rafmacalaba/datause-encoder-data huggingface dataset:
- 34 -

overall against the average 39 percent value recorded in industrial market economies (see Table 3.9b). This can be explained by the generally lower taxable base of **LAC** countries and the more developed fiscal systems of industrial market economies, but remains a determining factor of the lower standard of social welfare so far achieved in Latin America.

3.10 Are LAC Countries Centralized? Given the picture outlined above one would not conclude that LAC economies are more government-controlled than the more developed countries. However, another characteristic can be examined: the share of general government expenditures and revenues which is directly handled by their central governments. In LAC countries, central authorities collect in the average 80 percent of total tax revenues (against only 73 percent in industrial market economies) (See Table 3.10a) and implement 80 percent of their Table 3.10a: REVENUES BY LEVEL OF GOVERNMENT a/ -In Percentage, General Govt-100%-

||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|Average<br> <br>-|
|---|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE AVERAGE b/||||||||||||
|Subnatlonal Governmernt<br>Central Government|20.1<br>79.9|20.4<br>79.6|20.6<br>79.4|20.0<br>80.0|19.8<br>80.2|21.5<br>78.5|19.8<br>80.2|19.8<br>80.2|18.6<br>81.4|20.5<br>79.5|20.1<br>79.9|
|KARKET ECONOMIES||||||||||||
|Subnational Government|28.8|28.3|27.7|27.2|26.3|26.6|26.4|26.3|26.6|27.0|27.1|
|Central Government|71.2|71.7|72.3|72.8|73.7|73.4|73.6|73.7|73.4|73.0|72.9|

a/ Total expenditures include expenditures and lending minus repayment.

b/ Weighted averages. Comparability over time affected by the availability of data.

Table 3.10b: CAPITAL EXPENDITURES AS PERCENTAGE OF TOTAL EXPENDITURES BY LEVEL OF GOVERNMENT

||1975|1976|'977|1978|1979|1980|1981|1982|1983|1984|Average<br>-|
|---|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE AVERAGE_a/_||||||||||||
|Subnational Government<br>Central Government|25.7<br>19.0|28.3<br>23.0|23.5<br>18.5|23.6<br>19.0|22.7<br>17.0|24.5<br>17.9|21.3<br>19.2|17.8<br>16.9|15.4<br>14.2|19.0<br>11.7|22.2<br>17.6|
|MARKET ECONOMIES||||||||||||
|Subnational Government|25.0|23.7|21.9|22.9|22.2|18.7|17.1|16.3|15.2|14.8|19.8|
|Central<br>Government|8.8|8.5|9.8|8.5|7.8|8.3|9.2|9.4|8.8|8.0|8.7|

---------------------------------------------------------------------

__------__----------------_a/_ Welghted averages. Comparability over time affected by the availability of data.

Source: IMF, Government Finance Statistics, various years.
worse (Redfern, 1989). Undocumented migrants are, by definition, not captured at all since they would not want to register with local authorities. Another shortcoming is that departures are significantly underreported since many people avoid deregistering in order to retain residency rights, and some of the benefits that come with it (OECD, 2009).

# **_Administrative data_**

There are various sources of administrative data that may provide detailed information about international migration. Again, most of these sources come from destination countries, as they collect data on the migrants themselves. The most common are residency permits, which give migrants many rights and are implemented across a wide range of countries. Many destination countries regularly publish such data, both on the stock of migrants with permits, as well as the flow of new permits issued within a given time frame. Data may be disaggregated by different qualification criteria, such as family reunification, professional qualifications, humanitarian reasons and lotteries. One has to be careful in interpreting residency permit data since issuances need not equal the number of new immigrants. Permits might be based on issuance but taken up by migrants with an extended delay. Another possibility is that permits are issued to those already in the country as they change their legal status, without a real change in the number of migrants within the country. Most importantly, the legal criteria and rights of permits vary greatly. In some countries, residency permits lead to citizenship, while in others they are granted for limited stays. In cases where free mobility agreements between countries exist, such 7
# **5. Gender differentials: Evidence from the VHLSS**

As mentioned in the introduction, cross-country research by the World Economic Forum (2010) suggests that, relative to its rank in terms of overall gender equality, Vietnam is doing well in terms of gender equality in the labor market. Vietnam ranks 12<sup>th</sup> out of 134 countries in terms of women’s labor force participation, and 39<sup>th</sup> in terms of pay equality for similar work (versus a rank of 72 for overall gender equality).

