rafmacalaba/datause-encoder-data huggingface dataset:
and extensive information system that allows the municipality to channel resources to these especially poor neighborhoods.

**_Table 4:  Poverty Headcount Rates_**<sup>**_1_**</sup> **_by Comunas, Cali, 1999_**

|_Comuna_<br>---------------|Headcount Poverty<br>-------------------------------|Headcount extreme poverty<br>-------------------------------------------|Contribution to Total Poverty<br>-----------------------------------------------------|
|---|---|---|---|
|<br>1|<br>54.5 (6.71)|<br>14.7 (4.77)|<br>2.8|
|2|15.3 (2.11)|11.8 (1.89)|1.7|
|3|42.0 (8.34)|19.5 (6.70)|2.4|
|4|45.3 (10.61)|<br>14.6 (7.53)|1.5|
|5|19.0 (4.53)|10.9 (3.60)|2.6|
|6|51.2 (4.47)|23.4 (3.79)|9.4|
|7|47.3 (4.99)|31.8 (4.66)|8.8|
|8|31.2 (4.88)|11.3 (3.34)|4.8|
|9|22.4 (4.81)|12.2 (3.78)|3.1|
|10|25.9 (4.28)|12.9 (3.27)|4.1|
|11|36.2 (4.93)|15.8 (3.74)|7.0|
|12|28.9 (10.14)|<br>17.3 (8.46)|1.3|
|13|42.9 (4.52)|27.0 (4.05)|6.4|
|14|72.8 (3.63)|37.0 (3.94)|15.9|
|15|55.7 (5.55)|32.7 (5.24)|6.0|
|16|55.4 (3.99)|24.2 (3.44)|7.7|
|17|6.7 (3.37)|3.7 (2.55)|0.4|
|18|53.1 (5.12)|26.4 (4.52)|7.3|
|19|19.9 (2.95)|7.8 (1.98)|3.4|
|20|45.7 (6.72)|15.3 (4.85)|3.4|
|---------------|<br>--------------------------------<br>40.0|<br>-------------------------------------------<br>20.0|-----------------------------------------------|

1 The poverty lines were endogenously defined as those providing a 40 percent poverty and a 20 percent extreme poverty rate in the city. Standard errors are in parentheses Source: Own calculations from EPSOC (1999).

Using the geographical information on poverty, we derived two poverty maps (figures 1 and 2). While poverty rates by _comuna_ are important (figure 1), of equal importance is the geographical distribution of the poor (figure 2). Since _comunas_ varied widely in size – from 61,000 in _comuna_ 4 to 254,000 in c _omuna_ 14 -- these two angles of looking at the same picture showed different results. Visually, map 2 shows the distribution of the poor population in the city with the size of the circles indicating the size of the poor population group in the respective _comuna_ . While by far the largest amount of poor people also lived in the _comuna_ with the highest poverty rate ( _comuna_ 14 with about 185,000 poor people), a comparison of the maps also shows important differences, e.g. for the ranking of _comuna_ 11 (moderate poverty rate but large population) or _comuna_ 1 (high poverty rate, low population).

Closely linked to the spatial distribution of poverty in the city were a number of population characteristics. First, the _comunas_ with the highest headcount poverty rates were also the _comunas_ with the most rapid population growth rates in the last decade – which also explains why the share of the migrant population was highest in these 16
Table 7: The 2012 reform effect on Employee cash or near cash income

||OLS|OLS|OLS|OLS|
|---|---|---|---|---|
|Mom(six-year-old) _×_ Post2012|-271.4<br>(508.3)|52.99<br>(371.1)|36.88<br>(381.7)|36.86<br>(382.2)|
|Mean (Dep. Var)|2172.895|2172.895|2172.895|2172.895|
|St. Dv. (Dep. Var.)|3690.202|3690.202|3690.202|3690.202|
|Demographic controls|No|Yes|Yes|Yes|
|Year fxed efects|No|No|Yes|Yes|
|State fxed efects|No|No|Yes|Yes|
|Region by year unemployment rate|No|No|No|Yes|
|N|1279|1279|1279|1279|

Standard errors in parentheses Source: EU-SILC (2010-2020). Standard errors are clustered. The sample consists of employed mother of children between 3 and 4 or 6 years old.

# **7 Ruling out Contemporaneous Shocks**

A causal interpretation of DD requires that there are no other contemporaneous factors that influence the relative employment of mothers of six-year-old children. Even though it is unlikely that parallel introduced policies or other contemporaneous shocks only influenced the employment outcomes of mothers of six-year-olds, while leaving the employment of mothers of three-to-four-year-olds unaffected, we investigate this possibility in more detail.

