rafmacalaba/datause-encoder-data huggingface dataset:
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles Table 9 below provides a break down of the projected expenditure by major cost categories for the main baseline variant 1 (assuming general population mortality rates apply to the covered population).

|**Table 9: Projected total pension**|**expend**|**iture (ba**|**seline: va**|**riant 1)**|||
|---|---|---|---|---|---|---|
|**Year**<br>**2**|**003/04**|**2004/05**|**2005/06**|**2020/21**|**2050/51**|**2075/76**|
|(In Million Shs, nominal)|||||||
|Total pension expenditures|126,026|133,089|149,552|575,191|10,638,752|137,492,853|
|**Teachers**|||||||
|Regular pensions|19,924|22,905|26,791|165,623|3,386,972|43,950,374|
|_of which old age_|_19,055_|_21,964_|_25,754_|_161,967_|_3,322,487_|_43,452,673_|
|_survivors_|_870_|_941_|_1,037_|_3,656_|_64,485_|_497,701_|
|Lump-sum payments (CPGs)|25,000|24,443|27,534|103,990|1,730,532|17,963,356|
|Other expenditures|4,776|5,058|5,761|27,101|509,485|5,881,504|
|**Traditional Civil Servants and Local G.S.**|||||||
|Regular pensions|35,503|39,488|44,051|157,492|2,592,485|40,692,922|
|_of which old age_|_30,358_|_34,000_|_38,237_|_139,513_|_2,072,755_|_36,500,046_|
|_survivors_|_5,145_|_5,488_|_5,814_|_17,979_|_519,730_|_4,192,877_|
|Lump-sum payments (CPGs)|25,000|24,429|27,053|63,932|1,225,743|14,986,110|
|Other expenditures|15,822|16,767|18,362|57,054|1,193,535|14,018,588|
|GDP<br>|12,121,000|13,529,291|14,870,991|60,443,332|530,418,048|2,962,347,008|
|(As % of GDP)|||||||
|Total pension expenditures|1.04%|0.98%|1.01%|0.95%|2.01%|4.64%|
|**Teachers**|||||||
|Regular pensions|0.16%|0.17%|0.18%|0.27%|0.64%|1.48%|
|_of which old age_|_0.16%_|_0.16%_|_0.17%_|_0.27%_|_0.63%_|_1.47%_|
|_survivors_|_0.01%_|_0.01%_|_0.01%_|_0.01%_|_0.01%_|_0.02%_|
|Lump-sum payments (CPGs)|0.21%|0.18%|0.19%|0.17%|0.33%|0.61%|
|Other expenditures|0.04%|0.04%|0.04%|0.04%|0.10%|0.20%|
|**Traditional Civil Servants and Local G.S.**|||||||
|Regular pensions|0.29%|0.29%|0.30%|0.26%|0.49%|1.37%|
|_of which old age_|_0.25%_|_0.25%_|_0.26%_|_0.23%_|_0.39%_|_1.23%_|
|<br>_survivors_|_0.04%_|_0.04%_|_0.04%_|_0.03%_|_0.10%_|_0.14%_|
|Lump-sum payments (CPGs)|0.21%|0.18%|0.18%|0.11%|0.23%|0.51%|
|Other expenditures|0.13%|0.12%|0.12%|0.09%|0.23%|0.47%|

**Source** : PROST output files

# **_IV.D Required contribution rate_**

The required contribution rate is an analytical indicator measuring the rate that contributors should pay on their covered wages (regardless of whether it is levied against the employee or employer – in this case the government) to balance the system, assuming the system expenditure is solely financed through contributions. It can be calculated for each year or as a sustainable contribution rate balancing the system over the whole simulation period. This is a useful measure of what the system would cost to contributors if it were contributory.

- 42 -
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21
# Appendix 3: Labor demand and share of migrants per occupation

In order to formally assess the link between labor demand and the prevalence of migration in each occupation we estimate the following regression: Log(pct_migrants) ck = α + β log(pct_new_hires)ck + ηc + φk + ε ck (3) where log(pct_migrants)ck is (the natural logarithm of) the percentage of migrants in 2018 in each ISCO 3-digit occupation _c_ in UTS2 region of residence _k_ . Log(pct_new_hires) ck is the logarithm of the share of new hires migrant population from origin region _o_ in each occupation _c_ residing in NUTS2 division _k_ in 2005; ηc is a vector of dummy variables for each ISCO 3-digit occupation; and φk is a vector of dummy variables for each NUTS2 division of residence K.