The descriptive analysis above gives us a useful picture of the performance of the Vietnamese labor market, and confirms that Vietnamese women have good access to the labor market. The extent of the gender differentials are better analyzed using econometric modeling (see Annex 2 for methodology).

# _6.1 Women’s participation_

The labor force participation decision of women tends to be of a different nature compared with that of men. Although single women tend to have similar labor force participation to men, married women, and in particular women with children, have to make a choice between staying at home and participation in work outside the home. This is because they retain the main role in doing housework and caring for children/sick or elderly relatives. While it is generally assumed that men participate in the labor market unless they are incapacitated, the labor force choice of women is very much related to intra-household labor supply decisions.

Since Vietnamese women have a high labor force participation, which is similar to men’s, it is interesting to see whether there are gender differences in the determinants of participation, and how these compare with the usual patterns found in countries with very dissimilar participation rates for men and women. Moreover, gender differences are likely to exist in other dimensions, such as the type of employment.

The findings suggest that statistically significant gender differentials exist in participation with respect to age, educational attainment, regions, children ratio and marital status.<sup>16</sup> Participation in various economic activities and inactivity varies over the lifecycle and in a different manner for men and women (controlling for other characteristics). Women are

> 16 The multinomial logit model results are not shown.

30
Policy Research Working Paper 10616

# **Abstract**

Close to half of the population of Afghanistan was living below the national poverty line prior to the regime change in August 2021, with no additional information on poverty collected in the country since the last official household survey in 2019/20. This paper fills this knowledge gap through survey-to-survey imputation using a SWIFT-plus methodology. The analysis trains a predictive model on data from the 2019/20 Expenditure and Labor Force survey and imputes poverty in the latest Afghanistan Welfare Monitoring Survey. The analysis accounts for seasonality in welfare patterns and implements several tests to assess the model’s predictive capacity. The results show that 48.3 percent of the Afghan population was poor as of April–June 2023, a relative decline of 4 percentage points compared to poverty levels observed over the same months in 2020. The reduction in poverty was concentrated among rural households, with a decline from 51 to 44 percent, while it stagnated in urban areas at around 58 percent. Although no poverty data exists since 2020, the evolution of self-reported welfare and food security makes it reasonable to conclude that poverty first increased during the immediate economic contraction following the regime change and has progressively declined since then.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at obarriga@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(difference in the legal and regulatory framework between companies).

# **6.2.** - **Main types of indicators in the railways industry.**

Despite these difficulties, a large number of indicators are commonly used nowadays to monitor the performance of firms within the rail industry around the world. The definition of each particular indicator depends on the objective it must achieve and its informative value.

**Box 6.1. Contextual indicators in the rail industry.**

<!-- Start of picture text --> railc tereni act seea xea atr, hc aywdl rm onr ocutyo<br>conexua ind00icators, arei required Cd to;-Xi-S.,, assis S thi ompartAive analysis andR to define desirablem<br><!-- End of picture text --> leverof permtofrmance. these suandicat incluene thei socimalrisdeonomic firstctyperfisicsacto railway sector as well as other elements associated with the economy as a whole, and are mainly

- 51 -
Table 11: Simulation Results - Long Run Effects (Percent Change)

||Change in|Wage, _β_ =|0_._97||
|---|---|---|---|---|
||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|_White Collar_|1.18|0.89|1.34|1.37|
|_Tech/Sales_|1.56|1.03|1.71|1.47|
|_Service_|2.67|0.92|1.89|2.85|
|_Craft_|1.74|1.12|1.71|1.94|
|_Laborer_|2.02|1.51|2.36|2.18|

Change in Welfare, No-College, _<u>β</u>_ = 0 _._ 97

||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|---|---|---|---|---|
|_White Collar_|1.16|1.10|1.15|1.18|
|_Tech/Sales_|1.16|1.08|1.15|1.15|
|_Service_|1.15|1.03|1.16|1.19|
|_Craft_|1.15|0.99|1.16|1.19|
|_Laborer_|1.16|0.99|1.16|1.19|

