Policy Research Working Paper 9750 Protectionism and Gender Inequality in Developing Countries Erhan Artuc Nicolas Depetris Chauvin Guido Porto Bob Rijkers Development Economics Development Research Group August 2021 Policy Research Working Paper 9750 Abstract How do tariffs impact gender inequality? Using harmonized of their budget on agricultural products, which are usually household survey and tariff data from 54 low- and mid- subject to high tariffs in developing countries. Consistent dle-income countries, this paper shows that protectionism with this explanation, the anti-female bias is stronger in has an anti-female bias. On average, tariffs repress the real countries where female-headed households are underrep- incomes of female headed households by 0.6 percentage resented in agricultural production, are more reliant on points relative to that of male headed ones. Female headed remittances, and spend a larger share of their budgets on households bear the brunt of tariffs because they derive a food than male-headed ones. smaller share of their income from and spend a larger share This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted ateartuc@worldbank.org; nicolas.depetris-chauvin@hesge.ch; guido.porto@depeco.econo.unlp.edu.ar; and brijkers@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Protectionism and Gender Inequality in Developing Countries* Erhan Nicolas Guido Bob Artuc„ Depetris Chauvin… Porto§ Rijkers¶ The World Bank HES-SO Dept. of Economics The World Bank DECRG Geneva UNLP DECRG Keywords: Gender inequality, globalization, international trade, tariffs, poverty JEL Classification: F1; J16; O13; J43 * We thank M. Olarreaga, M. Porto, and N. Rocha for comments and N. Gomez Parra for excellent research assistance. This research was supported in part by ILO-World Bank Research Program on Job Creation and Shared Prosperity, the Knowledge for Change Program, the Multidonor Trust Fund for Trade and Development, the Research Support Budget, and the Strategic Research Program of the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank of Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the countries they represent. All errors are our responsibility. Depetris Chauvin and Porto acknowledge support from the R4D program on Employment funded by Swiss National Science Foundation and the Swiss Development Cooperation. „ Development Economics Research Group, Trade and Integration, The World Bank. email: eartuc@worldbank.org … HES-SO Geneva School of Business Administration, Switzerland. email: nicolas.depetris-chauvin@hesge.ch § Universidad Nacional de La Plata, Departamento de Economia, Calle 6 e/ 47 y 48, 1900 La Plata, Argentina. email: guido.porto@depeco.econo.unlp.edu.ar ¶ Development Economics Research Group, Trade and Integration, The World Bank. email: brijkers@worldbank.org 1 Introduction After decades of progressive globalization, spurred in part by trade tariff liberalization, protectionism is on the rise. Own tariff protection boosts nominal incomes by raising firm and farm profits as well as wages. But protection also results in higher prices, which increase the cost of living and hurt consumers. Since tariffs vary across goods, and because households have different sources of income and spending habits, trade protection has highly heterogeneous welfare impacts across the rich and the poor, across urban and rural households, across workers in different sectors and with different skills, and across women and men. This paper examines whether tariff protection exacerbates gender inequality in real incomes because of differences in the extent to which tariffs impact the earnings and the cost of living of male and female headed households. We combine tariff and household survey data from 54 low and middle income countries. These are countries with important gender differences and high protection. We quantify the level of tariff protection and we establish differences in the sources of income and expenditure across female-headed and male-headed households. We first document that developing countries still levy substantial tariffs, both on manufacturing and agricultural goods. In turn, female-headed households are under-represented in agricultural production and spend a greater share of their budget on food purchases than their male-headed counterparts. As a consequence, female-headed families are hurt more by tariffs. In 42 of our 54 countries, protectionism has an anti-female real income bias, which exacerbates gender income inequality. The paper is organized as follows. In Section 2, we discuss the methods used in the analysis, and, in Section 3 we describe the data. Section 4 presents the results and Section 5 ends with a summary and conclusions. 2 Method Our framework is based on the two-step approach of the trade and poverty literature. In the first step, the imposition of import tariffs leads to changes in domestic prices. Subsequently, 1 those changes in prices and wages affect households as consumers, producers and wage earners. The impact would depend on the degree of households’ dependence on various goods and factors of production, and the economic sectors in which they are employed. In our study, we are interested in assessing if the effect of trade protection is different for male and female-headed households. We follow Artuc, Porto and Rijkers (2019), who use an extended agricultural household model to define household welfare (Singh, Squire and Strauss, 1986; Benjamin and Deaton, 1993), and we derive the welfare effects using first order approximations (Deaton, 1989; Porto, 2006; Nicita, Olarreaga and Porto, 2014). In these models, household well-being is measured with the indirect utility function, which depends on prices and expenditures. Assuming that households spend all their income on consumption goods (both traded and non-traded), we can directly focus on the impacts of trade policies on the level of household real income. As in the literature, then, trade policy affects prices and prices affect households as consumers and as income earners. The real income xh of household h is given by the ratio of nominal income y h and a household-specific index price P h : yh (1) xh = . Ph We define a Cobb-Douglas price index: (sh i) (2) P h = Πi pi , where pi is the price of good i and sh i is the expenditure share of good i by household h (Friedman and Levinshon, 2002). Trade policy changes, and thus price changes, affect households as income earners (the numerator in 1). We use an extended definition of household income. Different authors have focused on different components of household income and two main approaches have been developed. Deaton (1989) and Benjamin and Deaton (1993), for instance, work with income earned from sales of agricultural production. Porto (2006) introduces wage income in a study 2 of Argentina. Nicita (2009) and Ural Marchand (2012) adopt a similar strategy for the cases of Mexico and India. Nicita, Olarreaga and Porto (2014) investigate both sources of income, sales of agricultural products and wages. Here, on top of labor and agricultural sales income, we also explore impacts on income earned in household enterprises or small businesses. This is a priori important because the development literature has recently emphasized the role of home businesses in the economy of poor households (Banerjee and Duflo, 2011). Concretely, it is useful to think about household income as being determined in a farm-household model, as in Sign, Squire and Strauss (1986). In these models, household income is given by: h h (3) yh = yw (p) + πj ( p) + T h , j h h where yw is labor wage income and πj are profits obtained from various household production activities j , and T h are transfers (e.g., public transfers or taxes). Labor income includes wages earned in potentially different activities in both traded and non-traded sectors. This could capture wage earnings in traded manufacturing sectors or in services, retail trade or in h the government. In πj , we include both net income from the sales of agricultural products and profits from household enterprises. This distinction is useful. In the case of trade policy affecting primary products such as maize, agricultural income captures income from the sales of maize grains, while enterprise income may capture instead income from sales of ground maize. In other cases, such as trade policy affecting processed goods, there will only be income from family enterprises. Both labor income and enterprise income consequently depend on a vector of product prices p. To explore how, we begin with labor markets and labor income. To simplify, we assume that households supply one unit of labor inelastically and that labor is homogeneous. There are many sectors in the economy, and we assume that the household supplies all its labor to one of these sectors. Furthermore, we assume a specific factor, Ricardo-Viner model with sector-specific labor. This