import costs, exacerbating inflationary pressures. This is especially true for countries that heavily rely on imports to meet domestic food demand. Even in cases where a country does not directly import food, exchange rates and international prices for commodities such as oil, seeds, and fertilizer indirectly impact food prices through agricultural input costs. Meanwhile, domestic labor costs and the money supply also contribute to the dynamics of food prices in the country (Figure 7). Figure 7: Drivers of food prices Price transmission Non-price determinants External Demand Supply • Global food prices • Population growth • Productivity • Global input prices • Income growth • Production decisions (oil, seeds, fertilizer) • Dietary changes • Weather and climate • Exchange rate • Speculation; hoarding • Pests and diseases • Seasonality • Seasonality Domestic • Agriculture and trade policies • Labor costs • Market structure and information • Money supply • Logistics Source: Authors’ illustration based on World Bank (2015) and World Bank (2020). Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 6 When the international and domestic markets are not integrated, domestic supply and demand factors play a significant role in determining domestic food prices. When the country does not engage in international trade, domestic food prices are primarily influenced by local factors. On the demand side, population growth and rising incomes drive higher consumption needs, while changes in dietary preferences determine the composition of food demand. In the short term, speculative trading, hoarding, and stockpiling in anticipation of future shortages or price increases can reduce the availability of food and exacerbate price volatility. On the supply side, productivity levels and technological advances determine the country’s food production capacity. Production decisions made by farmers and agribusinesses—such as shifting crop selection and land use practices away from food crops toward cash crops—can put pressure on the food supply. Furthermore, weather-related shocks and pest infestations can reduce yields, leading to immediate shortages and price spikes. Seasonal patterns such as harvest cycles or demand peaks during holidays or festivals can also influence short-term price fluctuations throughout the year. Agriculture and trade policies, market structure, and logistics also influence domestic food prices. Subsidies to farmers can reduce production costs, leading to increased supply and lower prices for certain products, while tariffs or import quotas may limit imports, potentially raising domestic prices. The level of competition within the market determines the pricing power of key players along the food supply chain, including farmers, producers, traders, and retailers, with more competitive environments typically resulting in lower prices. In addition, access to accurate market information fosters more-efficient production decisions and price setting. Improved logistics can also reduce transportation and distribution costs and minimize food spoilage and wastage, resulting in lower food prices for consumers. Price transmission Food prices in Laos have diverged notably from global trends, particularly since mid-2022. Amid the onset of the pandemic, monthly domestic food price inflation averaged 8.7 percent year-on-year in 2020, more than double the global average of 3.3 percent. Between January 2021 and May 2022, world food prices rose by 38.8 percent, outpacing the modest rate of 11.6 percent in Laos. However, a dramatic divergence then occurred. Between June 2022 and December 2023, Lao food prices rose by 54.4 percent, contrasting with a 12.4 percent decline in world prices (Figure 8). To better understand these inflationary trends, Vector Error Correction Model (VECM) and Autoregressive Distributed Lag Model (ARDL) analyses were conducted for different subperiods between 2000 and 2023, using three exchange rates: the nominal effective exchange rate (NEER), the official kip/dollar exchange rate, and the parallel kip/dollar exchange rate (Figure 9).2 During the 2000-2023 period, the depreciation of the kip was a significant driver of food inflation. Empirical evidence from the VECM and ARDL analysis for the 2000–2023 period indicates that a 1 percent depreciation in the nominal effective exchange rate leads to a 0.56-0.59 percent increase in the Lao food CPI (Table 1). It took between 18 and 25 months for full pass-through to occur. This slow price adjustment suggests that the pass-through mechanism combines both direct transmission to food prices and indirect transmission to prices for agricultural inputs—such as fertilizer, fuel, and animal feed—which takes longer to materialize. As Laos has become more integrated into the global market, the exchange rate has lately played a larger role in shaping domestic food prices. The test for structural breaks suggests that the shift in price transmission dynamics might have begun in December 2013. The ARDL analysis was therefore conducted for sub-periods. It suggests that while the exchange rate did not significantly influence domestic food prices in Laos during 2000–2013, its impact has grown since then as the country is exporting and importing more food (Table 1 and Figure 10A). The pass-through of the kip/dollar exchange rate appears to be stronger than that of the nominal effective exchange rate. During 2017–2023, a 1 percent depreciation in the kip/dollar parallel exchange rate led to food inflation of 1.09 percent, compared to 0.87 percent when using the nominal effective exchange rate. 2 Lao employs a parallel exchange rate system: a market-determined rate, and an official reference rate provided by the Bank of the Lao PDR. Commercial banks are required to set their exchange rates based on the reference rate. The gap between the official reference exchange rate and the parallel market rate has widened in recent years. Parallel exchange rate data are available from January 2017. 7 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Figure 8: Key price indices (2015=100) Figure 9: Exchange rates 250 21000 1.6 200 19000 1.4 17000 150 1.2 15000 13000 1.0 100 11000 0.8 9000 50 0.6 7000 0 5000 0.4 May-07 May-18 Nov-01 Nov-12 Sep-03 Sep-14 Mar-09 Mar-20 Jan-00 Jan-11 Jan-22 Jan-01 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-12 Jan-14 Jan-16 Jan-18 Jan-20 Jan-22 Jul-05 Jul-16 Lao food CPI NEER Kip/$ reference Kip/$ parallel Oil prices World food prices 1/NEER (right axis) Source: International Monetary Fund, Lao Statistics Bureau, and Darvas (2021). Note: Nominal effective exchange rate (NEER) for 51 trading partners. An increase in NEER indicates appreciation of the home currency against the basket of trading partners' currencies. Food and Beverage Price Index for world food prices. Brent crude oil price index for oil prices. Table 1: International-to-local food price transmission Period Exchange Method Adjustment World food Exchange Int. oil Money rate speed price rate price supply (months) Jan 2000 – NEER VECM 25 0.30*** 0.56*** -0.17*** 0.40*** Dec 2023 NEER ARDL 18 -0.10 0.59*** 0.02 0.52*** Kip/$ ARDL 20 0.11 0.67*** 0.08 0.51*** (reference) Jan 2000 – NEER ARDL No significant long-run relationship Dec 2013 Kip/$ ARDL No significant long-run relationship (reference) Jan 2014 – NEER ARDL 8 -0.20** 0.59*** -0.03 0.63*** Dec 2023 Kip/$ ARDL 7 -0.02 1.26*** -0.07*** -0.51** (reference) Jan 2017 – NEER ARDL 9 -0.34*** 0.87*** 0.01 0.20** Dec 2023 Kip/$ ARDL 7 0.02 1.50*** -0.08** -0.88** (reference) Kip/$ ARDL 9 -0.26** 1.09*** 0.00 -0.26 (parallel) Note: The table shows a percentage change in Lao food CPI or a percentage point change in Lao food inflation associated with a one percent increase in world food prices, international oil prices, money supply, and a one percent depreciation in the kip. The results shown in this table draw on long-run cointegrating relationships. Short-run coefficients are reported in the Annex tables. Speed of adjustment is the number of months for a full pass-through to occur. *, **, and *** denotes significance at 10%, 5% and 1% level. See Annex 1 for more details on VECM and ARDL methodology for price transmission and estimation results (Table A. 2, Table A. 3, Table A. 4). Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 8 International commodity prices have a limited impact on food prices in Laos. Global oil and food prices rose in mid-2020 amid the COVID-19 pandemic and rising geopolitical tensions, before starting to decline in 2022. In contrast, since mid-2022, Laos has witnessed its most rapid increase in food prices in two decades. Empirical evidence from the ARDL analysis for the 2014–2023 period indicates that the cost of food in Laos is not substantially influenced by the global oil and food market per se. Instead, a faster increase in domestic food and fuel prices compared to international prices since May 2022 has been driven by a rapid depreciation of the kip. This depreciation has deepened the impact of international commodity price changes on domestic food prices in Laos, which otherwise would have been negligible. Table 2: International-to-local food commodity price transmission Period Exchange Commodity Adjustment Int. food Exchange Int. oil Money rate speed price rate price supply (months) Jan 2000 – NEER Rice No significant long-run relationship Dec 2016 Chicken No significant long-run relationship Pork 9 0.05 0.38*** 0.05*** 0.28*** Jan 2017 – NEER Rice 5 0.45*** 1.13*** 0.06 0.13 Jun 2023 Chicken 6 0.16 0.74*** -0.03 0.25** Pork No significant long-run relationship Jan 2017 – Kip/$ Rice 6 0.58*** 1.45*** 0.17 -0.28 Jun 2023 (parallel) Chicken 6 -0.00 0.80*** -0.03 0.10 Pork 4 0.11 0.46*** -0.09** 0.39*** Note: The table shows a percentage change in local commodity prices associated with a one percent increase in international commodity prices, international oil prices, money supply, and a one percent depreciation in the kip. International prices are those of Laos’ main trading partners for which data is available: Thai rice, German pork, and Thai chicken. Thai chicken prices are available for 2010-2023. The results shown in this table draw on long-run cointegrating relationships from ARDL estimation. Short-run coefficients are reported in the Annex tables. Speed of adjustment is the number of months for a full pass-through to occur. *, **, and *** denote significance at 10%, 5%, and 1% level, respectively. See Annex 1 for more details on ARDL methodology for price transmission and estimation results (Table A.5, Table A.6, Table A.7). Nevertheless, the transmission of rice prices from international to local markets has become more evident in recent years, reflecting growing market integration and rising trade values. Empirical evidence suggests that while the exchange rate and international rice prices did not significantly influence domestic rice prices in Laos during 2000–2016, their impact has recently started to tell. The ARDL analysis for 2017–2023 indicates that a 1 percent increase in the international price of Thai glutinous rice and a 1 percent depreciation of the kip lead to a 0.58 and 1.45 percent increase, respectively, in the domestic price of second-quality glutinous rice. The adjustment speed is also fast, requiring about 6 months for full pass-through to occur. International-to-local price transmission is still not evident for chicken and pork, however, potentially due to the lack of integration between the domestic and international markets.3 Between 2016 and 2021, the average total trade value (imports plus exports) for rice amounted to $70 million, but only $1 million and $6 million for chicken and pork, respectively (Figure 10B, Figure 10C, Figure 10D). While the kip/dollar exchange rate plays a role, potentially by affecting production and transportation costs, pork and chicken prices in Laos are primarily determined by domestic factors. 