rafmacalaba/data-use-mentions-extended huggingface dataset:
The evaluation of the Nicaragua’s reform has been underway since 1995, undertaken by a team of the Ministry of Education and the World Bank’s Research Group, and has produced a number of special school-household surveys and informal assessments<sup>29</sup> which have allowed a continuous monitoring of the reform in time, constituting, as such, a unique case in Latin America. In 1998, King and Ozler (1998), building on these past assessments and data collection, attempted to undertake a particularly thorough evaluation by estimating an expanded education production function which allows for the estimation of the impact of decentralization by comparing autonomous schools with non-autonomous and private schools<sup>30</sup> . Their study finds that school management, measured by the degree of autonomy of the school, is an important determinant of student performance and, more specifically, disaggregating the school autonomy measure, that schools that take more decisions on teacher management (hiring and firing of teachers, evaluation, supervision, training, etc) are likely to be more successful in increasing student achievement<sup>31</sup> .

A similarly thorough evaluation was also made of the El Salvador’s EDUCO model in 1998 by Jimenez and Sawada (1998) and in 2004 by Sawada and Ragatz (2004). The first thing to say is that there is clear evidence that EDUCO, which was firstly conceived as a way to expand educational access in the rural areas, was successful in doing so (starting from a much lower point in 1990, the gross and net enrolment rates are currently higher in the rural than in the urban area in El Salvador [NER: 100% and GER: 132%] and we know that EDUCO schools were the main enrolment driving force in the rural area in the 1990’s). Taking this as a basis, the first study, estimating expanded education production functions which use three measures of educational achievement<sup>32</sup> and allowing the estimation of
the transition to individualized land rights, they have often maintained land-related social safety nets to provide insurance for the poor. One example for a mechanism to do so is to allow continuing uses of communal pastures and forest areas of low productive value as well as an universal right to collect leftovers after the harvest or graze animals on harvested fields. Another one is the provision for periodic redistribution of at least part of the land available to the community - for example the Bible mandates land redistribution every 50 years.<sup>22</sup> Such redistribution of cultivation rights is likely to decrease productive efficiency by attenuating individuals'

> 2' Jodha (1990) provides evidence on the importance of access to the commons as a safety net for the poor. Based on panel data from China, Burgess (1997) finds that the equitable allocation of land use rights under communal tenure has an effect similar to a lump sum transfer that provide insurance against low nutritional outcomes in a way that is more incentive-compatible than arex _post_ redistribution. The fact that land ownership has a more significant impact on improving nutrition than on income can be explained by the fact that, with imperfect rural grain markets, considerable cash outlays would be required to achieve a similar effect through market purchases of grain. The presence of equity benefits from periodic redistribution of land rights in China would be consistent with peasants' strong support for the system of periodic redistribution (Kung, 1995).

> **21** Note that this is historically well-founded, as the many examples in Binswanger et al. (1995) demonstrate.

> **22** If incentive structures and enforcement mechanisms to ensure that such provisions are actually implemented at the local level are non-existent, the provision for regular redistribution can actually give way to arbitrary behavior and rent extraction by local leaders. For a theoretical and empirical discussion of these issues see Tumer et al. (1998).

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proxies for the more volatile cross-border lending conducted primarily by the parent banks of foreign affiliates in the ECA region. We interact the foreign participation variable with year dummies to test whether firms were better able to weather the crisis in countries with wellestablished foreign banks. The pattern of results that we find is robust to using different measures of foreign bank participation and different years between 1999 and 2002.<sup>21</sup> S is a matrix of sector dummy variables because financial constraints and the probability of survival could vary across industries. Finally, T represents variables that control for the timing of surveys. In the case of the financial constraints, these are simple dummy variables corresponding to the round of the survey. For the survival analysis, we use time dummies indicating the month that the firm was surveyed in the 2008-9 BEEPS survey. Although the FCS were conducted within a tight two-month window (June and July 2009), most of the 2008-9 BEEPS were conducted over a three to six month window in individual countries. The dummies indicating the month of the 2008-9 BEEPS are included to control for differences in survival possibilities for firms first interviewed before the start of the global crisis and those that were first interviewed in the early part of the crisis.<sup>22</sup>

# **V. Regression Results**

This section presents results from the survival analysis and the financial constraints analysis. The results from the firm survival regressions are discussed first.

# **A. Firm Survival**

Table 3 shows results from the firm survival regressions. The first column presents base results omitting the financial and performance variables, the second and third include the financial variables and the fourth and fifth include both financial and performance variables.

