rafmacalaba/datause-encoder-data huggingface dataset:
6. Bellemare, M.F. and C.B. Barrett, 2006, “An Ordered Tobit Model of Market Participation: Evidence from Kenya and Ethiopia," American Journal of Agricultural Economics, Volume 88(2), p.324-337.

7. de Janvry, A., M. Fafchamps, and E. Sadoulet, 1991, “Peasant Household Behavior with Missing Markets: Some Paradoxes explained," Economic Journal, Vol. 101 (409), p.1400-1417.

8. Fafchamps, M. R.V. Hill, and B. Minten, 2006, “The Marketing of Non-Staple Crops in India," mimeo, World Bank.

9. Fafchamps M. and R. Hill, 2005, “Selling at the Farmgate or Traveling to Market," American Journal of Agricultural Economics, Vol 87(3), p.717-734.

10. Fafchamps M., 2004, Market Institutions in sub-Saharan Africa, MIT Press, Cambridge, Mass.

11. Fafchamps, M., E. Gabre-Medhin and B. Minten, 2005, “Increasing Returns and Market E ffi ciency in Agricultural trade," Journal of Development Economics, 78, p. 406-442.

12. Fafchamps, M. and F. Shilpi. 2003. “The Spatial Division of Labor in Nepal.” Journal of Development Studies, Vol. 39, p. 23—66.

13. Goetz, S. J., 1992, “A Selectivity Model of Household Food Marketing Behavior in Sub-Saharan Africa," American Journal of Agricultural Economics, Vol. 74(2), p.44452.

14. Holloway, G.C., C.B. Barrett and S. Ehui, 2005, “The Double Hurdle Model in the Presence of Fixed Costs, Journal of International Agricultural Trade and Development, forthcoming.

30
1.50 -

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Figure I.C.7 COCOA ACREAGE ADJUSTMENT TO PRICE UNDER POPULATION GROWTH <!-- Start of picture text --> a.<br>3 - s 2~~~~~~~~~~~~# / / / "_ _<br>2<br>1- .4 - ..<br>1987 1997 2007 2017 2027 2037<br>PERIOD MIDPOINTS<br>o f140 o + ISO 3 0 I<br>*~~~~~~ , .<br><!-- End of picture text -->
recorded refugees sheltered in the country increased from 125,910 at the end of 2009 to 660,987 at the end of 2014. Equation (1) further has denoting round-specific fixed effects. These fixed effects eliminate all round-specific correlated shifts in the outcome variable of interest, explanatory vv αα variables, and any other variables that are not directly controlled for in the regression, such as policy and economic shifts, political events, and other disasters that might affect the outcome variable of interest. hv is a vector of (12) different time-varying demographic (e.g., household size, number of young dependents in the household, and household head characteristics such as XX jj age, gender, and education) and environmental (e.g., rainfall and enhanced vegetation index) characteristics that may affect welfare outcomes. These variables are measured at the household or zone level.<sup>24</sup> hv is a white noise residual. (a scalar) and (a vector of elements) are the parameters to be estimated. jj εε ββ θθ jj The ESS sample selection followed a two-step process in which the selection of sample EAs is carried out followed by the selection of sample households in the selected EAs. This clustering in the sampling design, as only the EAs that are included in the final sample are observed while all other EAs from the population of EAs in the country are unobserved, and the likelihood of having heterogeneity in the effect of hosting refugees necessitate clustering of standard errors (Abadie et al., 2017). I thus cluster the standard errors at the EA level, which allows for crosssectional and temporal correlation in the errors within an EA. Such clustering of standard errors further assumes that the correlation in the errors that comes because of how the main explanatory variable of interest is constructed is at the EA level. The possibility of this correlation at a larger geographical level than the EA is addressed in the robustness checks section.

Moreover, the sample selection led to the inclusion of 143 urban EAs of the total 433 sample EAs. This created oversampling of EAs from urban areas, where only approximately onefifth of the total population resides. Additionally, the second stage of the sample selection process involved the selection of an equal number of households (12 from rural and small urban areas and 15 from large urban centers) from each EA. This, in turn, led to the oversampling of households in less densely populated areas. In light of these, assuming an equal probability of inclusion in the sample for every sample unit (household/individual) could bias estimates of the population effects

> 24A complete list and summary statistics of these variables is available in panel (d) of Table 1, while their description is given in panel (d) of Table A10.

15
17 standardized death rates for appendicitis and hernia and intestinal obstruction—as proxies for the average quality of hospital care. Finally, alternative infant mortality and under-five mortality rates were obtained from WB-WDI and the _TransMONEE 2006 Database_ , a UNICEF IRC (Florence) database which contains data for ECA countries except Turkey on 146 economic and social indicators divided into ten different topics and ranging from 1989 to 2004.