3 The transmission of chicken prices from international to local markets can be observed in selected provinces (Section 4). 9 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Figure 10: Food import and export values (in $ millions) A. Food B. Rice 1000 1000 7070 800 800 4040 600 600 2020 400 400 00 200 200 (20) (20) -- (40) (40) (200) (200) (400) (400) (60) (60) (600) (600) (80) (80) 2010 2010 2011 2011 2010 2010 2011 2011 2012 2012 2019 2019 2020 2020 2021 2021 2012 2012 2019 2019 2020 2020 2021 2021 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018  Exp Exp – China – China Imp Imp – Thailand  – Thailand Imp Imp – Vietnam – Vietnam Imp Imp – ROW – ROW Exp Exp – Vietnam – Vietnam  Exp Exp – Thailand – Thailand  Exp Exp – Thailand – Thailand  Exp Exp – – Vietnam Vietnam Imp – Imp ROW – ROW  Imp Imp – – Thailand Thailand Imp – Imp United – United States States  Exp Exp – – China China  Imp Imp – China – China Exp Exp – ROW – ROW Net – export – export Net Exp Exp – ROW – ROW Imp Imp – Vietnam – Vietnam Net Net – export – export C. Chicken D. Pork 22 2 22 2 00 0 11 1 (2) (2) (2) 00 0 (4) (4) (4) (1) (1) (1) (6) (6) (6) (8) (8) (8) (2) (2) (2) (10) (10) (10) (3) (3) (3) 2010 2010 2011 2011 2010 2011 2010 2010 2011 2011 2010 2011 2012 2012 2012 2019 2019 2020 2020 2019 2021 2021 2020 2021 2012 2012 2012 2013 2013 2014 2014 2013 2015 2015 2014 2016 2016 2015 2017 2017 2016 2018 2018 2017 2018 2013 2013 2014 2014 2013 2015 2015 2014 2016 2016 2015 2017 2017 2016 2017 2019 2019 2020 2020 2019 2021 2021 2020 2021 2018 2018 2018 Exp Exp–– China China Exp – ChinaImp  Imp–– Thailand Thailand Imp – Thailand Imp Imp–– Vietnam Imp – Vietnam Vietnam Exp Exp––China Exp China– China Imp  Imp–– Imp Germany Germany  – GermanyImp Imp –– Imp Thailand – Thailand Thailand   Exp Exp ––Thailand Exp Thailand –   Exp Thailand Exp––Vietnam Exp Vietnam –  Vietnam  Imp Imp –– Imp ROW ROW – ROW   Exp Exp –– Thailand Exp Thailand –   Thailand Exp Exp –– Vietnam Vietnam Exp –  Vietnam  Imp Imp –– Imp ROW ROW – ROW Imp Imp––China Imp – China China Exp  Exp–– ROW Exp ROW – ROW Net Net –– export export Net – export Imp Imp––Vietnam Vietnam Imp  – VietnamExp  Exp–– Exp ROW ROW – ROW Net Net export –– export Net – export Source: World Integrated Trade Solution. STIC Rev. 4 for food (01-09), rice (042), chicken (174, 123), and pork (175,122). Note: ROW = Rest of World. Non-price determinants Despite a high level of exchange rate pass-through, food prices in Laos are less affected by international commodity prices, with domestic factors more likely to play a significant role. Factors such as demand and supply dynamics, agriculture and trade policies, market structure, and logistics conditions can impact food prices through various channels. Especially for food items with low international trade volume—such as pork and chicken—where international and local markets are not fully integrated, domestic prices are largely determined by home-grown factors, including food supply and demand. In cases where food items are imported or exported and there is some degree of integration between international and local markets, trade policies can affect domestic prices. For example, import substitution policies designed to protect domestic production—such as import tariffs or quotas—may lead to temporarily higher domestic prices, especially if domestic production cannot quickly expand to meet the shortfall caused by reduced imports. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 10 A growing population and increasing incomes have put upward pressure on food demand. The population has increased by more than one million in the past decade, while real average income (as measured by GDP per capita in constant prices) has swelled by 66 percent. With more people enjoying greater purchasing power, the overall demand for food has increased. If this increase in demand is not matched by a corresponding increase in food supply, it could eventually lead to tighter market conditions and subsequent price increases. While consumers tend to change their dietary patterns when incomes rise, such changes so far appear to be minimal in Laos. Between 2012 and 2018, the share of rice, cereals, bread, and fish in household food consumption decreased from 51 percent to 46 percent, whereas that of dairy and eggs, fruit and vegetables, and other foods increased from 26 percent to 31 percent. Meat consumption went mostly unchanged (LSB and World Bank 2020). Insufficient production is constraining the growth of food supply. Both absolute and per capita paddy and vegetable production have declined in the past decade (Figure 11A, Figure 11D). Per capita paddy production began declining in 2015 and experienced a sharp drop in 2018 due to severe flooding.4 Although per capita production rebounded to 508 kg in 2022, it remains well below the 2015 level of 632 kg. As a result, despite being considered self-sufficient in rice and a rice exporter, the country had to import rice in certain years when production fell short Figure 11: Domestic Food Production A. Paddy B. Chicken 5.0 650 60 7.0 4.8 4.6 55 600 6.5 4.4 50 4.2 4.0 550 45 6.0 3.8 3.6 40 500 5.5 3.4 35 3.2 3.0 450 30 5.0 2015 2016 2017 2018 2019 2020 2021 2022 2015 2016 2017 2018 2019 2020 2021 2022 Total (le axis, million tonne) Total (le axis, million headcount) Per capita (right axis, kg) Per capita (right axis, headcount) C. Pig D. Vegetables 5.0 0.60 1.8 290 1.7 270 4.5 0.55 250 1.6 230 4.0 0.50 1.5 210 1.4 190 3.5 0.45 1.3 170 3.0 0.40 1.2 150 2015 2016 2017 2018 2019 2020 2021 2022 2015 2016 2017 2018 2019 2020 2021 Total (le axis, million headcount) Total (le axis, million tonne) Per capita (right axis, headcount) Per capita (right axis, kg) Source: Lao Statistics Bureau, Ministry of Agriculture and Forestry. 4 Laos was severely affected by floods between July and September 2018. Tropical Storm Son-Tinh caused heavy rains and flooding in 55 districts across 13 provinces. A breach in the Xe Pien-Xe Nam Noy hydropower saddle dam caused an unprecedented flash flood in Attapeu. Agriculture, including crops, fisheries, livestock, forestry, and irrigation, were hardest hit, amounting to losses in terms of value (GoL, 2019). 11 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts of consumption (Figure 10B). Moreover, per capita vegetable production experienced a 40 percent decline from 259 kg to 156 kg between 2015 and 2021. Despite a steady increase in meat production (Figure 11B, Figure 11C), the production of pork and chicken falls short of consumption levels: with deficits of 2 percent and 10 percent of per capita consumption of 13.5 kg and 6.5 kg per year, respectively. (See Section 4). While Laos is a net exporter of other food products—including fruit, vegetables, roots and tubers, cereals, coffee, tea, and spices—the country is a net importer of animal products, meat, and dairy, with consumers paying high prices for them. Sluggish agricultural productivity growth, changes in land use, and extreme weather events have held back food production. Productivity has remained persistently low over the past decade, with the value added per worker in the agricultural, forestry, and fishery sector only 10 percent that of the industrial and service sectors. Paddy rice yields declined from 4.25 metric tons per hectare in 2015 to 3.95 tons per hectare in 2022. The prevalence of subsistence and smallholder agriculture—coupled with the lack of agricultural infrastructure and limited technological adoption—has hindered productivity growth, undermining competitiveness compared to neighboring countries (Figure 12). At the same time, farmers have increasingly allocated land to commercial crops for export such as cassava and rubber, leading to a gradual decrease in the area cultivated for rice and vegetables since 2015 (Figure 13). While commercialization has lifted farmer incomes, if this decline in land allocated for food production for the local market is not matched by improvements in yields or agricultural productivity, food supply could be undermined, potentially driving prices upwards. Figure 12: Agriculture, forestry, and fishing, value Figure 13: Land utilization (10,000 hectares) added per worker (constant 2015 $) 160 3600 3150 140 2700 120 2250 100 1800 80 1350 60 900 40 450 20 0 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2015 2016 2017 2018 2019 2020 2021 2022 Cambodia Laos Myanmar  Rice  Vegetables  Maize  Starchy foods Thailand Vietnam Source: World Development Indicators. Source: Lao Statistics Bureau, Ministry of Agriculture and Forestry. Laos is highly susceptible to extreme weather events, and climate change could further disrupt crop production. The severe floods of 2018, for example, resulted in a steep decline in rice production. Climate change could further hamper food production through alterations to carbon dioxide availability, rainfall patterns, and temperatures, as well as more frequent extreme weather events (World Bank, 2021). Rice, the staple food, is particularly vulnerable to high overnight temperatures, and minimum night-time temperatures are expected to rise much faster in Laos than average temperatures. One study has suggested that the influence of climate change on temperature and rainfall patterns could depress local rice yields by between five and 20 percent by the 2040s (Li, Wang, and Chun 2017). Other indirect effects of climate change include impacts on water resource availability and seasonality, transformations in soil organic matter, soil erosion, changes in pests and diseases, the arrival of invasive species, and loss of arable areas due to flooding or desertification. Soil erosion is evident in declining agricultural land productivity in Laos, with some areas experiencing losses of up to 50 percent (World Bank, 2019). Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 12 4. Spatial Price Transmission: The Case of Rice, Pork, and Chicken Domestic food market integration and price transmission have positive welfare impacts. First, they help stabilize food prices by aligning supply and demand across provinces, reducing price volatility faced by both consumers and producers. Secondly, they facilitate the efficient allocation of resources, enabling food to move from surplus to deficit areas. Third, they improve food security by ensuring a more even distribution of food across the country. Finally, they enhance farmer income by providing access to markets with higher prices. However, when domestic food markets are not fully integrated—whether due to information asymmetries or logistical costs and barriers— price transmission remains incomplete. This implies that food prices and the welfare impact of price increases can vary widely across provinces, influenced by local factors. This section explores the spatial dimension of price transmission for staple foods—namely rice, pork, and chicken—to shed light on how incomplete price transmission may affect Lao households across provinces. International-to-local price transmission of staple foods varies across provinces. Empirical evidence from the Mean Group (MG) estimation for panel data covering 2016 to 2023 indicates that rice price transmission is strongest and fastest in Savannakhet and Bokeo (Figure 14). In Savannakhet, for example, a one percent increase in the international price of Thai glutinous rice leads to a 0.60 percent increase in the price of second-quality glutinous rice, with price adjustment completed in less than three months. This suggests that residents in these provinces are more susceptible to international price shocks. Meanwhile, in Sekong and Khammuan, prices are almost entirely determined by domestic factors. Despite no evidence for international-local price transmission for chicken at the national level, such a phenomenon can be observed in Champassak, Oudomxay, Bokeo, and Savannakhet, with price adjustment completed in approximately five months, and in Luang Prabang, where prices are adjusted in under three months (Figure 15). These provinces are likely to import chicken from Thailand. There is no evidence of international-to-local transmissions for pork either at the national nor the provincial level, reflecting the low trade value of pork. Figure 14: International-to-local price transmission Figure 15: International-to-local price by province (rice) transmission by province (chicken) 0.5 0.5 Savannakhet Luang Prabang 0.4 Luang Namtha 0.4 Xieng Khuang Bokeo Champassak Adjustment speed Adjustment speed Xayaboury 0.3 Huaphan Oudomxay 0.3 Attapeu Vientiane Province Borikhamxay Oudomxay Champassak Vientiane Capital Bokeo 0.2 0.2 Phongsaly Savannakhet Luang Prabang Saravan 0.1 0.1 Sekong Other provinces Khammuan 0 0 0 0.2 0.4 0.6 0.8 0 0.5 1 1.5 2 Price transmission degree Price transmission degree Source: Authors’ calculation using the Mean Group (MG) panel data estimation (17 provinces, 2016–2023). Note: Adjustment speed is 1/(the number of months required for a full pass-through to occur). The larger the value, the faster the adjustment. Price transmission degree is the percentage change in local prices in response to a 1 percent increase in international prices. See Annex 1 for more details on MG panel data estimation and estimation results (Table A.8). 