> 21 For example, although we use the 1999 share of total banking assets held by foreign-owned banks from Claessens and Van Horen (2008a), we derive similar results when we use the foreign claims on the countries in the ECA region (as a share of GDP) as
with the exception of the GSOEP, do not typically allow to observe completed migration spells and the timing of returns. In addition, they may miss part of the temporary migrant population as temporary migrants can be left out of traditional survey sampling frames.

# **3 Labor supply**

The first and foremost impact of temporary migration and return is on the labor market, especially on labor supply in the origin country. First, past migration overseas may affect incentives to participate in the labor force (extensive margin) or the quantity of labor supplied (intensive margin) by temporary migrants upon return. Second, temporary migration and the remittances sent by migrants can impact the labor supply of household members left behind, by increasing household income and possibly reducing domestic labor supply at the household level through an income effect. Finally, temporary migration, by acting as a negative labor supply shock in the domestic economy, may have general equilibrium effects on the labor supply of non-migrant households in the origin country. This subsection summarizes the evidence on the impact of temporary migration on the labor supply of return migrants, household members in migrant households, and non-migrant households.

## **3.1 Labor supply of temporary migrants after return**

The sign of the impact of past migration overseas on the labor supply of temporary migrants after return is theoretically ambiguous. On the one hand, past migration may decrease the labor supply of return migrants. Higher earnings and savings overseas may raise reservation wages and reduce incentives to find employment back home among returnees, especially among older individuals who are close to retirement age. In addition, at least in the short run, return migrants may want to allocate more time for leisure after spending several years away from their families, as part of their life cycle optimization plan (Dustmann and Kirchkamp 2002). Third, if the human capital and experience accumulated overseas do not match labor demand in home labor markets, job search frictions may lead to higher unemployment among return migrants (Lucas 2005). On
y��� �α� �α�Corruption��� �α�Regulation��� � α�Corruption��� ∗Regulation��� �α�X��� �α�D� �α�D�� �α�D�� �α�D� �e��� _(1)_ where _y_ is labor productivity (henceforth, productivity), _X_ is control variables; _i_ , _j, s, c_ denote firm, country, industry and cell, respectively; D�, D��, D��, D� denote country, industry, sector and region dummies (fixed effects), respectively. We also include year trend in the model to control for the potential impact of global shocks in different years on productivity, given that data across 111 countries were collected throughout 2009-2017.

The results of the baseline model without any interaction terms are reported in Table 1. As observed, irrespective of the set of controls in place, there is a significant negative correlation between productivity and corruption. Specifically, a 1 percent increase in corruption is associated with around a 0.32 percent decrease in productivity (Table 1).

For the remaining variables, there is no statistically significant relationship between regulation and productivity, once the control variables are included (Table 1). Firm-size has a significant positive relationship with productivity, but this relationship becomes insignificant once we control for training provided to employees and the innovative capacity of the firm ( _Certificate_ ). Thus, one possibility is that the impact of firm-size on productivity travels mainly through its impact on innovation and skills acquired through training. As expected, productivity is significantly higher for exporting firms; firms with higher share of foreign ownership; better access to finance; higher innovative capacity; and higher training opportunities for their employees. As Table 1 shows, these findings hold irrespective of the set of controls in place. In all regressions, we also included per capita income, prevalence of
costs. However, with greater availability of increasingly high-resolution and low-cost satellite imagery, improvements in remote sensing techniques for extracting useful data from satellite images, and declining computational costs

> 5 OECD/SWAC (2020) identify 7,617 urban agglomerations as of 2015. In addition to SSA, their analysis covers North Africa.

> 6 For a more detailed description of the differences between _WorldPop_ ’s constrained and unconstrained data sets see <u>https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained/. In both cases, population is</u> allocated to grid cells using machine learning methods described in Stevens et al. (2015).

> 7 In the GHS-BUILT data set, the quality of the satellite imagery used to derive built-up area is also likely to be lower for earlier years which introduces an additional source of measurement error for any dynamic analysis of SSA’s urbanization process. Similar considerations apply to OECD/SWAC’s (2020) dynamic analysis of urbanization patterns in Africa based on the Africapolis data set. Hence, while Google Earth provides the underlying source of imagery for 2015 for this data set, it is unclear, from the descriptions of the data in OECD/SWAC (2020) and Heinrigs (2021), what the underlying sources of imagery are for the earlier years, going back to 1950.

3
Africa, but many other countries have followed since (Lesotho, Nigeria, Niger, and Rwanda), and an increasing number of countries are considering its implementation.

