rafmacalaba/datause-encoder-data huggingface dataset:
**TABLE 2: Data by income quintiles**

|||1<br>_f_ |2<br>_f_ |3<br>_f_ |4<br>_f_ |5<br>_f_ |
|---|---|---|---|---|---|---|
|Populationofworkers (*)||1,050,962|1,156,764|1,156,764|1,220,245|1,156,764|
|Wage (RMB/hr)||6.61|8.47|10.56|12.66|23.59|
|Nonwage income, including form<br>assumedinformal nonwageinco|al and<br>me|1,044|2,832|10,560|20,451|70,770|
|Income share of housing in dispo<br>economicincome perworker|sable|0.63|0.50|0.445|0.40|0.34|
|Aggregate floor space (**)<br> (millions ofsquaremeters)||28.63|31.90|35.50|40.30|58.32|
|Carownershiprate||0.005|0.05|0.20|0.50|0.90|
|Cars owned||3,028|33,027|132,222|348,611|594,890|
|Share of car owners commuting|by car|30%|6.5%|9.2%|18.4%|42%|
|Commute shares|_Walk_|_0.17_|_0.12_|_0.12_|_0.09_|_0.18_|
|of carless|_Bike_|_0.29_|_0.27_|_0.25_|_0.24_|_0.19_|
||_Bus_|_0.54_|_0.61_|_0.63_|_0.67_|_0.63_|
|Commute shares of|_Walk_|_0.17_|_0.11_|_0.11_|_0.07_|_0.10_|
|car owners|_Bike_|_0.29_|_0.25_|_0.22_|_0.20_|_0.11_|
||_Bus_|_0.54_|_0.58_|_0.58_|_0.54_|_0.37_|
||_Auto_|_0.00_|_0.06_|_0.09_|_0.19_|_0.42_|

# (*) Source: _Beijing Statistical Yearbook._

(**) Sources: _Beijing Statistical Yearbook_ and survey conducted by the Beijing Municipal Construction Committee.

**TABLE 3: Calibrated elasticity by income quintile**

||1<br>_f_ |2<br>_f_ |3<br>_f_ |4<br>_f_ |5<br>_f_ |
|---|---|---|---|---|---|
|Income elasticity of car ownership with respect to the car<br>acquisition price for car owners|10.06|9.57|8.03|5.01|1.00|
|Price elasticity of car ownership with respect to the car<br>acquisition price for car owners|-4.69|-2.97|-1.47|-0.63|-0.05|
|Travel time elasticity of the demand for the choice of mode<br>(average over modes)|-0.44|-0.23|-0.14|-0.09|-0.04|
|Rent elasticity ofthe demandfor housing (floorspace)|-0.82|-0.75|-0.72|-0.70|-0.67|

Free flow (uncongested) traffic speed is assumed to be 80 km/hr. Thus, in the congestion function given by (16), we set _a=_ 1/80 = 0.0125. In order to calibrate the road capacity (Z) and the coefficients _b_ and _c_ , we proceeded as follows. We first set _b_ = 0.24 and _c_ = 4.0. Having done 21
# 1. Introduction

In the late-1990s to early-2000s, Sub-Saharan Africa experienced a surge in primary school enrollments linked to a movement to make schooling at that level free—a reform dubbed Free Primary Education (FPE). FPE has been linked to a number of positive impacts: more children enrolling in and completing schooling, and positive impacts on health, fertility, and other noneducation outcomes. But the rapid scale-up, sometimes within education systems that were not prepared for the personnel and infrastructure demands that the surge in students would require, has led many to comment on the potential harm to the quality of the average education delivered after scale-up. In particular, there were many reports of overcrowding (i.e., very high student-teacher ratios) and a lowering of teacher qualifications to address that overcrowding in the short run after the implementation of FPE in many countries.

