rafmacalaba/datause-encoder-data huggingface dataset:
# **5.3 Impact of Market Access**

Tables 3 and 4 show the correlation between changes in market access and changes in outcome variables using Kebeles and Woredas as units of analysis, respectively. Both tables show OLS results (columns 1–6) and results when instrumenting market access with the 50km doughnut market access variable (columns 7–12). Focusing on IV results using Kebeles as the unit of analysis, results show that market access is primarily associated with gains in nighttime lights across non-dark Kebeles (i.e., Kebeles with some positive nighttime light pixels at baseline), with gains largest in areas with the highest baseline nighttime lights. Nighttime light results are consistent across OLS results and results using Woredas as the unit of analysis.

Results using urban land as the dependent variable tend to be sensitive to using OLS or IV models and to the unit of analysis. Results using Kebeles as the unit of analysis show that gains in market access are associated with a small reduction in urban land in areas with low initial light but no significant association between market access and urban land in areas with medium or high initial light. Results using Woredas as the unit of analysis—particularly the IV models—show a positive association between market access and urban land in areas with higher initial light.

Results showing that gains from road upgrades tend to be concentrated in areas with higher baseline nighttime lights is consistent with our results on the impacts of road upgrades on the immediate areas surrounding the upgrades. Results examining the impact on areas incidentally connected to RSDP similarly show the greatest benefits to areas with the highest initial light. In addition, difference-in-difference results do show benefits to areas with below-median initial nighttime lights, but coefficients tend to be larger in areas with higher initial lights.

Results using different values of _θ_ when calculating market access, different doughnut market access sizes (using 20km and 100km), and two groups of baseline nighttime lights (Woredas with no baseline light versus Woredas with some baseline light) all show similar results (see Appendix S15). Results using a panel, two-way fixed effects approach also show similar results; however, here results show a significant association between market access and urban land using both Kebeles and Woredas as the unit of analysis (see Appendix S12).

Overall, the results using market access show that the gains in terms of nighttime lights from road upgrades are concentrated in areas with moderate-to-high initial levels of economic activity. This finding is consistent with the results based on long-differences specifications. Similarly, the areas with higher initial light show a positive association between market access and urban land, which is consistent with the difference-in-difference and long-differences results.

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# **B Constructing consumption aggregates and poverty rates**

To construct the consumption aggregates, we use the detailed module on household food consumption that collects information on (i) quantities and expenditures on a 7-day recall for most food items; (ii) expenditure on non-food items (electricity, water bills, etc.) on a monthly recall; (iii) imputed monthly rent and (iv) expenditure on miscellaneous items (such as school fee, clothes, etc.) on annual recall. The total consumption expenditure includes food and non-food items purchased from the market, produced at home (important component for farming households), food received as gifts, and the imputed value of rent and durables.

To capture the effect of seasonal variations, the data were collected over a 12-month span (October 2019 to September 2020) by randomly allocating sampled clusters to different months.<sup>a</sup> Temporal and spatial price deflators are used to adjust for price variations across regions and space to create comparable consumption measures that account for the different cost of living.<sup>b</sup> Additionally, to adjust for variations in household size and composition, consumption expenditure is divided by the officially used adult-equivalent scales, which are based on calorie requirements and vary by age and sex.

The poverty line is based on a cost of basic needs method to achieve the minimum daily calorie requirement of 2,100 kilocalories per adult in Afghanistan and adjusted upwards to include non-food consumption expenditures using the upper bound on the poverty line proposed in Ravallion (1998). The consumption expenditures per person per adult equivalent and poverty lines are estimated for each of the fourteen regions (urban and rural separately). The national poverty line, which is 2240 Afghani per adult equivalent, per month is used for construction of poverty rates. Afghanistan has fourteen poverty lines for rural and urban areas separately in eight regions across the country: North, Northeast, West, West-Central (only rural), South (only rural), Southwest, East, Central regions.