To this end, we search for important policy reforms between 2010 and 2014. The Romanian government introduced several policy reforms that could potentially have affected employment decisions, such as a cut in public sector wages, or the introduction of a flat tax rate. Nevertheless, most of them were concluded by 2010. In addition, we believe that none of these should have impacted the maternal employment rates of mothers with six-year-old children differently from those mothers with three to four-year-old children.

21
The Heterogeneous Effects of a Food Price Crisis on Child School Enrollment and Labor – Evidence from Pakistan Xiaohui Hou and Seo Yeon Hong<sup>1</sup> Key words: Food price crisis, school enrollment, child labor JEL: I24, J13, D01 Sector Board: Social Protection (SOCPT) 1 Xiaohui Hou (xhou@worldbank.org) is a Senior Economist at the World Bank, Health Nutrition Population Sector Unit (EASHH) and Seo Yeon Hong (shong1@worldbank.org) is a Consultant at the World Bank, South Asia Human Development Education Sector (SASED). Authors thank the Pakistani government counterparts, especially the Benazir Income Support Program, Federal Bureau of Statistics, and the Ministry of Finance, for facilitating technical work. We also thank the Trust Fund for Environmentally & Socially Sustainable Development (TFESSD), and the Gender Action Plan (GAP) Trust Fund for their financial contribution to this task. The authors also thank Andrea Vermehren, Amjad Zafar Khan, Cem Mete, Iftikhar Malik, Khalid Mahmud, Pablo Gottret, Dhushyanth Raju, and other members of the Pakistan Human Development team for their comments and support in the overall context of Social Protection work in Pakistan. As usual, all findings, interpretations and conclusions expressed in the paper are entirely those of the authors, and do not represent the views of the World Bank, its Executive Directors, or the countries they represent.
**TABLE 3: ZNPF INDICATORS** (Ratios %)

||1981|1985|1990|1995|
|---|---|---|---|---|
|Administration<br>Expenses/Contributions|22<br>|35|72|102|
|Administration<br>Expenses/Income from<br>Investment|42|54|223|66|
|Interest to|||||
|Members/Income from<br>Investment|64|59|41|77|

Source:ZNPF Annual Reports, various years Until recently, the investment of the Fund was made according to fixed guidelines assigning 50 percent of the reserves to the Government, 35 percent to parastatals and 15 percent to the private sector. Funds were channeled into long term loans to the Government and to parastatals at low fixed interest rates. Funds were also invested in shares of loss-making parastatal companies. In 1995, almost 50 percent of assets were invested in real estate, 27 percent in government securities, 16 percent were held in fixed deposits, and 6 percent in equity. A high proportion of the residential property owned by the ZNPF is occupied by staff and thus only earning residual income, there are difficulties in the collection of rents, especially from Government departments, and rents on properties outside of the urban areas are very low. Moreover, maintenance costs for buildings have risen at a much faster pace than rents.

The administrative expenses of the ZNPF have been increasing strongly. Measured as a share of investment income, they reached a peak of 254 percent in 1992. As a share of total revenue from contributions, administrative expenditure peaked in 1995 at 102 percent. In spite of recent personnel reductions, the ZNPF is still considerably overstaffed with currently more than 1,300 employees. Under the new management increased efforts have been made to reduce administrative costs and scale down expenses for non-essential activities. But steps to speed up the process through a substantial retrenchment program would impose a severe financial burden on the Fund, particularly since its staff pension fund is also believed to be considerably underfunded.

3The experience of the provident funds in Singapore and Malaysia shows that this system can be managed successfully with higher returns on investment and low administrativ **e** xpenditure. SeeVittas 1993, Queisser 1991, Valdes-Prieto 1994 8
26 Bolivia, Ghana, Malawi, Thailand, Bangladesh and Nepal the government provided technical services related to group formation. The most striking example with regard to group formation costs is the Zimbabwe Agricultural Finance Corporation's (AFC) group lending scheme. This program only lends to groups that were formed and formerly involved in another group lending program with limited liability. The AFC's administrative costs for its group lending scheme are minuscule (1% of loan capital) compared to the costs of the group 'ending scheme where groups have to be formed and costs of lending tr individual small farmers (12% and 11% of loan capital, respectively). In fact, administrative costs of lending to groups of small farmers compare favorably to those of lending to large-scale commercial farmers (Bratton, 1986). AFC's low administrative costs in the group lending scheme suggest that subsequent to startup costs associated with group formation, group lending programs become much more advantageous in terms of decreased administrative costs.

