rafmacalaba/datause-encoder-data huggingface dataset:
# **_Rainfall shocks_**

# **Burundi’s economy is mainly rural, and agriculture dominates as a means of livelihood for**

**a majority of its population.** Around 90 percent of the labor force is said to be employed in agriculture. With the exception of commercial tea and coffee production—which contribute less than 5 percent of GDP—agriculture remains largely subsistence, rain-fed farming. Rainfall in Burundi is highly volatile, and draughts are recurrent events (Figure 10). Higher precipitation clearly contributes to faster growth in agriculture. Importantly, growth in agriculture is said to have a large spillover to the rest of the economy: recently, the IMF has estimated that a 1 percent increase in agricultural sector activity leads indirectly to 0.7 percent increase in real GDP growth in other sectors.<sup>20</sup> Thus, weather shocks can have a large impact on Brandi’s economic performance, and hence fiscal outcomes.

**Figure 10. Burundi: Annual Rainfall and Agricultural Growth Rate**

<!-- Start of picture text --> mm Annual precipitation, millimeter<br>1,700 Agriculture GDP growth (RHS) 50%<br>1,600 40%<br>1,500<br>30%<br>1,400<br>20%<br>1,300<br>10%<br>1,200<br>0%<br>1,100<br>1,000 -10%<br>900 -20%<br>1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> _Source_ : Jefferson and O'Connell (2004) and World Bank.

> 20 IMF (2006).

15
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53
Which side of this debate is correct is an empirical question. However, while a large and increasing number of studies explore the topic, virtually all of them aggregate effects at the household level rather than allowing for heterogeneity within the household and focus on contemporaneous effects during the immediate reservation period. For a variety of reasons, these may be very different from longer-term changes in behavior, the change of which is the ultimate goal of reservations. To assess the extent to which such policies achieve their objective, it will be important to complement them with evidence on the extent to which reservation-induced effects persist over time. Exploring longer-term effects could also help in the understanding of the channels through which such effects materialize an issue of great policy interest.

India is of particular interest in this respect because, in an effort to overcome long-standing discrimination by gender and caste, far-reaching political decentralization in the early 1990s was combined with regulations mandating that a share of elected leadership positions be „reserved‟ for women and other disadvantaged groups. As villages subject to reservation are chosen randomly in any given period, differences between reserved and non-reserved locations can be interpreted causally. We draw on a large national sample to assess impacts of female reservations on the perceived quality with which public goods are delivered, individuals‟ level of political participation and willingness to make a matching contribution to provide local public goods, and their self-reported ability to hold leaders to account. As our data were collected some 15 years after this policy had taken effect, yet still with about one-third of villages never having been reserved, we can examine whether – and, if yes, how – reservations have affected long-term outcomes. Beyond academic interest in such results, recent debates in India about extending female reservations to the state and national levels imply that such evidence can also inform the country‟s ongoing policy debate.

From a methodological perspective, reservation-induced effects may be gender-differentiated and persist beyond the „reserved‟ period. The latter may be due to learning about political processes by previously excluded voters or reversal of policies after reservations expire. Keeping this in mind, we expect female reservations to have two potentially countervailing effects: On the one hand, reserved (female) leaders are likely to be less experienced and less qualified than other candidates, something that could reduce the quality of public service provision in the short term. On the other hand, by increasing inclusiveness of political processes and enabling (female) voters to voice concerns and hold leaders to account, the effects of this policy in the medium term could be very different from immediate impacts.

We indeed find that reservation prompted a decline in perceived leader attributes and the quality with which public goods are provided. While it is unclear to what extent this can be ascribed to prejudice or 3
**’ ’ -** **<u>Table 2: Comparing labor market outcomes at the individual s level for refugee s hosting and non hosting districts (excluding Kampala)</u>**

