Sowing the Seeds for Rural Finance: The Impact of Support Services for Credit Unions in Mexico

This paper studies the impact of a program that provides grants for technical assistance on the interest rates and outreach of credit unions in Mexico. Credit unions financing rural borrowers received grants in different years. The study uses propensity score matching and relies additionally on the timing of the grants to identify effects. The analysis shows that the program lowered lending interest rates by up to 2.6 percentage points (from a pre-program average of 17.8 percent). This drop appears to be due to lower operating costs and better risk management, as reflected in a lower nonperforming loan ratio. The program also raised credit unions' returns on assets and significantly increased the value of their loan portfolio.


Introduction
Despite recent progress in increasing access to finance for low-income populations, rural areas remain underserved by financial services in many countries. In developing economies, the unbanked live predominantly in rural areas (Demirguc-Kunt et al. 2015). The lack of finance creates challenges for agricultural producers and other agents in the rural economy. When formal financial services are not available, these agents resort to informal sources to meet their credit and savings needs that tend to be costlier and less reliable (Besley 1995, Fafchamps 2003, Ghate 1992, Onchan 1992, and Udry 1994and 1990. They may also forgo investment and income-generating activities and have a more limited ability to guard against risks (Dupas and Robinson 2013, Dercon 2002, Jacoby and Skoufias 1997, Kazianga and Udry 2006, and Morduch 1999. Rural areas typically have lower population density and more limited infrastructure, which can raise transaction costs and operating expenses for financial institutions. Often, these institutions also face other market failures in rural areas, for example related to lack of credit information and increased credit risk. In many countries, commercial banks have low credit volume in rural areas and concentrate their lending activities in urban areas instead. For some of these countries, non-bank financial institutions such as credit unions and cooperatives play a critical role in servicing the rural sector. However, small financial institutions catering to rural borrowers often lack economies of scale and tend to be constrained by poor governance, low technical capacity and limited funding sources (Branch and Evans 1999). These constraints are in turn passed on to the borrowers in the form of higher interest rates and credit rationing. This paper asks whether providing technical assistance to rural financial institutions can help lower their interest rates and increase outreach to more borrowers. We study these effects in the context of a support program to rural financial institutions provided by the Mexican 3 development finance institution Financiera Nacional de Desarrollo Agropecuario, Rural, Forestal y Pesquero (Rural Finance Development Agency, FND). FND's support program began in 2004 and primarily gives grants to rural financial institutions for technical assistance, which is provided through a network of accredited specialists.
Mounting evidence shows that technical assistance in the form of consulting services can increase productivity and growth of firms in a broad range of non-financial sectors (Bloom et al 2013;Bruhn, Karlan, and Schoar 2018;Anderson, Chandy, and Zia forthcoming). However, we are not aware of any study that examines the effect of technical assistance on financial institutions, which is of relevance also since it can have important spillovers to their clients.
The technical assistance financed through FND's support program may help financial institutions lower their lending interest rates and increase outreach for several reasons. First, the technical assistance may allow financial institutions to become more efficient, lowering operating costs. Second, it may teach financial institutions how to better screen and monitor loans, which can lower credit risk. Third, financial institutions may learn how to keep their books and financial statements in order, helping them raise funding at a lower interest rate. Depending on the market conditions, financial institutions can then pass these cost savings on to their clients in the form of better credit terms, or they might increase their profitability without any spillovers to their clients.