# D. Borrowers' Costs

Except from India and the Phiiippines, all group lending studies which reported information on borrowers, costs indicated that borrowers incurred lower costs when borrowing as a group member rather than as individuals. For example, a comparative study of borrowing expenses in the Dominican Republic found that the effective rate of borrowing costs on an annual basis was 15% for a group and 18% for individual borrowers (Adams, et.al. 1981). Generally, group borrowers enjoyed advantages of savings on fees for collateral registration, expenses on certificates needed for loan application and on time and transportation costs of visiting lenders. It
somewhat. Informal salaried workers now show no significant standard deviation in any specification. By contrast, civil employment shows a modest rise in heterogeneity in job, life and financial satisfaction relative to the previous exercise. The standard deviation for self-employment with employees is also higherthan was the case in the previous exercise both in the job and financial satisfaction specifications, but not in the life satisfaction regression. This appears to be due to the fact that the random effect is only significant in the life satisfaction case and we know from table 4 that this reduces, by roughly the difference observed here, the estimated variance. The same phenomenon is seen for the self-employed with no employees which lose the significance of the standard deviation in the case of life satisfaction.

Despite the weaker power resulting from the smaller sample size, the results with the life and financial satisfaction variables broadly confirm the findings with the larger sample using just job satisfaction. Self-employment of both types appears more favorable than before. Overall, the self-employed and informal salaried are certainly no less satisfied with their lives and their financial situation, ceteris paribus, than those in the formal sector. Further, there does not appear to be a strong tradeoff between financial and life satisfaction on the one hand, and job satisfaction on the other. Since satisfaction measures are strongly correlated and because work satisfaction has been argued to be the most important determinant of life satisfaction (?), these findings should perhaps not come as a surprise.

# 5.3 Survivor/Attrition Bias

Finally, we address the potential biases due to attrition from the panel. Each year approximately 30% of the respondents drop out, but many of these drop-outs are temporary, as a significant share of the dropouts re-appears in later waves.<sup>26</sup> Moreover the attrition does not seem systemic, but instead appears driven largely by difficulties associated with tracking people as addresses were not recorded and the initial waves of the survey did not use GIS mapped data. Simple probit models (not presented here to conserve space) suggest that location and survey wave are the strongest predictors of attrition; age, education and employment status have little explanatory power. Hence we would expect little systematic

> 26Of the 1164 individuals interviewed in 2004, 622 were still in the sample in 2010.

26
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# **1. Introduction**

Although considerable research efforts are being made, current understanding of the regional impacts, magnitude, and rate of climate change remain uncertain. Nevertheless, it is generally accepted that climate change will in many places adversely affect farming, fishing, forestry and many other industries that rely on weather and natural ecosystems. The African continent is particularly susceptible to climate change because it includes some of the world’s poorest nations. Furthermore, high spatial and temporal variability in rainfall, as well as high evaporation rates, already place great stress on agricultural systems from which 70% of the continent’s population derive their livelihoods. One of the most significant impacts of climate change is likely to be on the hydrological system, and hence on river flows and regional water resources. This will be particularly true in arid and semi-arid areas of Africa where water resources are very sensitive to climate variability, particularly rainfall.

The GEF/World Bank project, _Regional climate water and agriculture: Impacts on and adaptation of agro-ecological systems in Africa_ , is an investigation of the sensitivity of agricultural systems to climate change and an economic assessment of various adaptation strategies. The principal method adopted for the study is the Ricardian approach, which measures the effect of climatic variables on agriculture (Mendelsohn et al. 1994). The method is based on the assumption that, by examining the relationship between climatic data and a variety of geographical, agricultural, economic and demographic factors on land value and farm revenue, it is possible to isolate the intrinsic value of climate. The approach presupposes that farmers at any given location have optimized their farming systems (i.e. crops grown and inputs) to maximize their land value and/or farm revenue. Thus, the approach investigates farmers’ adaptation to local climate across a range of conditions by observing the relationship of farm values to climate. Basically, with this approach, regression relationships are derived to determine the links between climate variables (e.g. temperature and precipitation) and land values and farm revenues across different locations. In theory, by directly measuring farm prices or revenues it is possible to take into account not only the direct impacts of climate on yields, but also potential adaptations to climate change (e.g. the indirect substitution of different crops and different inputs, and even the introduction of different activities) (Mendelsohn et al. 1994). The implications of climate change for land values and farm revenues can then be deduced.

5
regimes<sup>26</sup> . Columns 3 and 4 of Table 7 display the results without and with controls. The estimates in both columns are positive, statistically significant and economically relevant. A one standard deviation increase in overrepresentation is associated with an increase in the dependent variable by between 0.35 and 0.50 standard deviations.

All in all, these results imply that the degree of political concentration, particularly in favor of parties that benefit from overrepresentation after transition to democracy, increases when overrepresentation increases, as suggested by our motivating theory.

# **4.4 Malapportionment and Government Transfers**

Our theoretical discussion and the previous literature also suggest that malapportionment could affect the distribution of public transfers across regions. We estimate the relationship between malapportionment and transfers per capita using within country data<sup>27</sup> and a model similar to Equation 3, where the dependent variable is transfers per capita from the central government to region _j_ . We use two alternative measures of transfers. “Total transfers” includes all transfers that the central government makes to a region, including transfers to state and municipal governments, social transfers, direct expenditures and investment by the central government, as well as transfers to public universities. The categories included vary from country to country, depending on availability. A more uniform variable is “transfers to sub-national government” which includes only transfers to state and/or municipal governments within a region.