The results of the above regression in Equation 3 are reported in Table A2. The strong relationship between labor demand and the share of migrants per occupation in the EU holds even when we control for ISCO-08 3-digit occupation and NUTS2 region of residence fixed effects (Column 3). In particular, a 1 percent increase in the share of new hires in an occupation in a given NUTS2 division –which we use as a proxy for the dynamism of the labor demand– is associated with a 0.02 percent increase in the share of migrants per occupation. The coefficient is statistically significant at the 1 percent level.

Table A2: Regression analysis on the role of labor demand in occupational choices of migrants in the EU

|Dependent variable: Log (% of migrants) per<br>3-digit occupationand NUTS2division in 2018|Model 1<br>(OLS)|Model 2<br>(OLS)|Model 3<br>(OLS)|
|---|---|---|---|
|Log (% new_hires (<1 year))|0.102***<br>(0. 003)|0. 089***<br>(0.003)|0.020***<br>(0.003)|
|Constant|-1.793***<br>(0.012)|-1.883***<br>(0.112)|-2. 675***<br>(0.678)|
|Observations|23,803|23,803|23,803|
|R-squared|0.066|0.147|0.408|
|NUTS2 division of residence FE|NO|YES|YES|
|ISCO 2-digit Occupation FE|NO|NO|YES|

Note: Robust standard errors are reported in parentheses; ***: p<0.01, **: p<0.05, *: p<0.1.

57
holdings.<sup>4 6</sup> At the same time, the reduction in the stock of external debt reduces interest payments, and the increase in income yields higher government revenues. These two effects have a positive impact on private capital formation, because both contribute to a reduction in confiscation risk. Higher private investment implies a higher demand for domestic loans to firms, which in turn leads to an increase in the premium charged by domestic banks<sup>4 7</sup> and a proportional increase in the loan rate.

The higher lending rate increases the effective price of labor in the agricultural traded sector, thereby reducing labor demand and output in that sector. Laid-off workers are absorbed in the agricultural nontraded sector, leading to an increase in output and a reduction in the product wage in that sector. By contrast, in the formal urban sector, the rise in investment increases the private sector capital stock; because skilled labor is (to some degree at least) a substitute to private physical capital, this implies that the demand for skilled labor goes down, resulting in an initial decrease in output of the formal private good. However, the subsequent switch of unskilled workers from the informal economy to the formal sector leads to a rise in output in the private formal sector. At the same time, it reduces output in the informal sector, but increases both the product wage and the consumption wage in the informal economy.

The resulting smaller differential between average expected urban consumption wages and rural wages eventually leads to a dampening of migration flows into urban areas, which tend to support the initial increase in informal sector wages. There is also an increase in the expected skilled consumption wage and a fall in the expected unskilled consumption wage (due, in the case of the latter, to the increase in the price index of the unskilled consumption basket). This leads to a higher rate of skills acquisition and increased unemployment relative to the baseline scenario. In terms of real per capita consumption, all working households benefit from the increase in transfers, except the urban unskilled labor group.

Poverty, as measured by the economy-wide consumption-based poverty headcount index, decreases in the short run when savings from lower debt service are applied to transfers to households. The reduction in poverty becomes somewhat larger in the medium run, but is reduced in the long run.

> 46Specifically, consumption of the agricultural traded and private formal sector goods increases, whereas consumption of informal and public sector goods decreases.

> 47See our discussion of the terms-of-trade shock section for a more detailed explanation of this effect on the premium.

70
focus on nutrition for mothers in utero and best practices during pregancy, as well as practices with infants regarding breastfeeding, care when sick, and supplemental feeding when older. In the meeting, the health worker used cards portraying the issue at hand to lead a group wide discussion. For example, one card was a drawing of a woman breastfeeding, where the discussion might then revolve around issues related to frequency depending on age of the child, proper attachment, and ways to try to solve issues such as not creating enough milk. The overall curriculum was developed by Helen Keller International, based on World Health Organization standards.