To trace out these channels, we first study the effects of the support program on lending interest rates and the following four key drivers of lending rates: (i) operating costs, (ii) credit risk, measured by the non-performing loan (NPL) ratio, (iii) the funding interest rate, and (iv) profits, measured by returns-on-assets (ROA). We then examine whether the program allowed financial institutions to expand by looking at the effect on their loan portfolio. 4 We measure the impact of FND's support program using data from the financial statements of 124 credit unions for the years 2002 through 2012. We obtained these data from the National Banking and Securities Commission (CNBV) and combined it with data from FND on the grants disbursed to credit unions. While other types of financial intermediaries also obtained grants through the program, historical financial statement data are not publicly available for these other intermediaries. A total of 65 credit unions received grants through the program between 2005 and 2008. We estimate the impact of the program on these credit unions by using a difference-indifference estimation that relies on the comparison of never treated and ever treated credit unions over time, as well as by considering the timing of treatment.
Selection into the program is likely driven by proximity to FND offices as well as percentage of the credit union loan portfolio going to borrowers in rural areas. We do not have information on these variables and thus initially compare all treated credit unions with untreated credit unions. We then use four different matching techniques to examine the robustness of the results. Samples created through caliper matching display identical averages and trends in outcome variables in the pre-program years 2002, 2003, and 2004. Our results suggest that the program lowered lending interest rates by up to 2.6 percentage points (from a pre-program average of 17.8 percent). This drop seems to be due to reduced operating costs and a lower NPL ratio. That is, the support program helped credit unions to make their operations more efficient and to better screen and/or monitor borrowers. We find no consistent change in the funding interest rate. However, we do see an increase in ROA as a result of the program. That is, increased operating efficiency and a lower NPL ratio are reflected in higher returns for the credit unions, but they also passed on some of these returns to their borrowers, in the form of lower interest rates. 5 We then study whether the reduced lending interest rate was accompanied by an increase in outreach and find that the program increased the value of the loan portfolio by about 50 percent. 2 Some grant recipients also received credit lines from FND. To make sure these credit lines are not driving the increase in outreach, we conduct two robustness checks. First, we control for borrowing from FND. Second, we restrict the sample to credit unions that did not borrow from FND during our period of observation. The estimated effects on outreach remain unchanged in these alternative specifications, suggesting that the program had a stand-alone effect and did not simply operate through increased borrowing from FND. Consistent with this conclusion, we find that even credit unions that did not borrow from FND during our period of observation increased their borrowing from members, banks, or other institutions due to the program (our data are not disaggregated enough to examine credit unions' borrowing by source).
Overall, our results suggest that high interest rates that reflect high operating costs/low efficiency and weak capacity to screen and monitor loans are an important constraint for credit unions in servicing more borrowers in rural areas. Our findings add to the literature that has examined the determinants of high intermediation costs of financial institutions in developing countries. Several studies have shown that intermediation costs are negatively correlated with a financial institution's operating efficiency and positively associated with its credit risk and cost of funding (Beck 2007;Beck and Hesse 2007;Brock and Rojas Suarez 2000;Martinez Peria and Mody 2004;Maudos and Solisa 2009).
Our findings also relate to the literature on matching grants, which evaluates interventions in which funds are provided to institutions or firms for technical assistance. This literature has faced many difficulties in studying the effectiveness of such grants (Campos et al 2014). Bruhn, Karlan, and Schoar (2018) may be the only paper so far to illustrate that matching grants for consulting services can have a positive effect on productivity and firm growth, suggesting that these grants do not simply subsidize services that firms would have hired in any case. Our paper adds to this evidence by showing that grants that cover 100 percent of the cost of technical assistance can promote the growth of credit unions.
The rest of this paper is organized as follows. Section 2 provides background information on rural finance in Mexico and on FND's support program. Section 3 describes the data. Section 4 discusses the identification strategy. Section 5 presents the results and section 6 concludes.