> aThe idea here was to assign clusters in the same month as was done in the previous rounds of the survey. A total of ten households were sampled from every cluster. bThe price data is used for four quarters and 14 geographical regions, rural and urban areas separately in South (all rural), Southwest, North, Northeast, West, West-Central (all rural), Central, and East regions.

45
and less developed economies such as Greece, Estonia, Latvia, and Lithuania. Finally, we put Germany and the United States in a separate group because these are two developed economies for which we observe an increase in the borders to services trade, however, these are also the two countries with the lowest borders in the initial and in the final year of our sample.

In order to check whether the effects of globalization are indeed related to country size and economic development, we split the countries in our sample into quintiles according to real GDP and we plot the evolution of the corresponding SMCTRY estimates in Figure 5. As before, motivated by the fact that Germany and the US are the only large developed economies that experience a small increase in the SMCTRY estimates over time and are also the two countries with the smallest borders to start with, we put those two countries in a separate group. Figure 5 captures several interesting features. First, as established before, smaller countries face higher borders in services trade. Second, it reveals that the border estimates for the countries in the lowest quintile have remained stable (or increased a bit) over the period of investigation. Third, we observe a decrease over time for the border estimates for the countries in the four upper quartiles in our sample. Our interpretation of this result as a reflection of globalization forces contributes to the extensive literature concerned with the “missing globalization” puzzle.<sup>18</sup> Specifically, we demonstrate that, in the case of services, globalization is an active force that is captured by the gravity model of trade.

Fourth, the decrease in the border effects on services trade has been faster for the countries in the second and third lowest quintiles as compared to the larger countries which, as noted before, face lower border barriers to begin with. This points to a convergence story among

> 18Coe _et al._ (2002) coin the term “missing globalization” and Disdier and Head (2008) provide a survey of the robust evidence for stable gravity estimates of distance over time. Our findings are in accordance with the results for goods trade from Yotov (2012) and Bergstrand _et al._ (2015). Yotov (2012) uses manufacturing goods data and demonstrates that the “distance puzzle” is resolved once the effects of international distance are measured properly relative to the corresponding effects of internal distance. Bergstrand _et al._ (2015) generalize this result to resolve the “missing globalization puzzle” with both sectoral goods data as well as with aggregate data.

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Not all studies, however, find an increase in bank risk taking. For instance, Wheelock and Wilson (1994) and Alston et al. (1994) find that deposit insurance has not led to an increase in bank failures in the United States. Karels and McClatchey (1999) also fail to find evidence that the adoption of deposit insurance increased the risk taking of U.S. credit unions. Peria and Schmukler (2001) using data from Argentina, Chile and Mexico during the 1980s and 1990s, find that depositors withdraw funds and require high interest rates during times of distress thus providing market discipline. They attribute their finding to deposit insurance schemes not being fully credible. Wagster (2007) finds an increase in idiosyncratic risk of Canadian banks and trust companies after adoption of deposit insurance, but finds that the overall risk declines.

The reason for the mixed findings could be due to other important factors, such as banks’ charter values, credibility of the insurance scheme or the effectiveness of monitoring by nondeposit creditors, affecting the impact of deposit insurance on risk taking. Failure to take into account some of these factors may explain the mixed findings in the empirical literature. It is also difficult to disentangle the positive stabilizing effects deposit insurance from negative effects that result from moral hazard. While deposit insurance can increase moral hazard and make financial systems more vulnerable to crises during good times, it can also enhance depositor confidence and reduce the likelihood of contagious bank runs during crisis periods. The net effect of deposit insurance on bank risk and stability depends on whether the benefits of deposit insurance outweigh its costs. Some recent papers have tried to determine whether the “moral hazard” effect dominates the “stability effect” of deposit insurance.