13 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts The domestic food market in Laos is not fully integrated, meaning that price changes in one market are not necessarily transferred to another. The VECM analysis across four key provinces (Vientiane Capital, Luang Prabang, Savannakhet, and Champassak) suggests that there is a certain degree of market integration for glutinous rice, but not for chicken and pork. For glutinous rice, a 1 percent price increase in Savannakhet and Vientiane Capital corresponds to a 0.78 and 0.63 percent increase in Champassak. The adjustment takes approximately two and a half months for a full pass-through to occur, indicating almost full integration (Table A.9). The domestic food market is more integrated within the southern region. Price transmission analysis was conducted within the northern region (Luang Namtha, Bokeo, Oudomxay, Xayaboury), central region (Vientiane Province, Borikhamxay, Khammuan, Savannakhet), and southern region (Saravan, Sekong, Champassak, Attapeu). The VECM results indicate a strong degree of market integration for pork in the north and for sticky rice and chicken in the south. The pork markets in Luang Namtha, Bokeo, and Oudomxay are linked, taking approximately 14 months for a full price pass-through to occur (Table A.10). In the southern region, glutinous rice markets are linked in all four provinces, with a price adjustment period of less than two months. For chicken, integration is observed among three provinces—Saravan, Sekong, and Attapeu—requiring approximately nine months for price differences to adjust (Table A.12). Food production varies greatly across provinces, with some exhibiting a surplus and others experiencing shortages. The country has consistently produced rice at approximately 21 percent above consumption levels over the last three years.5 However, there are significant differences in rice sufficiency levels among provinces. Net production in the north is lower than that required for consumption (Figure 16A and Figure 16B): six out of the seven northern provinces face a deficit of roughly 31 percent of the estimated per capita consumption of 206 kg per year. Conversely, provinces in the central and southern regions typically enjoy a rice surplus, although three central provinces and one in the south experience a deficit of about 26 percent. The situation with vegetables is different, with a national surplus of 40 percent over the estimated annual per capita consumption of 131 kg, but deficits in several provinces across all regions. At the national level, both pork and chicken production fall short of consumption, with respective deficits of 2 percent and 10 percent of the estimated per capita consumption of 13.5 kg and 6.5 kg per year. The sufficiency levels of pork and chicken also vary across regions. The central region has the largest deficit: approximately 36 percent and 41 percent for pork and chicken consumption, respectively (Figure 16C and Figure 16D). Rice prices in the deficit provinces are higher than in surplus provinces. The price of second-quality sticky rice in Luang Prabang (highest) was almost double that of Sekong (lowest). Since Luang Prabang contains some of the most-visited tourism sites in Laos, it often has higher prices than other parts of the country. However, second-quality sticky rice in Oudomxay is still about 25 percent more expensive than in Sekong. Figure 17 and Figure 18 show the negative relationship between per capita production and prices: the higher per capita production, the lower the price. This suggests that prices are largely influenced by local supply. A lack of market integration and incomplete price transmission could hurt consumers and producers alike, as consumers struggle to take advantage of low prices in surplus regions, while producers are unable to take advantage of higher prices in deficit regions. Potential reasons for incomplete price transmission include transportation costs, market structure, and asymmetric information. Transportation costs in Laos are relatively high for the region, reflecting poor road conditions, lack of competition in the trucking industry, and the absence of domestic consolidation and logistics facilities (World Bank, 2022). High transportation costs can hinder the transmission of prices from one market to another. In uncompetitive markets, prices may not be transmitted as efficiently as in a more competitive one. Furthermore, when buyers have more information about market conditions than sellers, they may be able to negotiate lower prices, which can result in incomplete price transmission. 5 In 2022, Laos exported $44.7 million worth of rice. The top five primary destinations were China, Vietnam, Belgium, Australia, and France (OCE World https://oec.world/en/profile/bilateral-product/rice/reporter/lao). Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 14 Figure 16: Net supply of rice, vegetables, pork, and chicken by province (% of est. per capita consumption) A. Rice B. Vegetables Phongsaly Phongsaly Luang Luang Namtha Namtha Bokeo Luang Bokeo Luang Oudomxai Huaphan Oudomxai Huaphan Prabang Prabang Xayaboury Xayaboury Xieng Xieng Khouang Khouang Xaysomboun Xaysomboun Vientiane Vientiane Province Province Borikhamxay Borikhamxay Vientiane Vientiane Capital Khammuane Capital Khammuane Savannakhet Savannakhet Rice Vegetables  80 – 125  80 – 400  40 – 80 Saravan  40 – 80 Saravan Sekong Sekong  0 – 40  0 – 40 Champassak Champassak  -40 – 0 Attapeu  -40 – 0 Attapeu  -80 – -40  -80 – -40 C. Pork D. Chicken Phongsaly Phongsaly Luang Luang Namtha Namtha Bokeo Luang Bokeo Oudomxai Huaphan Oudomxai Luang Huaphan Prabang Prabang Xayaboury Xayaboury Xieng Xieng Khouang Khouang Xaysomboun Xaysomboun Vientiane Vientiane Province Province Borikhamxay Borikhamxay Vientiane Vientiane Capital Khammuane Khammuane Capital Pork Savannakhet Chicken Savannakhet  80 – 200  80 – 100  40 – 80 Saravan  40 – 80 Saravan Sekong Sekong  0 – 40  0 – 40 Champassak Champassak  -40 – 0 Attapeu  -40 – 0 Attapeu  -80 – -40  -80 – -40 Source: Authors’ calculation based on data from Lao Statistics Bureau, World Food Programme, and Food and Agriculture Organization. Note: Net supply is calculated by subtracting consumption from production and then dividing the result by consumption. A positive figure implies excess supply, while a negative figure indicates excess demand. Consumption estimates are derived by multiplying per capita consumption by the total population in each province. The annual estimated per capita consumption figures for rice, vegetables, pork, and chicken are 206kg, 131kg, 13.5kg, and 6.45kg, respectively (World Food Programme 2023, Food and Agriculture Organization Corporate Statistical Database). See Annex Table A. 13 and Table A. 14 for detailed calculations. 15 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Figure 17: Price and production of glutinous Figure 18: Price and production of chicken by paddy rice by province province 24 120 LB 22 110 BK Average price (thousand kip/kg), 2023 Average price (thousand kip/kg), 2023 100 20 90 18 XK HP XK LT 80 16 LT LB SR XR PL BK 70 OX PL XR CS SR VC HP 14 OX VC AP 60 KM SK AP SV BX VT KM BX SV 12 CS SK 50 VT 10 40 0.1 0.3 0.5 0.7 0.9 1.1 0 5 10 15 Per capita production (tonnes), 2022 Per capita production (headcount), 2022 Source: Lao Statistics Bureau. Source: Lao Statistics Bureau. Note: AP=Attapeu, BK=Bokeo, BX=Borikhamxay, CS=Champassak, HP=Huaphan, KM=Khammuan, LB=Luang Prabang, LT=Laung Namtha, OX=Oudomxay, PL=Phongsaly, SV=Savannakhet, SR=Saravan, SK=Sekong, VC=Vientiane Capital, VT=Vientiane Province, XR=Xayaboury, XK=Xieng Khuang Improving transportation infrastructure, market competition, and market information can help address these issues. The government has built new roads and bridges to connect rural and urban areas, but there are still many parts of the countryside that lack adequate transportation infrastructure, making it difficult for farmers to access markets. The government has also supported the creation of cooperatives to help small farmers to access markets, and established market information centers to provide farmers with the latest data on market prices and trends. However, challenges to increased market competition remain, including limited access to credit, technical assistance, and technology. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 16 5. Welfare Implications of Food Price Inflation Food inflation can severely damage wellbeing, particularly for vulnerable populations, and has likely tipped many Lao households into poverty since 2018. As food prices rise, households may spend a larger portion of their income on food, reducing their ability to afford other essential goods and services. For low- income families already living on tight budgets, higher food prices can exacerbate financial strain, leading to reduced consumption of nutritious foods, limited dietary diversity, and increased food insecurity. This can have long-term implications for health and education outcomes: particularly for children and pregnant women, who require adequate nutrition for proper growth and development. Purchased food makes up a third of household consumption in Laos, making Lao families susceptible to high food inflation. Urban, low-income households are the most vulnerable, with purchased food accounting for nearly 40 percent of their consumption (Figure 19). This is compounded by their limited financial cushion for coping with rising food prices. While food overall represents a larger proportion of consumption for rural households, purchased food comprises a smaller share: particularly for the rural poor, who mainly produce their own. But inflation may still affect this group indirectly through rising costs of inputs such as seeds, fertilizers, and fuel. Meat and fish constitute the largest share of food consumption for the average Lao household, followed by rice. Meat and fish; rice and grains; fruit and vegetables; and eggs and processed foods make up 38 percent, 28 percent, 20 percent, and 14 percent of food consumption, respectively (Figure 20). However, households in the richest quintile allocate a higher share of their food budget to meat and fish and eggs and processed foods— together accounting for almost 70 percent of their food consumption. By contrast, rice holds primary importance in the diets of less well-off households, accounting for 44 percent of food consumption among the poorest quintile, compared to 20 percent among the wealthiest. Given that rice prices are more volatile than other food groups, urban low-income households—nearly all of which buy rice for consumption—will likely be hit harder by high inflation. Figure 19: Purchased/self-produced food (% share Figure 20: Consumption food group (% share of household consumption) of food consumption) 15 8 10 13 20 16 20 16 19 26 16 34 16 15 15 15 14 14 14 12 29 26 13 18 57 47 37 30 11 27 12 21 32 36 17 34 39 42 5 38 36 47 36 36 39 39 42 43 33 34 34 45 26 31 31 38 35 38 33 20 28 30 25 28 18 22 2 3 4 2 3 4 2 3 4 2 3 4 Richest Richest Poorest Poorest Richest Richest Poorest Poorest Lao Consumption quintile Consumption quintile Lao Consumption quintile Consumption quintile PDR Urban Rural PDR Urban Rural  Purchased food  Self-produced food  Rice and Grains  Meat and Fish  Fruit and vegetables  Eggs and processed food Source: Authors’ calculation based on the Lao Expenditure and Consumption Survey 2018/19 (LECS 6). Note: Households are grouped into quintiles based on their per capita consumption nationwide ranking. Consumption share is calculated using the sum of consumption for each household group. 