Rural Finance in Mexico
Mexico has relatively low credit depth in general: credit to the private sector in Mexico was at 27 percent of GDP in 2016, less than the average for the Latin America and Caribbean region (LCR, 44 percent) and Mexico's upper-middle-income country peers (51 percent). The supply of financial services in rural Mexico is low. The Mexican financial system is bank dominated, with many smaller financial institutions serving more remote areas. In rural areas, banks are focused on larger and more profitable agribusiness clients. Bank branches make up just 15 percent of total branches in rural areas, in comparison to 88 percent of total branches in urban areas (CNBV 2017). Non-bank financial institutions such as non-deposit taking financial institutions (SOFOMES), popular savings credit institutions (SOCAPs) and credit unions 7 primarily serve smaller enterprises and lower-income households. Many of these institutions lack economies of scale and some are constrained by limited funding sources and low technical capacity. However, they play a significant role in providing access to finance, particularly in rural areas where banks' operational costs may be high. Mexico also has six development banks, six government trusts, and four development finance institutions to support key sectors such as the rural economy.

FND and the Capacity Building Support Program
FND is a development finance institution that plays a key role in channeling finance to the rural economy in Mexico. It lends directly to producers in agriculture, livestock, fisheries, forestry, and other rural activities, and provides second-tier financing and capacity building support to rural financial intermediaries. 3 The most recent Mexican agricultural census (2007) showed that FND was the most common source of agricultural credit, reaching 17 percent of rural production units.
FND has worked with more than 500 financial intermediaries, including credit unions, cooperatives, SOFOMES, and producers' associations, as an important part of its strategy to increase access to credit in rural areas. Leveraging the distribution networks of these financial intermediaries helps not only increase FND's outreach but also to support the sustainable longterm development of private sector supply of rural finance, as these intermediaries can more easily service smaller loan amounts and low-income borrowers. As per its mandate, FND can provide financing to projects located within rural areas (both on the first and second tier).
Before a financial institution can receive an FND loan, it needs to go through an extensive accreditation process which includes questions about the institution's objectives as well as a 8 detailed review of products, financial statements, organizational structure and governance, quality of financial reporting, systems, and manuals for operation and risk management. To help improve institutional performance and outreach, FND uses public funds from the national budget (administered by the Ministry of Finance, SHCP) to provide a capacity building support program (Programa de Apoyos). This program provides grants for capacity building projects to financial intermediaries with the goal of getting them ready to receive FND loans and more broadly to develop sound rural financial institutions that can responsibly reach more rural borrowers. 4 Funds from FND's support program are allocated to financial intermediaries on a firstcome, first-served basis as described each year in the operating rules for FND's support program which are published in Mexico's Diario Oficial. The support program is not widely advertised and financial institutions that have interacted with FND for loans or that are located closer to FND offices may be more likely to find out about the program. This paper focuses on the impact of the support program on credit unions. Credit unions are member-based institutions that by law can offer credit only to their members. Credit unions are a common source of finance in rural areas, serving 8.8 percent of agricultural units (Agricultural Census 2007). They are supervised by the CNBV. Credit unions make up 20 percent of the financial intermediaries that have received grants through the support program. We do not study other types of financial intermediaries, such as cooperatives, SOFOMES, and producers' associations, due to a lack of data. Most of these other entities are not supervised by the CNBV, and we are not aware of a repository of their financial data.
The support program started in 2004 and continues until today. We have data for the period up to 2012. Table 1  The support program provides grants for distinct types of projects. We group these projects into three categories. The first category is technical assistance, which is provided through a network of accredited specialists with extensive experience in supporting financial institutions focused on lower-income populations. Examples of technical assistance include credit risk management, trainings to increase the skills and capacity of management and staff and IT systems selection. The second category includes equipment (i.e. computers, copiers and desks) to support the opening of branches and/or expansion of operational capacity in rural areas. The third category refers to financial support in the form of capitalization, guarantees to support credit access and interest rate subsidies. The median grant amounts were about 70,000 Mexican pesos (4,000 USD) for technical assistance, 340,000 pesos (19,000 USD) for equipment and 1,600,000 pesos (89,000 USD) for capitalization. Table 3 lists the number of grants disbursed in each year by type of project. About 64 percent of the grants disbursed between 2004 and 2012 were for technical assistance, followed by 23 percent for capitalization and 13 percent for equipment. All 69 credit unions in our sample obtained a grant for technical assistance at some point between 2004 and 2012. Of these, 46 also received a grant for either equipment or capitalization. In our analysis, we are not able to separate the effects of distinct types of grants since most credit unions received more than one type of grant often in the same year. However, since most grants were for technical assistance, a robustness check suggests that this is the primary channel through which the program operates. 5