This study explores the narrow question of whether the impacts of this rapid increase in the number of teachers associated with FPE can be felt “now” (at the time of the survey), years after the reform, in classrooms in terms of lower student learning outcomes and worse teacher “quality.” While the question has an interesting historical dimension, what makes it even more important and relevant is that at least two rapid scale-up efforts are currently being advocated for— universal access to early childhood education, and universal free secondary education. Understanding the consequences of the FPE effort can shed light on how to think about these new efforts.<sup>1</sup> The data used to analyze this question are from six Sub-Saharan African countries (Kenya, Madagascar, Mozambique, Tanzania, Togo, and Uganda) and include detailed student and teacher data collected as a part of the Service Delivery Indicators project. The analysis compares the learning outcomes of students at the time of the survey (which is, depending on the country, between 5 (Togo) and 16 (Uganda) years after FPE was implemented) whose teachers were hired just before versus just after the policy. It also compares the characteristics of teachers at the time

> 1 In addition, at the time of writing, many countries are ramping up the delivery of in-person learning after many months or years of closures due to the COVID-19 pandemic. There may also be lessons to learn from the FPE experience for how to manage this recovery effort.

1
4 This secti discusses each of these questions in some detail. It should be pointed out that most of the concerns raised in this section have been discussed by the conductors of cross-country studies themselves. This section's major practical theme (expressed in Section II.E.) is that insufficient effort has been devoted to examining **tho** fragility of existing empirical conclusions. The econometric work in Levine and Renelt (1990) suggests that almost all cross-country tegression results are fragile: they are not robust to slight alterations in the list of explanatory variables.

# A. Azgregation and Sampling

In thinking about studying the determinants of growth, a very basic question comes to mind: What is the appropriate unit of study? In particular, are countries the appropriate unit to study, or should we conduct analysis at a more disaggregated level? Since countries are composed of productive sectors, any country's growth rate depends on world-wide trends in the country's key sectors as well as country-specific factors that influence all sectors within the economy.' Decomposing growth into country and sectoral components would quantify the importance of country-specific factors in growth.<sup>2</sup> Furthermore, empirically isolating the country- and sector-specific components of grcth is important even if se are only interested in dissecting the country-specific component of growth. For example, cross-country 'Similarly, the performance of any industry within a given country depends on worldwide trends in that industry and country-specific factors that influence all industries within that country.

> **2** Stockman (1988) cor.ducts such a decomposition for seven European countries and the United States. He finds that there are both important industry-specific and country-specific components to industrial performance. It would be worthwhile to extend this analvsis to more countries.
sector. Since the WBES includes only formal firms, we cannot directly test for these two alternative explanations. Yet, suggestive evidence indicates that our effect is driven by formal firms becoming informal. First, Table A23 columns 1 and 3 show that conflict intensity does not change the (self-reported) total number of competitors of the firm. At the same time, conflict is associated with higher exit rates among formal firms (see Table A5). In sum, the overall level of competitive pressure faced by the firm remains unchanged, conflict makes some formal firms to exit the market, and competition from informal firms increases. These three effect are consistent with conflict inducing some of the previously formal firms to become informal.

These results align with prior studies showing that informality tends to rise in response to negative shocks, as firms seek to exploit the cost advantages of non-compliance with formal regulations (Ulyssea, 2018). This is also the case for an increase in conflict exposure. For instance, Neri-Lain ́e (2025) documents that —in the context of Afghanistan— an increase in insecurity reduces formal firms’ existence, increasing informality. These results documenting the shock-induced increase in informality are relevant for our analysis because of the evidence showing that more competition of informal firms can have negative consequences for formal ones. An increase in informal competition reduces the output of formal firms (Rozo and Winkler, 2021), lowers their employment (Amin, 2023), weakens innovation incentives (Avenyo et al., 2021), and dampens demand for credit, thereby curbing investment (Brancati et al., 2024). More broadly, the expansion of informality poses challenges for the functioning of the overall economy. It contributes to resource misallocation, reduces total factor productivity, and is often associated with broader welfare losses (Ulyssea, 2018). Taken together, this evidence suggests that the conflict-induced increase in informal competition contributes to explaining the reduction in firms’ sales documented in Table 1.