||**M**||**_In the base w_**<br>**M**|**_ave=2009/_**|**_10_**||**M**||**_In the last w_**<br>**M**|**_ave=201_**|**_2_**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variables**<br>**Levels**|**ean**<br>**(Non-hosting)**|**Obs.**|**ean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std. Error**|**ean**<br>**(Non-hosting)**|**Obs.**|**ean**<br>**(Hosting)**|**Obs.**|<br>**Mean Diff.**|**Std. Error**|
|Labor force participation (12 months)|0.525|13160|0.569|1864|-0.044***|0.012|0.441|15889|0.477|2193|-0.036***|0.011|
|Wage workers|0.157|9780|0.174|1421|-0.017|0.011|0.126|9939|0.142|1440|-0.016|0.010|
|Self-employed|0.121|9778|0.130|1421|-0.010|0.010|0.098|9939|0.101|1440|-0.003|0.008|
|Unpaid workers|0.046|9780|0.037|1420|0.009*|0.005|0.049|9938|0.049|1439|-0.000|0.006|
|Apprenticeship|0.010|9782|0.001|1421|0.009***|0.001|0.007|9938|0.005|1440|0.002|0.002|
|Family workers|0.632|9782|0.678|1421|-0.046***|0.013|0.619|9936|0.672|1440|-0.054***|0.013|
|Worked months (12 months)|8.669|721|8.591|93|0.077|0.431|9.654|5496|9.797|807|-0.142|0.117|
|**Main source of income**|||||||||||||
|Subsistence farming|0.560|12708|0.563|1805|-0.004|0.012|0.575|14760|0.616|2118|-0.041***|0.011|
|Commercial farming|0.032|12708|0.029|1805|0.003|0.004|0.016|14760|0.017|2118|-0.001|0.003|
|Wage employment|0.148|12708|0.143|1805|0.005|0.009|0.121|14760|0.148|2118|-0.027***|0.008|
|Non-ag. Self-employment|0.198|12708|0.202|1805|-0.003|0.010|0.210|14760|0.180|2118|0.030***|0.009|
|Remittances & others|0.062|12708|0.063|1805|-0.001|0.006|0.079|14760|0.040|2118|0.039***|0.005|
|**Other sources of income**|||||||||||||
|HH enterprises|0.764|12738|0.817|1805|-0.052***|0.010|0.742|14779|0.813|2118|-0.071***|0.009|
|Property & investments|0.049|12738|0.033|1805|0.016***|0.005|0.045|14779|0.016|2118|0.029***|0.003|
|Transfers & other benefits|0.187|12738|0.151|1805|0.036***|0.009|0.213|14779|0.171|2118|0.042***|0.009|
|Income received in cash|1001406.66|12738|755617.68|1805|245788.98***|55264.39|1462539.37|14716|827723.97|2094|634815.40***|58819.71|
|Income received in kind|117858.05|12738|139507.10|1805|-21649.05**|10815.08|198902.89|14640|259817.51|2085|-60914.61***|11997.49|
|Av. monthly revenue from non-ag.|||||||||||||
|activities|532430.61|6859|276228.80|1014|256201.81***|61756.55|914609.66|6080|541996.37|909|372613.29***|62027.61|

_Two sample t-test with unequal variances between refugee hosting and non-hosting districts. Significance level: *** p<0.01, ** p<0.05, * p<0.1 The different sources of income (main and other) are measured at the household level and then attributed to all household members who are part of the active population (age between 15 and 64 years)_ 15
in our paper shows that the enrollment falls in private schools which are expensive rather than in the public schools and there is heterogeneity even within private schools. Low quality schools drive the fall in enrollment in private schools. Second, from identification perspective, a clear advantage of our paper is that we have three years of pre-treatment data for a large subset of our schools. Thus, we can strengthen the confidence in our findings by demonstrating that there are no differential pre-trends. However, their study design is limited by only 1 year of pre-treatment data in the ASER sample.<sup>6</sup> Finally, our paper also complements the literature on child labor.<sup>7</sup> Basu and Van (1998) provide a theoretical model that examines conditions under which children work in the labor market. Edmonds (2005) uses data from Vietnam to examine whether improving standards of living reduces child labor. Edmonds and Pavnick (2005) examine the effect of international trade on children’s outcomes. Jacoby and Skoufias (1997) study the effects of financial market incompleteness on human capital accumulation and document that seasonal fluctuation in school attendance is a form of self-insurance by households. More recently Cascio and Narayan (2015) demonstrate that economic booms resulting from increased fracking in the US increase high school dropout rates due to an increase in the demand for unskilled labor. We contribute to this literature by examining the effects of employment guarantee schemes on child labor. Program induced increased labor opportunities for adults induce an increase in child labor.

Our findings have important policy implications: without adequate changes in incentives to attend school, large scale safety net programs designed to smooth household consumption may result in decreased school enrollment and worsening of performance in schools.

The rest of the paper is organized as follows: In Section 2, we offer more detailed information on the MGNREGA in India. Section 3 presents the data used, and Section

> 6Both these studies use the National Sample Survey Office (NSSO) employment and unemployment survey data to substantiate that the program indeed results in higher labor supply of children.

> 7See Basu (1999) for a review of this literature.