Data
We combine two data sets in our study. The first data set contains administrative information from FND about all credit unions that obtained a grant through FND's support Based on the CNBV data, we generate seven variables to assess the impact of FND's support program on lending interest rates, their components, and outreach.
We calculate the lending interest rate by dividing annualized interest income by the average size of the loan portfolio. We then define four components that are likely to drive lending interest rates. First, the operating cost ratio, calculated by dividing annualized operating expenses by average annual total assets. A lower ratio signals more efficiency in the form of lower operating costs for the same size. Second, the NPL ratio, which we calculate by dividing the value of nonperforming loans by the total loan portfolio. Third, the funding interest rate, which is calculated as the ratio of interest expenses to average liabilities. Fourth, the return-to-assets (ROA) ratio, which is a frequently used measure of financial institutions' profitability and performance. We calculate ROA by dividing annualized net income by average annual total assets.

12
To study outreach, we look at the value of the total loan portfolio, which we calculate as the sum of all outstanding loans, including performing and non-performing loans. We also study loans from credit union members, banks or other institutions, which we obtained directly from the financial statements.
Finally, we noticed that a significant fraction of credit unions in our sample end up closing during the eight years in our data. We thus check whether the support program had an effect on survival of credit unions since this has implications for sample selection and how we deal with the observations for closed credit unions. Table 4 shows summary statistics of our variables for the pre-program period for the 124 credit unions in our sample. All credit unions remained open in the pre-program years, as we drop credit unions that closed before 2005. Credit unions in our sample charged an average interest rate of 16.9 percent. The operating cost ratio was 8.2 percent on average. The average NPL ratio was 19 percent, with a substantially lower median of 4.4 percent. The average funding interest rate was 7.3 percent. ROA was -1.5 percent on average, but the median credit union was not making losses, with an ROA of 0.3. There is large heterogeneity across credit unions in terms of the loan portfolio: while the median value of the loan portfolio was around 25 million Mexican pesos (about 1.4 million USD), the average credit union lent more than double that amount. Finally, credit unions borrowed on average about 69 million Mexican pesos (close to 4 million USD).

