rafmacalaba/datause-encoder-data huggingface dataset:
Cumulative distribution functions of consumption are presented for movers versus stayers (Figure 2) and by move direction (Figure 3). Consumption levels of stayers and movers started off with relatively similar distributions in 2005/06. In 2009/10, however, not all movers experience higher consumption levels as compared to stayers. Indeed, the bottom 50% of movers fare the same when compared to stayers in 2009/10 (Figure 2). In fact, movers follow two different welfare paths in 2009/10, one corresponding to moves to rural destinations and the other corresponding to moves to urban destinations. As compared to stayers, movers to rural destinations had a consumption gap in 2005/06, which was almost closed after migration in 2009/10. The consumption distribution of movers to urban destinations dominates that of stayers in 2005/06 and 2009/10 (Figure 3). This descriptive analysis suggests that the welfare impact of out-migration will be strongly positive, regardless of the move direction. Furthermore, it indicates that the welfare impact of migration to urban destinations will be stronger than the welfare impact of migration to rural destinations.

_Figure 2_ <!-- Start of picture text --> CDFs between stayers and movers<br>Panel Sample 2005/09<br><!-- End of picture text --> <!-- Start of picture text --> Year 2005/06 Year 2009/10<br>9 10 11 12 13 9 10 11 12 13<br>Log of real p.c. expenditure Log of real p.c. expenditure<br>Stayer Mover Stayer Mover<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>Cumulative % of p.c. expenditure Cumulative % of p.c. expenditure<br>0 0<br><!-- End of picture text --> Source: UNPS 23
**Table 11: Robustness tests – Relationship between the employment rate of older persons (55-64) and the labor market outcomes of youth (15-24)**

||(1)<br>Youth|(2)<br>Youth|(3)<br>Youth|(4)<br>Youth|(5)<br>Youth|
|---|---|---|---|---|---|
||Employment|Unemployment|Hours of work(log)|Hourlywage(log)|Monthlyincome(log)|
|Generalized least squares||||||
|Employment rate of old persons|0.492***|-0.509***|-0.002**|-0.001|-0.004|
|(55-64)|(0.058)|(0.055)|(0.001)|(0.003)|(0.003)|
|Lagged independent variable||||||
|Lag of employment rate of old|0.406***|-0.491***|-0.001|-0.003|-0.005*|
|persons(T-2) (55-64)|(0.071)|(0.073)|(0.001)|(0.003)|(0.003)|

Source: Author’s calculations using Sakernas data Notes: Province-level control variables included are the log of hourly income of youth (for models 1-3), the unemployment rate (for models (3-5), the poverty rate, share of employment in the agriculture sector, share of employment in the industrial sector, share of youth, share of population with a primary education or less, log of GRP per capita (constant 2010 IDR), and growth in GRP per capita . Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

**Table 12: Robustness tests – Relationship between the employment rate of older persons (55-64) and the labor market outcomes of prime-aged persons (25-54)**

||(1)<br>Prime-aged<br>Employment|(2)<br>Prime-aged<br>Unemployment|(3)<br>Prime-aged<br>Hours of work(log)|(4)<br>Prime-aged<br>Hourlywage(log)|(5)<br>Prime-aged<br>Monthlyincome(log)|
|---|---|---|---|---|---|
|Generalized least squares||||||
|Employment rate of old persons|0.665***|-0.069***|0.000|-0.002|-0.002|
|(55-64)|(0.031)|(0.011)|(0.001)|(0.002)|(0.002)|
|Lagged independent variable||||||
|Lag of employment rate of old|0.505***|-0.077***|0.001|-0.003*|-0.002|
|persons(T-2) (55-64)|(0.053)|(0.011)|(0.001)|(0.002)|(0.002)|

Source: Author’s calculations using Sakernas data Notes: Province-level control variables included are the log of hourly income of prime-aged persons (for models 1-3), the unemployment rate (for models (3-5), the poverty rate, share of employment in the agriculture sector, share of employment in the industrial sector, share of prime-aged persons, share of population with a primary education or less, log of GRP per capita (constant 2010 IDR), and growth in GRP per capita . Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