Ngalawa, Tchana and Viegi (2016) argue that if the adoption of deposit insurance is associated more with bank insolvency than it is with bank runs, then one can conclude that the moral hazard effect dominates. Using an empirical approach that distinguishes liquidity problems arising from panic withdrawals of deposits from problems arising from insolvency, they find that the moral hazard costs outweigh the stability benefits of deposit insurance. Anginer, Demirguc-Kunt and Zhu (2014) examine the effect of deposit insurance on bank standalone and systemic risk before and after the global financial crisis. They find that more generous deposit insurance schemes increase bank risk and reduce systemic stability in non-crisis years. During the global financial crisis, they find that the bank risk is lower and systemic stability is greater in countries with more generous deposit insurance coverage. The overall effect of deposit insurance over the full sample they study remains negative, however.

13
scribes the data collection protocol, present selected summary statistics, and show that by chance the means of some covariates were not balanced at baseline, highlighting the importance of controlling for baseline characteristics in our estimates (the adjusted and unadjusted estimates remain qualitatively similar). Section 4 presents the conceptual framework that guided the study design and the interpretation of the results provided in Section 5. The cost effectiveness of the interventions is evaluated in Section 6. Finally, Section 7 concludes and highlights limitations of the results.

# **2 Program Description and Study design**

This study was carried out in Sonargaon, a 171 km<sup>2</sup> sub-administrative unit (or _upazila_ ) of Narayanganj district, located approximately 25 kilometers south-east of the capital Dhaka. According to the 2011 Census of Bangladesh, Sonargaon had a population of about 400,000, and at the time of the study it included 365 villages. According to the BAMWSP blanket testing, conducted locally in 1999-2000, about 40% of villages in Sonargaon contained _>_ 90% of wells that did not meet the national standard of 50 ppb.<sup>4</sup>

## **2.1 Study Area and Program Description**

The study area for this trial was initially formed by the list all 128 villages in Sonargaon with more than 10 wells and with a 40-90% share of unsafe wells based on the BAMWSP testing conducted years earlier. A lower bound was chosen to focus on areas where a sizeable fraction of new untested wells were likely to be unsafe, while the upper bound was designed to avoid areas where switching to safe wells was not likely to be a viable option for most households.

Between January and June 2016, surveyors conducted home visits to identify all wells in the selected villages, regardless of whether they had been tested before. Privately owned wells were linked to household who owned it, while public wells were linked to the main caretaker or user. Almost all wells (98.6%) were privately owned, and for simplicity in the rest of the paper the term ‘owner’ will be used to refer to the household who owned the well, or to the household who was the primary user of community wells.

During the home visits, surveyors explained the risk of consuming arsenic-contaminated tube well water to an adult—typically the most senior woman—and offered to test the well for a fee. When a test was purchased, tube well water was tested in the field using the Arsenic Econo-Quick (EQ) test kit, which has been shown to be reliable, and can deliver results within ten minutes (see George et al. 2012 for details). The result was immediately communicated to the buyer. The kit’s test strip is evaluated visually and the result classified in the following sequence (in ppb) _{_ 0 _,_ 10 _,_ 25 _,_ 50 _,_ 100 _,_ 200 _,_ 300 _,_ 500 _,_ 1000 _}_ . The tests cost USD0.30 for volume purchases, although the total cost per test is higher at about USD2.4 per test based on a testing campaign that also covered

> 4Blanket testing in Sonargaon was carried out by BRAC, a partner NGO of BAMWSP. A total of 25,048 tube wells were tested for arsenic.

6
applicant. (We do not differentiate by gender to keep sample sizes reasonable; however, results that disaggregate by the gender of both the applicant and her sibling are similar to those we report, but substantially less precise.) Table 7 shows that our results are not an artifact of aggregation: sibling effects are not significantly (or substantively) different depending on the relative age of the sibling.<sup>20</sup> E. _School visits_ : A final possible concern is the possibility of systematic reporting biases in our measure of school enrollment based on household survey data. Conceivably, parents of scholarship recipients could be more likely to lie to enumerators about school enrollment than those of non-recipients (although it is less clear why they would lie about the school enrollment and work status of ineligible siblings). As we report elsewhere, however, results from an analysis of data on directly observed school attendance from four unannounced school visits are very similar to those that use the household survey (Filmer and Schady 2009c).21

# **5.3.** **<u>Magnitude of program effects</u>**

The CSP program effects on the enrollment of eligible children are large. One way of placing the magnitude of the effects in context is by calculating the elasticity of enrollment with respect to cost. To do this, we calculated the total (direct and indirect) cost of schooling for children affected by the CSP program, using data we collected in the survey, and limiting the sample to children who applied for a scholarship but did not receive one.