Identification Strategy
We use a difference-in-difference framework to estimate the effects of the program. The estimating equation is where yit is an outcome variable of interest for credit union i and year t, αi is a credit union fixed effect, and βt is a year fixed effect. The variable Treatmentit is equal to one for a given credit union i in the year t where it received the first grant through the program and for all years thereafter. It is equal to zero for all years before the credit union received the first grant through the program.
For credit unions that did not participate in the program by 2012 (the last year in our data), Treatmentit is equal to zero in all years. εit is an error term, clustered at the credit union level. The If the full control group is a valid comparison group for the treatment group depends on how selection into the program happened. For example, equation 1 would overestimate the effects of the program on the volume of the loan portfolio if credit unions that expected to grow their loan portfolio received the program right before this growth was to happen. In practice, we do not know what determined if a credit union applied to the program. Although, as described in section 2, we do know that the program was not widely advertised, and it is likely that credit unions located closer to FND offices had more information about the program. Also, credit unions that had a 14 higher fraction of their loan portfolio located in rural areas were more likely to participate in the program. 7 While we cannot say for certain what determined selection into the program, we can compare pre-program outcomes for the credit unions in the treatment and control group. Column 1 of table 5 shows the pre-2005 averages of our outcome variables for the 65 credit unions in the full treatment group. Column 2 shows the normalized differences between the averages of the credit unions in the full treatment group and the full control group and column 3 shows the pvalues corresponding to these differences. Credit unions in the full treatment and control groups have similar values of their loan portfolio and loans from members, banks or other institutions.
However, they differ in their lending interest rate and its components. On average, credit unions in the full control group have a higher NPL ratio than credit unions in the full treatment group and they pay a lower interest rate for their funding. These differences in means do not per se invalidate our identification strategy, but they do suggest that the full control group may not be a good comparison group for the full treatment group.
We now examine the identification assumptions for equation 1 in more detail. These assumptions are that, (i) if the credit unions in the treatment group had not participated in the program, their outcomes variables would have followed a trend parallel to those of the credit unions in the control group, and (ii) the timing of treatment in the treatment group is exogenous to the outcomes variables. While we cannot explicitly test these assumptions, we can check whether the outcomes for treatment and control group credit unions followed a parallel trend in the pre-2005 period. If this was the case, it is more plausible that the outcomes would have continued to follow a parallel trend in the post-2005 period. Table 6 shows the results from the following regression that tests the parallel trends assumption in the pre-2005 period * * where yit is an outcome variable of interest for credit union i and quarter t and α is the constant term. Trendt is a linear time trend and Treatmenti is equal to 1 for the credit unions in the full treatment group and is equal to 0 for the credit unions in the full control group. εit is an error term, clustered at the credit union level. If the coefficient  is not statistically different from zero, we can conclude that outcome yit followed a parallel trend for treatment and control group credit unions in the pre-2005 period. That is, matching on past values of the outcome variables is likely to yield the most comparable groups of credit unions in the post-program period. As before, we create one sample that keeps only credit unions on the common support of this propensity score and another sample based on caliper matching as described above. This procedure yields a different number of credit unions in each group for each outcome. These numbers are reported in columns 3 and 5 of tables 10 to 16. 9 Austin (2014) used Monte Carlo simulations to compare 12 algorithms for propensity score matching in terms of bias, variability, and MSE of the resulting estimates. He concludes that nearest neighbor caliper matching without replacement performs best in most situations. Austin (2011) conducted simulations to determine the optimal caliper width for propensity score matching. 10 For the first two samples where we match on more variables, we do not use all years of data separately and match on the average and growth rate instead to reduce the number of matching variables.
Tables 5 and 6 summarize the differences in pre-2005 averages and trends for each sample.
Columns 4 through 11 in table 5 first show the normalized difference between the average outcome in the treatment group and control group obtained with each method. in outcomes across these two groups as analyzed through equation 2 were also similar (table 8).

5.a. Survival
We first examine whether the support program had an effect on the survival of credit unions ( significantly increased the probability of survival. We thus make assumptions for filling in the observations for closed credit unions, as explained in the following section.

5.b. Lending interest rate and channels
The fact that not all credit unions survive during our period of study implies that we face sample selection for studying the effect of the program on the lending interest rates since the lending interest rates are not observed for credit unions that closed. We address this issue by filling in the years after a credit union closed with values based on two different assumptions.
First, we assume the lending interest rate would have stayed the same as in the last year where we observed the credit union. We view this as a best-case scenario and consider the results obtained with this assumption as a lower bound of the effect of the program on lending interest rates. 11 That is, we believe that credit unions would likely have raised their interest rates had they remained in operation instead of closing. Presumably they closed because of low returns and would thus have had to try to increase revenues by raising interest rates. Our second and alternative assumption is that the lending interest rates would have been the highest observed for the same credit union. We view this as a worst-case scenario and consider the results obtained with this assumption to be an upper bound of the effect. We use analogous assumptions for the other four variables that we do not observe for closed credit unions: the operating cost ratio, the NPL ratio, the funding interest rate, and ROA.
In table 10 we examine whether the program changed the average interest rate charged to borrowers using the five different samples described in section 4. All but one specification suggest that the program lowered lending interest rates, but the effect is only statistically significant in three specifications. We interpret the consistency of signs the coefficients as weak evidence that the program lowered lending interest rates. We now study the components that are likely to drive interest rates and that could have been affected by the program, starting with operating efficiency. 12 The results in table 11 show that the program lowered the operating cost ratio by up to 1.5 percentage points, compared to a pre-program mean of 7.7 percent (column 1 of table 7). Table 12 shows the impact estimates for the NPL ratio. The estimated effect is negative in all specifications, but it is only statistically significant in one specification. We interpret this as suggestive evidence that the program lowered the NPL ratio.
The results for the borrowing interest rates are mixed (table 13). One specification shows a significant positive effect of the program on the borrowing interest rates, while three of the upper bound estimates show a significant negative effect.
Next, we examine the effects of the program on ROA (table 14) and find evidence that the program increased ROA by up to 4.6 percentage points, corresponding to about two-thirds of a standard deviation (table 4).
Overall, we conclude that the program increased operating efficiency, suggesting that the technical assistance helped credit unions streamline their operations. We also find some evidence of reduced risk in the loan portfolio, as reflected in a lower NPL ratio. The program thus seems to have improved the way in which credit unions screen and monitor borrowers. The lower operating costs and lower NPL ratio are reflected in higher ROA, providing higher returns to credit unions, but part of this gain was passed on to the borrowers in the form of lower lending interest rates.
12 Operating costs tend to make up the largest part of the intermediation costs of financial institutions in most countries (Beck 2007) and they are often particularly relevant for smaller institutions. The effects of the program on operating efficiency may thus vary by size of the credit union. For smaller credit unions, operating costs tend to represent a higher share of their total costs, implying that the benefits from an intervention that raises their operating efficiency should be greater. We conducted a test along these lines in our data. Following suggestions from FND, we classified credit unions as small or large based on whether their capital from partners one year before participating in the program was below or above 20 million pesos. We do not find different effects of the program on operating efficiency by size of the credit union, which may be due to the relatively small sample size.   We interpret the increase in the loan portfolio as an increase in outreach, i.e. credit unions lending to more borrowers. Since credit unions can only lend to their members this interpretation implies that treatment credit unions increased their membership. We do not have data on number of members. However, we do have data on the value of paid-in capital, which is a proxy for number of members since each new member needs to contribute a minimum amount of capital. Using paidin capital as an outcome variable gives similar results to using the value of the loan portfolio.

5.c. Outreach
We now ask whether the program had a standalone effect on outreach or whether the effect operated through borrowing from FND. That is, the program may have allowed credit unions to obtain FND funding for the first time or to increase their borrowing from FND, which could have led to increased survival and a higher loan portfolio. In panel B of . We thus conclude that the program had a standalone effect on outreach and did not simply operate through increased borrowing from FND.

Conclusion
This paper studies the impact of a program that provides grants for technical assistance on the lending interest rates and outreach of credit unions that finance borrowers in rural Mexico. We find that the program lowered credit unions' lending interest rates by up to 2.6 percentage points, compared to a pre-program rate of 17.8 percent. The program led to an increase in the value of credit unions' loan portfolio by about 50 percent. A robustness check using paid-in capital as a proxy for number of members also suggests that the program increased credit unions' outreach.
Digging into the mechanisms behind the drop in interest rates, we find that the program lowered operating costs, suggesting that the technical assistance allowed credit unions to streamline their operations. We also find weak evidence of decreased NPL ratios, indicating that the program helped credit unions learn how to better screen and monitor borrowers. The increased efficiency and lower NPL ratio are reflected in higher ROA, but credit unions passed at least some of these gains on to borrowers in the form of lower lending interest rates.
All in all, our findings indicate that limited technical capacity constrains credit unions from financing more borrowers in rural areas and that technical assistance can help overcome this constraint. In the case of FND, providing grants for technical assistance seems to have worked well in helping them achieve their goal of expanding access to credit in rural areas.