The cash transfer was set at NPR 700 per month (approximately $7 USD), which represents about 8-20% of median monthly income in our four districts. This figure is approximately in line with standard CCT’s (Fiszbein, Schady, and Ferreira, 2009). The transfer was distributed at the regular village meeting, meaning that there was a conditionality on the cash transfer that can additionally be viewed as an incentive to take up the information treatment. The transfer was “labeled” as for use on the child. Given that the conditionality is on simply going to collect the money, not on any type of behavior, as is standard in conditional cash transfer programs, and that the transfer was only distributed for five months, this should not be viewed as a standard CCT program. Rather, the cash provided a short term safety net, spurring the critical question of whether a shorter, cost effective intervention can have similar improvements in child outcomes as would a more elaborate, prolonged CCT program.

# 3 Data

To estimate the impacts of the intervention, we gathered detailed data on eligible households in each experimental village. A household was deemed eligible if there was a woman present who was either pregnant or had a child younger than two years old. Baseline data collection occurred in August-October 2013, prior to the start of the intervention. In order to better measure effects of how the cash was used, and because effects of cash have been shown to fade very quickly after the cash is no longer being distributed (Baird, McIntosh, and Ozler, 2016), we conducted a midline and endline survey. One-fourth of counties were randomly assigned to be surveyed at midline in August-September 2014, with the remaining three-fourths surveyed at endline in November-December 2014.

7
These relationships are shown in three figures. Figure 2 shows that it is operating costs, rather than capital costs or loan loss provisions, that that drive the differences in total costs between different kinds of microfinance institutions. Figure 3 shows that the institutions that make the smallest loans on average are also the institutions that face the highest costs per unit lent (a result that holds up in regressions after controlling for institutions’ age, inflation, countrylevel governance, GDP growth, region, and lending method). Figure 4 shows that the institutions with the highest costs per unit also charge the most to their customers.

The figures come together to yield a very weak correlation between profitability and average loan size (our proxy for the income level of customers). The correlation between the financial sustainability ratio and average loan size (relative to the per capita income of the bottom 20 percent) is positive but very small (0.07 with a standard deviation of 0.06; 293 observations).

In criticizing the Compartamos stock offering, Muhammad Yunus (2007) declared: “A true microcredit organization must keep its interest rate as close to the cost-of-funds as possible...My own experience has convinced me that microcredit interest rates can be comfortably under the cost of funds plus ten percent, or plus fifteen percent at the most.” The evidence presented in Table 3 shows that most nongovernmental finance organizations in our sample in fact charge more than Yunus’s desired upper range. More important, the cost data suggest that, if most nongovernmental microfinance organizations charged much less, they would require larger subsidies to continue operating along current lines.

# _Are profits high enough to attract profit-maximizing investors?_

It is one thing to earn profits, and quite another to earn profits that are high enough to attract investors who have no concern with social missions. Banco Compartamos took this idea to heart in creating the high-profit strategy behind their IPO. To them, below-average profit rates would have been a “non-event” and would have failed to bring competitors into the sector. We find that the median nongovernmental organization does earns profits, thanks to the relatively high interest rates they charge (bear in mind, once more, that this data is a selected sample of leading 18
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74
Similarly, some proxies of economic development like the share of employment in industry and agriculture at the beginning of the sample period seem to be associated to the levels of mobility at the district level.

This paper contributes to several strands of the economic literature. First, it adds to the literature on intergenerational mobility in general (see Black & Devereux, 2011, for a survey) but specifically to the literature focusing on the geography of socioeconomic mobility that recently received more attention in part because of the work of Chetty et al. (2014), which shows important variation across commuting zones in the United States. Second, it adds to the recent wave of research that looks at intergenerational mobility in education (see Emran & Shilpi, 2021; Torche, 2019, for recent surveys focused on developing countries). This set of papers includes on the one hand those that use household survey data or opinion surveys. For example, Hertz et al. (2007), Narayan et al. (2018), and Van der Weide, Lakner, Gerszon Mahler, Narayan, and Ramasubbaiah (2021) that document IGM for a very large set of countries across the world,<sup>2</sup> and Neidh ̈ofer et al. (2018) that focus on 18 countries from Latin America. This paper expands over them in terms of country coverage of the region and cross-country comparability. In addition, these estimates use the same type of data of recent estimates available for Africa, allowing a cross-regional comparison that was not available.<sup>3</sup> On the other hand and more closely related to this paper, it contributes to the pool of studies using administrative data or census data. For instance, Asher, Novosad, and Rafkin (2021) study mobility among different marginalized groups and analyze geographic differences in India; Card, Domnisoru, and Taylor (2022) use 1940 census data to study the role of school quality in mediating upward mobility in the US; Van der Weide, Ferreira de Souza, and Barbosa (2020) study mobility at the sub-national level in Brazil; and most closely related to this paper, Alesina et al. (2021) document patterns of IGM in Africa using

> 2The former documents mobility for 42 countries (7 from LAC) and the latter 153 countries (16 from LAC).