is a simple yet convenient representation of the short-run impacts in labor markets because, with specific labor, the wage in one sector is affected 3 one-to-one by the price change. It follows that h ∂yw ∂wi (4) = = 1. ∂pi ∂pi Many households own land and use it to produce agricultural products, either food crops such as maize, wheat, or rice, or cash crops, such as cotton, tobacco, cocoa, and so on. Cash crops are directly sold in the market. Instead, food crops are often partly consumed by the household (this is auto- or own-consumption) while the surplus is also sold in the h market. These are sources of agricultural production income, which we include in πj . The household may also own a (small) business. Some households do some basic processing to the agricultural product and sell the processed food. Others may own tools and perform odd-jobs or may operate (small) shops in various non-traded sectors. In these cases, profits in non-traded sectors are assumed not to be affected by the tariffs. If a household earns profits in a traded sector i, e.g. cotton, then the first order effect of h a change in price pi on income is the quantity produced qi (e.g., kilograms of cotton). This is the Hotelling Lemma: ∂πi h (5) = qi . ∂pi To wrap up, equation (4) describes how wage income responds to prices, while equation (5) does so for profit income. We now turn to the impacts of trade policy on households as consumers (the denominator in (1)). To do this, we need to derive how the household-specific index price (2) changes in response to a price change pi . We have that ∂P h Ph (6) = sh i . ∂pi pi Combining (4), (5) and (6), the proportional change in the real income of household h is 4 given by: ∂ ln xh (7) = φh h h w + φi − si , ∂ ln pi where, again, sh h i is the share of good i in the consumption bundle of household h, φw is the share of labor income in total income, and φh i is the share of income from sales or from home businesses in traded sector i. The interpretation of this equation is straightforward. Following an exogenous price change d ln pi and given the endogenous responses of wages, the first order effects on real income can be well-approximated with the corresponding expenditure and income shares. This is an extended version of the net-consumer, net-producer proposition (Deaton, 1989). A price increase hurts net-consumers and benefits net-producers, with the net position of a household defined in an extended model including not only consumption and production of traded goods, but also labor income and enterprise income. As in Nicita, Olarreaga and Porto (2014), we want to have a measure of the welfare effects generated by the entire structure of protection and for different trade policy instruments. To do this, we sum the changes in welfare in (7) over all traded goods i to obtain a formula for the proportional change in real income: (8) d ln xh = φh h h w + φi − si d ln pi . i As a final step to operationalize the formulas, we need an expression for the price change d ln pi . A convenient assumption is to work with a full pass-through model because then we can translate our measures of trade protection directly into domestic price changes. Inasmuch as the pass-through rate is homogeneous across households and, in particular, homogeneous across males and females, this assumption will not affect our conclusions at all. If the free trade price of the good is p∗ ∗ i (the world price), a tariff τi raises it to pi = pi (1 + τi ). Consequently, we can think of protection as increasing prices by (9) d ln pi = τi . 5 In the end, the estimable welfare effects of protection are given by (10) d ln xh = φh h h w + φi − si τi . i We now introduce an index of the anti-female bias in the structure of trade protection. We define trade policy as being anti-female if the existing structure of protection harms female-headed households more than male-headed households. If the existing structure of protection is anti-female, that would imply that the elimination of this protection structure would be pro-female, in the sense that the proportional change in welfare following trade liberalization would be larger for female-headed households than for male-headed households. Accordingly, we propose an indicator of anti-female bias in the structure of trade protection given by the difference between the percentage change in welfare due to trade protection of a-vis the average male-headed household: the average female-headed household vis-` (11) F = E [d ln xh |H h = f ] − E [d ln xh |H h = m], where H h is an indicator variable equal to m for male-headed households and equal to f for female-headed households. A negative indicator (F < 0) implies that the current trade protection harms female-headed households more than male-headed households. In this situation, trade liberalization would be biased in favor of female-headed households because the percentage change in income for female-headed households would be larger than for male-headed households. It is very important to understand the scope and limitations of our measure of gender bias F . The index is a conditional mean and, as such, it should be interpreted as a differential welfare effect for female-headed households relative to male-headed households. This captures gender inequality issues related to decisions mostly made by household heads, but not necessarily to other features of gender differences. For instance, the index captures the role of some consumption decisions (such as for instance food and education expenditures) and of some income-generating decisions or limitations (such as crop growing choices or barriers to labor employment). We explore some of these mechanisms below. Of course, 6 our measure of gender bias does not necessarily apply to females in general and it would be wrong to extrapolate our conclusions to the female population. But we believe that nevertheless, our results provides very useful insights into gender inequality considerations and trade protection. 3 Data The estimation of the welfare impact of trade policy requires a combination of household survey and trade policy data. The household surveys provide information on consumption and production of traded goods, and labor and enterprise income. These data are needed to calculate the income and the expenditure shares. The trade policy data provide the information on tariffs needed to calculate the price changes. To quantify the anti-female bias of trade policy, we use harmonized data on incomes and expenditures from 54 representative household surveys (Artuc, Porto and Rijkers, 2019). The data comprises 521,639 households which are representative of approximately 1.8 billion people in developing countries. On the expenditure side, we cover 53 agricultural and food items, such as corn, wheat, rice, oils, cotton and tobacco; 5 manufacturing items; 5 non-tradeable services; and 4 other expenditure categories. On the income side, we keep track of income derived from the sales of the same 53 food items we cover on the expenditure side, as well as from wage income across 10 sectors, non-farm household enterprise sales across 10 sectors, and various types of transfers. The household surveys are harmonized with detailed tariff data from WITS, the World Integrated Trade Solution. For each product classification in the household surveys, we calculate the average tariff from WITS, using import value shares as weights. Table 1 presents the household surveys for the 54 developing countries considered in the analysis. The table reports the name of the survey, the year when the data was collected, and the number of households in each survey. The analysis includes 28 Sub-Saharan African (SSA) countries, 4 countries in the Middle East and North Africa (MENA) region, 8 countries in Europe and Central Asia, 5 countries in South Asia, 5 countries in East Asia and Pacific, 7 and 4 countries in the Latin American and Caribbean region. We use information on the gender of the household head to classify households as male-headed and female-headed households. Table 2 provides summary statistics for each household survey. We report the average log per capita expenditure and average household size for the whole survey and by gender of the head of the household. It is interesting to note that the average level of livelihood for male-headed household is not systematically higher than for female-headed households. In addition, outside the Sub-Saharan Africa (SSA) region, male-headed household are on average larger than female-headed households in all countries. In SSA, male-headed households are larger in 22 of the 28 countries. The import tariff data is summarized in Table 3 and in Figure 1. For our analysis we have grouped the import tariff data in three categories of goods: staple agricultural products, non-staple agricultural products and manufactured imported products. Table 3 reveals a large dispersion in the average import