Simple comparisons of the average outcomes presented in Table 2 indicate that SHI countries tend to spend more in health care, both in the public and private sectors, and a higher fraction of the government health spending seems to be absorbed by salaries. On the other hand, there is some indication that mortality and disease incidence rates are generally lower in SHI countries, whilst no clear pattern emerges for immunization rates. As far as hospital indicators are concerned, total length of stay, in-patient admissions and beds tend all to be lower in SHI countries; most of our diagnosis-specific hospital discharges indicators are higher for SHI countries, and there is no clear pattern concerning our quality-of-care proxy measures. Visual comparisons of the evolution of SHI adoption in our sample vis-à-vis two health outcomes, average total health expenditures per capita and WHO’s average infant mortality rate (Figure 4 and Figure 5), show somewhat clear patterns: average health spending slightly decreased during the first period of growing SHI adoption by ECA countries (1990-93) but experienced a sustained increase during and after the second period of SHI growth (1995-98, when SHI prevalence reached more than 50%), while the average infant mortality rate tended to remain stable around 22 per thousand births during the first period but continuously decreased during and after the second period, when SHI prevalence reached half of the countries. Determining whether the differences and patterns described above are due to SHI adoption (that is, a _causal_ effect) or whether they merely reflect pre-existing differences—observable and/or
(statistically adjusted) are compared. The cut-off years differ from sector to sector and for different states.

**Table 4: Comparison of Means After Adoption of Variable Pay with Other States in the Region**

||**Before**||**After**||
|---|---|---|---|---|
||**Difference**|**State**|**Other States**<br>**in the**<br>**Region**|**Difference**|
|**Pernambuco (Northeast)**|||||
|Child Mortality (per 1,000 born alive), 2007–2011|‐0.25|19.84|21.81|‐1.97|
|Homicide Rate (per 100,000 inhabitants), 2007–2011|32.56***|45.52|31.20|14.32**|
|IDEB Public Secondary School Score, 2007–2013|0.05|3.10|2.88|0.22*|
|IDEB Public Secondary School Score, 2008–2013|0.01|3.23|2.90|0.30**|
|**Minas Gerais (Southeast)**|||||
|Child Mortality (per 1,000 born alive), 2004–2011|5.75***|17.38|13.94|3.44***|
|Homicide Rate (per 100,000 inhabitants), 2004–2011|‐18.99***|20.68|36.64|‐15.96***|
|IDEB Public Secondary School Score, 2004‐2013||3.56|3.30|0.26*|
|Child Mortality (per 1,000 born alive), 2008–2011|3.32|16.53|13.07|3.46***|
|Homicide Rate (per 100,000 inhabitants), 2008–2011|‐22.67***|19.63|33.79|‐14.17**|
|IDEB Public Secondary School Score, 2008–2011|0.36|3.62|3.43|0.20|
|**Rio de Janeiro (Southeast)**|||||
|Homicide Rate (per 100,000 inhabitants), 2008‐2011|17.64***|33.58|29.14|4.43|
|IDEB Public Secondary School Score, 2010‐2011|‐0.59***|3.40|3.60|‐0.20|

_Source_ : Authors’ calculations, based on data from IBGE and MEC. _Note_ : *** Significant at the 0.01 level (2-tailed); ** Significant at the 0.05 level (2-tailed).

In the case of Pernambuco, while child mortality has decreased by a sizable amount after 2007, the reduction is not significantly different from that of other states in the northeast region. In the education sector, the state has surpassed the average for the region, and it is improving at a faster rate its IDEB score. In the security sector, the change is not only statistically significant but also goes in a different direction than the regional average. During that period, the gap between the state and the rest of the region has decreased by 18.3 points is the combined result of a sizable reduction of the homicide rate in Pernambuco and its increase in the rest of the states.

When comparing the changes in Minas Gerais to other states in the southeast region, it is possible to conclude that only improvements in education outcomes go beyond the general trend. In child mortality, the change is very similar, and the difference between the state and the region remains fairly constant. The homicide rate for the period after the introduction of the Management Shock policy is lower than the rest of the region, but on average other states have had larger reductions.

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## ANNEX 2. VARLABLE DEFINIONS

All country data sets refer to individuals age 15 to 65, inclusive. Observations with missing values for education variables have been dropped from the analysis. All individuals have positive earnings from employment.

# 1. Education

The education variables are of two types. One is continuous in terms of years of schooling (S). The other is a series of educational dummy variables:

# NOSCHOOL No education.

- PRIMDROP Primary education incomplete.

- PRIM Primary education completed.