17 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts While Laos has an agriculture-based economy with a significant number of farming households, most households are net food buyers. Rising food prices have mixed effects on households: net sellers of food can benefit from higher prices, whereas the purchasing power of net buyers decreases. In Laos, over 80 percent of Lao households are net food buyers, while only 18 percent are net food sellers (Figure 21A), meaning the effects of food inflation tend towards the negative. Urban households—who buy nearly all of their food—are especially vulnerable to food inflation. Figure 21: Net buyer/seller status (% of households) A. Food B. Rice and grains 1 1 0 0 0 0 3 1 1 0 0 12 6 3 12 18 17 15 21 16 24 27 25 26 32 4 41 41 39 47 43 39 9 64 57 11 17 12 12 16 87 94 97 24 81 82 85 79 84 84 24 70 74 73 67 20 47 50 57 19 43 45 23 29 33 16 2 3 4 2 3 4 2 3 4 2 3 4 Richest Richest Richest Richest Poorest Poorest Poorest Poorest Lao Consumption quintile Consumption quintile Lao Consumption quintile Consumption quintile PDR Urban Rural PDR Urban Rural  Net buyer  Net seller  Self-su iciency  Net buyer  Net seller  Self-su iciency C. Meat and fish D. Fruit and vegetables 5 5 3 1 1 2 8 5 2 1 9 6 5 2 2 11 7 9 5 4 17 12 15 4 2 17 11 6 8 13 9 8 9 15 17 12 28 9 7 9 15 43 7 96 7 89 88 90 95 94 85 87 85 87 94 84 84 77 78 77 80 73 80 68 66 49 2 3 4 2 3 4 2 3 4 2 3 4 Richest Richest Richest Richest Poorest Poorest Poorest Poorest Lao Consumption quintile Consumption quintile Lao Consumption quintile Consumption quintile PDR Urban Rural PDR Urban Rural  Net buyer  Net seller  Self-su iciency  Net buyer  Net seller  Self-su iciency Source: Authors’ calculation based on LECS 6. Note: Households are grouped into quintiles based on their per capita consumption nationwide ranking. Consumption share is calculated using the sum of consumption for each household group. Autarky denotes households whose net buying or selling values are zero. However, Laos could capitalize on the potential of higher food prices to raise the income of poor, rural households. The proportion of net food sellers is notably higher among rural and low-income households, with 26 percent of rural households from the bottom 60 percent falling into this category. Among these households, 21 percent are net sellers of rice, and 15 percent are net sellers of meat and fish (Figure 21B, Figure 21C, Figure 21D). Moreover, a significant share of households from the bottom 40 percent grow just enough rice and vegetables for their own consumption. With appropriate policy interventions aimed at stimulating agricultural investment and commercialization (e.g., infrastructure development), high food prices could be made to translate into greater earnings for poor farmers and their families. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 18 Food inflation has exacerbated the impact of COVID-19 on food insecurity. Among the most common strategies adopted by Lao households to cope with high food inflation are scaling up their own-food production (70 percent), switching to cheaper food (59 percent), and reducing food consumption (48 percent). The prevalence of food insecurity increased from 13 percent of households in late 2021 to 32 percent in January 2023 (Figure 22). This surge coincided with rising food price inflation, and more households reportedly running low on food or going hungry. Food insecurity settled at 29 percent in February 2024 as food inflation eased, but remained well above 2021 levels. Persistently high food inflation in Laos is expected to have long-term effects on nutrition and health. Figure 22: Moderate to severe food insecurity (% of households) 60 50 40 30 20 10  Food inflation 0 Food insecurity Jul-21 Jul-22 Jul-23 Oct-21 Oct-22 Oct-23 Jan-21 Apr-21 Jan-22 Apr-22 Jan-23 Apr-23 Jan-24 Source: Authors’ calculation from World Bank rapid monitoring phone survey rounds 2,4,6,8 conducted in November-February to minimize the seasonal effect. Note: Based on the method developed by the Food and Agriculture Organization (FAO) for analysis of Food Insecurity Experience Scale (FIES) data. FIES is a measure of household food security using self-reported behaviors and experiences associated with increasing difficulties in accessing food due to resource constraints. The reference period is 30 days. The net effect of food price changes varies across socioeconomic groups. Considering food price changes alone (assuming non-food prices and income from sources other than food production remain unchanged), the additional spending required to maintain food consumption from December 2018 to December 2023 is estimated at 14 percent of household consumption for Lao households (Figure 23).6 This figure rises to between 24 and 40 percent for urban households, and is negligible for the bottom 60 percent of rural households, most of whom are net food sellers. Net buyers of rice and grains on average face a larger impact than buyers of meat and fish or fruit and vegetables, partly due to the steeper increase in rice prices and the fact that these households tend to be net buyers of other food items. For net food sellers, additional income from selling food is worth about 76 percent of household consumption, partially offsetting the extra spending required to cover other food purchases. This figure is lower—at around 40 percent— for net sellers of rice and grain, meat and fish, and fruit and vegetables. The likelihood of different socioeconomic groups falling into poverty was estimated, taking into account the impact of non-food inflation and potential changes in non-farm income. The most recent poverty data available for Laos is from 2018. To estimate the likelihood of different socioeconomic groups falling into poverty due to inflation and projected income changes associated with inflation, the 2018 poverty line is inflated using food (115 percent) and non-food inflation (87 percent) between December 2018 and December 2023.7 Additionally, the 2018 consumption aggregate is adjusted to reflect estimated increases in household income: i) household farm income is adjusted based on product sales and changes in product prices, with input costs (fuel, tractor services, fertilizer, and wages) assumed to represent 20 percent of total sales and to increase at the same rate as fuel prices; ii) self-produced food value is adjusted using raw food inflation, with input costs accounting for 10 percent of total values; iii) non-farm business income is estimated to increase at a rate equal to 64 percent of the average increase in farm income, as suggested by the World Bank phone survey; iv) wage income is assumed to increase by 45 percent, mirroring the rise in the minimum wage; and v) other non-labor income, such as financial income and transfers, is assumed to rise at the same rate as overall inflation. 6 Due to the lack of farm-gate price data in Laos, it is assumed that the ratio between consumer and farm-gate prices remains unchanged. 7 The simulation abstracts from potential income changes from other factors, such as COVID-19. 19 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Figure 23: Additional spending required to maintain food consumption due to food price increases (% of household consumption) 60 40 20 0 -20 -40 -60 -80 -100 2 3 4 2 3 4 Poorest Richest Richest Meat and Fish Meat and Fish Poorest Food Food Rice and Grains Rice and Grains Fruit and Vegetables Fruit and Vegetables Lao Consumption quintile Consumption quintile Net buyer Net seller PDR Urban Rural Source: Authors' calculations based on LECS6 data and changes in the price index between December 2018 and December 2023 of food (115 percent), rice (147 percent), meat and fish (99 percent), and fruit and vegetables (100 percent). Note: Non-food prices and income from sources other than food production are assumed to remain unchanged. Households are grouped into quintiles based on their per capita consumption nationwide ranking. Under this scenario, low-income urban households and net food buyers are more likely to have fallen into poverty as of mid-2024. Approximately 50 percent and 20 percent of urban households from the second and third quintiles respectively are estimated to have fallen into poverty, compared to 31 percent and 6 percent of rural households from the same quintiles (Figure 24). These urban households tend to be net food buyers and rely heavily on wage and non-farm business incomes, which have struggled to keep pace with inflation. Notably, while net food buyers exhibited lower poverty rates in 2018, they face heightened risks of poverty during periods of high food inflation compared to net sellers or self-sufficient households. Figure 24: Chances of living in poverty (% of population) 100  Baseline 2018  New poor 90 80 70 60 50 40 30 20 10 2 3 4 2 3 4 Poorest Richest Richest Meat and Fish Meat and Fish Meat and Fish Poorest Food Food Food Rice and Grains Rice and Grains Rice and Grains Fruit and Vegetables Fruit and Vegetables Fruit and Vegetables Lao Consumption quintile Consumption quintile PDR Urban Rural Net buyer Net seller Self-su iciency Source: Authors’ calculation based on LECS6 and the World Bank rapid monitoring phone survey. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 20 Box 1: Welfare cost of exchange rate depreciation Assessing the distributional impacts of exchange rate depreciation is a crucial aspect of welfare analysis, especially in countries vulnerable to exchange rate shocks. Between 2017 and 2023, a 1 percent depreciation in the kip/dollar parallel exchange rate led to a 0.9 and 1.1 percentage point increase in headline inflation and food inflation, respectively. The average welfare cost of a 1 percent depreciation of the kip in the parallel exchange rate market—through its effect on both food and non-food prices—is estimated to be 0.6 percent of consumption for Lao households (Figure 25).8 The impact varies across socioeconomic groups. Among the poorest 20 percent of rural households, the welfare cost is 0.4 percent, compared to 0.7 percent among urban families, which purchase more food than they produce. Figure 25: Welfare cost of a 1 percent depreciation in the kip/dollar parallel exchange rate (% household consumption) 1.0 0.8 0.6 0.4 0.2 0.0 2 3 4 2 3 4 Richest Poorest Richest Poorest Lao PDR Consumption quintile Consumption quintile Urban Rural Source: Authors’ calculation. Note: Welfare cost represents the additional spending required to afford the consumption basket in 2018, expressed as a percentage of household consumption. The exchange rate pass-through to the overall CPI is applied to the non-food portion, while the exchange rate pass-through is assumed to be 0 for self-produced food. See Annex 2 for the methodology. 