# **4.2.4 Demand**

An alternative explanation for the observed reduction in sales and total costs is that conflict reduces consumer demand. In this case, firms would respond to falling consumer expenditure by scaling back production and, consequently, reducing input purchases—leading to lower sales. While we cannot directly test this channel due to the lack of consumer demand data in the WBES, Table A24 provides suggestive evidence that this mechanism alone is unlikely to fully account for our results.

Column 1 examines whether the effect of conflict exposure on sales varies by the firm’s main destination market—local, national, or international. If the decline in sales were driven solely by a conflict-induced drop in domestic demand, we would expect firms selling primarily to international markets to be less affected. However, this is not what we find: the effect of conflict exposure on sales does not differ significantly across the three market categories. This suggests that the negative impact on sales is not confined 18
margin (percentage of disposable money saved?) in columns (3) and (4). The results show statistically significant improvements in variables among students in treatment schools. Compared to 40 percent of students in the control group, the financial education program prompted an additional 5 percent of students in the treatment schools to save up for their purchases. This result is similar and highly significant (at the 1 percent level) in both follow-up rounds. Table 5 also shows that students’ average percentage of money saved increased significantly, by 1.4 percentage points in follow-up 2 (14.3 percent of disposable money saved in the treatment group as compared to 12.9 percent in the control group).<sup>14</sup> This 1.4 percentage point improvement represents a 0.07 standard deviation increase.

We find significant treatment effects on spending behavior as well. In line with the concepts taught in the curriculum, the results show a 2.1-2.5 percentage point greater likelihood of comparison shopping before making purchases, and a 3.1-4.1 percentage point greater likelihood of negotiating price or payment method prior to purchases by students in treated schools. Further, the results show that 16 percent of students in treated schools made a list of monthly expenses as part of a budgeting exercise compared to 13 percent in the control schools in follow-up 1. These numbers are 17 percent and 14 percent respectively, in follow-up 2. All treatment effects are statistically significant at the 1 percent level.

Overall, these results show a clear improvement in student behavior as a result of the financial education course, in line with concepts taught in the curriculum.

# **7.3. Student Participation in Household Finance**

Apart from students’ own financial behavior, we investigate whether students become more proactive in their households’ financial decisions. Indeed, the curriculum included several takehome exercises that were meant to be completed with parents such as making a household budget and exploring savings account options.

Data on student participation in household financial decisions come from the parent questionnaires. The parent surveys included questions on socio-demographic characteristics, measured financial literacy through standard questions used in the literature, and elicited parents’ financial behavior regarding budgeting and savings. The parent questionnaires also asked whether parents discuss financial matters with students and whether students help to organize the household budget.

We find that a significantly larger percentage of students in the treatment group talked to their parents about finances and participated in organizing the household budget. Table 6 shows that 71 percent of students in treatment schools participated in household financial decisions compared to 67 percent in control schools in follow-up 1, and 74 percent compared to 70 percent

> 14 This question was not asked in follow-up 1.

20
- 25 -

the African region (Williams _et al_ ., 2006). Most of the agricultural economics literature indeed focuses on the effects of road development (Pinstrup-Anderson, Shimokawa, 2006).

There is a good measurement which is most relevant to rural road development, i.e., “rural access index” developed by Roberts _et al_ . (2006). Unfortunately, however, the time series data are unavailable. Although the level of infrastructure cannot be changed in the short run, more variation in data is desirable for econometrics purposes. The current paper relies on two conventional road development indices: the share of paved roads and road density, which appear broadly positively correlated with the rural access index as far as data are available (Figure 7).<sup>37</sup> Of particular note, the quality of roads is expected to have an important role to play in expanding trade opportunities in many earlier studies (Buys _et al_ ., 2006; Broadman, 2007).

<!-- Start of picture text --> Figure 7. Consistency Check between Road Accessibility Index and Paved Road Share, 1993–2004<br>120<br>100<br>80<br>60<br>40<br>y = 0.443x+42.519<br> (0.105) (4.848)<br>20 R 2 = 0.271<br>0<br>0 20 40 60 80 100 120<br>Share of paved roads (%)<br>Rural accessibility index (%)<br><!-- End of picture text --> Sources: Roberts _et_ al. (2006); and _World Development Indicators_ .