tariffs. For instance in Sub-Saharan Africa, for staple agricultural products the average tariff applied in Burundi is 23.8 percent but only 1.8 percent in the Comoros. The same applies for non-staple agricultural products where Rwanda applies the highest average import tariff at 30.1 percent while Liberia’s average tariff for this category is 5.6 percent. In manufacturing, tariffs in Sub-Saharan African countries vary from 6.8 percent (Zambia) up to 23 percent (Cameroon). Tariff dispersion across countries and within regions is also observed in other developing regions. For instance, in South Asia, import tariffs are very high for agricultural products in Bhutan (43.7 percent) and very low in Pakistan (3.7 percent), while there is far less dispersion of tariffs for manufactured products (ranging from 15.3 to 23.5 percent). In Latin America, Ecuador applies lower average tariff across all good categories than the other three countries in the region. In our sample, there are countries like Georgia, Indonesia, Ukraine and Iraq that apply average import tariffs that are 5 percent or lower, and countries like the Central Africa Republic, Rwanda and Bhutan where they are 20 percent or higher. Since we are assuming perfect pass-through from tariff cuts to domestic prices, Table 3 not only displays the average tariff but also the corresponding price change caused by protection. Given this, we would expect to observe the largest welfare changes in highly protected countries like Cameroon, Rwanda or Bhutan 8 but the overall impact will depend on the combination of consumption and income effects that sometimes cancel out. We will discuss this in detail in section 4 below. However, before doing that, we need to consider the incidence of the three categories of goods in the expenditure and income of the households in our sample. How much households are affected as consumers depends on the level of exposure to the price change each household faces. This is captured by the expenditure shares sh i . Table 4 reports the share of expenditures that male-headed and female-headed households spend on tradable goods (staple agriculture, non-staple agriculture, and manufacture), non-tradeable goods, other goods and home consumption. Not surprisingly, in most countries, the share households spend on agricultural goods (food) tend to be very large. Of the 54 countries in our sample, households in 50 countries dedicate on average more than one quarter of their resources for staple agricultural products. This implies that tariffs in food products may have negative welfare impact, particularly for urban households that tend to be net food consumers. Manufactured goods are on average the second largest expenditure component in developing countries but there is a lot of variability across countries. They account for more than a quarter of total expenditures in Ghana, Malawi, South Africa, Iraq, Kyrgyz Republic, Moldova, and Bhutan but less than 10 percent in Guinea Bissau, Mali, Zambia, Armenia, Uzbekistan, Sri Lanka, and Papua New Guinea. Home consumption also accounts for an important part of total expenditures in some countries. In eleven countries in our sample, it accounts for more than one-quarter of total expenditures but in seven countries home consumption is on average less than 5 percent of total expenditures. Table 4 also presents the difference in consumption patterns depending on the gender of the head of the households. A close observation of the data shows that in 47 of the 54 countries, female headed households spend on average a larger share of their budget on staple agricultural products than male-headed households do. Except for Bhutan, all the other exceptions are in Sub-Saharan Africa: Benin, Kenya, Malawi, Mozambique, Uganda and Zambia. When considering all tradable goods together, female-headed households are slightly more exposed as consumers to international trade than male-headed households (share of tradable goods in expenditure of 64.6 percent versus 62.2 percent). 9 Table 5 displays the income shares for male and female-headed households in our sample of developing countries. These shares capture household exposure to trade for households as income earners (φh h i and φw in the notation of the model). Our methodology allows us to identify six sources of income: the sales of staple agricultural goods, the sales of non-staple agricultural goods, wages, income from family enterprises, other income, and production of goods for home consumption. In our sample, wages are the largest source of income on average. However, there are significant differences between the share of income coming from wages among male-headed households (31 percent) and female-headed households (23 percent). In fact, in all countries but Benin, Guinea, Mali, Niger, Cambodia, Vietnam and Nicaragua, the share of income coming from wages is larger for male than for female-headed households. The second largest source of income in our sample is other income. This category is the largest source of income for female-headed households (29.3 percent) and includes transfers and remittances. Home consumption is on average the third largest source of income in our sample. However, once again, there is a lot of heterogeneity across countries. It accounts for more than 40 percent in six countries in our sample but less than 5 percent in seven countries. When looking at the gender of the head of the household, there is not a significant difference between the share of income from home consumption among male (22.4 percent) and female-headed households (21.8 percent). 4 The Anti-Female Bias of Tariff Protection 4.1 The Anti-Female Bias The main finding of this paper is that the tariff protection of developing countries creates a gender bias in trade policy: In our sample, tariff protectionism is anti-female in 42 of the 54 countries. The level and intensity of the gender bias are illustrated in Figure 2. In the map, more intense shades of violet mean more intense anti-female bias. Countries with pro-female biases are plotted in shades of orange. As explained above, throughout our discussion, we refer to a female-head bias of trade policy only (rather than a more general female bias). The gender bias is presented in Table 6 for the 42 countries with an anti-female bias. At 10 –2.5 percent, the most negative female bias is estimated in Burkina Faso. This bias means that female-headed households lose 2.5 percent more than male-headed households in terms of their economic well-being. In particular, women lose 3 percent from protection but men lose less, 0.5 percent. We find similar patterns in other African countries, such as Cameroon, Mali and The Gambia, where the bias is –2.2 percent. This pattern also generalizes to other continents. In Nicaragua, for instance, the female bias is –2.1 percent; in Uzbekistan, it is –1.5 percent; in Vietnam, –1.2 percent; and in Bangladesh, –1.2 percent. All the anti-female biases are statistically significant at the 1 percent level, except for Azerbaijan which is significant at the 5 percent level. In the remaining 12 countries, there is a pro-female bias instead. These are shown in Table 7. In Benin, for example, the bias is 2.2 percent and it is the result of higher losses for males (–4.0 percent) than for females (–1.8 percent). Note that the pro-female bias is actually low in most cases. It exceeds 1 percent only in Bhutan, Uganda and Benin. Moreover, the pro-female bias is statistically significant in only 6 of the 12 countries. Together, these results illustrate the ubiquity of an anti-female bias: the bias is in general negative and highly statistically significant; when it is positive, it tends to be very small in magnitude and often not statistically significant. These differential impacts on household well-being exacerbate gender inequality. Across countries in our sample, the real income of male-headed households is 2.6 percent higher, on average, than the real income of female-headed households. Tariff protection contributes to 0.6 percentage point of this 2.6 percent difference. This means that, worldwide across poor and lower-middle-income countries, protectionism accounts for about a fourth of the status-quo gender income inequality. 4.2 Mechanisms Why does this happen? The anti-female bias occurs because tariffs affect households both as consumers and as income earners and there are inherent differences in the income sources and spending patterns of male and female headed households. This creates a “female nominal income bias of trade policy” and a “female cost-of-living bias of trade policy.” 