The results in Columns 1 and 2 of Table 8 confirm the previous findings in the literature that overrepresentation translates into higher transfers per capita from the central government. In terms of economic significance, these results imply that a one standard deviation increase in malapportionment at the local level increases transfers per capita by about ten percent of a standard deviation.

Columns 3 and 4 of Table 8 check whether overrepresented areas are either poorer or more unequal than underrepresented areas. If this were true, then the higher transfers to these

> 26 The interaction term can be interpreted as the probability that two random voters vote for a party that is associated with a former non-democratic regime, because it is the probability that two people vote for the same party (the HH index) times the probability that a person votes for a party associated with a former non–democratic regime (the vote share going to this party).

> 27 Our data on government transfers covers Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Paraguay, and Uruguay.

19
number of firms pursuing them. Consequently, companies often source consulting services from South Africa, Portugal, and other more advanced markets.

Furthermore, Mozambique has a significant skills gap. Compared to other countries in sub-Saharan Africa, the educational attainment in Mozambique is low. In 2010, less than half of adults were estimated to be literate, the result of Mozambique’s long civil war which – in the affected regions - prevented almost an entire generation from attending school. Only one-fourth of those above 30 years living in urban areas attended post-primary education. Nevertheless, there is some reason for optimism: the younger generation is significantly more likely to be literate and to have attended education beyond primary school (Lachler & Walker 2018).

**Figure 4. Mozambique’s management practices scores**

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# **3 Data and measurement**

The analysis relies on representative data of firms that was newly collected in 2018 by the _World Bank Enterprise Survey_ , covering 650 formally registered non-farm firms with five or more employees.<sup>4</sup> Of these firms, 222 were firms also interviewed as part of the 2007 Enterprise Survey, while the other firms were newly sampled. The 650 firms were randomly sampled from the

> 4 Additional surveys were conducted among 120 formally registered micro-enterprises (with fewer than five employees) and among 554 informal business entrepreneurs. For these enterprises instead of questions on management practices, questions on business practices were asked, based on the methodology by McKenzie & Woodruff (2017). Instead of only management practices, these questions focus on organizational practices more broadly, such as marketing, stock-keeping, record-keeping and financial planning. The evidence for these firms is discussed in Aga et al. (2019).

10
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effects separately on school-age siblings and on other school-age girls within treatment communities.<sup>5</sup> Using the General Health Questionnaire 12 (GHQ-12), a screening instrument widely used in clinical settings, we show that a positive income shock can cause a substantial temporary reduction in psychological distress. While the intervention was ongoing, baseline schoolgirls who were offered unconditional cash transfers were approximately 14 percentage points (pp), or 38%, less likely to be classified as suffering from mental health problems. Those who had been offered transfers _conditional_ on regular school attendance also experienced a statistically significant improvement in their mental health, but at roughly 6 pp (17%) the size of this impact is significantly lower than that for the unconditional treatment group. The likelihood that a baseline schoolgirl suffers from mental health problems increased by approximately 3 pp with each additional dollar offered to her parents _conditional_ on her school attendance, which is responsible for this muted impact in the conditional treatment arm. The intervention resulted in detrimental spillover effects on the mental health of girls who lived in a treatment area but were not invited to participate in the intervention and in positive spillover effects on untreated female siblings of girls who had been offered a transfer. The impact of the intervention on mental health does not appear to be permanent, as the measured differences between the treatment and control groups disappeared to a large extent after the intervention ended.

> 5 There is now a substantial literature examining the impact of cash transfer interventions on physical health outcomes (See Fiszbein and Schady, 2009, for a review). Evidence is accumulating on the impact of cash transfers on physical health outcomes for infants and children under the age of six, such as stunting, anemia, and body-mass (e.g. Fernald, Hou and Gertler 2008; Fernald, Gertler and Neufeld 2009; Gertler 2004; Lagarde, Haines and Palmer 2007; Rivera et. al. 2004). There is also evidence on the impact of cash transfer interventions on behavior, cognition, and stress levels in infants and young children in Latin America (e.g. Fernald and Gunnar 2009; Fernald, Gertler and Neufeld 2009; Macours, Schady and Vakis 2008; Ozer et. al. 2009; Paxson and Schady 2007). Filmer and Schady (2009) present evidence that a cash transfer intervention in Cambodia had a small effect on the mental health of its adolescent beneficiaries. Except for this latter study, however, there is little evidence on the impact of cash transfer interventions on the mental health of adolescents.

4