- SECDROP Secondary education, either general or vocational, incomplete

- SECGEN Secondary general education completed. In cases where information on the general/vocational split of secondary education is not available, this variable refers to both types of the curriculum.

- SECVOC Secondary vocational education completed.

- SECTEAC Teachers' college graduate.

- NOUNI Higher non-university education such as post-secondary education or technical institute.

- UNIVDROP University education incomplete.

- UNIV University education completed.

In the case of the Paraguay data sets (1983 and 1990), the sample size of those with noeducation (NOSCHOOL) is very small and hence we grouped this category with the less-thanprimary category ,PRIMDROP). In Peru's 1985 and 1990 data sets, the two education groups are defined in a different way:

# NOSCHOOL No education or primary education incomplete.

PRIM Primary education completed or secondary education incomplete.
18 limitations of the WHS consumption data, our results should be viewed as illustrative.

We also need a threshold for catastrophic spending (we set _X_ = 0.4) and a poverty line. We use variants of the international “dollar-a-day” poverty line (Chen and Ravallion 2010): the low poverty line (which is actually currently $1.25-a-day at 2005 prices and purchasing power parities), and the higher poverty line (currently $2-a-day).<sup>9,10</sup> We also use a $5-a-day poverty line which is more relevant for middleincome countries.

# _Results_

Figure 4 illustrates immiserizing and impoverishing payments for one WHS country, Figure 5 illustrates catastrophic payments defined as non-immiserizing and non-impoverishing payments that exceed 40 percent of discretionary consumption, and Figure 6 illustrates catastrophic payments defined as nonimmiserizing and non-impoverishing payments that leave a family’s nonmedical consumption below 120 percent of the poverty line. Comparing the left-hand panels of Figure 5 and Figure 6, we see many households incurring payments that are catastrophic according to the former definition but not the latter (the shading gets lighter moving from Figure 5 to Figure 6). Figure 7 and Figure 8 compare the incidence of non-catastrophic payments according to the two definitions: in Figure 7

> 9 If _L_ is the number of dollars per day in 2005 PPP dollars, the corresponding poverty line in local currency for a survey undertaken in year _t_ is equal to 365 x _L_ x ( _CPIt_ / _CPI_ 2005) x _PPP_ 2005, where _CPIt_ is the consumer price index (CPI) for year _t_ for the country in question, and _PPP_ 2005 is the PPP for that country in 2005. 10 The World Bank’s data on poverty, and a discussion of the data and methods used to derive them, are available via the interactive tool PovCalNet available at <u>http://iresearch.worldbank.org/povcalnet/index.htm.</u>
Table 3: Local Supply of Higher Education Institutions (HEIs), at the Student- and Municipality-level

||Other HEI=0<br>_H_ = 0||Other <br>_H_|HEI=1<br>= 1|
|---|---|---|---|---|
||HEI only ofers SC=0<br>HEI only ofers SC<br>_Z_ = 0<br>_Z_ = 1|=1|HEI only ofers SC=0<br>_Z_ = 0|HEI only ofers SC=1<br>_Z_ = 1|
|Variable|**Stu**|**den**|**t level**||
|Share of students|19.9%<br>3.0%||20.1%|57.1%|
|N|64,053<br>9,917||66,302|188,086|
|_Higher education enrollment_|||||
|Not Enrolled (NE)|73.6 %<br>68.6%||61.8%|56.4%|
|Short-Cycle Program (SC)|6.2%<br>8.8%||6.3%|11.6%|
|Bachelor’s Program (BP)’|20.3%<br>22.6%||31.9%|32.0%|
|Total|100%<br>100%||100%|100%|
|_Municipality size (population)_|||||
|Small municipality (<0.4 million)|100%<br>69.7%||87.9%|24.1%|
|Medium city (0.4-2.2 million)|30.3%||12.1%|28.9%|
|Big city (>2.2 million)<br>Total|100%<br>100%||100%|46.9%<br>100%|
||**Municipality level**||||
|Share of municipalities|68.0%<br>3.9%||17.9%|10.2%|
|N|624<br>36||164|94|

Note: The sample corresponds to the universe of students who took the high school exit exam (Saber 11) in 2005. The information on population size comes from the Municipal Panel from CEDE (Center of Studies on Economic Development). The information on higher education programs and institutions is from SNIES (National System of Information on Higher Education).
_understanding of the development process must not only take account of the initial conditions and the role of existing institutions in shaping the response to modernization and globalization, but must also consider how these traditional institutions are shaped in turn by the forces of change_ ”.