This paper assesses the impacts of the PSI-PMI program on students’ learning outcomes. To assess the program’s impact, we focus mainly on the performance of 12<sup>th</sup> grade students on the compulsory high school leaving examination, the West African Secondary School Certificate Examination (WASSCE). We used matching procedures to construct two groups of non-program students to serve as the control group. In the first control group, <mark>given that the selection of pilot schools was not random, we used administrative data and propensity score matching to select a comparable group of control schools. We then obtained student-level data through a survey,</mark> significantly oversampling control group student <mark>s to further match at the student level. The second control group exploits the fact that within the pilot schools, not all students were exposed to the program due, among other things, to capacity constraints. As a result, we were able to build a within-school control group, which addresses the limitation of the matching at the school level given the small sample. A</mark> t the time of data collection, the sampled students were assessed on an exam based on the Gambian curriculum but designed by educators and the researchers. In addition to our survey data, we have access to a significant amount of administrative data, including the students’ performance on their nationwide grade 9 examination, the Gambia Basic Education Certificate Examination (GABECE).

<mark>The results of our analyses show</mark> that the PSI-PMI program in The Gambia significantly improved student performance in mathematics and English.<sup>3</sup> The program improved students’ average mathematics score by 0.54 standard deviation or 11.9 percentage points, from 38.8 to 50.7, on the researcher designed exam and by
16 power, such as the Indian National Congress, or advocate the restriction of rights of minorities, such as the French National Front Party, are notable examples.

A third dimension coded in the DPI is whether a party is rural or not. A party is classified as rural if either the platform or the constituency is mainly rural. Finland’s and Sweden’s Center Parties are prominent examples of rural parties. Similarly, a party is classified as regional if either the platform or the constituency pertains mainly to a specific region of the country. Examples include most of the Belgian parties, since separate parties exist in the Wallonian and the Flemish part of the country.

Finally, we classify a party as religious if government policy towards religion or the members of a particular religion are among the party’s core issues. We distinguish such parties using, once again, the source characterizations of either the party’s platform or constituency. In predominantly Islamic countries, parties are defined as religious if the chief executive or party leader is also a religious leader, as is the case in Iran. The database also captures, among others, religious parties in Israel, the Buddhist Komeito party in Japan and the Hindu-based Bharatiya Janata Party (BJP) in India. With respect to Christian-Democratic parties, if any one of our sources indicated that religious issues were a central part of their party platform, they are indicated as Christian. Both the Italian and German Christian Democratic parties are therefore coded as having religion as an important element ofparty orientation. Since religious issues are arguably not the most central or salient concern of party members, this suggests that this variable is somewhat biased towards identifying a religious orientation where none exists. On the other hand, for social policy issues, or on questions such as immigration, the coding is likely to distinguish these parties from others in the respective political systems in ways that are both relevant and useful.
the distributive effect, excluding the significant distributive effects due to different average changes for rural and urban incomes.

For the trade reform scenarios analyzed here, the aggregate income change rates – consistently estimated by aggregation from the micro data shocked with the factor and goods prices changes – are shown in Table 14.

_<u>Table 14: Real income rates of change (percent) estimated by aggregation of the household surveys</u>_

||**All**<br>**Urban**|**Rural**|
|---|---|---|
|<br>**_Equation (1) use_**|**_d for all households_**||
|**Full unilateral liberalization**|**1.43**<br>**1.66**|**0.82**|
|**CAFTA bilateral liberalization**|||
|**Nicaragua and US liberalize**|**0.84**<br>**1.01**|**0.38**|
|**Nicaragua liberalizes only**|0.59<br>0.82|-0.01|
|**US liberalizes only**|0.24<br>0.19|0.38|
|<br>**_Equations (1) and (2) used fo_**|**_r different groups of households_**||
|**Full unilateral liberalization**|**1.48**<br>**1.73**|**0.82**|
|**CAFTA bilateral liberalization**|||
|**Nicaragua and US liberalize**|**0.91**<br>**1.05**|**0.55**|
Melitz-Pareto model (with a continuum of single product firms, multi-product firms or a finite number of firms) to the EDD facts. Section 4 shows how the implications of the Melitz model change when we drop the Pareto assumption and instead assume that the firm productivity distribution is lognormal. Section 5 describes the quantitative impact of trade cost shocks estimated based on “exact hat algebra.” Section 6 concludes.