27
**Table 1. An Overview of Some Key Concepts**

||**Broad/ Traditional**|**Narrow/ Relational**|
|---|---|---|
|**Definition**|Basic: Increased used of_foreign_<br>_value added in production_,<br>especially in production<br>destined for exports|Basic: Increased used of foreign<br>value added in production,<br>especially in production<br>destined for exports, but<br>_focusing on inter‐firm and intra‐_<br>_firm transactions involving_<br>_customized inputs and_<br>_relational contracting_(distinct<br>from anonymous, spot trades in<br>homogeneous goods)|
|**Conceptual framework**|GVCs essentially entail a_finer_<br>_international division of labor_<br>(trade in parts and components,<br>tasks)|GVCs entail a_finer international_<br>_division of labor_, but also<br>involve:<br>‐<br>nontrivial matching<br>between importers and<br>exporters<br>‐<br>relationship‐specific<br>investments by all parties<br>‐<br>inter‐firm and intra‐firm<br>flows of goods, technology<br>and credit in environments<br>with limited contractual<br>security<br>‐<br>governed by incomplete<br>contracts enforcement|
|**Empirical measures**|‐<br>Country‐ and industry‐level<br>measures of (i) foreign<br>value added in production<br>and in exports (related to<br>backward and forward GVC<br>participation indices);<br>‐<br>Analogous measures at the<br>firm‐level|‐<br>Case studies;<br>‐<br>Analyses of intrafirm trade<br>flows or global ownership<br>patterns;<br>‐<br>Empirical work on<br>persistence in firm‐level<br>trading relationships;<br>‐<br>Measures based on HS<br>products (anonymous vs<br>relational)|

4
false discovery rate (the expected proportion of falsely rejected null hypotheses). We use this approach to examine which dimensions of heterogeneity are robust to this concern.

# **5. Impacts on Employment and Well-being**

We begin by looking at the overall impacts on employment for the pooled sample, and then look separately by the four age*gender strata, and for heterogeneity with respect to pre-specified human capital variables. We then examine impacts on measures of current and expected future well-being.

# **5.1 Overall Impacts on Employment**

Table 3 reports the results of estimating equation (2) for different employment outcomes measured in our follow-up survey data. The first two columns show small and insignificant positive impacts of training on the likelihood of working at all, and of working at least 20 hours or more a week. The ITT for working at all is for a 2 percentage point impact, with a 95 percent confidence interval of [-0.5, +4.4] percentage points. This is thus a relatively precise zero or small effect. Lee bounds to adjust for differential attrition are also fairly narrow, consistent with the amount of differential attrition being small. The LATE estimates are for a 2.9 percentage point increase in being employed at all, and a 1.9 percentage point increase in the likelihood of working at least 20 hours per week.

We also find small and statistically insignificant impacts on weekly hours worked, income from work, and on a transform of income from work which is less sensitive to outliers. In contrast, we find statistically significant (at the 10 percent level) impacts on several measures of job quality: the socioeconomic occupational status of the job, being formally employed, and the income earned from formal jobs. For example, we find a 2 percentage point increase from being assigned to training in the likelihood of being formally employed, with the LATE impact being 3 percentage points. Given that 29 percent of the control group is formally employed at follow-up, this LATE estimate is equivalent to a 10 percent increase in formal sector employment.

All of these different employment outcomes show modest positive impacts.<sup>14</sup> Averaging them together to account for multiple testing and examine the mean impact finds a positive overall impact which is significant at the 10 percent level. However, the size of the impact is

> 14 The modest size of the treatment effect cannot be explained by trainees being more likely to pursue additional education and training.  The impact of treatment on the probability of being in education or training at the time of the follow-up survey is very small, negative and marginally significant (-.014, p-value = .095).

14
distributed this value for the 20 regions using a re-proportioning method through domestic demand. For instance, _Piemonte_ ’s share is equal to the domestic demand for the Piedmont electricity sector, divided by the domestic demand for the Italian electricity sector. The shares of each region were then multiplied by the amount of €1.6 billion to obtain the regional figure for importing the electricity sector;

- Import of services: data refer to 2016 and are taken by the ISTAT-ICE report from the Bank of Italy. Data refers to “ _debiti per importazioni di servizi per tipologia e ripartizione territoriale_ ” (i.e., debts on imports of services). These data are reported for macro-regions (northwest, northeast, middle, south, and islands), so we need to regionalize these macro-regions into our 20 Nuts-II levels. For the regionalization of the import of services, the domestic demand is used in the same way it is presented for the import of goods. The initial data are not broken down by macro-areas for the transport category but fall under the item "non-distributable data" (approximately €20 million). For this reason, data on the number of companies in the transport sector whose reference markets are foreign countries were used to distribute the €20 million (data on permanent ISTAT censuses). Once the regional shares were calculated, they were applied to the €20 million to obtain the final regional figure;