5
combined public and semipublic workers in one group. In the 55<sup>th</sup> round, workers were presented with eight categories: proprietary male, proprietary female, proprietary partnership with members of the same household, proprietary partnership with members of a different household, public sector, semi-public sector, and cooperative society or an enterprise covered by the Annual Survey of Industries. The last category (5% of the sample of wage earners) could include enterprises from both public and private sectors. In addition, nine percent of the sample of wage earners had their answers coded as “0” corresponding to “not recorded”. For the respondents with unidentified enterprise type we impute the value for sector-of-employment indicator, based on existing auxiliary information. We considered the 3-digit industry code in NSS 55<sup>th</sup> round and examined the public/private composition in the same industry code in NSS 50<sup>th</sup> Round. If employment within an industry code exceeded 95% public in 50<sup>th</sup> Round, we assigned the same indicator for the missing values in 55<sup>th</sup> Round. We applied an additional filter for assigning an individual to the public sector: whether that individual is covered by Government Provident Fund (GPF) or not. Because certain industries are reserved for public activities only and GPF covers only public sector workers, we believe, we were able to predict quite accurately the public/private status for the workers with this category missing<sup>6</sup> .

The sample for the present analysis consists of 82,283 individuals in 1994-94 and 86,616 individuals in 1999-2000. Among those, in 1993-94, 16% worked in formal public, 14% in formal private and 66% in informal casual sectors. The corresponding numbers for 1999-00 are 14%, 16%, and 66%. In both rounds, the “don’t know” response about the sector of employment among workers in the formal sector averages between 2 and 4% of the sample.

Characteristics of the workforce in these sectors varied substantially. In 1999-00, slightly less than 30% of the total selected sample resided in urban areas, while more than 60% of all public workers and about 70% of all formal private workers did so. Males represented about 85% in the public and formal private sectors and 70% in the salaried

> 6 Changes in the definitions of the public/private categories between the 50th and 55th rounds of the EUS complicate the comparability of 1993 and 1999 employment data. We examined whether the implemented imputations resulted in biasing for or against our main conclusions (i.e. whether the estimated wage differential was smaller or larger with imputation than it was without). It turns out that the public/private differential is lower with imputation, indicating that imputation biases against our results. The results for a sub-sample that excludes individuals with unidentified enterprise type are available from the authors upon request.

11
# FINANCING URBAN SERVICES IN LATIN AMERICA:

# Spatial Distribution Issues

# I. THE RISING DEMAND FOR URBAN SERVICES

1.01 The Region's Urbanization Process. The proportion of total population living in cities has been rising at a markedly faster pace in Latin America than it has in other regions of the world. In 1960, less than half of the Region's population (49 percetit) was urbanized. By 1980, the proportion had reached about two thirds (65 percent) and UN projections suggest an overall urbanization ratio of 77 percent by the end of this century.11 In year 2025 the population of LAC cities would attain an average ratio of 83 percent, matching the values already achieved today in Venezuela and in the three countries of the Southern Cone (Argentina, Chile and Uruguay). In most other LAC countries, the overall structure of the population is young and the fertility rates quite high in both the cities and the countryside, resulting in sustained rates of demographic increase. Especially prominent are the positions of Brazil in Tropical South America and Mexico in Central America: the combined urban populations of these two countries alone (128 million) represent more than half the regional total (54 percent) and they are still growing at average rates of over 3.2 percent per annum.

1.02 PoRulation Distribution. In LAC countries, the territorial distribution of the population is characterized by two main features: the golarization of demographic settlement on a number of large metropolitan areas (see Table 1.2) and, at the same time, the dispersion of population in tens of thousands of smaller cities and towns, scattered over vast territories. At the root of these settlement patterns are the inappropriate terms of trade between the urban and the rural sectors (an enduring legacy of the colonial past) and the corresponding, sharp income differentials which drove millions of poor immigrants to the cities. To these factors, one must add the persistence of adverse conditions in the countryside which is plagued with poor living standards, generalized unemployment, land tenure problems and recurrent natural disasters.'

- 1/ United Nations Center for Human Settlements (HABITAT): Global Report on Human Settlements, Oxford University Press, New York, 1987.

- 2/ Severe droughts, floods, hurricanes, landslides, volcanic eruptions or earthquakes have hit Argentina and Brazil (1985), Chile (1939 and 1986), Colombia (1984 and 1986), the Dominican Republic (1979 and 1982), Ecuador (1987), El Salvador (1987), Haiti (1982), Guatemala (1976), Mexico and Nicaragua (1973) and Peru (1970 and 1983).
_religiosity_ : IMPORTANCE OF RELIGION ranges in four categories from “very important” to “not at all important.” How often a respondent ATTENDS SERVICES is coded as days per year.