11 4.2.1 The female nominal income bias The “female nominal income bias” of trade policy occurs because tariff protection raises the incomes of females relatively less than the incomes of males. The magnitudes of the nominal income female biases are reported in Tables 6 and 7, columns 3-6. The nominal income bias is very strong: in 47 of the 54 countries, the nominal income bias is anti-female. Moreover, countries with larger income female biases are countries with larger overall biases. As can be seen in panel a) of Figure 3, the correlation between the nominal income female bias and the overall female bias is extremely strong, 0.76, and the slope of the linear fit is 1.04, very close to (and statistically undistinguishable from) 1. The anti-female income bias of protection is a major source of gender inequality. The major underlying driver of the female nominal income bias is that female headed households participate proportionately less in agriculture than male-headed ones and, consequently, benefit relatively less from the protection of agricultural incomes offered by agricultural tariffs. To illustrate this mechanism, we compute the difference in the share of income derived from agricultural sales between female- and male-headed households, φf m ag − φag in terms of the notation of our theoretical framework. This difference captures how much more exposed to tariff protection females are relative to males. A positive (negative) difference implies women would benefit more (less) from protection as producers. In panel b) of Figure 3, we present the strong correlation between the nominal income female bias and the differential share of income derived from agricultural sales, that is, the relative exposure to agricultural income. Countries where female headed households derive a smaller share of their income from agricultural sales than male-headed ones (i.e., where relative agricultural exposure φf m ag − φag is negative) tend to have larger anti-female income biases. By the same token, countries where relative female agricultural sales exposure is positive (φf m ag − φag > 0) tend to be countries with a pro-female income bias. Across countries, on average, female-headed households enjoy lower income gains than male-headed ones. There are several theories that can explain why females participate less in market agriculture than males. A review can be found in the World Development Report (2012). In many less developed countries, social norms that affect marriage and fertility decisions, 12 and that determine the role of women outside her household, often lead to lower female labor force participation (Duflo, 2012; Jayachandran, 2015). In the case of agriculture, the nature of the production process in these economies often requires physical strength, endowing men with a comparative advantage in agricultural work (Jayachandran, 2015). As pointed out by Alessina, Giuliano and Nunn (2013), these explanations often interact with each other. Culture and social institutions combine with the strenuous labor requirements of agriculture to further limit female labor participation. In addition, there is evidence that the need to utilize non-labor inputs up-front such as seeds, fertilizers and pesticides often imposes additional barriers to female participation (because of credit constraints and insufficient productive assets). This happens in commercial staple agriculture and, especially, in non-staple agriculture such as cotton or tobacco (Porto, Depetris Chauvin and M. Olarreaga, 2011). Another (complementary) explanation is that female-headed households are more reliant on remittances and transfers. Indeed, Appleton (1996) shows that higher remittances receipts in female-headed households have been instrumental in preventing increases in gender inequality in Uganda, while Amuedo-Dorantes and Pozo (2006) show that remittances adversely affected female but not male labor force participation in Mexico (see algo De la Briere, Sadoulet, De Janvry, and Lambert 2002). We find evidence consistent with their hypothesis in the context of trade policy. Panel c) of Figure 3 presents a scatter plot of the nominal income bias of tariff protection (as before) and the bias in exposure to remittances and other transfers from relatives and friends (that is, the differences between the share of income derived from remittances and transfers between female- and male-headed households, φf m r − φr ). Unlike the case of agricultural income, we observe that when female-headed households are more exposed to remittances and transfer income, the anti-female bias of trade policy is amplified. This is consistent with the notion that women as income earners enjoy less protection from trade policy than males because of a higher reliance on remittances and transfers. 13 4.2.2 The female cost-of-living bias There is also a negative “female cost-of-living bias” of trade protection: tariffs raise consumer prices and the cost of living for female-headed households more than the cost of living for male-headed households. As consumers, females thus lose more from tariff protection than males (see columns 7-9 of Tables 6 and 7). The cost-of-living bias is strong as well. As shown in panel a) of Figure 4, the correlation between the female cost-of-living bias and the overall female bias is 0.69: countries with larger anti-female cost-of-living biases are countries with large anti-female bias overall. However, the cost-of-living bias is weaker than the female nominal income bias. In fact, the cost-of-living bias is negative (that is, there is an anti-female bias) in 33 of the 54 countries, while the anti-female nominal income bias is negative in 47 countries. The major underlying driver of this result is that female headed households spend a larger share of their budget on food products than male-headed ones. This can be seen in panel b) of Figure 4, which shows the strong negative correlation between the cost-of-living female bias and the relative female exposure to agricultural spending (the difference in the budget share spent on agricultural goods between female- and male-headed households, sf m ag − sag ). When female headed households spend a larger share of their budget on food items than male ones, so that sf m ag − sag > 0, the cost-of-living bias turns negative and large. Several interrelated theories can rationalize the anti-female cost-of-living bias. The fact that female-headed households are less reliant on agriculture implies that, ceteris paribus (i.e., at a given level of food requirement), they need to rely more on purchases of agricultural products on the market. Moreover, evidence from economics (Angelucci and Attanasio, 2013; Braido, Olinto and Perrone, 2012; Hoddinott and Haddad, 1995; Doss 2006), medicine e, (Johnson and Large Rogers, 1993) and behavioral science (Christov-Moore, Simpson, Coud´ Grigaityte, Iacoboni, and Ferrari, 2014) shows that women are more altruistic and care more about child nutrition than males, which raises food budget shares. When tariffs increase food prices, female-headed households are disproportionately hurt. 14 5 Conclusion Countries use tariffs to raise government revenue and protect the incomes of producers and workers. Yet, evidence from 54 low and middle income countries shows that tariff protection creates an (inadvertent) anti-female welfare bias that exacerbates gender inequality. In the absence of trade protection, across the countries in our sample the real incomes of female headed households would be 2.4 percentage points higher, while those of male headed households would be 1.8 percentage points higher. The prevailing pattern of tariffs thus exacerbates inequality in the incomes of female- relative to male-headed households by 0.6 percentage points on average. Tariff protection accounts for about a fourth of the gender income inequality across countries. The reason can be found in the seminal work of Angus Deaton: female-headed households derive a smaller share of their income from and spend a larger share of their budget on agricultural products than male-headed households. Tariff protection in low-income and developing countries is characterized by relatively high duties on food and agriculture. Female headed households not only benefit less from the protection of agricultural incomes but are also disproportionately impacted by higher food prices as consumers. Female-headed households consequently bear the brunt of protectionism. Figure 5 neatly summarizes these findings. It plots the female bias in trade protection index against the female net exposure to agricultural protection, which is the difference between the net agricultural sales income share (i.e. the income share minus the expenditure share, (φf f m m ag − sag ) − (φag − sag ), for female-headed households vis-a-vis male-headed ones. The correlation between net agricultural sales exposure and the female bias is strongly positive: in those countries where female-headed households are net producers in agriculture relative to male headed ones and thus benefit more from protectionism, tariffs have a pro-female bias. By contrast, in those countries in which female-headed households are net consumers relative to male-headed ones—the majority of the countries in our sample—the female bias turns negative. 