- Export of goods: as for goods imports, the first step is to split the electricity/water category through 2 digit data. However, the 2 digit data shows €350 million not distributed among the regions. While we solved the import problem using shares calculated on domestic demand, shares on the number of employees by region and sector have now been used for services exports. It is plausible that the value of exports of goods can be approximated using the shares of the number of employees in the same sector within a specific region. In addition to the shares on employees, shares relating to the number of companies with more than ten employees operating within the electricity sector were also used. In this way, a double re-proportioning was carried out to obtain the final data;

- Export of services: data from 2016 from the Bank of Italy are used for the export of services. They refer to “ _Crediti per esportazioni di servizi per tipologia e ripartizione territoriale_ ” (i.e., credits on exports). The services reported by the Bank of Italy are grouped as for import of services. The unique difference is that data are regionalized using shares calculated on the number of employees. We used the same procedure for the transport sector as used for the import of services.

15
## **Policy Research Working Paper**

# **Can Public Credit Schemes Improve Access to Finance for Small Businesses?** _Evidence from Indonesia_

Salman Alibhai Hillary C. Johnson Cecile Thioro Niang Francesco Strobbe

**JEL Classification: G21, H81, E43, O16**

**Keywords: Government credit programs, subsidized credit, partial credit guarantees, micro-small and medium enterprises, financial inclusion**
# **Table 9:  Methodological Breaks in PRS Indicators**

||**1996**|**1996**|**1998**|**1998**|**Change**|**Change**||
|---|---|---|---|---|---|---|---|
||**DRI**|**EIU**|**DRI**|**EIU**|**DRI**|**EIU**||
|**_Series With Breaks in 1997_**||||||||
|Democratic Accountability (VA)|..|0.83|..|0.75|..|-0.08||
|Bureaucratic Quality (GE)|0.67|0.79|0.68|0.62|0.01|-0.18||
|_Median Change_|||||||-0.08|
|_Fraction Increase_|||||||0.33|
|**_Series Without Breaks in 1997_**||||||||
|Military in Politics (VA)||0.72||0.73|..|0.02||
|Internal Conflict (PV)|0.62|0.60|0.74|0.77|0.12|0.17||
|External Conflict (PV)|0.33|0.43|0.51|0.51|0.19|0.08||
|Ethnic Tensions (PV)|0.50|0.56|0.61|0.66|0.11|0.10||
|Government Stability (PV)|0.04|0.14|0.02|0.10|-0.02|-0.04||
|Investment Profile (RQ)|0.56|..|0.54|..|-0.02|..||
|Law and Order (RL)|0.72|0.71|0.74|0.77|0.03|0.05||
|Corruption (CC)|0.64|0.75|0.69|0.68|0.04|-0.07||
|_Median Change_|||||||0.05|
|_Fraction Increase_|||||||0.71|
||**2000**|**2000**|**2002**|**2002**|**Change**|**Change**||
||**DRI**|**EIU**|**DRI**|**EIU**|**DRI**|**EIU**||
|**_Series With Breaks in 2001_**||||||||
|Internal Conflict (PV)|0.68|0.78|0.76|0.75|0.08|-0.03||
|External Conflict (PV)|0.50|0.51|0.44|0.48|-0.06|-0.03||
|Government Stability (PV)|0.09|0.19|0.19|0.20|0.10|0.01||
|Investment Profile (RQ)|0.55||0.68||0.13|||
|Corruption (CC)|0.75|0.75|0.74|0.84|-0.02|0.09||
|_Median Change_|||||||0.01|
|_Fraction Increase_|||||||0.56|
|**_Series Without Breaks in 2001_**||||||||
|Military in Politics (VA)||0.71||0.70||-0.01||
|Democratic Accountability (VA)||0.75||0.79||0.05||
|Ethnic Tensions (PV)|0.46|0.52|0.37|0.44|-0.09|-0.08||
|Bureaucratic Quality (GE)|0.75|0.82|0.73|0.86|-0.02|0.04||
|Law and Order (RL)|0.74|0.70|0.71|0.70|-0.03|0.00||
|_Median Change_|||||||-0.01|
|_Fraction Increase_<br>Note: Break in series identified by fraction of<br>Series identified as having breaks have 40 pe<br>Series identified as having no breaks have les<br>Correlations based on common sample of 97|countries that cha<br>rcent or more of c<br>s than 18 percent<br>countries|nge from mont<br>ountries chang<br>of countries c|h to month in<br>e in a single<br>hange in a sin|PRS<br>month<br>gle month.|||0.38|

44
women in the household in columns 5 and 6 respectively. This could give an indication of how reliant the household is on someone outside the household (potentially the absent male members). We see that the main effect loses significance, indicating a convincing mechanism explaining the change in FDI.