Seven WVS variables capture a respondent’s _attention to international affairs_ . Individuals who respond that they are a MEMBER OF THE WORLD are distinguished (1) from those who chose towns, regions, or countries (0). NATIONAL PRIDE is measured in four categories from “very proud” to “not at all proud.” INTEREST IN POLITICS is coded in four categories from the question, “How interested would you say you are in politics?” TELEVISION CONSUMPTION ranges from 0 hours of television viewing to over 3 hours per day. Two occupations with an international focus are included: members of the ARMED FORCES and professional workers including LAWYERS AND TEACHERS. Finally, respondents who were BORN IN COUNTRY are distinguished from all others.

The WVS analyses also include four variables to measure _Left/Right Position_ . First respondents were asked to place themselves on a 1 to 10 scale indicating their LEFT/RIGHT POSITION, where higher values indicate positions closer to the right. Next, three variables capture traditional left/right views on poverty. First, individuals who feel that others “are poor because of laziness and lack of will power” (POOR ARE LAZY = 1) are contrasted to those who believe others are poor because society treats them unfairly (0). Individuals who believe the POOR CAN ESCAPE POVERTY (1) are distinguished from those who believe they have very little chance (0). Finally, an individual’s assessment of whether there are MORE POOR today than ten years ago is coded in three response categories.

Lastly, the WVS analyses include three _psychological characteristics_ .

Respondents indicated their SATISFACTION WITH THEIR FINANCIAL SITUATION on a 1 to 10 scale with higher values indicating greater satisfaction. AGENCY is also measured as a 1 to 10 scale, with higher values indicating a feeling of “free choice and control.” TRUST is assessed with the question, “generally, speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” The WVS measures INCOME as quintiles within each country’s income structure. Three other demographic variables are included in the WVS analysis: a respondents AGE, whether the respondent is FEMALE, and whether a respondent has ANY CHILDREN.

9
incorporating any of these additional potential benefits into the analysis not only would require building up an appropriate analytical framework-something that goes beyond the scope of this paper-but also appears to be of secondary importance given the immediate needs facing these countries.

**Table 4. International Reserves by Region: Stocks and Months of Imports**

||**_Average_**|**_Average_**|
|---|---|---|
|**African sample**|**1984-88**|**1989-93**|
|Intemational Reserves/Monthly Imports|1.46|1.76|
|International reserves (US$ mill.)|4,638|6,810|
|**Middle East**|||
|International Reserves/Monthly Imports|4.61|4.55|
|International reserves (US$ mill.)|52,643|59,187|
|**Latin America**|||
|International Reserves/Monthly Imports|4.07|4.36|
|International reserves (US$ mill.)|47,351|78,422|
|**East Asia**|||
|International Reserves/Monthly Imports|3.50|3.81|
|International reserves (US$ mill.)|45,153|96,723|
|**All developing countries**|||
|International Reserves/Monthly Imports|4.93|3.77|
|International reserves (US$ mill.)|296,636|334,530|

The data on the ratio of international reserves to months of imports refer to all the countries in the sample. However, when excluding Nigeria these numbers become 1.42 and 1.46 (instead of 1.46 and 1.76), which makes the point about the lack of international reserves even stronger.

# _What is the impact of ODA debt forgiveness?_

This section assesses the impact of ODA debt relief on the recipient countries using the import bill capacity indicator introduced above. We do this in two steps, first using averages and second on a marginal basis.

_Have countries receiving more ODA debt relief, on average, been able to import more?'_<sup>_4_</sup> Over the past decade African countries, as a whole and on average, have been able to finance an increasing import bill, as defined above, in part because of an increasing trend in grants. In fact, import capacity and pure grants to the sample countries almost doubled between 1984-88 and 1989-93 (measured in current U.S. dollars). Still, pure grants were insufficient to finance the transfer of resources from abroad, and the gap was covered by new lending from both multilateral and bilateral sources (Chart 4).'5 Indeed, during 198493 the share of financial needs import capacity covered by pure grants for all the sample

> example, there is an improvement in the balance sheet of lenders (typically export promotion agencies) in donor countries.

> **_14_** The results reported have refer only to 31 countries. The smaller sample resulted from excluding Nigeria, which in preliminary estimates and analysis appears as an outlier. This pattem repeated systematically throughout the analysis. Nigeria's large oil reserves and its strong export performance may be causing such divergence. Several examples of this are provided in footnotes. IS For all data sources see appendix 3 below.

10