The direct cost is given by the sum of various school fees, including annual fees (which include exam fees, various “allowances” and fees for various school events and ceremonies); fees incurred at the beginning of the school year (including registration fees, uniforms, books, and school material); and daily

> 20 We also explored whether restricting the analysis to siblings who are close in age to the applicant alters our findings. For this purpose, we estimated our basic model, but restricted the sample to children (both applicants and siblings) ages 14 to 18. Results from these estimates are very similar to those we report in the paper—that is, strong own effects on schooling and work for pay but small and insignificant impacts on siblings. Finally, we restricted the sample to male applicants only and analyzed the effect on brothers, and to female applicants only and analyzed the effects on sisters—on the grounds that same-sex siblings might be closer substitutes for each other. As with the age restriction, we do not find that restricting the sample in these ways changes our findings. These results are not included in the paper but are available from the authors upon request.

> 21 The impact of being offered a scholarship on physically verified attendance is equal to 25 percentage points when pooling across all four visits (February/March 2006; April/May 2006; June 2006; June 2007), and equal to 20 percentage points when restricting the analysis to June 2007 when the applicants would have been in 8<sup>th</sup> grade, if they did not repeat school grades.

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on actual values and Column 2 those based on residual values. The treatment regions for the difference-in-difference estimates in Akgunduz, van den Berg and Hassink (2015a, 2015b) and Ceritoglu et al. (2015) are along the border (plus Adana).<sup>28</sup> These are the first five regions in Column 1. Once we control for distance the rank order changes substantial, see Column 2. Some subregions on the border, specifically Gaziantep and Hatay, still have disproportionately high refugee inflows. However, Mardin drops to 17<sup>th</sup> and Adana drops to 24<sup>th</sup> . The correlation between the actual and residual refugee flows is 0.68. Clearly, controlling for distance from the border quite substantially changes the identifying source of variation.

# **4. RESULTS**

In this section we first describe the impact of the inflow of Syrian refugees on the employment of the Turkish population, and discuss plausible underlying mechanism for our findings. We then estimate the impact on wages, and decompose this impact into changes due to the impact on the observed composition of Turkish employment (caused by the inflow of refugees) and changes in marginal products (and unobserved characteristics). Finally, we discuss adjustment mechanisms for the Turkish population.

# **4.1 Impact on Native Employment**

Tables 5a and 5b present OLS and IV estimates, respectively, of the impact of the refugee inflow on Turkish employment. We present results for total private sector, paid employment, formal, informal, regular and irregular, full-time and part-time. The baseline estimates without individual controls, only controlling for subregion and year fixed effects and the time-varying impact of log distance, are presented in Panel A of each table. Panel B shows the results for a full specification including individual covariates: fully interacted dummy variables for gender, education and year, as well as a gender, education and year-specific second-order polynomial in potential experience.

The OLS estimates, Table 5a, show that refugee flows are positively correlated with Turkish employment. This positive correlation is driven by increasing formal, full-time employment and employment in irregular workplaces. The IV estimates, see Table 5b, show that the

> 28 Akgunduz, van den Berg and Hassink (2015a, 2015b) also include Malatya.