# **Indirect channels**

The conflict in Yemen has led to high levels of displacement and movements in the population as people and households escape violence and seek better conditions. Calderón, Gáfaro, and Ibáñez (2011) find that women from displaced households in Colombia are more likely to participate in the labor force because their skill sets are more transferable than those of men. Evidence in other contexts points to a similar phenomenon as women are more likely to work in the informal sector while male IDPs are excluded. As conflict exposure is likely to be correlated with displacement, in column 7 we control for whether the household has been displaced and find that the main effect of medium intensity conflict on FDI is slightly smaller, but that women living in strong intensity conflict districts also a see a significant increase in the female decision-making index. This result could be explained by some of the changes to household composition described earlier, and deserves further research to be fully understood.

_Table 5: Mechanisms: Additional controls and interactions_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Female|||||||
||<br>employment|FDI|FDI|FDI|FDI|FDI|FDI|
|||Work<br>differences|Female<br>headed<br>households|Female<br>headed<br>households|Remittances|Proportion<br>of men to<br>women|IDP<br>status|
|Medium x 2021|-0.088|0.189**|0.173**|0.110*|-0.012|-0.049|0.135***|
||(0.074)|(0.088)|(0.079)|(0.061)|(0.088)|(0.082)|(0.046)|
|Strong x 2021|0.041|0.025|0.062|0.050|0.070|-0.127|0.312***|
||(0.057)|(0.079)|(0.061)|(0.055)|(0.076)|(0.076)|(0.043)|
|Indicator for female head|||0.274***|0.538||||
||||(0.084)|(0.335)||||
|Medium x 2021 x female head||||0.611||||
|||||(0.393)||||
|Strong x 2021 x female head||||0.285||||
|||||(0.382)||||
|PSU dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Women controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Partner controls|No|Yes|No|No|No|No|No|
|Observations|2,315|2,033|2,315|2,315|2,315|2,315|2,315|
|R-squared<br>Standard errors clustered at the|0.216<br>district level.|0.244|0.162|0.174|0.154|0.136|0.335|

20
Pakistan’s poverty is determined using a standard methodology. The official poverty line used was defined in 2001 based on a food energy intake approach (minimum caloric intake of 2,350 calories per adult equivalent per day), using 1999 data. The poverty line is currently updated using the inflation rate from the Consumer Price Index (CPI), and the consumption data comes from the integrated household study known as the Pakistan Social and Living Standards Measurement (PSLM) Survey. This survey is collected by the Pakistan Bureau of Statistics over an entire year, every three years.<sup>12</sup> Despite the use of standard methods, poverty measurement in Pakistan is the subject of an ongoing controversy. The latest official estimate of poverty found that 29.5 percent of the total population was poor in 2013. There are several concerns among experts regarding the limitations of the current methodology. First, questions are raised about the appropriateness of using the CPI to deflate the poverty line, because the reference basket is based on a typical urban household, which spends a lower share of its budget on food than poor households in the PSLM. Since food prices have been increasing more than non-food prices, using the CPI underestimates the actual price increases faced by the poor, which leads poverty to be understated about 3 percentage points (Newman 2013). Second, the most recent population census was carried out in 1998, and the lack of a more recent sampling frame makes recent rounds of the PSLM less representative of the national population. In addition, it is particularly difficult to collect data in the province of Baluchistan because it is a vast territory with a highly dispersed population.<sup>13</sup> These are important methodological issues that should be addressed, but initial indications

> 12 The first integrated household survey was implemented in 1998-99. Since then, consumption data was collected in 2001-02, 2004-05, 2005-06, 2007-08, 2010-11, and 2014-2015, and the consumption module has experienced minor changes.

13 Additional concerns of lesser importance are the exclusion of durable goods from the poverty line and the fact that spatial deflation is based purely on food prices.

24
WPS4247

# **The Impact of Remittances on Poverty and Human Capital: Evidence from Latin American Household Surveys**

**Pablo Acosta, (Corporación Andina de Fomento) Pablo Fajnzylber and J. Humberto Lopez**<sup>*****</sup> **(The World Bank)**

## **Abstract:**

This paper explores the impact of remittances on poverty, education, and health in eleven Latin American countries using nationally representative household surveys and making an explicit attempt to account for one of the inherent costs associated with migration: the potential income that the migrant may have made at home. The main findings of the study are the following: (i) regardless of the counterfactual used remittances appear to lower poverty levels in most recipient countries; (ii) yet, despite this general tendency the estimated impacts tend to be modest; and (iii) there is significant country heterogeneity in the poverty reduction impact of remittances’ flows. Among the aspects that have been identified in the paper that may lead to varying outcomes across countries are the percentage of households reporting remittances income, the share of remittances recipient households belonging to the lowest quintiles of the income distribution, and the relative importance of remittances flows with respect to GDP. While remittances tend to have positive effects on education and health, this impact is often restricted to specific groups of the population.

