rafmacalaba/data-use-mentions-extended huggingface dataset:
FUNDEF is associated with a rapid increase in enrolment rates in primary and lower-secondary education. Although this increase cannot be attributed solely to FUNDEF, there appears to be a strong association between changes in enrolment rates and in the composition of enrolment between the state and municipal school networks, which has been changing over time in favor of the municipal school network. Based on evidence for a data set including 26 state capitals in the period 1991, 1994, and 1998-2001, enrolment rates were estimated to have risen rose by more than 2 percent per year faster after the introduction of FUNDEF than in the period 1991-98.<sup>2</sup> _____________________

1. See de Mello (2001 _a_ , 2001 _b_ ), Afonso and de Mello (2002), and Ministry of Education (2003), for more information.

2. See de Mello and Hoppe (2005) and OECD (2005), for empirical evidence.

22. The municipalities face downward rigidities in their budgets, constraining their ability to reallocate funds to finance outlays on investment. The existence of minimum spending levels for education and health care impose downward rigidity in current spending, as in the case of the federal government, although revenue earmarking is less pervasive than at the federal level. The municipalities (as well as the states) are required by the Constitution to earmark 25 percent of revenue (18 percent for the federal government) to finance spending on education and 15 percent of revenue (12 percent for the states) to finance the provision of health care. At the same time, spending on personnel, including pensions to retired civil servants, places a heavy burden on municipal budgets (Figure 2). Outlays on debt service, consistent with continued compliance with the debt restructuring arrangements with the National Treasury, reduces the room for manoeuvre in indebted jurisdictions.

23. The ability of the municipalities to finance infrastructure investment is also constrained by their revenue raising capacity, which tends to be lower than at the state and federal levels. The municipalities tax services
***|
||(0.034)|(0.039)|(0.036)|
|Ethiopia-Nigeria|0.000|0.107||
|Ethiopia-Uganda|0.919|0.000||
|Nigeria-Uganda|0.000|0.000|0.000|
|Observations|4,604|6,792|5,297|
|R-squared|0.072|0.038|0.030|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent answered “yes” to a question about the inability to access a basic necessity. Inability or ability to access a basic necessity is conditional on the household needing the item or attempting to purchase the item. The binary variable is regressed on indicators for each country, with Malawi as the base case, and each pre-COVID-19 household annual per capita consumption quintile, with the lowest consumption quintile as the base case. Note that the question regarding soap was not asked in Ethiopia. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

54
Moderate Obstacle, a Major Obstacle, or a Very Severe Obstacle to the current operations of this establishment?‖ This question provides a comprehensive measure of managers‘ views about financial constraints.

> 13 The first round of the BEEPS was conducted in 1999 but the coverage of firms and format of questions was not sufficiently similar to later rounds to include that data in the regression analysis. For purposes of this analysis, we refer to the 2002 survey as being the first round.

> 14 These changes are described in detail in World Bank (2009).

11
40 investing across borders: do fdi-specific regulations matter?

I formally test for the exogeneity of FDI regulations with three alternative instrumental variables related to land. The first index on Access to Land Information measures aspects of whether the land registry or cadastre have a publicly accessible inventory of private and public land. The second index on the Availability of Land Information scores the richness of 18 pieces of land-related information (for example, plot size, land value, address, previous contracts). And the third index captures the time taken (number of days) to lease public or private land in the host country on average (World Bank Group 2010). These instrumental variables are chosen because they are associated with the soundness of domestic institutions; but they are not a determinant of FDI in their own right because a substantial share of aggregate global FDI is in services for which access to industrial land is not as important as for FDI in manufacturing.

The tests follow a three-stage process. _First_ , I conduct the Durbin-Wu-Hausman (DWH) test for the endogeneity of each of the Openness, Arbitration, and Procedures variables. After confirming a high degree of correlation between the potentially endogenous variable and its instrument, Openness is instrumented by the Access to Land Information index, FDI Arbitration is instrumented by the Availability of Land Information index, and FDI Procedures is instrumented by the time it takes to lease private land. I also instrument for all the three variables together. In all the four cases, the p-value of the DWH test is greater than 0.15, which fails to reject the null hypothesis of exogeneity of regressors.

_Second_ , I check for the validity of the instruments in an over-identified model. None of the p-values of the Hansen’s _J_ chi-squared is less than 0.44; the null hypothesis that the overidentifying restriction is valid is not rejected. Finally, the results reject the null hypothesis of weak instruments for the Openness and Arbitration variables, but not the Procedures variable. However, when the Arbitration and Procedures variables are instrumented
countries, where these costs are known to be very high relative to workers’ earnings (International Labour Organization 2015; Farole, Cho, Bossavie, and Aterido 2017).<sup>13</sup> The survey asks workers about the expectations that they held prior to their migration experience regarding labor market outcomes in the destination countries, such as wages and savings. As a result, we can compare their expectations with actual outcomes. Borjas and Bratsberg (1996) postulate that lower-than-expected earnings may be an important reason for return migration from the United States, and McKenzie, Gibson, and Stillman (2013) provide empirical evidence on over-optimistic expectations for migrants from Tonga. Even

> 12One exception is the Labor Market Panel Survey for Egypt, one of the few labor market surveys in sending countries that include a detailed module on past migration, and collects information on intermediary fees paid by migrants. Migration from Egypt, however, is distinct from the migration from Bangladesh in that the former includes a larger share of undocumented migrants, many of them headed to European countries.

> 13In a recent study, Mobarak, Sharif, and Shrestha (2020) examine a government-mediated program between Bangladesh and Malaysia where the migration costs are significantly lower. The migrants are chosen via lottery which allows the authors to address the selection problems directly and show how migration improves the welfare of the families. The authors attribute the gains, at least in part, to the low fees.

10
Policy Research Working Paper 10376

# **Abstract**

This paper uses an exogenous shock to wages from the world’s largest anti-poverty program to show that higher wages can lead to increased air pollution, likely by inducing farmers to shift into a labor-saving and mechanized production process. Using a difference-in-differences approach on the staggered roll-out of India’s Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA), combined with data on nearly 1 million fires, the paper shows that the frequency of agricultural fires increases by 21 percent after the shock. The increase in fires is concentrated in districts that appear more likely to mechanize the harvest. MNREGA did not lead to changes in area planted or tonnage produced in fire intensive crops. The estimates show that nationally, the shock increased the rate of particulate emissions from biomass burning by 30 to 50 percent. The results suggest that absent policies to correct for environmental externalities of mechanization at all stages of development, labor market shocks may lead to inefficient levels of mechanization.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at abehrer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent
plants for which the measures of TFPR and TFPQ are available (column 2).

To the extent that preschool expansion relaxes firms’ skill availability constraint, these impacts should be larger precisely for those plants that are initially most constrained. To test for this differential effect, we exploit a question in the 2006 SI data that asks if the lack of workers’ skill is the primary reason not to expand. Column (3) shows that those plants which answered ‘yes’ to this question increased wages as a result of preschool expansion significantly more than 19
*|(0.24)|[0.04]|-0.79***|-0.22|2.33|(2.49)|1,031|
|Child alertness (s.d. units)|-0.73***|(0.20)|[0.00]|-0.50***|-1.45***|0.00|(1.00)|975|
|Diffculty with self-care (z-score)|0.38***|(0.14)|[0.08]|-0.43***|0.43***|-0.00|(1.00)|1,610|
|Number of learning activities|0.19**|(0.09)|[0.08]|-0.21***|0.27***|0.22|(0.56)|1,385|

_Notes:_ The table shows the regression results on statistically significant results from the midline survey. Each row is its own dependent variable. Panel A outcomes are Clean Water, defined as an indicator for households having treated drinking water (such as by a filter). Rooms and rooms occupied by family members are reported by the respondents. Panel B outcomes are food consumption, Respondent (and adult) food insecurity equals 1 if the respondent went to bed hungry on at least one day in the last week and 0 otherwise. Panel C outcomes are self-explanatory. Panel D outcomes are COVID symptoms, which is the sum of indicators that increase the likelihood of COVID contraction by the respondent including leaving the house, attending social gatherings, not keeping distance from others, going to mosque or other religious institutions, going to grocery store or market, and leaving village/neighborhood. The other outcomes are as explained in the labels
## **4.3 Discussion**

Secondary education seems to partly explain why mission areas get more aid, although the evidence is not conclusive. Even if these results are taken at face value, it is not obvious why higher levels of education should make areas attractive targets for aid. It could be that some level of human capital is necessary for successful implementation of projects, in which case this finding would be indirect evidence of the “effectiveness” hypothesis examined in Section 3. On the other hand, if the evidence of a political aid cycle is interpreted as favoritism (as argued here), human capital could play the role of a necessary condition for the formation of political connections. These are certainly questions that could be investigated empirically and which merit further research.

# **5 Conclusion**

This article has documented that 19th- and 20th-century Christian missionary activity in Africa predicts the location of present-day World Bank aid allocation. The findings suggest that the probability of receiving development projects is about 40 percent higher in areas that contain a historical Christian mission station. This result is the main contribution of the article, although attempts have been made to explain the source of the correlation and to explore policy implications.

First, the article tested whether aid effectiveness is higher in areas that hosted historical missions. As missionary activity had long-lasting positive effects on social and human capital, it is possible that donors channel aid to such areas in the hope that these endowments will facilitate successful implementation of the projects. Using data on project ratings and survey-based development indicators, the empirical analysis in this article was unable to find evidence of superior aid effectiveness in areas exposed to early missionary activity. These results are not conclusive, however, due to data limitations.

Second, the article investigated three potential mechanisms: political favoritism, religious favoritism, and education. Education is positively correlated with aid allocation, and its inclusion among the controls in the baseline regression reduces the coefficient on missions. There is no evidence of religious favoritism, but
History, measurement, and policy_ . Oxford University Press, 2015.

Riumallo-Herl, Carlos, David Canning, and Joshua A. Salomon. "Measuring health and economic wellbeing in the Sustainable Development Goals era: development of a poverty-free life expectancy metric and estimates for 90 countries." _The Lancet Global Health_ 6.8 (2018): e843-e858.

Salecker, Lukas, Anar K. Ahmadov, and Leyla Karimli. "Contrasting monetary and multidimensional poverty measures in a low-income Sub-Saharan African Country." _Social indicators research_ 151.2 (2020): 547574.

Sen, A., 1999. Development as freedom. Anchor Books, New York.

Unicef. "A Roadmap for Countries Measuring Multidimensional Poverty." (2021).

Whelan, Christopher T., et al. "Monitoring poverty trends in Ireland: Results from the 2001 Living in Ireland Survey." (2003).

World Bank (2018). "Poverty and shared prosperity 2018: Piecing together the poverty puzzle." World Bank (2022a). _Poverty and Shared Prosperity 2022: Correcting Course_ . The World Bank, 2022.

World Bank (2022b) A better future for all Nigerian. Nigeria Poverty Assessment 2022.

34
the host community. As host communities’ often have negative attitudes and behavior towards refugees (Adida et al., 2018; Hangartner et al., 2019; Valli et al., 2019; Wike et al., 2016), a failure to stimulate positive change among the host community indicates that the intervention does not address their underlying concerns regarding employment and refugees.

Given that our endline survey was collected right after the completion of the training, it is perhaps not surprising that economic impacts failed to materialize. Indeed, we imagined such an outcome in our pre-analysis plan. Endline data was collected, literally, on the last day of a set of trainings that required significant time commitment from participants. That the control group, who were free to pursue and undertake employment, did not manage to do so more so than the treatment group suggests significant labor market constraints. This should temper expectations about the potential longer-term impacts of this program, which are reinforced by more recent macro-economic problems in the region. Both countries have suffered economically due to Covid, and Lebanon has had additional political and financial crises. Broadly speaking, these concerns are reflected by null findings in terms of optimism and experience / perceptions of economic scarcity in the host community.

Similarly, the program did not assist refugees in finding employment. However, in contrast to the host community, the program stimulated a range of positive attitudinal and behavioral outcomes among the refugee community. In particular, the program made refugees in the treatment group more optimistic, left them feeling more capable to meet their current needs and has resulted in a reduction of relative group bias in the dictator game. These social outcomes appear to be driven by a direct “program effect” as identified by Brück et al. (2021) as opposed to an indirect effect via employment gains. For these specific outcomes, we imagine, simply, that involvement in the program sent a positive signal to refugee participants about their future competitiveness in the labor market, which improved optimism. Ability to meet
first hypothesis. For subsequent imputation purposes, the omitted observations with INCRA land values greater than R$1700 hectares were left with their original land value; we do not believe that these values are in error but rather reflect characteristics we are unable to measure.