# **2. The Intensive Margin in the Data**

## **The Exporter Dynamics Database**

We use the Exporter Dynamics Database (EDD) described in Fernandes et al. (2016) to study the intensive and extensive margins of trade. The EDD is based on firm-level customs data covering the universe of export transactions provided by customs agencies from 59 countries (53 developing and 6 developed countries). For each country, the raw firm-level customs data con-
# **_Trade_**

Goods and services move within and across countries. Within each country, these flows are determined by the relative demand and supply of goods in each cell together with iceberg trade costs. � From the network data we derive iceberg trade cost factors τ�� , i.e. the cost of shipping goods from sector s across the transport network from origin cell i to destination j. The experiment we study is a � change in these costs. Internationally, in addition to network trade costs τ�� , there are country origin‐ destination shift parameters, A��� , which are sector specific, and capture national preferences for goods from other countries. For origins and destinations i, j both in the same country these take value unity. Between countries they may differ from unity but take a common value , A��� , for trade between all origin cells i in one country and destination cells j all in one other; i.e. for each sector s this is a block matrix, with values of A��� the same for any particular bilateral international trade.3 The parameter applies both for final consumption and intermediate usage of such goods. These shift parameters are calibrated such that international trade flows match the trade flows in the data. They apply only to international flows, since we only have trade data internationally, and they calibrated on top of the trade barriers created by the transport network.

The initial international trade flows are defined by the data, in which national trade balances are non‐ zero. We assume that net imports are paid for by remittances from migrants or other capital flows; the base levels of remittances/ capital flows do not change with the experiments, and neither therefore do national trade balances.

# **_Equilibrium and calibration_**

Given the transport network, endowments of labor and land, and parameters of the model (including � productivity parameters z�� and country origin‐destination shift parameters A�� ),
lower DRD is to be expected, assuming that the death rate declines monotonically with income, though this would have a hard time explaining a _DRD_ so close to unity. However, one cannot rule out the possibility of bias. One reason to suspect underestimation of the _DRD_ using the BR method is greater attenuation bias due to greater noise in the poverty data for Africa.

There are other possible factors. It might also be conjectured that the HIV/AIDS epidemic in Africa may has weakened the correlation between the death rate and poverty. At the early stages of the epidemic, it appears that HIV/AIDS incidence was higher among non-poor groups (at least as indicated by education and living in urban areas). However, there is evidence that this has changed over time, as better educated (and hence less poor) groups were better able to protect themselves from the disease (de Walque, 2004).<sup>12</sup> The very low death rate differential for Africa suggested by the _BR_ method is also hard to reconcile with the aforementioned DHS analyses, which indicate a differential in infant and child mortality rates for SSA between the poorest quintile and overall mean of 1.1, though this is still lower than that for low and middle income countries as a whole (at around 1.4).<sup>13</sup>

# _3.2 Evidence on differential fertility_

Turning next to fertility differentials, one faces similar data problems. The overall Crude Birth Rate (CBR) for low and middle income countries in 2001 is 23 births per year per 1,000 people (World Bank, 2004). Similarly to the death rate, we know less about the conditional mean birth rates, though recent analyses of DHS data have provided some valuable new information.

> 12 There is evidence of lower usage rates for condoms among the poor; the aforementioned analyses of the DHS data indicate a condom usage rate of 18% for the poorest quintile, versus 27% on average (
of enrollment in _Oportunidades_ focuses on urban households; Leroy et al. (2008) compare urban and rural enrollment and discuss utilization of the different components of the program.

4 For the subsample of households in the ENCELURB that applied for benefits according to administrative sources, we compared the program’s calculated eligibility index to our index (based on the proxy means model and the characteristics in the ENCELURB survey). These two criteria for eligibility coincide in approximately 80 percent of the cases. Differences are likely due to different selfreports of household characteristics between the two visits.

5 In the survey, households that were recipients of the transfer were assumed to have applied, and households that applied were assumed to know about the program.

6 Coady and Parker (2009) study the participation rates across different socio-economic groups based on household consumption per capita.

7 Note that potential maximum benefits have been previously used as an instrument for actual benefits (see, e.g., Gertler, Martinez, and Rubio-Codina 2012 and Dubois, de Janvry, and Sadoulet 2011).

8 The applicant's characteristics, detailed in table 4, are those of the mother, who was the recipient of the transfer and the person actually approaching the registration center in the vast majority of cases.

- 9 The marginal effect of benefits corresponds to a hundred pesos.

10 Martinelli and Parker (2009) also find that applicants who are more educated tend to omit reporting at the registration center that they lack a concrete floor or a toilet, potentially excluding themselves from the program; this omission may be attributed to embarrassment.

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