Change in Welfare, College, _<u>β</u>_ = 0 _._ 97

||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|---|---|---|---|---|
|_White Collar_|1.15|1.01|1.15|1.18|
|_Tech/Sales_|1.16|1.01|1.16|1.18|
|_Service_|1.21|0.98|1.18|1.17|
|_Craft_|1.16|1.06|1.16|1.16|
|_Laborer_|1.16|1.08|1.16|1.16|

31
|llages within 50 km<br>(5)<br>ar<br>Casualty count|0.001<br>(0.001)<br>0.000<br>(0.001)<br>0.000<br>(0.001)<br>-0.001<br>(0.001)<br>4,421<br>0.040<br>Yes<br>Yes<br>Yes<br>383<br>151.4|a dierence-in-dierence<br>on cohort is age 16 to 21<br>e and month of interview|
|---|---|---|
|Including vi<br>(4)<br>Months of w|0.011<br>(0.016)<br>-0.011<br>(0.013)<br>-0.006<br>(0.016)<br>-0.036*<br>(0.020)<br>4,421<br>0.040<br>Yes<br>Yes<br>Yes<br>383<br>151.4|reported based on <br>he war. Comparis<br>cator for high cast|
|Including contiguous villages<br>(2)<br>(3)<br>Months of war<br>Casualty count|0.022<br>0.004<br>(0.022)<br>(0.006)<br>-0.006<br>-0.002<br>(0.017)<br>(0.006)<br>-0.013<br>-0.006<br>(0.020)<br>(0.006)<br>-0.068***<br>-0.019***<br>(0.023)<br>(0.007)<br>4,421<br>4,421<br>0.042<br>0.042<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>383<br>383<br>151.4<br>151.4|el. Treatment on the treated estimates are <br>es in the table refer to age at the start of t<br>placebo test. Other controls include an indi|
|Casualty count<br>(1)<br>Own village|0.005<br>(0.014)<br>-0.013<br>(0.009)<br>-0.030***<br>(0.011)<br>-0.050***<br>(0.012)<br>4,421<br>0.042<br>Yes<br>Yes<br>Yes<br>383<br>151.4|the 1% (5%) (10%) lev<br> at village level. All ag<br> group that serves as a|
|variable =|o 29 _×_ Conict<br> 15 _×_ Conict<br> 8 _×_ Conict<br> 3 _×_ Conict<br>ions<br> R-squared<br>r and month xed eects<br> trends and other controls<br>xed eects<br>of clusters<br>mean (Age 16 to 21)|**) (*) indicates signicance at<br>. Standard errors are clustered<br>2 to 29 is a second comparison <br>|
|Conict|Age 22 t<br>Age 9 to <br>Age 4 to <br>Age 0 to <br>Observat<br>Adjusted <br>Birth yea<br>Regional <br>Village <br>Number <br>Control|Note: *** (<br>specication<br>and cohort 2<br>xed-eects.|

43
little visual indication of non-linearity; these plots are designed to roughly correspond to the fixed effects regressions by using dependent and independent variables that have both been demeaned twice, first by district then by year.

Fourth, the effect of the TSC on learning outcomes is not different depending on whether the child’s mother is literate. The effect on reading, for example, is slightly less for children of literate mothers, but with a _t_ statistic of 0.38 on the interaction. This is consistent with disease externalities as a mechanism of the effect. Spears (2013) has shown that exposure to local open defecation stunts the growth of rich and poor children both, and even those living in households which use a toilet or latrine.

# **5.2 Assessing effect magnitudes**

Are these effect sizes plausible? One method of evaluating the effect size is to compare this paper’s estimates, causally identified using heterogeneity in program timing, with OLS cross-sectional estimates from another data source. Section 2 reported a gradient between sanitation and cognitive achievement using the IHDS. These results are not directly comparable because they are for 8 to 11 year olds, not 6 year olds, and therefore focus on the higher achievement levels of reading paragraphs or better, which is approximately a median split in that sample. Nevertheless, regressing an indicator for reading paragraphs on local area (PSU) latrines per capita, and including all of the controls used in figure 1, finds a slope of 0.82 (clustered standard error = 0.25). Because of measurement error from the larger geographic area of districts rather than villages, and because of the general tendency of OLS to overestimate, it is unsurprising that this OLS finding is slightly larger than this paper’s estimates around 0.75.