Non-transport infrastructures, such as electricity and telecommunications, may also be conducive to improving efficiency in livestock production and marketing, along with education (Timmer, 2002; Williams _et al_ ., 2006). In fact, a common perception in Africa is that livestock is a last resort for livelihood, implying that a full set of basic rural infrastructures may be necessary for supporting livestock activities in rural areas. Rural areas with low standards of living cannot rein rural-urban migration.

> 37 Also see Queiroz and Gautam (1992).
# Table 5: Robustness IV, Survival and Firms’Type.

The dependent variable is the hazard rate of trade relationships for product k of ...rm i operating in country c to destination country j: All regressions are estimated using the Cox Proportional Hazard Model. We control for destination country, year, ...rm and exporting country x HS2 ...xed e¤ects, and allow the baseline hazard to vary across HS8. Sample description: ...rms only exporting to African countries (column 1), total sample excuding ...rms only exporting to African countries (column 2), total sample excuding trading and international companies (columns 3-4), and total sample excuding higher order spells (column 7). Robust standard errors clustered at (exporting country) � HS4 sector level are in parentheses. *, **, and *** denote statistical signi...cance at the 10%, 5%, and 1% levels, respectively.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Firms<br>exporting to<br>Africa only|Firms<br>exporting to<br>di¤erent regions|No<br>international<br>companies|No<br>trading<br>companies|...rst<br>spell<br>only|
|FDc x sanitary_riskk|0.018|-0.461***|-0.243**|-0.235**|-0.237**|
||(0.142)|(0.151)|(0.108)|(0.107)|(0.118)|
|initial_exportcikj|-0.087***|-0.094***|-0.089***|-0.089***|-0.093***|
||(0.015)|(0.008)|(0.007)|(0.008)|(0.007)|
|NProductsci|0.010***|-0.016***|-0.004|-0.002|-0.006**|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|NDestinationscik|-0.033***|-0.046***|-0.057***|-0.060***|-0.055***|
||(0.010)|(0.010)|(0.010)|(0.009)|(0.010)|
|Contiguitycj|-0.085|-0.204|-0.086|-0.152|-0.110|
||(0.155)|(0.170)|(0.125)|(0.109)|(0.102)|
|Com_languagecj|-0.225***|-0.074|-0.072|-0.124***|-0.113***|
||(0.065)|(0.048)|(0.045)|(0.044)|(0.043)|
|Colonycj||-0.123|-0.166**|-0.076|-0.103|
|||(0.088)|(0.083)|(0.085)|(0.104)|
|Distancecj|0.109|0.066|0.141***|0.085*|0.121***|
||(0.084)|(0.064)|(0.052)|(0.050)|(0.047)|
|Observations|2494|12376|14163|13522|13191|

26
countries that already trade significantly (in particular geographically close countries and those that share cultural characteristics that reduce transaction costs, such as language) are the ones most likely to be trade creating. Although theoretically this does not need to always hold, as Bhagwati and Panagariya (1999) point out, Frankel (1997) finds evidence consistent with the natural trade partners hypothesis in a number of regression analyses based on the gravity equation with country-level trade flows.

Lee and Shin (2006) extend the approach of Frankel (1997) and estimate a gravity model with year dummies and with both random and fixed effects to assess trade creation and trade diversion in 175 countries using data from 1948 to 1999. The key trade creation variable is a dummy that is one if both countries are members of a common RTA; the key trade diversion variable is a dummy that is one if one country belongs to an RTA and the other does not belong to that RTA. Lee and Shin interact these variables with geographical and common language variables to identify whether trade creation and trade diversion are different for “natural” trade patterns.

In most specifications, Lee and Shin (2006) confirm that RTAs increase bilateral trade between members. The magnitudes are around 50 percent, but if the countries share a common border this effect increases to up to 200 percent. Similarly, the closer the countries are from each other, the larger is trade creation. On the other hand, RTAs are never found to reduce trade between members and nonmembers significantly. In fact, in most specifications RTAs are estimated to _increase_ trade between members and nonmembers, from 6 to 15 percent. Trade with nonmembers grows more for RTAs with a smaller average distance between their members and when more members of the RTA have common borders or share a common language. Having the trade creation and trade diversion estimates in hand, Lee and Shin then predict the average trade impact of several proposed RTAs in Asia. They find in particular that the trade effects of AFTA are significantly positive.<sup>1</sup>

> 1 Lee and Park (2005) develop a similar analysis, also predicting significant trade creation but no trade diversion from the formation of Asian RTAs, including the expansion of AFTA to incorporate China, Japan and Korea.