Keywords: remittances, poverty, human capital.

## World Bank Policy Research Working Paper 4247, June 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * The views expressed in this paper should not be attributed to the Corporación Andina de Fomento, the World Bank, its Executive Directors or the countries they represent.

1
23

# <u>Table 4: Multiple regressions explaining international differences in out-of-pocket expenditure measures</u>

||(1)<br>OOP per cap PPP $|(2)<br>CV|(3)<br>Q90/Q50|(4)<br>Budget Share|(5)<br>CATA10|(6)<br>Kakwani|(7)<br>CI CATA10|(8)<br>IMPOV190|(9)<br>IMPOV PG 190|
|---|---|---|---|---|---|---|---|---|---|
|Per capita GDP 2011 int. $|11.042<sup>***</sup>|-111.962|0.162|0.016|0.052|-0.003<sup>*</sup>|-0.003|-0.060<sup>***</sup>|-0.001<sup>***</sup>|
||(0.000)|(0.327)|(0.536)<br>|(0.388)|(0.445)|(0.081)|(0.161)|(0.000)|(0.000)|
|Gini index of income inequality|0.022|2.043|0.530<sup>*</sup>|0.010|0.074|-0.001|-0.000|0.004|-0.000|
||(0.967)|(0.844)|(0.075)|(0.382)|(0.108)|(0.554)|(0.996)|(0.496)|(0.874)|
|Total Health Exp. (THE) as % GDP|9.061<sup>**</sup>|91.114|-0.217|0.146<sup>**</sup>|0.562<sup>**</sup>|-0.009<sup>**</sup>|-0.009|-0.109<sup>**</sup>|-0.000|
||(0.002)|(0.444)|(0.750)|(0.012)<br>|(0.005)<br>|(0.040)<br>|(0.124)<br>|(0.002)<br>|(0.243)|
|SHI schemes as % THE|-0.497|28.365|0.095|-0.019<sup>**</sup>|-0.053<sup>*</sup>|-0.001<sup>*</sup>|-0.001<sup>*</sup>|-0.012<sup>**</sup>|-0.000|
||(0.236)|(0.333)|(0.509)|(0.017)|(0.052)|(0.093)|(0.056)|(0.005)|(0.190)|
|Govt. Financing Arrang. as % THE|-1.611<sup>***</sup>|2.726|-0.012|-0.034<sup>***</sup>|-0.104<sup>***</sup>|-0.001<sup>**</sup>|-0.001|-0.008<sup>*</sup>|0.000|
||(0.000)|(0.540)|(0.926)|(0.000)|(0.000)|(0.025)<br>|(0.111)|(0.095)|(0.613)|
|Comp. PI as % THE|-3.590|-1.758|-0.505|-0.042|-0.156|-0.007<sup>**</sup>|-0.003|-0.039|-0.000|
||(0.258)<br>|(0.950)|(0.404)|(0.313)<br>|(0.282)<br>|(0.035)|(0.673)|(0.151)<br>|(0.530)<br>|
|Nonprofit schemes as % THE|-3.568<sup>**</sup>|21.545|1.005|-0.074<sup>***</sup>|-0.190<sup>**</sup>|-0.001|0.001|-0.030<sup>**</sup>|-0.000<sup>*</sup>|
||(0.026)|(0.230)|(0.124)|(0.000)|(0.002)|(0.458)|(0.835)|(0.002)|(0.051)|
|Vol. Schemes as % THE|-0.426|34.959|-0.140|-0.008|-0.032|0.000|0.002|-0.006|-0.000|
||(0.683)|(0.353)|(0.597)|(0.719)|(0.701)|(0.817)|(0.281)|(0.613)|(0.232)|
|No. observations|422|422|361|422|422|422|421|422|422|
|R-squared|0.750|0.008|0.087|0.248|0.244|0.227|0.254|0.445|0.278|
|p: Soc. Sec.=Other Govt.|0.001|0.345|0.276|0.019|0.032|0.597|0.988|0.213|0.028|

Note: Numbers in parentheses are p-values. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.001