Another approach to assessing the plausibility of this result is to compare the magnitude of the estimate effect with what is implied by other estimates in the literature. Case and Paxson (2010) found that taller children in the U.S. perform better, on average, on cognitive tests. Spears (2012b) not only replicated this result among Indian children, but found that 20
22

# **Figure 3**

<!-- Start of picture text --> Gross Exports<br>(EXP)<br>Domestic Value Foreign Value<br>Added Added<br>(DV) (FV)<br>Exported in Exported in<br>Exported in<br>Exported in Final Intermediates Intermediates Re‐ Other Countries DV<br>Intermediates the<br>Goods absorbed by Direct exported to Third in Intermediates<br>Return Home<br>Importers Countries (IV)<br>(1) (5)<br>(4)<br>(2) (3)<br><!-- End of picture text --> Source: Koopman _et al._ (2010)

# **Figure 4**

<!-- Start of picture text --> 70%<br>Percentage Decline in Elasticity<br>60%<br>52% 53%<br>50%<br>40% 39% 38% 20th Percentile<br>36%<br>Mean<br>30%<br>25% 25% 80th Percentile<br>20% 20%<br>12%<br>10%<br>0%<br>Participation Position Backward<br><!-- End of picture text -->
The Worldwide Governance Indicators (WGI) by the World Bank (2017) offer a consistent and quantifiable measure of governance standards. These indicators report estimated governance standards for over 200 countries and territories along six dimensions: (i) voice and accountability, (ii) political stability and absence of violence, (iii) government effectiveness, (iv) regulatory quality, (v) rule of law, and (vi) control of corruption. For each country, these governance indicators reflect surveys of a large number of enterprises, citizens, and experts, based on over 30 individual data sources.

<mark>To illustrate the correlation between infrastructure quality and governance, Figure 2 presents the relationship between the WGI sub-index on corruption, in comparison to the Infrastructure Quality Index of the Global Competitiveness report (WEF, 2018). The same positive correlation between corruption and infrastructure quality can be observed for infrastructure in general, electricity, and roads. Similar patterns can be observed for different WGI sub-indices.</mark> _Figure 2 Infrastructure quality correlates strongly with governance standards. The WEF Infrastructure Quality Index ranks countries on a scale from 1 (worst) to 7 (best). (Data source:_ WEF, 2018) <!-- Start of picture text --> 3<br>Infrastructure<br>2<br>1<br>0<br>-1<br>-2<br>1 2 3 4 5 6 7<br>WEF Infrastructure Quality Index (1-7)<br>3<br>Electricity<br>2<br>1<br>0<br>-1<br>-2<br>1 2 3 4 5 6 7<br>WEF Infrastructure Quality Index (1-7)<br>3<br>2 Roads<br>1<br>0<br>-1<br>-2<br>1 2 3 4 5 6 7<br>WEF Infrastructure Quality Index (1-7)<br>Governance index<br>(Control of Corruption)<br>Governance index<br>(Control of Corruption)<br>Governance index<br>(Control of Corruption)<br><!-- End of picture text --> 4
18 61. Smuggling due to fuel price differentials between countries is another problem. The price of gasoline in Tanzania is well below that of neighboring Kenya. Fuel is smuggled between the two. The problem in Tanzania was complicated in 1987 by an overvalued exchange rate. As one World Bank study noted, "...fuel is imported into Tanzania, jaid for in foreign currency, and then resold over the border for local currency." The cost to the Tanzanian government is a revenue loss, assuming that motor fuel is set below market price. Fuel prices in Ghana are also relatively low. This leads to fuel carrying from Ghana to neighboring Ivory Coast. It is estimated that such leakage is around 1 percent of total domestic sales with a concentration on kerosene. Thus, rather than undercollecting the Govemnment of Ghana is modestly overcollecting. However, with Ghana's ex-refinery prices well below world prices, it is doubtful if the tax revenue collected on illegally exported fuels compensates for the subsidy lost.