> 3Narayan et al. (2018) and Van der Weide et al. (2021) allow regional comparison but pooling together estimates generated with retrospective information and those with coresident samples, which may be problematic (see Munoz & Siravegna, 2021).

4
46

**Table 11**

**Robustness: contractionary current account reversals**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Baseregression<br>||With remitta<br>|nces:linearproba|bilitymodel<br>|
||probit|linear|linear|median|mean|75th|
|CA<br>REM|-8.651<br>[1.138]***|-0.641<br>[0.107]***|-0.669<br>[0.108]***|-0.827<br>[0.181]***<br>-0.327<br>[0.321]|-0.835<br>[0.180]***<br>-0.356<br>[0.331]|-0.851<br>[0.180]***<br>-0.077<br>[0.314]|
|RES<br>EXTD|-3.995<br>[1.948]**<br>0.401<br>[0.248]|-0.209<br>[0.078]***<br>0.030<br>[0.021]|-0.506<br>[0.150]***<br>0.095<br>[0.034]***|-1.020<br>[0.351]***<br>0.104<br>[0.053]*|-0.843<br>[0.323]***<br>0.109<br>[0.059]*|-0.810<br>[0.309]***<br>0.149<br>[0.061]**|
|SHORTD|1.549|0.145|0.145|0.068|0.068|0.084|
||[0.746]**|[0.100]|[0.100]|[0.104]|[0.106]|[0.106]|
|CREGRO|-0.070|-0.001|-0.001|0.001|0.000|0.000|
||[0.061]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|PPPGDP|0.555|0.042|0.040|0.022|0.021|0.019|
||[0.243]**|[0.014]***|[0.015]***|[0.024]|[0.024]|[0.024]|
|OPEN|0.705|0.020|0.050|0.063|0.067|0.079|
|OT|[0.738]<br>-5.507<br>[2.001]***|[0.045]<br>-0.152<br>[0.100]|[0.049]<br>-0.191<br>[0.102]*|[0.077]<br>-0.358<br>[0.176]**|[0.076]<br>-0.345<br>[0.184]*|[0.076]<br>-0.403<br>[0.173]**|
|CONCD|-0.219|-0.000|0.025|0.015|0.001|0.016|
|RES^2|[0.893]|[0.069]|[0.069]<br>0.450<br>[0.155]***|[0.077]<br>1.396<br>[0.613]**|[0.077]<br>1.338<br>[0.611]**|[0.078]<br>1.227<br>[0.585]**|
|EXTD^2<br>RES*DREM<br>EXTD*DRE<br>M|||-0.017<br>[0.006]***|-0.024<br>[0.011]**<br>0.458<br>[0.213]**<br>0.017<br>[0.031]|-0.023<br>[0.012]*<br>0.332<br>[0.204]<br>0.001<br>[0.038]|-0.031<br>[0.012]**<br>0.399<br>[0.194]**<br>-0.072<br>[0.030]**|
|Observations|1860|1860|1860|1190|1190|1190|
|R-squared|0.26|0.13|0.13|0.16|0.15|0.16|

_Notes_ : Estimation by probit and linear probability model. Robust standard errors in brackets. All regressions contain country fixed effect and year dummies. The dependent variable takes the value 1 if a reduction of a current account deficit of at least 4 percentage points of GDP and equal to at least half of the initial current account deficit and a contraction in the real per capita GDP occur at time t, and zero otherwise. DREM is a dummy variable that takes the value 1 if in a given year the level of workers’ remittances over GDP of a country is above the median or mean or 75<sup>th</sup> percentile value (as specified at the top of each column) of that year’s (cross-country) distribution. All other explanatory variables are described in Table A1 and lagged one period. For the probit models we report the pseudo – R<sup>2</sup> .

* significant at 10%; ** significant at 5%; *** significant at 1%.