15 References Alesina, A., P. Giuliano, and N. Nunn (2013). “On the Origins of Gender Roles: Women and the Plough,” The Quarterly Journal of Economics, 128(2), 469–530. Amuedo-Dorantes, C. and Pozo. S. (2006). “Migration, Remittances, and Male and Female Employment Patterns”, American Economic Review Paper and Proceedings 96.2: 222-226. Angelucci, M. and O. Attanasio (2013). “The Demand for Food of Poor Urban Mexican Households: Understanding Policy Impacts Using Structural Models,” American Economic Journal: Economic Policy, vol. 5, no. 1, pp. 146–178. Appleton, S. (1996). “Women-headed Households and Household Welfare: An Empirical Deconstruction for Uganda,” World Development, Volume 24, Issue 12, 1811–1827. Artuc, E., G. Porto, and B. Rijkers (2019). “Trading Off the Income Gains and the Inequality Costs of Trade Policy,” Journal of International Economics, forthcoming. Banerjee, A. and Duflo, E. (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty, Public Affairs. Benjamin, D. and A. Deaton. (1993). “Household Welfare and the Pricing of Cocoa and ote d’Ivoire: Lessons from the Living Standards Surveys,” The World Bank Coffee in Cˆ Economic Review vol. 7, pp. 293–318. Braido, L., P. Olinto, and H. Perrone (2012). “Gender Bias in Intrahousehold Allocation: Evidence from an Unintentional Experiment,” The Review of Economics and Statistics, 94:2, pp. 552–565. e, K. Grigaityte, M. Iacoboni, and P. F. Ferrari Christov-Moore, L., E. A. Simpson, G. Coud´ (2014). “Empathy: Gender Effects in Brain and Behavior,” Neuroscience and Biobehavioral Reviews, 46(4), pp. 604–627. 16 De la Briere, B., Sadoulet, E., De Janvry, A., and Lambert, S. (2002). The roles of destination, gender, and household composition in explaining remittances: an analysis for the Dominican Sierra. Journal of Development Economics, 68(2), 309-328. Deaton, A. (1989). “Rice Prices and Income Distribution in Thailand: a Non-parametric Analysis,” Economic Journal, 99 (Supplement), pp. 1–37. Doss, C. (2006). “The Effects of Intrahousehold Property Ownership on Expenditure Patterns in Ghana,” Journal of African Economies, 15(1), pp. 149–180. Duflo, E. (2012). “Women Empowerment and Economic Development,” Journal of Economic Literature, 50:1051–79. Friedman, J. and Levinsohn, J. (2002). “The Distributional Impacts of Indonesia’s Financial Crisis on Household Welfare: A ‘Rapid Response’ Methodology,” World Bank Economic Review 16, pp. 397–423. Hoddinott, J. and L. Haddad (1995). “Does Female Income Share Influence Household Expenditures? Evidence from Cote d’Ivoire,” Oxford Bulletin of Economics and Statistics, 57(1), pp. 77–96. Jayachandran, S. (2015). “The Roots of Gender Inequality in Developing Countries,” Annual Review of Economics, 7:63–88. Johnson, F.C., and B. Lorge Rogers (1993). “Children’s Nutritional Status in Female-headed Households in the Dominican Republic,” Social Science & Medicine, Volume 37, Issue 11, pp. 1293–1301. 8. Nicita, A. (2009). “The Price Effect of Trade Liberalization: Measuring the Impacts on Household welfare,” Journal of Development Economics, vol. 89(1), pp. 19–27. Nicita, A., M. Olarreaga, and G. Porto (2014). “Pro-Poor Trade Policy in Sub-Saharan Africa,” Journal of International Economics, Vol. 92(2), pp. 252–265. 17 Porto, G. (2006). “Using survey data to assess the distributional effects of trade policy,” Journal of International Economics 70, 1, 140–160. Porto, G., N. Depetris Chauvin, and M. Olarreaga (2011). Supply Chains in Export Agriculture, Competition, and Poverty in Sub-Saharan Africa, CEPR and the World Bank. Singh, I., L. Squire, and J. Strauss, eds. (1986). Agricultural Household Models: Extensions, Applications and Policy. Baltimore, Johns Hopkins Press for the World Bank. Ural Marchand, B. (2012). “Tariff Pass-Through and the Distributional Effects of Trade Liberalization,” Journal of Development Economics, 99(2), pp. 265–281. World Bank. World Development Report 2012: Gender Equality and Development. Washington, DC: World Bank, 2011. 18 Figure 1 Tariff Protection Across the Developing World 50 Bhutan Bhutan 40 30 Bhutan Cameroon Burundi 20 10 0 Non-staple Agriculture Manufactures Staple Agriculture Notes: Data come from the World Integrated Trade Solutions, Trade Analysis and Information System (WITS-TRAINS). The figure is a box-plot depicting variation in average tariffs by broad product category across countries. The box represents the interquartile range, with the line in the middle depicting the median average tariff across countries. Dots represent outliers. 19 Figure 2 The Gender Bias of Tariff Protection Across the Developing World Notes: World map of the male bias of tariff, which measures how much more male-headed households gain from tariffs than female-headed ones, expressed in percentage of household-status quo expenditure. Countries with anti-female trade protection are plotted in violet, with more intense shades of violet indicating more intense anti-female bias. The few countries with pro-female bias are plotted in shades of orange. 20 Figure 3 The Gender Bias and the Nominal Income Gender Bias (a) the nominal income female bias Benin 2 Uganda Bhutan female bias in trade protection 1 Central African Republic Burundi Indonesia Jordan Malawi Kenya Comoros Rwanda Ukraine 0 Georgia Iraq Azerbaijan Togo Nigeria Mozambique Kyrgyz Republic Moldova Tajikistan Egypt Zambia Sri Lanka Guinea Cote d'Ivoire Tanzania Yemen Mongolia -1 Guatemala Cambodia Papua New Guinea Vietnam Ghana Bangladesh Bolivia Pakistan Uzbekistan Niger Ethiopia -2 Nicaragua Gambia Mali Cameroon Burkina Faso -3 -2 -1 0 1 2 female nominal income bias in trade protection (b) market agricultural income (c) remittances and transfers 2 2 female nominal income bias in trade protection female nominal income bias in trade protection Benin Benin Bhutan Bhutan 1 1 Central African Republic Central African Republic Burundi Mali Burundi Mali Jordan Jordan Comoros Comoros 0 0 Ukraine Ukraine Iraq Rwanda Iraq Rwanda Guinea Bissau Guinea Bissau Kyrgyz Republic Indonesia Kyrgyz Republic South Africa Georgia Togo Nepal Nigeria Papua New Guinea Papua New Guinea Togo Azerbaijan Tanzania Kenya Nepal Mongolia Moldova Guinea Liberia Moldova Mongolia Yemen Uganda Uganda Guinea Yemen Mozambique Mozambique Sri Lanka Cote d'Ivoire Cote d'Ivoire Cambodia Cambodia Malawi Ecuador Malawi Ecuador Madagascar Madagascar Niger Niger Guatemala Bangladesh Guatemala Bangladesh Pakistan Pakistan -1 -1 Zambia Zambia Gambia Gambia Bolivia Bolivia Ethiopia Ghana Ethiopia Ghana Cameroon Cameroon Nicaragua Nicaragua Vietnam Vietnam Egypt Egypt -2 -2 Burkina Faso Burkina Faso -10 -5 0 5 10 -40 -20 0 20 40 60 female exposure to agricultural protection female exposure to remittances/transfers Notes: Panel a): plot of the total female bias of trade policy against the nominal income bias of trade policy. The total female bias measures how much more female-headed households gain from tariffs than male-headed ones, expressed in percentage of household-status quo expenditure. The female nominal income bias measures how much more female-headed households gain from tariffs than male-headed ones as producers, expressed in percentage of household-status quo expenditure. Panel b) plots the nominal income bias against the relative exposure of females to market agricultural income (the difference in the share of market agricultural income for female- relative to male-headed households). Panel c) plots the nominal income bias against the relative exposure of females to remittances and other transfers (the difference in the share of remittances and transfer income for female- relative to male-headed households). 21 Figure 4 The Gender Bias and the Cost-of-living Gender Bias (a) the cost-of-living female bias Benin 2 Bhutan Uganda female bias in trade protection 1 Central African Republic Burundi Jordan Indonesia Comoros Malawi Ukraine Rwanda Kenya 0 Iraq Georgia Azerbaijan Nigeria Togo Mozambique Kyrgyz Republic Tajikistan Moldova Sri Lanka Zambia Egypt Liberia Cote d'Ivoire Mongolia -1 Yemen Cambodia Papua New Guinea Guatemala Ecuador Bangladesh Bolivia Pakistan Vietnam Ghana Uzbekistan Niger Ethiopia -2 Mali Gambia Nicaragua Cameroon Burkina Faso -3 -3 -2 -1 0 1 2 female cost-of-living bias in trade protection (b) agriculture expenditures 2 Uganda female cost-of-living bias in trade protection 1 Egypt Malawi Benin Kenya Indonesia Zambia Vietnam Rwanda Mozambique Ghana Bhutan Comoros Burundi 0 Bolivia Mauritania Togo South Africa Cote d'Ivoire Nigeria Nepal Guinea Pakistan Tajikistan Madagascar Ecuador Yemen Ethiopia Tanzania Liberia Mongolia Burkina Faso Niger Papua New Guinea Cameroon Gambia -1 Uzbekistan -2 Mali -3 -10 0 10 20 female exposure to agriculture expenditure Notes: Panel a): plot of the total female bias of trade policy against the cost-of-living bias of trade policy. The total female bias measures how much more female-headed households gain from tariffs than male-headed ones, expressed in percentage of household-status quo expenditure. The cost-of-living bias is the difference between the effects of tariffs only on the cost of living index for female- and male-headed households. Panel b) plots the cost-of-living bias against the relative exposure of females to food expenditures (the difference in the share of agriculture and food expenditures for female- relative to male-headed households). 