8 Figure 25 illustrates the welfare impact of exchange rate depreciation through its effect on both food and non-food inflation. This is different from Figure 23, which illustrates the impact of food inflation, influenced by exchange rate depreciation, global food prices, oil prices, as well as weather-related and other domestic factors. 21 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 6. Policy Existing policy interventions The Bank of the Lao PDR (BOL) has recently implemented stricter monetary and foreign exchange policies with the aim of stabilizing the exchange rate and curbing inflation. The policy interest rate was raised three times during 2022–2023, from 3.1 percent to 7.5 percent, and most recently in March 2024, to 8.5 percent. During 2022–2023, the reserve requirement ratio for the kip was increased to 8 percent, up from 5 percent, while for foreign currencies it rose to 10 percent from 5 percent. Since June 2022, BOL has issued bonds amounting to 6.6 trillion kip with interest rates of 15–20 percent. To tighten foreign exchange regulations, the bank has limited individuals' access to foreign exchange bureaus, instructed businesses to conduct foreign exchange transactions solely through commercial banks, and—in March 2024—began enforcing the partial repatriation and conversion of export proceeds from key sectors (mining, power, agriculture, and services). Access to foreign exchange at the official exchange rate is also prioritized for imports of strategic goods, particularly fuel. The government has implemented controls over food prices and quantity. In March 2022, the Ministry of Industry and Commerce issued a list of 23 price-controlled necessities, including fuel, cooking gas, rice, pork, beef, fish, chicken, steel, and cement. These price caps would be adjusted periodically based on international fuel prices and exchange rates. However, enforcement was weak. In March 2023, the government strengthened the application of price caps through regular inspections and the imposition of fines. Additionally, the Ministry of Agriculture and Forestry has initiated the rice balancing scheme, aimed at measuring production quantities, redistributing rice away from surplus provinces, and ensuring food security in deficit provinces: primarily located in the northern region. Rice exporters require approval from provincial authorities, with regular assessments conducted to ensure enough rice is retained in-province for food security and whether a surplus exists for potential redistribution to deficit areas. Empirical evidence suggests that price controls are not effective in stabilizing food prices, particularly given weak enforcement, and that shocks are not temporary. After the Ministry of Industry and Commerce issued a list of 23 price-controlled necessities in March 2022, the prices of rice, pork, and chicken instead began to accelerate (Figure 26). Although enforcement efforts were strengthened a year later, the prices of rice and chicken have continued to rise. Price controls are, at best, a short-term solution and cannot address underlying structural issues contributing to inflation. Over-reliance on price controls can distort markets, lead to shortages (as has Figure 26: Price indices of food items subject to price controls 260 2015=100 240 220 200 180 160 Glutinous rice (average) 140 Pork (average) 120 Chicken (average) 100 Sep-21 Sep-23 Mar-21 May-21 May-22 May-23 Nov-21 Nov-22 Nov-23 Jul-21 Jul-22 Jul-23 Jan-21 Jan-22 Jan-23 Sep-22 Mar-22 Mar-23 Price controls imposed/tightened Source: Lao Statistics Bureau. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 22 proved the case for fuel), disincentivize producers, and encourage a shift towards lower-quality alternative foods, thereby exacerbating long-term inflationary pressures and household impacts. Quantity-control measures have the potential to induce market distortions, resulting in unintended negative consequences. The rice balancing scheme and export restrictions could discourage farmers from investing in and expanding rice production. Rice surplus areas are predominantly situated in the south, whereas rice deficit areas are concentrated in the north. This geographical division, coupled with insufficient connective infrastructure, may mean it is cheaper for consumers in deficit regions to buy imported rice rather than rice redistributed from another region and sold at market rates. This scenario would not only undermine the government's intended objective of ensuring food security but also hamper productivity improvement and exacerbate challenges for consumers in deficit regions. Cuts to fuel excise taxes are unlikely to significantly reduce inflation nor help those hit the hardest. In June 2022, the Ministry of Finance (MOF) lowered the gasoline excise tax from 30 percent to 16 percent, and the diesel excise tax from 20 percent to 11 percent. This reduction was further extended in August 2022, with the excise tax on diesel removed entirely. Despite aiming to mitigate the effects of inflation, such untargeted measures tend to incur significant fiscal costs and result in the inefficient allocation of resources. Moreover, they often fail to effectively reach those individuals and sectors most in need of support. The slashing of fuel excise tax rates is estimated to have resulted in a meager 1.1 percent decline in the price of the consumption basket and a 0.5 percent decline in the price of the food and non-alcoholic beverage consumption basket.9 These figures include the use of fuel for final consumption and as an intermediate input for food and non-food products. The reduction in fuel excise tax has disproportionately benefited better-off households while resulting in forgone revenue of over $160 million between mid-2022 and mid-2023 (World Bank 2023a, World Bank 2023b). Income support measures have been limited to increases in the minimum wage and allowances for civil servants. The monthly minimum wage was raised three times during 2022–2023: from 1.1 million kip to 1.2 million kip in August 2022, then to 1.3 million kip in May 2023, and finally to 1.6 million kip in October 2023. Despite these adjustments, the total 45 percent increase in the minimum wage fell significantly below the 72 percent surge in overall prices, and the 80 percent rise in food prices, during the same period. Evidence from rapid monitoring phone surveys conducted by the World Bank indicates that the average wage grew even at a slower pace, rising by just 13 percent between December 2022 and December 2023. In March 2024, the government announced a 150,000 kip ($7.15) increase in monthly allowances for civil servants, retirees, and volunteer teachers earning below 1.7 million kip. However, given the inflation-adjusted poverty line of 583,000 kip per person per month in 2023, these additional allowances are unlikely to meaningfully supplement incomes: especially for those supporting large families. Policy recommendations The findings of this report emphasize the need to move beyond short-term policy responses and adopt a comprehensive strategy focused on restoring macroeconomic stability, mitigating the impact of food inflation on vulnerable households, capitalizing on higher agricultural prices, and improving food market efficiency to enhance food price stability. Restoring macroeconomic stability. Given the significant impact of exchange rate depreciation on food inflation and poverty, policies to stabilize the currency are the most urgent. Measures to increase foreign currency reserves and reduce debt repayments through expediting renegotiations with creditors and mobilizing additional forex revenue—are essential to alleviate pressure on the exchange rate (World Bank 2023a). Scaling up targeted income support measures. Price controls have had limited success in curbing price increases in Laos, especially for staples like rice and chicken, and could inadvertently lead to market distortions and shortages of goods. Income support is therefore essential to assist those struggling the most with food inflation: particularly low-income urban households reliant on wage and non-farm business incomes, which have struggled to keep pace. Such support could take the form of allowances and/or cash transfers. The Ministry of Agriculture and Forestry’s Helping Hand Programme has proven effective in alleviating the impact of food inflation 9 Average household basket in 2018. 23 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts on nutritional outcomes among low-income households, and could be expanded. To implement these measures effectively, it is crucial for the government to invest more in targeting tools and benefit transfer systems. Leveraging high agricultural prices to boost productivity and raise incomes for the poor. Nearly 20 percent of Lao households are net food sellers, especially rural and low-income households. Subsistence agriculture is also predominant among poor households. While high food prices can stimulate agricultural investment and commercialization, unlocking this potential requires strategic policy interventions to support small-scale farmers. These include ensuring access to credit and agricultural inputs, providing targeted in-kind support and cash transfers, and offering training and technical assistance in farming techniques and commercial practices. This would enable farmers to expand production, invest in machinery, and capitalize on market opportunities in response to higher prices. Moreover, infrastructure development plays a crucial role in this process, with investments needed in irrigation systems and storage facilities to make commercial production possible and in roads to ensure access to markets. Enhancing local market integration and facilitating price transmission. The Lao domestic food market is currently not fully integrated, resulting in high price volatility, inefficient resource allocation, and limited benefits for farmers from price increases. This situation undermines the welfare of both consumers and farmers and discourages investment in productivity-enhancing technologies. Measures like the rice balancing scheme and export controls could unintentionally limit the benefits of higher agricultural prices by discouraging farmers from expanding production and improving productivity. At the same time, import restrictions may temporarily raise domestic prices, especially if local producers encounter high input and transportation costs, and local production cannot quickly ramp up to offset the reduced imports. By contrast, enhancing food market integration and price transmission, while enabling food to move freely between and within countries based on price signals, would facilitate more efficient resource allocation, reducing food price volatility and spatial disparities. Using price signals is less distortive than quantity controls which can have unintended negative consequences. Various policy interventions could be implemented to enhance market transparency, efficiency, and linkages. In the short term, improving agricultural market monitoring and information would increase farmers' awareness of opportunities, while helping traders to identify markets that offer profitable arbitrage opportunities. In the medium term, improving connective infrastructure to reduce logistical barriers and costs will entrench market integration. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 24 Annex 1: Price Transmission Methodology To examine price transmission and market integration, monthly time series data were obtained from various sources. International food prices and crude oil prices were obtained from the International Monetary Fund, nominal effective exchange rates for 51 trading partners from Darvas (2021), and official and parallel kip/USD exchange rates and M2 money supply from the Bank of the Lao PDR. Lao food CPI and domestic commodity prices in each province were obtained from the Lao Statistics Bureau and the Ministry of Agriculture and Forestry. The model incorporates the following primary explanatory variables: the world food price index, nominal effective exchange rate, international oil price, and money supply. The world food price index and international oil price capture the influence of global food and oil prices respectively, while the nominal effective exchange rate aims to measure the pass-through effects of exchange rate changes on domestic food prices. Lao money supply represents the demand-pull inflationary pressures. The selection of variables in the model is informed by several studies: Norazman et al. (2018) employed the world food price index, labor costs, real effective exchange rate, and oil price; Davidson et al. (2011) utilized world food commodity prices, oil prices, exchange rates, labor costs, and unemployment rates; Baek and Won (2010) considered agricultural commodity prices, energy prices, ethanol production, and exchange rates. These variables were further adjusted to fit the data availability for Laos and the study's objectives. Descriptive statistics are shown in Table A. 1. The price transmission analysis employs time series techniques: the Vector Error Correction Model (VECM); the Autoregressive Distributed Lag Model (ARDL); and the Mean Group (MG) estimation. VECM is a multivariate time series model that allows for an analysis of long-run equilibrium relationships. Like the Vector Autoregression (VAR), VECM describes the joint dynamics of multiple variables by regressing each variable on its lagged values and lagged values of all other variables in the system. However, unlike VAR, VECM allows for an analysis of long-run equilibrium relationships (cointegrating relationships) among variables and short-run deviations from that equilibrium. The presence of cointegrating relationships implies that prices are transmitted from one market to another. Moreover, the adjustment coefficients show how the short-run deviations or disequilibrium are corrected, and how long it takes for prices to fully transmit and adjust. The general form of VECM with variables, lags, and cointegrating relationships can be specified as: −1 ∆ = −1 + ∑ ∆− + =1 where ∆ is a 1 matrix of first differences of endogenous variables, ∆− are lagged differences of endogenous variables, is a matrix of short-run coefficients, is a matrix of adjustment coefficients toward long- run equilibrium, and is a matrix of cointegrating relationships. VECM with 2 variables, 2 lags, and 1 cointegrating relationship can be expressed as: ∆1, = 1(1,−1 − 2,−1) + 1,1∆1,−1 + 1,2∆2,−1 + 1, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) ∆2, = 2(1,−1 − 2,−1) + 2,1∆1,−1 + 2,2∆2,−1 + 2, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) where captures the long-run cointegrating relationship, while 1 and 2 are the speed of adjustment toward long- run equilibrium of 1 and 2 , respectively. The Autoregressive Distributed Lag Model (ARDL) is a single-equation model commonly used for analyzing the long-run and short-run dynamics between variables. If one cointegrating relationship is identified among variables, the ARDL model can be re-parameterized into the Error Correction Model (ECM), giving short-run dynamics and the long run relationship between variables. In this note, ARDL is used for studying international to local price transmission, with domestic prices treated as a dependent variable. ARDL is preferred when dealing with a small number of observations. However, it should be noted that ARDL does not accommodate more than one cointegrating relationship. Therefore, VECM remains the main method used for analyzing local price transmission between provinces, for which several long-run relationships can be expected. 25 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts ARDL with 2 variables, 2 lags, and 1 cointegrating relationship can be expressed as: ∆ = 1(−1 − −1) + ∆−1 + ,0∆ + ,1∆−1 + . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (3) The international to local price transmission analysis by province is estimated using the Mean Group (MG) estimation for panel data. The MG approach is used to estimate the non-stationary dynamic heterogeneous panel data, allowing for the long-run cointegrating relationship and short-run dynamics to differ across provinces (Pesaran and Smith 1995). Variables that exhibit seasonal patterns, including Lao food CPI, rice prices, and exchange rates, are seasonally adjusted. Variables are transformed into their stationary forms based on the results of unit root tests, namely the Augmented Dickey-Fuller test (ADF) and the Phillips-Perron test (PP) tests. For VECM, the number of lags was chosen based on the majority consensus of the following indicators: final prediction error (FPE), Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), and the Hannan and Quinn information criterion (HQIC) lag-order selection statistics. The Johansen cointegration test examines the presence and number of cointegrating relationships among the variables. For ARDL, the number of lags was chosen based on the Schwarz's Bayesian information criterion (BIC). The ARDL bounds test examines the presence of a cointegrating relationship among the variables. If a cointegrating relationship exists, these variables can be fitted into the VECM or ECM to analyze the long-run dynamics and the speed of price adjustments. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 26 Table A.1: Descriptive Statistics Obs Mean Std. dev. Min Max Key variables Lao food CPI (2015=100) 288 82 41 27 223 World food and beverage price index(2016=100) 288 99 24 55 162 Nominal EER (51 trading partners) 288 121 21 66 167 Kip/USD reference exchange rate 288 9,497 2,257 7,531 19,328 Kip/USD parallel exchange rate 84 11,762 4,384 8,311 23,754 Brent crude oil price (US$ per barrel) 288 66 29 19 134 Lao money supply(M2, billion kip) 288 49,203 56,055 1,622 252,993 International commodity prices Thai Rice (Glutinous 10%) (USD/tonne) 288 664 268 270 1,424 German pig meat (USD/tonne) 288 1,879 353 1,113 2,870 Thai chicken meat ( THB/kg) 168 52 5 43 67 National commodity prices Glutinous rice, secondquality (kip/kg) 288 5,467 2,995 1,397 16,470 Pork, first quality (kip/kg) 288 33,775 14,784 14,344 82,843 Meat-producing breed peeled chicken (kip/kg) 288 31,285 12,606 12,973 76,991 Vientiane Capital Glutinous rice, second quality(kip/kg) 282 5,865 2,677 1,575 13,491 Pork, first quality (kip/kg) 282 33,927 11,375 17,000 80,000 Meat-producing breed peeled chicken (kip/kg) 282 27,587 10,017 11,500 64,197 Phongsaly Glutinous rice, second quality (kip/kg) 95 8,563 2,994 5,745 17,000 Pork, first quality (kip/kg) 95 50,983 16,033 36,593 95,000 Meat-producing breed peeled chicken (kip/kg) 95 42,350 15,242 25,000 80,000 Luang Namtha Glutinous rice, second quality (kip/kg) 95 9,436 3,198 6,000 19,000 Pork, first quality (kip/kg) 95 58,821 13,549 43,500 90,000 Meat-producing breed peeled chicken (kip/kg) 95 51,796 14,318 37,000 90,000 Oudomxay Glutinous rice, second quality (kip/kg) 95 7,990 2,901 5,161 17,000 Pork, first quality (kip/kg) 95 53,651 14,276 40,000 86,635 Meat-producing breed peeled chicken (kip/kg) 95 47,167 9,696 35,000 70,000 Bokeo Glutinous rice, second quality (kip/kg) 95 9,632 2,843 6,000 18,000 Pork, first quality (kip/kg) 95 51,442 16,103 33,000 90,000 Meat-producing breed peeled chicken (kip/kg) 95 59,987 24,952 40,000 120,000 Luang Prabang Glutinous rice, second quality (kip/kg) 95 12,720 4,841 7,800 26,301 Pork, first quality (kip/kg) 95 50,310 15,372 35,000 85,000 Meat-producing breed peeled chicken (kip/kg) 95 48,853 12,170 27,000 80,000 Huaphan Glutinous rice, second quality (kip/kg) 95 9,257 3,587 6,481 19,000 Pork, first quality (kip/kg) 95 43,726 16,352 30,000 80,000 Meat-producing breed peeledchicken (kip/kg) 95 48,242 11,024 30,000 75,000 Xayaboury Glutinous rice, second quality (kip/kg) 95 7,812 2,756 5,500 15,000 Pork, first quality (kip/kg) 95 49,287 14,572 35,000 90,000 Meat-producing breed peeled chicken (kip/kg) 95 43,000 14,661 30,000 80,000 Table A.1: Descriptive Statistics (continued) Xieng Khuang Glutinous rice, second quality (kip/kg) 95 8,508 3,647 5,000 18,000 Pork, first quality (kip/kg) 95 49,923 13,862 40,000 90,000 Meat-producing breed peeled chicken (kip/kg) 95 51,213 15,323 35,000 90,000 Vientiane Province Glutinous rice, second quality (kip/kg) 95 7,909 2,492 5,398 15,000 Pork, first quality (kip/kg) 95 49,442 14,342 35,000 85,000 Meat-producing breed peeled chicken (kip/kg) 95 35,349 8,141 20,000 60,000 Borikhamxay Glutinous rice, second quality (kip/kg) 95 7,796 2,338 5,802 15,000 Pork, first quality (kip/kg) 95 46,068 14,879 35,000 80,000 Meat-producing breed peeled chicken (kip/kg) 95 29,373 15,025 18,000 80,000 Khammuan Glutinous rice, secondquality (kip/kg) 95 7,558 2,603 5,328 14,000 Pork, first quality (kip/kg) 95 52,608 14,192 40,000 85,000 Meat-producing breed peeled chicken (kip/kg) 95 34,844 11,894 25,000 65,000 Savannakhet Glutinous rice, second quality (kip/kg) 95 7,327 2,636 4,827 14,000 Pork, first quality (kip/kg) 95 49,643 14,151 33,988 80,000 Meat-producing breed peeled chicken (kip/kg) 95 36,637 10,344 30,000 65,000 Saravan Glutinous rice, second quality (kip/kg) 95 7,488 2,786 4,695 15,000 Pork, first quality (kip/kg) 95 43,628 13,231 32,441 75,000 Meat-producing breed peeled chicken (kip/kg) 95 42,618 11,083 32,019 83,125 Sekong Glutinous rice, second quality (kip/kg) 95 6,960 2,698 4,000 15,000 Pork, first quality (kip/kg) 95 46,585 13,815 35,000 80,000 Meat-producing breed peeled chicken (kip/kg) 95 39,658 10,909 30,000 70,000 Champassak Glutinous rice, second quality (kip/kg) 95 7,737 2,752 5,143 14,735 Pork, first quality (kip/kg) 95 46,310 12,815 32,000 76,631 Meat-producing breed peeled chicken (kip/kg) 95 34,460 10,695 22,662 65,000 Attapue Glutinous rice, second quality (kip/kg) 95 8,091 2,450 4,500 14,228 Pork, first quality (kip/kg) 95 49,022 12,548 35,000 76,517 Meat-producing breed peeled chicken (kip/kg) 95 38,380 11,772 25,000 68,000 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 28 International-to-local price transmission: food CPI Table A.2: International-to-local price transmission (food CPI, VECM, 2000-2023) VECM Short-Run ∆ Log Lao ∆ Log int'l ∆ Log NEER ∆ Log int'l oil ∆ Log money ∆ Log Lao ∆ Log int'l ∆ Log NEER ∆ Log int'l oil ∆ Log money food CPI (t) food price (t) (t) price (t) supply (t) food CPI (t) food price (t) (t) price (t) supply (t) Speed of convergence -0.04*** -0.03 0.06*** -0.25*** 0.01 Speed of convergence -0.04*** -0.03 0.06*** -0.25*** 0.01 ∆ Log Lao food CPI (t-1) 0.42*** 0.04 -0.02 -0.68 -0.01 ∆ Log Lao food CPI (t-1) 0.42*** 0.04 -0.02 -0.68 -0.01 ∆ Log int'l food price (t-1) -0.06*** 0.35*** -0.04 0.75*** -0.02 ∆ Log int'l food price (t-1) -0.06*** 0.35*** -0.04 0.75*** -0.02 ∆ Log NEER (t-1) 0.01 0.14 0.08 0.12 -0.02 ∆ Log NEER (t-1) 0.01 0.14 0.08 0.12 -0.02 ∆ Log int'l oil price (t-1) 0.00 0.02 -0.01 0.19*** 0.01 ∆ Log int'l oil price (t-1) 0.00 0.02 -0.01 0.19*** 0.01 ∆ Log money supply (t-1) 0.03 0.08 -0.15*** -0.18 -0.14** ∆ Log money supply (t-1) 0.03 0.08 -0.15*** -0.18 -0.14** Constant 0.00*** -0.00 0.00 0.00 0.02*** Constant 0.00*** -0.00 0.00 0.00 0.02*** VECM Long-Run Log Lao food CPI (t-1) 1 Log Lao food CPI (t-1) 1 Log int'l food price (t-1) -0.30*** Log int'l food price (t-1) -0.30*** Log NEER (t-1) 0.56*** Log NEER (t-1) 0.56*** Log int'l oil price (t-1) 0.17*** Log int'l oil price (t-1) 0.17*** Log money supply (t-1) -0.40*** Log money supply (t-1) -0.40*** Trend 0.00 Trend 0.00 Constant -4.95 Constant -4.95 No. lags (Information criteria for lag selection) 2 (FPE, AIC, HQIC, SBIC) No. lags (Information criteria for lag selection) 2 (FPE, AIC, HQIC, SBIC) No. of cointegrating relationships 1 No. of cointegrating relationships 1 Observations Jan 2000- Dec 2023 Observations Jan 2000- Dec 2023 Note: *, **, and *** denotes significance at 10%, 5% and 1% level A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. The optimal lag in the corresponding VAR at the levels. The number of cointegrating relationships is determined by the Johansen cointegration test at the 99% confidence level. Table A.3: International-to-local price transmission (food CPI, ARDL, NEER) ∆ Log Lao food CPI (t) (1) (2) (3) (4) Speed of convergence -0.05*** -0.13*** -0.11*** Long run Log int'l food price (t-1) -0.10 -0.20** -0.34*** Log NEER (t-1) -0.59*** -0.59*** -0.87*** Log int'l oil price (t-1) 0.02 -0.03 0.01 Log money supply (t-1) 0.52*** 0.63*** 0.20** Trend -0.00* -0.00*** Short run ∆ Log Lao food CPI (t-1) 0.31*** 0.30*** 0.29*** ∆ Log Lao food CPI (t-2) 0.16*** 0.30*** 0.28*** ∆ Log NEER (t) -0.18** Constant 0.38*** 1.01*** 1.04*** Information criteria for lag selection BIC BIC BIC BIC Cointegrating relationship Yes*** Yes*** No Yes*** Yes*** Yes* Yes** Jan 2000- Jan 2000- Jan 2000- Jan Jan 2014- Jan2014- Jan 2017- Jan 2017- Observations Dec 2023 Dec 2023 Dec 2013 Dec Dec 2023 Dec Dec 2023 Note: *, **, and *** denotes significance at 10%, 5% and 1% level A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. ARDL bounds testing for existence of a cointegrating relationship. The number of cointegrating relationships, if they exist, is one by default. 