# **Purchase** Taxation: **Duty** and Sales Tax

62. Tax on the sale and purchase of motor vehicles and parts are collected in four different forms: import duty, general sales tax, excise on domestic production, and income tax on manufacturers and dealers. Though the motor vehicle share of these tax bases is significant, it may not be meeting full revenue potential. To determine the extent of revenue potential reached, the tax base definitions and rate structures in various countries must be reviewed and matched against modem norms for a _good_ tax. An evaluation of administrative practice, and an exploration of divisions of responsibility for these taxes among central and subnational governments is also required. Lastly, we take up the reduction of avoidance and evasion.

63. Revenue **importance.** The revenue importance of sales and production taxes, as a share of GNP and as a share of total transport tax, is described for a small sample of countries in Table 3. Based on this small sample, one-third of all revenue collected from transportation activity seems a feasible level. Table 3: Revenue from Customs Duty, Sales Taxes and Excises on Motor Vehicles and Motor Vehicle Parts

|Country|Year|Percent<br>of**GDP**|Percent of Total<br>Transport Taxes|
|---|---|---|---|
|1. Argentina<br>|1987<br>|**0.7**<br>|22.4<br>|
|2. Nepal<br>|1985<br>|0.3<br>|33.7<br>|
|3. Tanzania<br>|1986<br>|1.1<br>|64.6|
|4. India<br>|1988<br>|0.4<br>|19.2|
|_5._Guatemala<br>|1988<br>|0.6<br>|32.2|
|6. Indonesia<br>|1980<br>|0.4|n.a.|
|7. Jamaica|1984|1.0|33.0|

64. Revenue from these **taxes** is rarely earmarked for expenditure on transportation services. The evaluation of practices in this area centers on the contribution of these transport taxes to general revenue, and to increasing their contribution through improved administration.
# Measuring Aversion to Debt: An Experiment among Student Loan Candidates<sup>_∗_</sup>

## Gregorio Caetano<sup>_†_</sup> Miguel Palacios<sup>_‡_</sup> Harry Anthony Patrinos<sup>_§_</sup>

**_JEL codes:_** _I22, J24, G11_

**_Keywords:_** _debt aversion, educational finance, human capital, investment decisions, student loans_

> _∗_ We thank Juan David Herre ̃no for valuable research assistance, the World Bank for providing funding for this project, and Lumni Inc. for conducting the surveys. We are grateful to seminar participants at the Australian National University, the University of Naples Federico II, the Darden School (University of Virginia), the University of Leicester and the World Bank for valuable suggestions and comments.

> _†_ Economics Department, University of Rochester. Email: gregorio.caetano@rochester.edu. 238 Harkness Hall, P.O. Box 270156, NY 14627 (USA). http://web.me.com/gregorio.caetano/Caetano/Home.html

> _‡_ Owen Graduate School of Management, Vanderbilt University. Email: miguel.palacios@vanderbilt.edu. Phone: +1(615)322-8059. Address: 401 21st Avenue South, Nashville, TN 37212 (USA). http://owen.vanderbilt.edu/palacios. Co-founder and shareholder of Lumni Inc.

> _§_ Lead Education Economist, The World Bank.

1
# 4.1 _Law Book_ index

Estimation results using the _Law Book_ index as the measure of labor regulation are reported in Table 4. Irrespective of the controls, the coefficient of the _Law Book_ index is negative. It is significant at less than 10% level without any controls (column 1, Table 4) and at less than 5% level once we control for _Age_ and _Income_ (column 2, Table 4). For the base specification, the estimated coefficient is significant at less than 1% level and equals -1.07 (column 3, Table 4). Adding the various controls discussed above lowers the coefficient value slightly to -.979 significant at less than 1% level (column 4, Table 4).

As for the _Perception_ index, the estimated coefficient of the _Law Book_ index declines sharply but remains significant at less than 5% level when we drop the largest 25% of the stores from the sample. For example, for the specification in column 4 of Table 4, the estimated coefficient of the _Law Book_ index drops from -.907 for the full sample to -.162 significant at less than 1% level when we drop the largest 25% of the stores. The corresponding value for the sample of the smallest 25% of the stores equals -.177 (p-value of .005).

# 4.2 _Temporary versus permanent employment_

We argued above that labor regulations are directed mostly towards permanent workers. One implication of this is that the negative effect of labor regulation should be stronger for permanent than temporary employment. Our results confirm this thinking. Columns 1 and 2 of Table 5 report the regression results with permanent and temporary employment as the dependent variables, respectively. In these two columns we use the _Perception_ 16