> 14 In fact, the municipio-level data set was derived from aggregating point level information. Hence, additional municipio observations were created by assigning all points of municipio x in agricultural land (pasture land) the INCRA land value for agricultural land (pasture land).

35
# **3. Data description**

The analysis in this paper relies on data from the Life in Transition Survey (LiTS), administered by the World Bank and the European Bank for Reconstruction and Development (EBRD). LiTS provides data from a unified survey for the entire set of Transition Economies,<sup>2</sup> and five Western European countries (France, Germany, Italy, Sweden, and the United Kingdom). For each of the countries, the LiTS survey provides a nationally-representative sample of households; in each household the responses are based on a face-to-face interview with a randomly-selected member of the household.<sup>3</sup> Data from the second round of the survey, collected in 2010, are used in this paper. A newer round of data was collected in 2016, but the questionnaire from the third round omits one of the two key variables used in the analysis, namely the question whether respondents have access to connections (see the empirical strategy section below for details).

The notion of expected socio-economic mobility is defined here based on the respondents’ assessment of their position on the society’s 10-step welfare ladder today and four years hence. These are derived from the respondent’s answers to the following questions: “Please imagine a ten-step ladder where on the bottom, the first step, stand the poorest 10% people in our country, and on the highest step, the tenth, stand the richest 10% of people in our country. On which step of the ten is your household today? And where on the ladder do you believe your household will be 4 years from now?” The distribution of responses to the current and future welfare ladder questions for the pooled sample is shown in Table 1. One can observe clustering of responses in the middle of the scale for both questions. About 60 percent of respondents in the sample place themselves on the 4th, 5th, or 6th step of the welfare ladder today, and more than
# **2. Data**

This paper takes advantage of publicly available census records from the IPUMS-International database and the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), a harmonization project of individual-level information on incomes from national household surveys in Latin America.

# **2.1. The IPUMS-International database on Census records**

The IPUMS-International database contains microdata based on samples of full population census records from 82 countries in the world since 1960, providing public access to anonymized samples from the official national censuses and constituting the world's largest available bank of censuses information.<sup>1</sup> The database is organized following similar criteria across time and countries to allow researchers to compare results from different periods and countries.

The IPUMS-International database provides information at the individual and household levels from 10 percent samples of census data—where possible—obtained by selecting every 10<sup>th</sup> household after a random start. These cross-sectional databases provide information at the individual level on demographic characteristics such as age, years of education and enrollment. This paper relies on the censuses’ information on household access to basic public services and

> 1 For an overview on the IPUMS-International project, see

<u>https://international.ipums.org/international/overview.shtml.</u> For an example of a historical census record, see Appendix A.1.

5
14 According to data collected in the LiTS 2010 (EBRD 2010), 45% of workers 18+ in the region would prefer to work for a state‐owned enterprise (30%) or the public administration (15%). Being self‐employed comes behind with 25% of the sample, 20% would prefer in a private firm, and 10% does not know. While these results hide some regional disparities, it is clear also that it highlights a preference for the types of positions offered in the public sector.

17 | P a g e
. In our model these could be captured as reducing the likelihood of finding employment abroad, _p_<sup>_E_</sup> _,_ and in lowering the health conditions associated with migration to Europe, _H_<sup>_E_</sup> _._ Prior to COVID-19, this calculation still resulted in many youth having the intention to migrate, as our data will show in the next section. We now discuss how COVID-19 may affect this decision.

# **2.2 How might COVID-19 change these intentions?**

There are several channels through which the COVID-19 pandemic can influence these migration intentions. First, the economic consequences of the pandemic in The Gambia are likely to lower the income-earning opportunities at home, while lower earnings for household members who are

> 5 Source: World Development Indicators, <u>https://data.worldbank.org/indicator/NY.GDP.PCAP.CD</u> [accessed 20 January, 2021].

> 6 At the time of our baseline survey in 2019, the exchange rate was 56 Gambian Dalasi (GMD) = 1 Euro, and 50 GMD = 1 USD.

> 7 Differences in the cost-of-living across locations will reduce these gaps, but the relative rankings will still hold, and _I_ can then be interpreted as the income net of living costs.

6