Figure 1. Propensity scores for the full treatment and control groups
Notes: The figure plots the histogram of the propensity scores of credit unions in the treatment and control group. The scores were obtained from a probit regression of a dummy variable that equals one for credit unions in the treatment group and zero otherwise on the averages and growth rates of all outcome variables listed in table 5.    Notes: Column 1 shows pre-program means of credit unions in the treatment group. Columns 2 to 11 show normalized differences and corresponding p-values in the different samples. The normalized differences are f calculated as where and are the sample mean and variance of the outcome for treated credit unions (j=T) and the comparison subsample from the control group (j=C) respectively (Imbens and Rubin, 2015).

p-values of parallel trends coefficients between treated and control credit unions in the pre-program years
Notes: The table reports the p-values for the coefficient  of the regression y it Trend t Treatment i  Trend t  it , where α is the constant term and y it corresponds to the outcome of interest (in rows) for credit union i and quarter t. Trend t is a linear time trend for the pre-program years and Treatment i is equal to 1 for credit unions in the treatment group and 0 for credit unions in the control group. ε it is an error term, clustered at the credit union level. Columns 1 to 5 present the p-values for  across the different samples.    t . α i is a credit union fixed effect, and β t is a year fixed effect. The variable Treatment it is equal to 1 for a given credit union i in the year t where it received the first benefit through the program and for all years thereafter. It is equal to 0 for all years before the credit union received the first benefit through the program. ε it is an error term, clustered at the credit union level. Columns 1 to 5 present the regression results across the different samples. For credit unions that close, lending interest rates after closing are filled in with values based on the two different assumptions presented in Panels A and B.  Table 11. Impact of grants on operating cost ratio Notes: The table reports the results of the regression y it =  i + t +Treatment it + it , where y it is the NPL ratio of credit union i in year t . α i is a credit union fixed effect, and β t is a year fixed effect. The variable Treatment it is equal to 1 for a given credit union i in the year t where it received the first benefit through the program and for all years thereafter. It is equal to 0 for all years before the credit union received the first benefit through the program. ε it is an error term, clustered at the credit union level. Columns 1 to 5 present the regression results across the different samples. For credit unions that close, their operating cost ratios after closing are filled in with values based on the three different assumptions presented in Panels A and B.  Notes: The table reports the results of the regression y it =  i + t +Treatment it + it , where y it is the lending interest rate of credit union i in year t . α i is a credit union fixed effect, and β t is a year fixed effect. The variable Treatment it is equal to 1 for a given credit union i in the year t where it received the first benefit through the program and for all years thereafter. It is equal to 0 for all years before the credit union received the first benefit through the program. ε it is an error term, clustered at the credit union level. Columns 1 to 5 present the regression results across the different samples. For credit unions that close, their NPL ratios after closing are filled in with values based on the three different assumptions presented in Panels A and B.  α i is a credit union fixed effect, and β t is a year fixed effect. The variable Treatment it is equal to 1 for a given credit union i in the year t where it received the first benefit through the program and for all years thereafter. It is equal to 0 for all years before the credit union received the first benefit through the program. ε it is an error term, clustered at the credit union level. Columns 1 to 5 present the regression results across the different samples. For credit unions that close, their funding interest rates after closing are filled in with values based on the three different assumptions presented in Panels A and B. 29 # o f c o n t r o l c r e d i t u n i o n s 5 1 3 1 3 7 2 4 2 9 Notes: The table reports the results of the regression y it =  i + t +Treatment it + it , where y it is the operating cost ratio of credit union i in year t . α i is a credit union fixed effect, and β t is a year fixed effect. The variable Treatment it is equal to 1 for a given credit union i in the year t where it received the first benefit through the program and for all years thereafter. It is equal to 0 for all years before the credit union received the first benefit through the program. ε it is an error term, clustered at the credit union level. Columns 1 to 5 present the regression results across the different samples. For credit unions that close, their ROA indicators after closing are filled in with values based on the three different assumptions presented in Panels A and B.