6
Figure B1: Average Share of Unspecified Category by COICOP <!-- Start of picture text --> Utilities, Telecom, Gas<br>Health & Education<br>Diverse G&S, Recreation<br>Other<br>BI CD NE MZ BF TD RW TZ BJ ST SN KM CM BO PG MA CG TN SZ DO EC CO RS PE ME ZA CR UY MX BR CL<br>60<br>50<br>40<br>30<br>20<br>10<br>Share of Unspecified Place of Purchase (in %)<br>0<br><!-- End of picture text --> an ’unspecified’ place of purchase is relatively large. Indeed when looking at what types of products compose the unspecified category, over half are utilities, while the remaining is principally education and health spending.

Finally, we assign the remaining places of purchase that are harder to categorize (as purchases over the internet or from abroad) to category (6) “services provided by institutions”. While this might not be accurate, we note that these PoP typically represent a very small share of total expenditure.

The countries-specific assignment of places of purchase to the broad categories presented above is detailed in Table B4, for each PoP representing more than 0.5% of total purchases. The table also reports the share of expenditures purchased from each category, including the unspecified category.

Finally, we note that, to the best of our knowledge, the only other project which constructs a common taxonomy of places of purchases across countries is the International Price Comparison (ICP) project, which builds purchasing power parity indexes. The ICP provides a store type classifier for marketed consumption which is used by individual countries to obtain price quotes from a variety of retailer types. Our classification mirrors that of the ICP.

78
letters of the alphabet, or numbers. Learning standards are met by 89 percent of children, while physical and social standards are met by 93 percent and 90 percent of children respectively.

When examining household arsenic exposure, about 27 percent of all children are exposed to arsenic based on the WHO standard of >10 ppb compared to 13 percent when using the Bangladesh standard of >50 ppb. About 73 percent, 14 percent, and 13 percent of children fall into the no-to-low risk, medium risk, and high risk exposure groups, respectively. Arsenic is most prevalent in water from tubewells. In contrast, less than 1 percent of households that use piped water and other sources are contaminated.<sup>3</sup> It should be noted that over 90 percent of the households in the sample use tubewells.

Figure 1 shows that arsenic risk levels are evenly distributed across wealth quintiles. This indicates that arsenic contamination is not influenced by wealth and affects households across all wealth quintiles. However, early childhood development performance does vary by wealth quintiles. When disaggregated by household wealth, more children are developmentally on track in Quintile 5 (76 percent) compared to Quintile 1 (62 percent). This trend is primarily driven by the literacy domain, where children from wealthier households perform better. In the other three domains, there is no clear difference between the number of children who are on track and those who are not across the different wealth quintiles.

# 4. Results

## **4.1 Overall ECDI**

Table 3 presents the marginal effects from the logit model used to examine the relationship between ECDI and three specifications of arsenic exposure. After controlling for individual, household and community characteristics, the results from the logistic regression models report that arsenic exposure is significantly and negatively associated with the overall ECDI. Specifically, the presence of arsenic in drinking water above the WHO standard of greater than 10 ppb (specification 1) reduces the likelihood of being developmentally on-track by about 7 percentage points while the presence of arsenic above the Bangladesh standard of 50 ppb (specification 2) reduces the likelihood by about 11 percentage points. Based on specification (3) where the explanatory variable of interest is the intensity risk levels of arsenic in drinking water, the analysis further reveals that there is a clear dose-response

> 3 The piped water category includes piped into dwelling, piped into compound, yard or plot, piped to neighbor, and public tap/standpipe. Other sources include protected well, protected spring, unprotected well, unprotected spring, cart with small/tank drum, and surface water (river, stream, dam, lake).

10