22 Figure 5 The Gender Bias and Women as Net-Consumers of Agriculture Benin 2 Uganda Bhutan female bias in trade protection 1 Central African Republic Burundi Jordan Comoros Indonesia Kenya Malawi Rwanda Ukraine 0 Georgia Iraq Nigeria Azerbaijan Togo Armenia South Africa Tajikistan Sri Lanka Zambia Guinea Egypt Cote d'Ivoire Mongolia Yemen Tanzania -1 Madagascar Guatemala Papua New Guinea Cambodia Vietnam Ecuador Ghana Bolivia Bangladesh Pakistan Niger Uzbekistan Ethiopia -2 Nicaragua Gambia Mali Cameroon Burkina Faso -3 -20 -10 0 10 20 female net exposure to agricultural protection Notes: plot of the total female bias of trade policy against the net relative exposure of females to agricultural protection. The total female bias measures how much more female-headed households gain from tariffs than male-headed ones, expressed in percentage of household-status quo expenditure. Relative exposure to agricultural protection is the difference in the income share, net of the expenditure share, for female- relative to male-headed households (i.e., a measure of the net-producer or net-consumer status of the household). 23 Table 1 Household Surveys Country Year Obs Survey Benin 2003 5296 Questionnaire Unifi´ ˆ e sur les Indicateurs de Base du Bien-Etre Burkina Faso 2003 8413 Enquˆ ete sur les Conditions de Vie des M´ enages Burundi 1998 6585 Enquˆ ete Prioritaire sur les Conditions de Vie des Populations Cameroon 2001-2002 10881 Deuxi` eme Enquˆ ete Camerounaise Aupr` es des M´enages Central Af. Rep. 2008 6828 Enquˆ ete Centrafricaine pour le Suivi-Evaluation du Bien-ˆ etre Comoros 2004 2929 Enquˆ egrale aupr` ete Int´ es des M´enages Cˆote d’Ivoire 2008 12471 Enquˆ ete sur le Niveau de Vie des M´ enages Egypt 2008-2009 23193 Household Income, Expenditure and Consumption Survey Ethiopia 1999-2000 16505 Household Income, Consumption and Expenditure Survey The Gambia 1998 1952 Household Poverty Survey Ghana 2005-2006 8599 Living Standards Survey V Guinea 2012 7423 Enquˆ ete L´ ere pour l’Evaluation de la Pauvret´ eg` e Guinea Bissau 2010 3141 Inquerito Ligeiro para a Avalic˜ ao da Pobreza Kenya 2005 13026 Integrated Household Budget Survey Liberia 2014-2015 4063 Household Income and Expenditure Survey Madagascar 2005 11661 Permanent Survey of Households Malawi 2004-2005 11167 Second Integrated Household Survey Mali 2006 4449 Enquˆ ete L´eg` ere Int´ ee aupr` egr´ es des M´enages Mauritania 2004 9272 Enquˆ ete Permanente sur les Conditions de Vie des M´ enages Mozambique 2008-2009 10696 Inqu´erito sobre Or¸ camento Familiar Niger 2005 6621 Enquˆ ete Nationale sur les Conditions de Vie des M´ enages Nigeria 2003-2004 18603 Living Standards Survey Rwanda 1998 6355 Integrated Household Living Conditions Survey Sierra Leone 2011 6692 Integrated Household Survey South Africa 2000 25491 General Household Survey Tanzania 2008 3232 Household Budget Survey Togo 2011 5464 Questionnaire des Indicateurs de Base du Bien-ˆ etre Uganda 2005-2006 7350 National Household Survey Zambia 2004 7563 Living Conditions Monitoring Survey IV Notes: List of household surveys, name, year of data collection and sample size. 24 Table 1 (cont.) Household Surveys Country Year Obs Survey Armenia 2014 5124 Integrated Living Conditions Survey Bangladesh 2010 12117 Household Income and Expenditure Survey Bhutan 2012 8879 Living Standards Survey Cambodia 2013 3801 Socio-Economic Survey Indonesia 2007 12876 Indonesian Family Life Survey Iraq 2012 24895 Household Socio-Economic Survey Jordan 2010 11110 Household Expenditure and Income Survey Krygyz Republic 2012 4962 Intergrated Sample Household Budget and Labor Survey Mongolia 2011 11089 Household Socio-Economic Survey Nepal 2010-2011 5929 Living Standards Survey Pakistan 2010-2011 16178 Social and Living Standards Measurement Survey Papua New Guinea 2009 3776 Household Income and Expenditure Survey Sri Lanka 2012-2013 20335 Household Income and Expenditure Survey Tajikistan 2009 1488 Tajikistan Panel Survey Uzbekistan 2003 9419 Household Budget Survey Vietnam 2012 9306 Household Living Standard Survey Yemen 2005-2006 12998 Household Budget Survey Azerbaijan 2005 4797 Household Budget Survey Georgia 2014 10959 Household Integrated Survey Moldova 2014 4836 Household Budget Survey Ukraine 2012 10394 Sampling Survey of the Conditions of Life of Ukraine’s Households Bolivia 2008 3900 Encuesta de Hogares Ecuador 2013-2014 28680 Encuesta de Condiciones de Vida Guatemala 2014 11420 Encuesta Nacional de Condiciones de Vida Nicaragua 2009 6450 Encuesta on de Niveles de Vida Nacional de Hogares sobre Medici´ Notes: List of household surveys, name, year of data collection and sample size. 25 Table 2 Summary Statistics Country log p.c. expenditure household size gender of head all male female all male female male female Benin 9.35 9.51 9.32 4.95 3.36 5.27 0.17 0.83 Burkina Faso 9.13 9.13 9.13 5.57 5.73 3.81 0.92 0.08 Burundi 7.49 7.61 7.16 4.97 5.22 4.25 0.74 0.26 Cameroon 10.18 10.17 10.20 4.94 5.30 3.81 0.76 0.24 Central African Republic 8.94 8.94 8.97 4.56 4.73 3.93 0.78 0.22 Comoros 9.88 9.85 10.01 5.77 6.06 4.70 0.79 0.21 Cote d’Ivoire 10.06 10.35 9.94 4.76 2.82 5.52 0.28 0.72 Egypt 5.72 5.69 5.87 4.68 4.95 3.38 0.83 0.17 Ethiopia 4.46 4.44 4.49 4.81 5.26 3.63 0.72 0.28 Gambia 5.14 5.08 5.49 7.78 8.06 6.30 0.84 0.16 Ghana 12.28 12.27 12.32 4.20 4.58 3.23 0.72 0.28 Guinea 5.69 5.69 5.74 6.45 6.65 5.11 0.87 0.13 Guinea Bissau 9.44 9.42 9.52 8.19 8.45 7.35 0.77 0.23 Kenya 7.67 7.67 7.67 5.09 5.08 5.19 0.90 0.10 Liberia 8.88 8.88 8.89 4.29 4.50 3.77 0.70 0.30 Madagascar 9.72 9.72 9.74 4.85 5.13 3.68 0.81 0.19 Malawi 7.21 7.25 7.07 4.52 4.74 3.77 0.77 0.23 Mali 10.36 10.38 10.15 8.53 8.77 5.97 0.92 0.08 Mauritania 9.57 9.55 9.66 5.66 5.95 4.41 0.81 0.19 Mozambique 6.87 6.88 6.84 4.68 5.02 3.85 0.71 0.29 Niger 9.15 9.14 9.31 6.37 6.55 4.05 0.93 0.07 Nigeria 7.37 7.34 7.54 4.91 5.23 3.22 0.84 0.16 Rwanda 8.44 8.53 8.23 4.96 5.29 4.26 0.68 0.32 Sierra Leone 11.81 11.80 11.84 5.60 5.66 5.45 0.73 0.27 South Africa 6.19 6.47 5.76 3.85 3.67 4.13 0.61 0.39 Tanzania 10.58 10.84 10.53 5.17 3.90 5.44 0.17 0.83 Togo 9.92 9.90 9.96 5.04 5.37 3.89 0.78 0.22 Notes: Authors’ calculations based on household survey data. The table reports the average log of per capita expenditure and the average household size for the entire sample and for male- and female-headed households separately. The proportion of male- and female-headed households are reported in the last two columns. 26 Table 2 (cont.) Summary Statistics Country log p.c. expenditure household size gender of head all male female all male female male female Uganda 10.45 10.70 10.39 5.80 3.93 6.24 0.19 0.81 Zambia 11.20 11.19 11.23 5.62 5.92 4.65 0.77 0.23 Armenia 10.83 10.82 10.85 3.84 4.25 3.00 0.67 0.33 Bangladesh 0.75 0.74 0.78 4.50 4.68 3.39 0.86 0.14 Bhutan 8.33 8.31 8.39 4.53 4.57 4.43 0.71 0.29 Cambodia 5.71 5.71 5.71 4.47 4.66 3.72 0.79 0.21 Indonesia 6.23 6.21 6.29 3.98 4.24 2.87 0.81 0.19 Iraq 12.13 12.12 12.18 6.74 6.88 5.62 0.88 0.12 Jordan 4.80 4.77 5.00 5.39 5.65 3.68 0.87 0.13 Kyrgyz Republic 7.94 7.88 8.03 4.11 4.55 3.37 0.63 0.37 Mongolia 11.28 11.25 11.36 3.80 4.00 3.10 0.78 0.22 Nepal 8.56 8.53 8.62 4.85 5.22 3.82 0.73 0.27 Pakistan 8.35 8.37 8.15 6.39 6.54 4.96 0.91 0.09 Papua New Guinea 5.07 5.08 5.02 5.12 5.26 4.27 0.86 0.14 Sri Lanka 9.31 9.33 9.26 3.88 4.06 3.29 0.77 0.23 Tajikistan 5.54 5.53 5.61 6.68 6.92 5.52 0.83 0.17 Uzbekistan 9.93 9.89 10.06 5.11 5.41 4.11 0.77 0.23 Vietnam 7.30 7.27 7.40 3.85 4.05 3.26 0.74 0.26 Yemen 8.80 8.80 8.76 7.53 7.74 4.95 0.93 0.07 Azerbaijan 12.61 12.59 12.69 4.85 5.07 3.71 0.84 0.16 Georgia -1.96 -1.96 -1.94 3.61 3.93 2.97 0.67 0.33 Moldova 7.51 7.51 7.50 2.57 2.82 2.20 0.59 0.41 Ukraine 7.09 7.06 7.09 2.58 3.10 2.45 0.20 0.80 Bolivia 6.62 6.60 6.68 3.86 4.09 3.18 0.75 0.25 Ecuador 5.38 5.38 5.37 3.66 3.84 3.14 0.74 0.26 Guatemala 6.85 6.82 6.98 4.77 5.00 3.93 0.78 0.22 Nicaragua 7.18 7.17 7.20 4.70 4.80 4.50 0.66 0.34 Notes: Authors’ calculations based on household survey data. The table reports the average log of per capita expenditure and the average household size for the entire sample and for male- and female-headed households separately. The proportion of male- and female-headed households are reported in the last two columns. 