29 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Table A. 4 International-to-local price transmission (food CPI, ARDL, Kip/USD) ∆ Log Lao food CPI (t) (1) (2) (3) (4) (5) Speed of convergence -0.05*** -0.13*** -0.14*** -0.11*** Long run Log int'l food price (t-1) 0.11 -0.02 0.02 -0.26** Log Kip/USD reference (t-1) 0.67*** 1.26*** 1.50*** Log Kip/USD parallel (t-1) 1.09*** Log int’l oil price (t-1) 0.08 -0.07*** -0.08** 0.00 Log money supply (t-1) 0.51*** -0.51** -0.08** -0.26 Trend -0.00** 0.01*** 0.01** 0.00 Short run ∆ Log Lao food CPI (t-1) 0.30*** 0.17** 0.21** ∆ Log Lao food CPI (t-2) 0.23** Constant 0.03 -0.32** -0.62*** 0.05 Information criteria BIC BIC BIC BIC BIC for lag selection Cointegrating relationship Yes*** No Yes*** Yes*** Yes** Jan 2000- Jan 2000- Jan2014- Jan 2017- Jan 2017- Observations Dec 2023 Dec 2013 Dec 2023 Dec 2023 Dec 2023 Note: *, **, and *** denotes significance at 10%, 5% and 1% level A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. ARDL bounds testing for existence of a cointegrating relationship. The number of cointegrating relationships, if they exist, is one by default. International-to-local price transmission: rice, pork, and chicken Table A.5: International-to-local price transmission (rice, ARDL) ∆ Log domestic price (t) (1) (2) (3) Speed of convergence -0.19*** -0.16*** Long run Log international price (t-1) 0.45*** 0.58*** Log NEER (t-1) -1.13*** Log Kip/USD parallel (t-1) 1.45*** Log international oil price (t-1) 0.06 0.17 Log money supply (t-1) 0.13 -0.28 Short run ∆ Log domestic price (t-1) 0.31*** 0.21** ∆ Log domestic price (t-2) 0.18* ∆ Log domestic price (t-3) -0.31*** ∆ Log international price (t) -0.07** -0.08*** ∆ Log international price (t-1) ∆ Log Kip/USD parallel (t) -0.27*** ∆ Log international oil price (t) -0.00 0.02 ∆ Log international oil price (t-1) -0.07*** -0.06*** Constant 1.27*** -0.66*** Information criteria for lag selection BIC BIC BIC Cointegrating relationship No Yes *** Yes** Observations Jan 2000- Dec 2016 Jan 2017-Dec 2023 Jan 2017-Dec 2023 Note: *, **, and *** denotes significance at 10%, 5% and 1% level International prices are Thai rice prices. A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. ARDL bounds testing for existence of a cointegrating relationship. The number of cointegrating relationships, if they exist, is one by default. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 30 Table A.6: International-to-local price transmission (pork, ARDL) Δ Log domestic price (t) (1) (2) (3) Speed of convergence -0.11*** -0.25*** Long run Log international price (t-1) 0.05 0.11 Log NEER (t-1) 0.38*** Log Kip/USD parallel (t-1) 0.46*** Log international oil price (t-1) 0.05 -0.09** Log money supply (t-1) 0.28*** 0.39*** Short run Δ Log domestic price (t-1) 0.31*** 0.29*** Δ Log domestic price (t-2) 0.31*** Δ Log international price (t) - 0.13*** Δ Log Kip/USD parallel (t) -0.25** Δ Log international oil price (t) 0.08*** Δ Log international oil price (t-1) -0.02 Δ Log international oil price (t-2) 0.11*** Δ Log money supply (t) -0.57*** Constant 0.50*** 0.12 Information criteria for lag selection BIC BIC BIC Cointegrating relationship Yes*** No Yes*** Observations Jan 2000-Dec 2016 Jan 2017-Dec 20 23 Jan 2017-Dec 2023 Note: *, **, and *** denotes significance at 10%, 5% and 1% level International prices are German pork prices. A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. ARDL bounds testing for existence of a cointegrating relationship. The number of cointegrating relationships, if they exist, is one by default. Table A.7: International-to-local price transmission (chicken, ARDL) ∆ Log domestic price (t) (1) (2) (3) Speed of convergence -0.17*** -0.17*** Long run Log international price (t-1) 0.16 -0.00 Log NEER (t-1) -0.74*** Log Kip/USD parallel (t-1) 0.80*** Log international oil price (t-1) -0.04 -0.03 Log money supply (t-1) 0.25** 0.10 Short run ∆ Log domestic price (t-1) 0.33*** 0.30*** Constant 1.01** 0.09 Information criteria for lag selection BIC BIC BIC Cointegrating relationship No Yes** Yes* Observations Jan 2000-Dec 2016 Jan 2017-Dec 2023 Jan 2017-Dec 2023 Note: *, **, and *** denotes significance at 10%, 5% and 1% level International prices are Thai chicken prices. A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. ARDL bounds testing for existence of a cointegrating relationship. The number of cointegrating relationships, if they exist, is one by default. 31 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Table A.8: International-to-local price transmission by province (MG) Sticky rice Chicken Pork Vientiane Capital Speed of convergence -0.21*** -0.06 -0.20*** Log international price (t-1) 0.47*** 0.19 -0.05 Log NEER (t-1) -1.83*** -2.26** -1.21*** Log international oil price (t-1) 0.28* 0.31 -0.15 Log money supply (t-1) -0.54*** -0.64 0.21 Phongsaly Speed of convergence -0.21*** -0.25*** -0.23*** Log international price (t-1) 0.63*** -0.62 0.19 Log NEER (t-1) -1.54*** -0.93** -0.64* Log international oil price (t-1) 0.02 0.02 -0.17 Log money supply (t-1) 0.05 0.44** 0.34* Luang Namtha Speed of convergence -0.41*** -0.38*** -0.36*** Log international price (t-1) 0.32*** -0.16 0.08 Log NEER (t-1) -0.94*** -1.05*** -0.76*** Log international oil price (t-1) -0.19*** -0.15** -0.03 Log money supply (t-1) 0.27** 0.12 0.11 Oudomxay Speed of convergence -0.29*** -0.21*** -0.20*** Log international price (t-1) 0.58*** 1.34*** -0.03 Log NEER (t-1) -1.92*** -0.57 -1.21*** Log international oil price (t-1) -0.09 0.01 -0.20** Log money supply (t-1) -0.18 0.02 0.13 Bokeo Speed of convergence -0.37*** -0.19*** -0.18*** Log international price (t-1) 0.57*** 0.99* -0.17 Log NEER (t-1) -1.00*** 0.07 -1.13** Log international oil price (t-1) 0.03 0.07 -0.38*** Log money supply (t-1) 0.09 0.80*** 0.43 Luang Prabang Speed of convergence -0.20*** -0.41*** -0.35*** Log international price (t-1) 0.45*** 1.04** -0.09 Log NEER (t-1) -1.07** -0.82* -1.51*** Log international oil price (t-1) -0.03 -0.11 -0.16 Log money supply (t-1) 0.40* -0.22 -0.07 Huaphan Speed of convergence -0.30*** -0.04 -0.26*** Log international price (t-1) 0.37*** -5.90 0.16 Log NEER (t-1) -2.10*** -5.97 -1.14*** Log international oil price (t-1) 0.12 0.49 -0.09 Log money supply (t-1) -0.40*** -2.55 0.20 Xayaboury Speed of convergence -0.28*** -0.24*** -0.30*** Log international price (t-1) 0.50*** 0.80 -0.02 Log NEER (t-1) -1.99*** 0.10 -1.24*** Log international oil price (t-1) 0.07 -0.11 -0.22*** Log money supply (t-1) -0.26 0.76** 0.11 Xieng Khuang Speed of convergence -0.35*** -0.11* -0.22*** Log international price (t-1) 0.41*** 0.69 0.20 Log NEER (t-1) -1.65*** -0.26 -1.20*** Log international oil price (t-1) -0.16* -0.01 -0.11 Log money supply (t-1) 0.10 0.51 -0.07 Vientiane Province Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 32 Table A.8: International-to-local price transmission by province (MG) (continued) Log NEER (t-1) -1.65*** -0.26 -1.20*** Log international oil price (t-1) -0.16* -0.01 -0.11 Log money supply (t-1) 0.10 0.51 -0.07 Vientiane Province Speed of convergence -0.22*** -0.18*** -0.21*** Log international price (t-1) 0.71*** -0.28 0.15 Log NEER (t-1) -1.65*** -1.20 -1.13*** Log international oil price (t-1) 0.08 -0.06 -0.38*** Log money supply (t-1) -0.05 -0.35 0.27 Borikhamxay Speed of convergence -0.23*** -0.12* -0.27*** Log international price (t-1) 0.61*** 0.84 -0.25* Log NEER (t-1) -1.35*** -0.20 -1.11*** Log international oil price (t-1) 0.01 0.61 -0.16** Log money supply (t-1) -0.07 0.35 0.31** Khammuan Speed of convergence -0.16** -0.18** -0.23*** Log international price (t-1) 0.19 0.66 0.09 Log NEER (t-1) -1.29*** -0.70 -0.87*** Log international oil price (t-1) 0.03 -0.03 -0.25*** Log money supply (t-1) 0.05 0.32 0.36** Savannakhet Speed of convergence -0.41*** -0.18** -0.11** Log international price (t-1) 0.60*** 0.84** -0.02 Log NEER (t-1) -1.62*** -1.14*** -0.74 Log international oil price (t-1) -0.11 0.11 -0.20 Log money supply (t-1) -0.02 -0.21 0.53 Saravan Speed of convergence -0.15** -0.15*** -0.22*** Log international price (t-1) 0.71** -0.24 -0.05 Log NEER (t-1) -2.37*** -1.67*** -1.34*** Log international oil price (t-1) 0.13 -0.28** -0.17*** Log money supply (t-1) -0.44* -0.09 0.07 Sekong Speed of convergence -0.09 -0.16** -0.31*** Log international price (t-1) 1.25 1.03 0.00 Log NEER (t-1) -0.94 -1.09 -0.79*** Log international oil price (t-1) 0.58 -0.05 -0.08 Log money supply (t-1) -0.19 -0.13 0.33** Champassak Speed of convergence -0.35*** -0.21*** -0.20*** Log international price (t-1) 0.37*** 1.97** 0.12 Log NEER (t-1) -1.35*** 0.85 -0.78* Log international oil price (t-1) -0.07 0.33 -0.21* Log money supply (t-1) 0.22 0.69 0.38* Attapeu Speed of convergence -0.29*** -0.22*** -0.14** Log international price (t-1) 0.40** -0.40 0.25 Log NEER (t-1) -1.05** -1.33* -0.38 Log international oil price (t-1) 0.04 -0.14 -0.26* Log money supply (t-1) 0.17 0.11 0.58* No. of lags 2 2 1 No. observations Jan 2016- Jun 2023 Jan 2016- Jun 2023 Jan 2016 -Jun 2023 Note: *, **, and *** denotes significance at 10%, 5% and 1% level International prices are Thai sticky rice, German pork, and Thai chicken prices. Only the long-run (cointegrating relationship) estimation results are presented. A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. The optimal lag in the corresponding VAR at the levels. The number of cointegrating relationships is one by default. 