Geographical mobility in rural Tanzania is associated with large income gains. Our data show that despite only minor welfare differences during the 1991-94 baseline survey, those who moved out of the region to other parts of Tanzania have grown roughly twice as rich as those who did not by the time we interviewed them again nearly two decades later. As we are measuring consumption and not income, it is clear that the main beneficiaries of this migration-led growth were the migrants themselves and certainly not their relatives who remained at home.

But did these migrants simply leave and never look back, or did they maintain links with the home community? We investigate this question by exploiting the fact that the 3,314 households interviewed in 2010 are grouped in 817 geographically disperse extended family networks. Using techniques from the risk-sharing literature, we quantify how migrants’ consumption responds to shocks experienced by others in their extended family network. Much of the migration literature has a very strong focus on dealing with the selectivity of the migration decision. Interestingly, in this paper, the endogeneity of migration turns out to be irrelevant for our most important contribution: the documentation of the long-run dynamics of risk-sharing arrangements among extended family members in a context of large internal migration flows. Whether or not migration is causally responsible for any of our findings is an interesting, but secondary question, which we will nevertheless attempt to answer in Section 8.

Our analysis departs from a number of other studies in the migration literature by focusing on consumption instead of transfers. This choice of the outcome variable is motivated by the fact that risk sharing and other economic exchange could happen through a multitude of different mechanisms, of which transfers is just one. Other mechanisms could include looking for a job for someone, employing them directly, providing them with tips, advice or a network link, or providing migration 3
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associated to privatization. Interestingly, regulatory agencies have a positive impact on public-firms average tariffs, a result that is likely to reflect improvements in cost recovery efforts and tariff rebalancing associated with the typical mandate assigned to independent regulators.

In Table 7 we apply the difference-in-differences approach to the sample restricted to the common support. Again, results corresponding to the difference-indifferences in common support are consistent with previous results.

Overall, our empirical analysis suggests that the establishment of regulatory agencies in developing and transition countries is associated with higher social welfare. Again, to validate our results we perform a number of robustness checks. First, the sign, magnitude, and significance of the coefficients of interest remain mostly unchanged when we drop one firm at the time or one country at the time. Second, results remain unaltered when we include country-level controls such as GDP per capita. Finally, conclusions in terms of the significance of the coefficients remain also unaltered when standard errors are clustered at country-year combinations.

# **5. Conclusions**

We have presented what we believe is the first attempt at using firm-level data to evaluate the impact of introducing a regulatory agency on firm performance and social welfare. Our analysis focuses on the electricity distribution sector in developing and transition countries, and it includes three measures of firm performance (labor productivity, operation expenditures per connection, and electricity losses) and three measures of social welfare (service coverage, frequency of interruptions, and residential tariffs).

15
INTRODUCTION XoXvil Figure 19 Percentage Contribution to World Population Growth-irom World Bank Regions and Nonborrowers, 1985-2100 I ~ <!-- Start of picture text --> 50 -<br>40 -<br>30 A I<br>20-<br>10<br>10 ~~~~~~~~~LAC<br>~~~~~ I ' ' ' ' -<br>19S5 2000 2015 2030 2045 2060 2075 2090<br><!-- End of picture text --> growth (Tables 17 and 18), age structure (Table 19), and vital rates.

China is the most populous country or economy, and remains so in these projections almost until the end, not quite 200 years in the future. Rankings by size among other large countries will be changing well before that point. Nigeria and Pakistan, in particular, will be moving up, from ninth-tenth today to seventh-eight by the turn of the century and to fifth-sixth by 2025. Japan will fall out of the group of ten largest countries, and by 2025 will have fewer people than Mexico, Iran, or Viet Nam.

India presently contributes more to world population growth than China or any other country, and will continue to do so throughout the projection period. After India and Cnina, the leading contributors to world population growth are Pakistan and Nigeria. At the other extreme, the Federal Republic of Germany is contracting by 100,000 people a year. Even recent mass migrations from the German Democratic Republic-which occurred too recently to be incorporated in these estimates--are unlikely to produce substantial population growth. Hungary is also losing population, but only a fifth as much as the Federal Republic of Germany.

Despite contributing so much to growth, none of the large countries is growing as fast as some states in the Persian Gulf and in Sub-Saharan Africa. In the Gulf, Qatar and Oman are growing by more than 4 percent a year, and Saudi Arabia, Djibouti, and Kuwait by more than 3.7 percent. In Africa, C6te d'Ivoire and Kenya are growing by more than 4 percent, and Zambia by about 3.7 percent.

Rapid growth pirtly accounts for Kenya having a dependency ratio as high as 120, well above any other national ratio. Other countries with ratios above 104 (other than small islands) are Botswana, the Yemen Arab Republic, and Zambia. By contrast, exceptionally low dependency ratios, indicating relatively large working-age populations, characterize such vigorous