27 Table 3 Average Tariffs Country Staple Non-Staple Manufactures Country Staple Non-Staple Manufactures Agric. Agric. Agric. Agric. Benin 12.2 16.9 10.8 Armenia 6.9 7.3 6.7 Burkina Faso 12.0 18.3 9.3 Bangladesh 7.4 4.9 18.8 Burundi 23.8 21.6 10.8 Bhutan 43.7 46.1 23.5 Cameroon 13.8 22.5 23.0 Cambodia 13.0 6.4 10.1 Central African Rep. 16.6 23.7 21.8 Indonesia 6.0 1.9 6.1 Comoros 1.8 10.4 8.9 Iraq 5.0 5.0 5.0 Cˆote d’Ivoire 10.4 10.2 9.2 Jordan 7.9 18.6 8.3 Egypt 7.1 28.0 18.0 Kyrgyz Republic 6.1 6.1 4.0 Ethiopia 10.1 13.3 12.4 Mongolia 5.3 6.5 4.9 Gambia 6.6 13.5 13.9 Nepal 9.0 11.7 13.9 Ghana 16.4 11.6 14.3 Pakistan 3.7 8.1 17.4 Guinea 13.9 18.9 9.5 Papua New Guinea 4.7 12.4 0.9 28 Guinea Bissau 13.5 15.7 12.8 Sri Lanka 7.8 16.3 15.3 Kenya 18.7 25.1 11.0 Tajikistan 7.4 5.8 8.3 Liberia 6.3 5.6 16.4 Uzbekistan 14.8 11.4 8.5 Madagascar 8.3 9.6 14.8 Vietnam 11.1 6.3 9.8 Malawi 8.2 22.0 9.3 Yemen 4.4 7.6 7.7 Mali 11.2 16.8 8.8 Mauritania 9.2 14.8 15.9 Azerbaijan 5.7 4.0 10.4 Mozambique 8.8 13.9 7.4 Georgia 6.0 6.4 0.5 Niger 12.2 17.6 9.3 Moldova 7.9 10.7 3.3 Nigeria 11.3 19.8 11.0 Ukraine 4.8 5.1 4.8 Rwanda 21.0 30.1 11.0 Sierra Leone 11.8 16.2 9.7 Bolivia 11.0 12.6 15.1 South Africa 7.1 6.4 16.8 Ecuador 14.4 15.4 14.0 Tanzania 12.6 29.1 10.7 Guatemala 10.3 10.2 7.4 Togo 11.6 18.6 9.5 Nicaragua 12.1 9.8 9.1 Uganda 11.4 29.7 10.0 Zambia 17.1 19.7 6.8 Average 10.8 14.4 10.9 Pop. weighted average 9.0 12.1 11.8 GDP weighted average 8.1 10.2 10.9 Notes: Authors’ calculations based on United Nations COMTRADE and UNCTAD TRAINS data. The average tariff is expressed in percentage points. Table 4 Expenditure Shares Male- and Female-Headed Households Country Agriculture Non-Staple Agric. Manufactures Non-trade Other Goods Home Consumption male female male female male female male female male female male female Benin 41.9 32.9 2.3 4.1 26.5 22.7 12.1 10.4 4.6 6.4 12.6 23.4 Burkina Faso 24.0 29.0 12.2 13.2 15.9 18.1 8.5 12.7 8.6 4.1 30.8 23.0 Burundi 40.1 46.7 11.5 5.2 20.3 19.8 12.3 13.7 10.8 10.7 4.9 3.9 Cameroon 45.5 50.8 6.6 4.7 17.7 15.4 14.5 15.2 6.3 4.5 9.4 9.3 Central African Republic 39.4 43.7 18.7 17.8 21.8 19.7 7.9 7.8 0.2 0.2 12.0 10.8 Comoros 47.5 50.2 9.4 9.7 11.0 9.9 17.5 16.4 5.5 4.4 9.1 9.4 Cote d’Ivoire 35.7 35.8 3.9 3.8 22.8 21.9 21.7 20.0 8.2 5.9 7.9 12.6 Egypt 45.1 47.6 5.1 3.7 13.9 13.1 31.3 31.9 2.1 1.9 2.6 1.8 Ethiopia 21.3 27.8 8.9 9.7 16.7 18.0 2.9 3.1 10.6 8.7 39.6 32.8 Gambia 44.9 47.6 11.6 10.9 11.2 11.7 11.6 14.4 10.5 9.5 10.2 6.0 Ghana 7.3 8.8 1.6 0.9 31.1 30.1 31.4 37.3 16.4 13.0 12.2 9.8 29 Guinea 32.4 37.1 12.0 11.3 18.2 19.0 12.4 13.9 5.0 5.0 20.0 13.7 Guinea Bissau 50.3 52.3 6.4 6.1 6.5 6.5 7.0 8.4 4.3 3.9 25.4 22.8 Kenya 30.2 30.1 9.8 9.5 23.5 23.2 24.9 24.6 2.3 2.5 9.3 10.1 Liberia 44.7 52.8 7.1 7.3 12.9 11.2 15.1 15.2 2.7 1.9 17.4 11.6 Madagascar 35.6 44.3 7.4 6.3 11.7 13.2 3.6 3.5 0.7 0.7 41.1 32.0 Malawi 26.3 24.1 6.0 4.6 28.8 30.0 7.3 5.3 0.8 0.4 30.7 35.5 Mali 24.1 42.4 7.4 10.1 3.9 7.1 4.8 6.4 0.6 0.2 59.3 33.8 Mauritania 46.6 49.7 11.4 11.6 14.7 14.3 6.8 6.3 0.7 0.6 19.7 17.5 Mozambique 45.1 43.9 5.3 5.3 14.4 15.3 3.9 3.8 1.5 1.6 29.8 30.1 Niger 35.2 42.9 8.7 9.2 17.0 18.0 6.5 7.1 10.3 8.9 22.3 13.8 Nigeria 47.3 51.2 3.7 3.2 18.4 16.1 9.3 10.0 0.5 0.4 20.9 19.2 Rwanda 23.7 25.6 5.2 4.4 10.8 10.3 9.4 8.2 30.9 24.9 20.1 26.6 Sierra Leone 45.8 47.0 10.5 10.0 12.4 12.5 10.5 11.9 4.5 4.1 16.3 14.4 South Africa 28.1 37.2 8.5 8.0 31.7 31.9 17.8 14.2 13.8 8.6 0.1 0.1 Tanzania 27.0 29.9 7.5 6.5 21.4 18.6 12.0 9.4 7.0 6.0 25.1 29.6 Togo 37.8 42.9 7.9 7.5 15.1 15.2 26.3 25.8 6.1 4.6 6.7 4.0 Notes: Authors’ calculations based on household survey data. The table reports the average expenditure share for different aggregates of goods for male- and female-headed households. Table 4 (cont.) Expenditure Shares Male- and Female-Headed Households Country Agriculture Non-Staple Agric. Manufactures Non-trade Other Goods Home Consumption male female male female male female male female male female male female Uganda 27.7 23.4 10.3 7.0 16.6 16.4 16.6 18.2 2.9 1.8 25.9 33.3 Zambia 53.6 53.3 5.0 4.4 6.7 5.2 10.1 10.1 0.7 0.4 21.2 23.9 Armenia 54.8 57.0 8.6 6.7 7.4 6.4 20.1 23.4 0.0 0.0 9.1 6.4 Bangladesh 45.0 47.4 9.2 8.1 13.9 15.3 16.3 15.6 4.4 4.6 11.3 9.0 Bhutan 27.7 25.1 7.2 7.1 25.0 26.6 16.3 14.4 12.4 12.3 11.4 14.4 Cambodia 30.3 34.6 12.5 11.9 16.2 15.4 19.1 17.8 8.6 8.3 13.3 12.1 Indonesia 28.9 30.6 12.2 9.7 11.2 12.0 22.7 23.3 14.2 10.3 10.7 14.1 Iraq 32.1 33.9 5.3 4.9 35.4 33.8 23.0 23.1 3.3 3.6 0.8 0.7 Jordan 34.7 37.4 15.4 14.5 19.0 20.3 29.6 26.2 1.2 1.5 0.2 0.1 Kyrgyz Republic 41.9 43.1 5.5 5.3 24.9 26.6 13.3 14.2 3.9 2.6 10.6 8.3 Mongolia 46.1 53.0 9.1 7.9 13.7 16.4 8.3 10.3 1.1 1.1 21.6 11.3 30 Nepal 26.7 28.8 5.0 4.1 11.6 12.1 27.2 28.7 4.8 4.5 24.7 21.7 Pakistan 27.5 34.1 7.6 8.2 23.8 19.5 12.7 14.1 6.7 6.6 21.7 17.5 Papua New Guinea 35.7 39.5 12.6 10.2 5.8 5.8 5.0 4.8 13.8 13.4 27.2 26.3 Sri Lanka 32.1 34.1 10.3 9.9 9.4 9.5 19.8 18.1 22.0 20.5 6.3 8.0 Tajikistan 37.4 39.7 5.4 5.9 25.0 23.9 15.1 13.6 3.1 3.5 14.0 13.3 Uzbekistan 35.0 41.5 5.0 5.4 7.4 8.1 10.1 11.9 1.9 1.8 40.6 31.3 Vietnam 36.5 39.6 6.8 5.9 19.9 18.8 15.0 16.0 10.5 10.8 11.4 8.8 Yemen 38.7 44.6 20.7 16.5 17.3 20.2 15.5 14.6 4.4 3.5 3.3 0.7 Azerbaijan 50.9 51.7 6.0 5.2 21.0 20.7 11.6 11.7 1.6 1.6 8.9 9.0 Georgia 33.6 35.2 8.1 7.0 23.4 24.2 27.6 27.6 4.9 4.2 2.3 1.7 Moldova 15.4 17.8 2.2 2.0 31.9 32.5 14.6 16.3 7.7 5.9 28.2 25.5 Ukraine 44.6 44.9 11.8 11.3 20.2 19.9 17.3 16.1 0.1 0.1 5.9 7.7 Bolivia 43.8 44.6 8.1 6.7 16.7 17.1 23.4 25.3 1.3 1.3 6.8 5.0 Ecuador 41.4 44.4 3.8 3.9 16.7 17.0 21.5 21.6 9.0 7.0 7.6 6.0 Guatemala 37.3 39.3 5.2 5.6 19.4 21.1 17.4 19.1 4.7 5.2 15.9 9.7 Nicaragua 38.6 45.0 5.0 4.7 15.9 17.8 18.5 20.2 1.0 0.9 21.1 11.4 Notes: Authors’ calculations based on household survey data. The table reports the average expenditure share for different aggregates of goods for male- and female-headed households. Table 5 Income Shares Male- and Female-Headed Households Country Agriculture Non-Staple Agric. Wages Businesses Other Home Production male female male female male female male female male female male female Benin 6.8 15.7 4.2 11.1 6.6 14.4 0.0 0.0 66.0 35.9 16.4 22.8 Burkina Faso 20.1 8.0 3.0 2.2 13.7 10.1 17.3 22.4 11.2 32.2 34.8 25.1 Burundi 38.8 41.3 30.8 25.5 9.8 3.3 8.9 3.6 6.9 22.8 4.8 3.6 Cameroon 16.1 13.3 0.2 0.0 28.4 23.4 22.6 24.6 0.0 0.0 32.7 38.7 Central African Republic 42.6 42.4 9.3 9.1 2.6 1.7 3.5 3.3 3.4 7.2 38.6 36.2 Comoros 25.5 19.2 4.0 2.3 28.9 18.4 16.4 16.4 8.8 20.4 16.4 23.4 Cote d’Ivoire 7.4 7.0 13.5 13.7 22.4 14.8 29.5 28.0 13.8 16.4 13.3 20.1 Egypt 7.6 3.8 7.6 3.8 45.2 20.6 16.7 7.3 22.8 64.3 0.2 0.1 Ethiopia 15.8 10.3 0.6 0.4 5.5 4.2 23.0 27.7 8.5 17.4 46.6 40.0 Gambia 2.8 2.0 7.8 0.9 47.0 39.1 21.3 25.2 5.8 23.3 15.3 9.5 Ghana 9.8 5.1 6.2 3.5 60.7 52.9 0.0 0.0 7.7 23.2 15.7 15.4 31 Guinea 17.5 17.2 3.3 2.8 7.0 7.2 18.3 17.5 11.6 26.4 42.2 29.0 Guinea Bissau 5.6 5.2 23.3 17.2 23.6 15.4 7.2 9.5 7.6 19.4 32.7 33.3 Kenya 21.8 21.8 3.1 3.3 35.5 34.5 5.3 5.5 17.8 17.3 16.5 17.7 Liberia 9.6 11.6 3.7 2.8 26.6 11.7 27.0 34.5 6.6 17.8 26.5 21.7 Madagascar 28.4 21.6 3.1 2.4 23.3 21.7 12.3 16.5 3.3 12.7 29.6 25.1 Malawi 17.9 20.4 5.2 2.8 23.6 14.2 13.3 10.8 3.2 5.8 36.9 46.0 Mali 8.4 12.3 2.8 2.7 8.2 9.5 10.4 11.2 13.7 34.6 56.5 29.9 Mauritania 14.1 10.3 0.0 0.0 4.1 2.3 10.6 7.7 27.1 46.2 44.0 33.5 Mozambique 11.4 8.2 8.7 3.2 17.0 10.6 10.6 9.7 7.2 16.9 45.1 51.5 Niger 17.7 12.3 3.2 1.3 4.0 4.3 1.4 2.1 36.8 56.5 36.8 23.5 Nigeria 15.6 15.1 6.2 2.5 33.2 32.8 9.6 13.8 3.4 10.8 31.9 24.9 Rwanda 11.1 9.3 3.8 3.2 26.9 20.0 2.8 2.0 11.9 11.8 43.5 53.6 Sierra Leone 19.8 15.8 4.8 4.0 12.1 7.9 12.3 16.2 17.1 26.6 33.8 29.5 South Africa 0.7 0.5 0.0 0.0 67.0 35.4 0.0 0.0 31.5 63.2 0.8 0.9 Tanzania 10.6 10.9 3.6 2.9 25.5 22.5 8.4 5.0 10.4 11.8 41.4 46.9 Togo 9.5 5.7 6.8 5.3 33.5 18.3 35.5 43.4 6.7 20.5 8.0 6.7 Notes: Authors’ calculations based on household survey data. The table reports the average income share for different production activities for male- and female-headed households. Table 5 (cont.) Income Shares Male- and Female-Headed Households Country Agriculture Non-Staple Agric. Wages Businesses Other Home Production male female male female male female male female male female male female Uganda 9.6 9.8 2.7 3.0 30.4 19.5 17.9 18.9 13.2 14.1 26.1 34.7 Zambia 6.3 3.9 1.9 1.0 23.5 12.8 13.5 12.7 16.8 25.2 38.0 44.4 Armenia 10.2 7.1 0.2 0.0 37.4 30.4 8.0 3.4 34.9 52.4 9.4 6.6 Bangladesh 32.5 36.0 2.2 1.8 33.6 17.4 16.0 4.0 8.1 36.5 7.6 4.2 Bhutan 11.9 15.5 0.0 0.0 49.6 30.9 8.8 10.4 7.7 10.7 22.0 32.5 Cambodia 25.5 19.3 0.5 0.3 30.0 33.8 24.1 21.8 4.2 9.4 15.7 15.4 Indonesia 5.3 1.9 1.4 0.6 40.9 27.1 0.6 0.4 19.2 28.1 32.6 41.9 Iraq 8.3 7.0 1.6 1.6 51.0 35.2 12.5 6.8 25.7 48.9 0.9 0.5 Jordan 1.4 3.4 1.9 3.5 47.8 28.2 10.0 2.0 37.9 61.4 0.9 1.5 Kyrgyz Republic 13.8 9.6 1.6 1.2 41.1 39.0 14.6 7.7 21.5 37.3 7.4 5.2 Mongolia 11.3 5.8 0.4 0.1 39.1 34.8 9.4 6.2 27.5 46.3 12.3 6.7 32 Nepal 4.4 3.3 1.4 0.7 29.0 17.2 12.9 6.0 16.3 38.0 36.0 34.9 Pakistan 7.8 4.7 3.2 2.3 48.5 21.1 12.9 4.3 9.7 53.0 17.8 14.6 Papua New Guinea 13.5 15.3 6.9 4.6 15.2 12.8 9.6 9.8 17.6 19.9 37.2 37.7 Sri Lanka 14.6 8.3 4.5 4.7 51.0 41.3 20.9 13.9 0.0 0.0 9.0 31.8 Tajikistan 0.8 1.6 1.7 0.9 38.9 37.5 8.8 7.1 21.2 28.0 28.6 24.9 Uzbekistan 8.3 4.5 0.2 0.1 20.5 19.5 11.3 10.9 17.8 30.2 41.8 34.8 Vietnam 23.7 13.7 3.9 2.6 34.2 38.4 20.0 18.4 11.1 19.0 7.2 7.9 Yemen 8.0 5.7 9.4 6.5 46.1 13.2 16.3 3.6 17.2 70.4 3.0 0.6 Azerbaijan 29.0 27.6 2.0 1.6 27.6 18.6 3.0 2.6 24.4 35.1 14.0 14.4 Georgia 8.9 4.1 2.0 1.8 31.8 24.0 9.1 5.2 46.0 63.6 2.2 1.3 Moldova 7.1 3.2 2.4 1.8 34.2 25.1 2.2 1.1 21.5 33.2 32.7 35.7 Ukraine 2.3 2.9 0.0 0.0 53.9 40.9 0.2 0.1 39.2 50.2 4.4 5.9 Bolivia 7.1 4.0 8.7 4.5 38.0 30.0 28.2 24.7 11.0 32.0 7.0 4.9 Ecuador 10.4 11.0 1.3 0.6 51.0 41.0 17.9 13.5 13.4 28.5 6.0 5.4 Guatemala 7.1 3.9 2.7 3.5 46.6 39.9 17.7 18.9 11.3 24.5 14.6 9.3 Nicaragua 12.8 7.4 3.8 1.0 39.8 41.5 18.5 18.3 8.7 22.7 16.5 9.1 Notes: Authors’ calculations based on household survey data. The table reports the average income share for different production activities for male- and female-headed households. Table 6 Countries with Anti-Female Bias From Protectionism Cuntry Welfare Effects Income Effects Expenditure Effects Males Females Bias Males Females Bias Males Females Bias Burkina Faso -0.50 -3.05 -2.55 5.58 3.52 -2.05 -6.07 -6.57 -0.50 (0.06) (0.15) (0.16) (0.05) (0.11) (0.12) (0.03) (0.08) (0.09) Cameroon -6.31 -8.52 -2.21 5.96 4.59 -1.37 -12.27 -13.11 -0.84 (0.08) (0.12) (0.14) (0.07) (0.10) (0.12) (0.04) (0.06) (0.08) Mali 0.48 -1.70 -2.18 2.95 3.27 0.32 -2.47 -4.97 -2.50 (0.05) (0.26) (0.27) (0.03) (0.16) (0.17) (0.05) (0.24) (0.24) Gambia -1.46 -3.61 -2.15 6.31 5.15 -1.16 -7.77 -8.76 -0.99 (0.14) (0.26) (0.29) (0.11) (0.19) (0.22) (0.09) (0.19) (0.21) Nicaragua -1.20 -3.26 -2.06 4.69 3.16 -1.54 -5.89 -6.41 -0.52 (0.08) (0.07) (0.11) (0.07) (0.06) (0.09) (0.04) (0.05) (0.07) Ethiopia -1.75 -3.45 -1.69 5.45 4.12 -1.33 -7.20 -7.57 -0.37 (0.06) (0.07) (0.09) (0.04) (0.05) (0.07) (0.03) (0.04) (0.06) Uzbekistan -3.13 -4.65 -1.52 3.52 3.18 -0.34 -6.65 -7.83 -1.18 (0.04) (0.08) (0.09) (0.03) (0.05) (0.06) (0.04) (0.07) (0.08) Niger -1.80 -3.30 -1.50 4.44 3.56 -0.88 -6.24 -6.86 -0.62 (0.06) (0.18) (0.19) (0.05) (0.14) (0.15) (0.03) (0.10) (0.10) Ghana 2.24 0.96 -1.28 6.16 4.80 -1.36 -3.92 -3.84 0.08 (0.07) (0.10) (0.12) (0.06) (0.09) (0.11) (0.03) (0.05) (0.06) Pakistan -2.28 -3.54 -1.26 3.36 2.42 -0.95 -5.64 -5.95 -0.31 (0.04) (0.10) (0.11) (0.03) (0.08) (0.08) (0.02) (0.06) (0.06) Vietnam -0.76 -2.00 -1.25 6.39 4.86 -1.53 -7.14 -6.86 0.28 (0.06) (0.10) (0.11) (0.05) (0.08) (0.10) (0.03) (0.05) (0.06) Bolivia -2.53 -3.72 -1.20 4.02 2.83 -1.19 -6.54 -6.55 -0.01 (0.09) (0.11) (0.14) (0.08) (0.10) (0.12) (0.04) (0.07) (0.08) Bangladesh -0.29 -1.48 -1.19 6.84 5.91 -0.92 -7.13 -7.39 -0.26 (0.06) (0.15) (0.16) (0.06) (0.13) (0.15) (0.02) (0.05) (0.06) Ecuador -2.70 -3.79 -1.09 4.54 3.80 -0.74 -7.25 -7.60 -0.35 (0.04) (0.05) (0.06) (0.03) (0.05) (0.06) (0.02) (0.04) (0.04) Madagascar 1.26 0.18 -1.08 5.15 4.35 -0.80 -3.88 -4.17 -0.28 (0.05) (0.09) (0.10) (0.04) (0.07) (0.08) (0.02) (0.04) (0.05) Guatemala -1.61 -2.67 -1.06 3.16 2.26 -0.91 -4.77 -4.92 -0.15 (0.03) (0.05) (0.06) (0.03) (0.04) (0.05) (0.02) (0.03) (0.04) Papua New Guinea -1.60 -2.63 -1.03 3.05 2.77 -0.28 -4.64 -5.39 -0.75 (0.05) (0.17) (0.18) (0.05) (0.12) (0.13) (0.05) (0.18) (0.19) Cambodia 3.26 2.27 -0.99 8.54 7.94 -0.60 -5.28 -5.68 -0.40 (0.12) (0.22) (0.25) (0.10) (0.20) (0.22) (0.04) (0.08) (0.09) Yemen -2.59 -3.54 -0.95 2.80 2.25 -0.55 -5.39 -5.79 -0.40 (0.03) (0.09) (0.10) (0.02) (0.06) (0.07) (0.02) (0.07) (0.07) Mongolia 0.11 -0.75 -0.85 3.38 2.96 -0.42 -3.27 -3.71 -0.44 (0.03) (0.05) (0.05) (0.02) (0.03) (0.03) (0.02) (0.03) (0.04) Liberia -1.35 -2.18 -0.83 3.08 2.69 -0.39 -4.44 -4.87 -0.44 (0.06) (0.08) (0.10) (0.05) (0.07) (0.09) (0.03) (0.04) (0.05) Notes: Authors’ calculations. The table presents the welfare effects of tariff protection, the gender bias and the nominal income and cost-of-living sources of gains and gender biases. Standard errors are reported in parenthesis. All numbers are expressed in percent of household status-quo expenditure. 33 Table 6 (cont.) Countries with Anti-Female Bias From Protectionism Cuntry Welfare Effects Income Effects Expenditure Effects Males Females Bias Males Females Bias Males Females Bias Tanzania -3.54 -4.37 -0.83 4.90 4.53 -0.37 -8.45 -8.90 -0.45 (0.26) (0.13) (0.29) (0.21) (0.09) (0.23) (0.19) (0.09) (0.21) Egypt -2.71 -3.51 -0.80 4.06 2.32 -1.74 -6.77 -5.84 0.93 (0.03) (0.04) (0.05) (0.02) (0.03) (0.04) (0.02) (0.04) (0.04) Cote d’Ivoire -2.91 -3.69 -0.79 4.16 3.57 -0.59 -7.06 -7.26 -0.20 (0.08) (0.05) (0.10) (0.07) (0.04) (0.08) (0.04) (0.03) (0.05) Sri Lanka 0.45 -0.31 -0.76 4.51 3.96 -0.55 -4.05 -4.26 -0.21 (0.04) (0.07) (0.09) (0.04) (0.07) (0.08) (0.02) (0.04) (0.05) Zambia -5.75 -6.51 -0.76 3.29 2.17 -1.11 -9.04 -8.69 0.35 (0.06) (0.08) (0.10) (0.05) (0.06) (0.08) (0.03) (0.05) (0.06) Guinea -2.74 -3.45 -0.72 5.03 4.63 -0.40 -7.77 -8.09 -0.32 (0.05) (0.10) (0.11) (0.04) (0.08) (0.09) (0.03) (0.06) (0.07) Tajikistan -1.84 -2.42 -0.58 2.81 2.54 -0.26 -4.65 -4.97 -0.32 (0.06) (0.12) (0.13) (0.04) (0.08) (0.09) (0.04) (0.10) (0.10) Nepal -1.24 -1.80 -0.56 3.09 2.73 -0.35 -4.33 -4.53 -0.21 (0.04) (0.05) (0.06) (0.03) (0.03) (0.04) (0.03) (0.04) (0.05) Moldova -0.52 -1.06 -0.54 2.29 1.87 -0.42 -2.81 -2.93 -0.12 (0.05) (0.04) (0.06) (0.04) (0.03) (0.05) (0.02) (0.03) (0.04) Sierra Leone -4.13 -4.64 -0.51 3.24 2.90 -0.34 -7.37 -7.54 -0.17 (0.07) (0.11) (0.13) (0.05) (0.08) (0.10) (0.04) (0.06) (0.07) South Africa -2.34 -2.84 -0.50 1.78 1.45 -0.33 -4.11 -4.29 -0.18 (0.03) (0.04) (0.05) (0.03) (0.03) (0.04) (0.02) (0.02) (0.03) Kyrgyz Republic -0.43 -0.91 -0.49 2.70 2.45 -0.24 -3.12 -3.37 -0.24 (0.03) (0.04) (0.05) (0.02) (0.03) (0.03) (0.02) (0.02) (0.03) Guinea Bissau -1.87 -2.34 -0.47 3.60 3.37 -0.24 -5.48 -5.70 -0.23 (0.08) (0.14) (0.16) (0.05) (0.09) (0.10) (0.08) (0.14) (0.16) Mauritania 1.40 0.98 -0.42 7.72 7.37 -0.35 -6.31 -6.39 -0.08 (0.05) (0.09) (0.10) (0.03) (0.06) (0.06) (0.04) (0.07) (0.08) Togo -2.02 -2.44 -0.42 5.11 4.76 -0.34 -7.13 -7.20 -0.08 (0.07) (0.11) (0.13) (0.06) (0.11) (0.12) (0.03) (0.06) (0.07) Mozambique -3.54 -3.95 -0.40 3.72 3.22 -0.50 -7.27 -7.17 0.10 (0.05) (0.07) (0.08) (0.04) (0.05) (0.06) (0.03) (0.05) (0.06) Nigeria -3.23 -3.60 -0.37 5.09 4.80 -0.28 -8.32 -8.41 -0.08 (0.04) (0.11) (0.12) (0.04) (0.09) (0.10) (0.02) (0.05) (0.06) Armenia -2.38 -2.64 -0.26 1.79 1.45 -0.34 -4.17 -4.09 0.08 (0.04) (0.05) (0.06) (0.03) (0.03) (0.05) (0.02) (0.03) (0.03) Azerbaijan -2.47 -2.70 -0.23 3.74 3.38 -0.36 -6.20 -6.08 0.13 (0.06) (0.11) (0.12) (0.05) (0.09) (0.10) (0.03) (0.07) (0.08) Georgia -0.94 -1.17 -0.23 1.32 1.00 -0.31 -2.26 -2.17 0.08 (0.03) (0.03) (0.04) (0.02) (0.02) (0.03) (0.01) (0.02) (0.02) Iraq -1.61 -1.73 -0.12 1.86 1.68 -0.18 -3.47 -3.41 0.06 (0.01) (0.02) (0.02) (0.01) (0.02) (0.02) (0.01) (0.02) (0.02) Notes: Authors’ calculations. The table presents the welfare effects of tariff protection, the gender bias and the nominal income and cost-of-living sources of gains and gender biases. Standard errors are reported in parenthesis. All numbers are expressed in percent of household status-quo expenditure. 34 Table 7 Countries with Pro-Female Bias From Protectionism Country Welfare Effects Income Effects Expenditure Effects Males Females Bias Males Females Bias Males Females Bias Rwanda 0.14 0.17 0.04 5.25 5.09 -0.16 -5.11 -4.92 0.20 (0.10) (0.15) (0.18) (0.07) (0.11) (0.13) (0.06) (0.09) (0.10) Ukraine -3.27 -3.20 0.07 1.39 1.34 -0.05 -4.66 -4.54 0.12 (0.03) (0.01) (0.04) (0.02) (0.01) (0.02) (0.02) (0.01) (0.02) Kenya -2.93 -2.80 0.13 5.70 5.29 -0.41 -8.63 -8.09 0.55 (0.06) (0.17) (0.18) (0.05) (0.15) (0.16) (0.04) (0.12) (0.13) Malawi -2.40 -2.26 0.15 4.66 3.96 -0.69 -7.06 -6.22 0.84 (0.05) (0.08) (0.10) (0.04) (0.06) (0.07) (0.03) (0.06) (0.06) Comoros 0.22 0.37 0.15 3.20 3.24 0.04 -2.98 -2.86 0.12 (0.06) (0.11) (0.12) (0.04) (0.09) (0.10) (0.04) (0.06) (0.07) Indonesia -1.90 -1.69 0.22 1.41 1.14 -0.27 -3.32 -2.82 0.49 (0.02) (0.04) (0.05) (0.01) (0.03) (0.03) (0.02) (0.04) (0.04) Jordan -4.09 -3.84 0.24 4.22 4.31 0.09 -8.31 -8.15 0.16 (0.04) (0.10) (0.11) (0.02) (0.05) (0.05) (0.04) (0.09) (0.10) Burundi -0.45 -0.09 0.36 8.58 8.89 0.31 -9.03 -8.98 0.05 (0.11) (0.20) (0.23) (0.10) (0.17) (0.20) (0.05) (0.10) (0.11) Central African Republic -4.30 -3.72 0.58 6.50 7.01 0.51 -10.80 -10.72 0.08 (0.08) (0.15) (0.17) (0.08) (0.14) (0.16) (0.05) (0.07) (0.08) Bhutan 0.33 1.73 1.40 14.16 15.49 1.32 -13.84 -13.76 0.08 (0.12) (0.20) (0.24) (0.10) (0.17) (0.20) (0.07) (0.11) (0.13) Uganda -3.02 -1.59 1.43 4.97 4.51 -0.46 -7.99 -6.10 1.89 (0.16) (0.07) (0.17) (0.12) (0.05) (0.13) (0.10) (0.04) (0.11) Benin -4.01 -1.83 2.18 4.10 5.77 1.67 -8.11 -7.60 0.51 (0.11) (0.08) (0.13) (0.07) (0.05) (0.09) (0.07) (0.04) (0.08) Notes: Authors’ calculations. The table presents the welfare effects of tariff protection, the gender bias and the nominal income and cost-of-living sources of gains and gender biases. Standard errors are reported in parenthesis. All numbers are expressed in percent of household status-quo expenditure. 35