33 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Sticky rice Pork Chicken Local price transmission: rice, pork, Speed of convergence and chicken -0.35*** Champassak Sticky1 rice Pork Chicken Table A.9: Local price transmission (four key provinces, VECM) Speed of convergence Savannakhet -0.35*** -0.76*** Champassak Laung Prabang 1 rice 0.82*** Sticky Pork Chicken Savannakhet Vientiane Capital Speed of convergence -0.76*** -0.70*** -0.35*** Trend Prabang Laung Champassak 0.82*** -0.01*** 1 Vientiane Constant Capital Savannakhet -0.70*** -3.23 -0.76*** Trend No. of lags Laung Prabang -0.01*** 1 0.82*** 1 1 Constant Capital Vientiane -3.23 (HQIC, SBIC) -0.70*** (FPE, AIC, HQIC, SBIC) (HQIC, SBIC) No. lags of cointegrating Trend relationships 1 -0.01*** 1 0 1 2 Observations Constant Jan(HQIC, 2016- -3.23SBIC) Dec 2023 (FPE, Jan AIC, 2016-HQIC, SBIC) Dec 2023 Jan(HQIC, SBIC) 2016- Dec 2023 No. of cointegrating No. of lags relationships 1 1 0 1 2 1 Observations 2016- Dec Jan(HQIC, 2023 SBIC) Jan 2016- (FPE, Dec 2023 AIC, HQIC, SBIC) Jan(HQIC, 2016- Dec 2023 SBIC) No. of cointegrating relationships 1 0 2 Observations Jan 2016- Dec 2023 Jan 2016- Dec 2023 Jan 2016- Dec 2023 Note: *, **, and *** denotes significance at 10%, 5% and 1% level Only the long-run (cointegrating relationship) estimation results are presented. A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. The optimal lag in the corresponding VAR at the levels. The number of cointegrating relationships is determined by the Johansen cointegration test at the 99% confidence level. The estimation for chicken fails the Lagrange Multiplier (LM) test for residual autocorrelation and the stability condition test. Sticky Table A.10: Local price transmission (northern riceVECM) region, Pork Chicken Speed of convergence -0.63*** -0.07*** Luang Namtha Sticky1 rice Pork 1 Chicken Bokeo of convergence Speed -0.63*** -0.28*** -0.07*** 1.54*** Luang Namtha Oudomxay 1 rice -0.60*** Sticky 1 -2.08*** Pork Chicken Bokeo Xayaboury Speed of convergence -0.28*** 0.06 -0.63*** 1.54*** 0.01 -0.07*** Oudomxay Trend Namtha Luang -0.60*** -0.00 1 -2.08*** -0.01*** 1 Xayaboury Constant Bokeo 0.06 -1.62 -0.28*** 0.01 -4.83 1.54*** Trend No. of lags Oudomxay -0.00 1 -0.60*** -0.01*** 1 -2.08*** 1 Constant Xayaboury -1.62 (HQIC, 0.06SBIC) -4.83 (FPE, HQIC, 0.01 SBIC) (FPE, AIC, HQIC, SBIC) No. lags of cointegrating Trend relationships 1 -0.00 1 -0.01*** 1 0 Observations Constant Jan(HQIC, 2016- SBIC) Dec -1.62 2023 (FPE, Jan HQIC, 2016- Dec -4.83 SBIC) 2023 (FPE, AIC, HQIC, Jan 2016 SBIC) -Dec 2023 No. of No. of cointegrating lags relationships 1 1 1 1 0 1 Observations 2016- Dec Jan(HQIC, 2023 SBIC) Jan 2016- (FPE, Dec HQIC, 2023 SBIC) Jan 2016 (FPE, -Dec 2023 AIC, HQIC, SBIC) No. Note: *, of cointegrating **, and relationships *** denotes significance at 10%, 5% and 1% level 1 1 0 Only the long-run (cointegrating relationship) estimation results are presented. A negative speed of convergence coefficient implies that prices adjust to return to the Observations Jan 2016- Dec 2023 Jan 2016- Dec 2023 Jan 2016 long-run equilibrium. The optimal lag in the corresponding VAR at the levels. The number of cointegrating relationships is determined by the Johansen -Dec 2023 cointegration test at the 99% confidence level. Table A.11: Local price transmission (central region, VECM) Sticky rice Pork Chicken Speed of convergence Borikhamxay Sticky rice Pork Chicken Speed Khammuan of convergence Borikhamxay Savannakhet Sticky rice Pork Chicken Khammuan Vientiane Speed Province of convergence Savannakhet Trend Borikhamxay Constant Province Vientiane Khammuan Trend No. of lags Savannakhet 1 1 1 Constant Province Vientiane (FPE, HQIC, SBIC) (FPE, AIC, HQIC, SBIC) (FPE, AIC, HQIC, SBIC) No. Trend of lags cointegrating 1 0 1 0 1 0 relationships Constant (FPE, HQIC, SBIC) (FPE, AIC, HQIC, SBIC) (FPE, AIC, HQIC, SBIC) No. No. of Observationscointegrating of lags Jan 2016 0 - Dec 2023 1 Jan 2016- 0 1Dec 2023 Jan 20160 1-Dec 2023 relationships (FPE, HQIC, SBIC) Note: *, **, and *** denotes significance at 10%, 5% and 1% level (FPE, AIC, HQIC, SBIC) (FPE, AIC, HQIC, SBIC) Observations Only No. Jan the long-run (cointegrating relationship) estimation results of cointegrating 2016 are - Dec presented. 0 2023 speed of convergence Jan 2016- A negative 0 Dec 2023 coefficient Jan implies that 2016 prices 0-Dec adjust 2023 to return to the long-run equilibrium. The optimal lag in the corresponding VAR at the levels. The number of cointegrating relationships is determined by the Johansen cointegration relationships test at the 99% confidence level. Observations Jan 2016 - Dec 2023 Jan 2016- Dec 2023 Jan 2016 -Dec 2023 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 34 Table A.12: Local price transmission (southern region, VECM) Sticky rice Pork Chicken Sticky rice Pork Chicken Speed of convergence -0.44*** Sticky rice Pork -0.11*** Chicken Speed of convergence -0.44*** -0.11*** Champassak Speed of convergence 1 -0.44*** 0.29 -0.11*** Champassak 1 0.29 Saravan Champassak -0.23*** 1 1 0.29 Saravan -0.23*** 1 Sekong Saravan -0.03*** -0.23*** -0.66*** 1 Sekong -0.03*** -0.66*** Attapue Sekong -0.78*** -0.03*** -0.53*** -0.66*** Attapue -0.78*** -0.53*** Trend Attapue 0.00** -0.78*** 0.29 -0.53*** Trend 0.00** 0.29 Constant Trend 0.45 0.00** -0.00** 0.29 Constant 0.45 -0.00** No. of lags Constant 2 0.45 1 2 -0.00** No. of lags 2 1 2 No. of lags (FPE,2 AIC) (HQIC,1 SBIC) (FPE, AIC, HQIC, 2 SBIC) (FPE, AIC) (HQIC, SBIC) (FPE, AIC, HQIC, SBIC) No. of cointegrating relationships (FPE,1 0 1 No. of cointegrating relationships 1 AIC) (HQIC,0 SBIC) (FPE, AIC, HQIC, 1 SBIC) Observations No. of cointegrating relationships Dec 2016 - Dec 2023 Mar 2016 - Dec 2023 Feb 2016 1- Dec 2023 Observations Dec 2016 1 - Dec 2023 Mar 2016 0 - Dec 2023 Feb 2016 - Dec 2023 Observations Note: Dec 2016 - Dec 2023 *, **, and *** denotes significance at 10%, 5% and 1% level Mar 2016 - Dec 2023 Feb 2016 - Dec 2023 Only the long-run (cointegrating relationship) estimation results are presented. A negative speed of convergence coefficient implies that prices adjust to return to the long-run equilibrium. The optimal lag in the corresponding VAR at the levels. The number of cointegrating relationships is determined by the Johansen cointegration test at the 99% confidence level. Table A.13: Production for consumption calculation approach Provincial Feed Seed Loss Paddy to Provincial production available for Provincial Feed Seed Loss Paddy to Provincial production available for production Provincial Feed Seed Loss rice loss Paddy to consumption Provincial production available for production rice loss consumption A production C D E riceGloss H consumption A C D E G H Paddy (kg) A 0.06 C 0.06 D 0.04 E 0.40 G H=(A-(A*(C+D+E))*(1 H -G) Paddy (kg) 0.06 0.06 0.04 0.40 H=(A-(A*(C+D+E))*(1 -G) Vegetables Paddy (kg) (kg) (kg) 0.06 0.06 0.08 0.04 0.40 H=A-(A*E) H=(A-(A*(C+D+E))*(1 -G) Vegetables 0.08 H=A-(A*E) Pig (number) Vegetables (kg) 0.08 H= A/∑ A*total meat production H=A-(A*E) Pig (number) H= A/∑ A*total meat production Chicken (number) Pig (number) H= A/∑ H= A/∑ A A*total A/∑ A *total meat *total meat production production meat production Chicken (number) H= Chicken (number) H= A/∑ A*total meat production Source: Lao Statistics Bureau (LSB) for provincial production. Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) for total pork and chicken meat production, Ministry of Agriculture and Forestry (2023) for paddy to rice conversion, World Food Programme (2023) and Burgos et. al. (2008) for feed, seed, and loss. Annual per approach Table A.14: Consumption calculation capita consumption Provincial population Provincial consumption Annual per capita consumption Provincial population Provincial consumption A consumption Annual per capita Provincial B population C Provincial consumption A B C Rice A kg 206 B C=A*B C Rice 206 kg C=A*B Vegetables Rice 131 206 kg C=A*B Vegetables 131 kg kg C=A*B C=A*B Pork Vegetables 13.5 kg 131 kg C=A*B C=A*B Pork 13.5 kg C=A*B Chicken Pork kg 6.45 kg 13.5 C=A*B C=A*B Chicken 6.45 kg C=A*B Chicken 6.45 kg C=A*B Source: FAOSTAT for annual per capita consumption. LSB for provincial population. 35 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts Annex 2: Welfare cost of exchange rate depreciation The technical note on assessing the distributional impacts of exchange rate reforms developed by Pimhidzai et al. (forthcoming) provides a comprehensive understanding of the conceptual framework and empirical approaches for quantifying the distributional effects of exchange rate reforms within a single country framework. The approach measures the welfare impact of a price shock by calculating the amount of money needed to compensate households for changes in prices. More precisely, the welfare cost is calculated as the aggregate of budget share of each item in household’s total expenditure and the respective price change for that item as presented in Equation (1), where ∆ℎ denotes the change in total expenditure for household ℎ, ℎ represents the budget share of item in household ℎ's total expenditure and ∆ is the change in the price of item . ∆ℎ = ∑ ℎ ∆ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) This method essentially estimates the exchange rate pass–through (ERPT) for each item. The ERPT is then multiplied by the magnitude of the devaluation to determine the measure of the price shock. This is expressed as ∆ = × ∆, where ∆ is the change in exchange rate. Δℎ=∑ [∑(Δ×)]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Following the proposed approach, ERPT for the overall CPI (food CPI) in Laos was estimated by employing ADLR model, with Lao overall CPI (food CPI) as the dependent variable and the world overall CPI (food CPI), parallel exchange rate, international oil price, and Lao money supply as independent variables. The ERPT value for the Lao overall CPI and food CPI were found to be 0.84 and 1.27, respectively. These values were multiplied by the share of non-food and food consumption for each household in LECS 6 (2018) to generate the welfare cost of the kip depreciation. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 36 References ASEAN+3 Macroeconomic Research Office. 2020. Annual Consultation Report: Lao PDR. Singapore. Baek, J. and W. W. Koo. 2010. Analyzing factors affecting US food price inflation. Canadian Journal of Agricultural Economics, 58(3), 303-320. Baffes, J., V. Kshirsagar and D. Mitchell. 2019. “What Drives Local Food Prices? Evidence from the Tanzanian Maize Market.” The World Bank Economic Review, 33(1), 160–184. Burgos, S., J. Otte, and D. Roland-Holst. 2008. “Poultry, HPAI and Livelihoods in Lao People’s Democratic Republic–A Review.” Mekong Team Working Paper No. 5, 38 pp. Darvas, Z. 2021. “Timely Measurement of Real Effective Exchange Rates.” Working Paper 15/2021, Bruegel. Davidson, J., A. Halunga, T. Lloyd, S. McCorriston, and C.W. Morgan. 2011. “Explaining UK food price inflation.” Transparency of Food Pricing. EU. International Monetary Fund. 2023. Article IV Consultation. IMF Country Report 23/171. Washington, D.C: IMF. Lao Statistics Bureau and World Bank. 2020. “Poverty Profile in Lao PDR: Poverty Report for the Lao Expenditure and Consumption Survey 2018-2019.” Li, S., Q. Wang and J.A. 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Chingozha. forthcoming. “A technical guidance note: Assessing the distributional impacts of exchange rate reform.” Washington, D.C.: World Bank Group. World Bank. 2017. “Country Partnership Framework for the Lao People's Democratic Republic.” Main Report. Vol. 2 of 2, Washington, D.C.: World Bank Group. World Bank. 2019. “Lao People's Democratic Republic: State of Environment Report.” Washington, D.C.: World Bank Group. World Bank. 2020. “Drivers of Food Price Inflation in Turkey.” Washington, D.C.: World Bank Group. World Bank. 2022. “Lao People’s Democratic Republic Systematic County Diagnostic: 2021 Update.” Washington, D.C.: World Bank Group. 37 Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts World Bank. 2023a. “Forging Ahead: Restoring Stability and Boosting Prosperity.” Washington, D.C.: World Bank Group. World Bank. 2023b. “Raising the Bar: Toward an Equitable and Inclusive Fiscal Policy, Fiscal Incidence Analysis, Lao PDR.” Washington, D.C.: World Bank Group. World Food Program. 2023. “Understanding the Rice Value Chain in Lao PDR: Defining the Way Forward for Rice Fortification.” Regional Bureau for Asia and the Pacific. Policy Note Food inflation in the Lao PDR: Trends, Drivers and Impacts 38 The World Bank Lao PDR Country Office, East Asia and Pacific Region Xieng Ngeun Village, Chao Fa Ngum Road, Chantabouly District, Vientiane, Lao PDR Tel: (856-21) 266 200 Fax: (202) 266 299 www.worldbank.org/lao The World Bank 1818 H Street, NW Washington, D.C. 20433, USA Tel: (202) 4731000 Fax: (202) 4776391 www.worldbank.org