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<u>Appendix Table 5a. Average household statistics in 2008 by attrition</u>

||<br>Overall<br>average|<br>Nonattrited<br>average|<br>Attrited<br>average|Difference<br>(Nonattrited -<br>attrited)|Difference-in-<br>difference<br>(DD) migration<br>& attrition|
|---|---|---|---|---|---|
|Total expenditures (Quetzales)|35161.01<br>(30319.71)|34196.60<br>(28867.70)|38996.24<br>(35443.61)|-4799.64<br>(4514.09)|-4868.20<br>(11412.73)|
|Per capita expenditures (Quetzales)|5074.35|4966.82|5501.96|-535.14|-304.64|
||(4069.83)|(4053.67)|(4129.60)|(627.37)|(1032.10)|
|Per capita food expenditures (Quetzales)|2753.62|2680.57|3044.12|-363.56|-556.99|
||(2695.92)|(2719.36)|(2595.67)|(300.99)|(678.92)|
|Food share (food / total expenditures)|0.57|0.57|0.57|0.00|-0.07|
||(0.19)|(0.19)|(0.19)|(0.02)|(0.05)|
|Poor = 1|0.83|0.85|0.78|0.07|0.11|
||(0.37)|(0.36)|(0.42)|(0.06)|(0.11)|
|Extremely poor = 1|0.49|0.50|0.44|0.06|-0.04|
||(0.50)|(0.50)|(0.50)|(0.05)|(0.15)|
|Reported their income adequate|0.41|0.42|0.37|0.05|0.01|
|necessities = 1|(0.49)|(0.49)|(0.49)|(0.07)|(0.14)|
|Reported their food expenditures|0.46|0.47|0.44|0.03|0.04|
|sufficient = 1|(0.50)|(0.50)|(0.50)|(0.07)|(0.15)|
|Children < 6 years old food secure = 1|0.38|0.41|0.27|0.14**|-0.08|
||(0.49)|(0.49)|(0.45)|(0.06)|(0.11)|
|Household size|7.47|7.45|7.56|-0.11|0.71|
||(2.86)|(2.83)|(2.98)|(0.36)|(0.93)|
|Head is male = 1|0.82|0.83|0.79|0.04|0.15|
||(0.38)|(0.37)|(0.41)|(0.04)|(0.14)|
|Head age (years)|41.29|40.62|43.97|-3.34|3.96|
||(14.16)|(13.57)|(16.10)|(2.05)|(3.97)|
|Head education (grades)|2.12|2.17|1.93|0.24|0.39|
||(2.25)|(2.27)|(2.16)|(0.27)|(0.48)|
|Head identifies as indigenous = 1|0.55|0.55|0.55|0.01|0.02|
||(0.50)|(0.50)|(0.50)|(0.06)|(0.12)|
|Own more than 1 hectare land = 1|0.24|0.26|0.16|0.10**|0.19*|
||(0.43)|(0.44)|(0.37)|(0.04)|(0.11)|
|Receives remittances = 1|0.27|0.25|0.31|-0.06|0.01|
||(0.44)|(0.44)|(0.47)|(0.05)|(0.11)|
|Number of household observations(N)|428|342|86|428|428|

Notes: Sample of households with a child under three years old in 2008. Migrant household is defined as a household with at least one migrant in the U.S. in three of the four years 2005-08. Nonattrited indicates child in the household has valid measurement in 2012. Standard deviations in parentheses under average and standard errors estimated allowing for clustering at the sample cluster level in parentheses under difference and difference-in-difference (DD). The DD shows averages for (nonattrited migrant - attrited migrant) - (nonattrited non-migrant - attrited non-migrant). Exchange rate was 7.7 Quetzales to U.S. dollar in 2008. *** indicates significantly different at p<0.01, ** at p<0.05, and * at p<0.10.
The price index is the standard C.E.S. ideal price index: Firms in the service sector are heterogeneous in the sense of Melitz (2003). The production function for each service variety is _ys_ = _aLs_ , where _a_ is a measure of labor productivity, drawn from a distribution with a cumulative distribution function _G_ ( _a_ ) over a support [ _aL, aH_ ]. Naturally, the most productive firm in a given country _i_ is _aiH_ .

Profit maximization in the manufacturing sector implies the following demand function for labor in manufacturing and intermediate services: Profit maximization in the service sector implies a standard optimal pricing rule as a markup over marginal costs: Profit maximization in the manufacturing sector and utility maximization implies the following demand for each service variety: To close the model, we need to impose market clearing. The three goods market clearing conditions read: