rafmacalaba/datause-encoder-data huggingface dataset:
sparsity.<sup>11</sup> In empirical analyses in practice, local-area level remoteness is often characterized through the urban-rural dichotomy, which is rather blunt. Alternatively, the size or density of a particular settlement can potentially provide a more nuanced indication of a resident population’s centrality or remoteness. However, this approach rests critically on the delineation of boundaries for any particular settlement, which is empirically fraught (for example, due to clustering of towns).

At the same time, one open question, which would be a fruitful direction for future work to refine our new indicators, relates to the ways in which the delineation of local areas affects the measures. Intuition suggests that the most precise and informative measures of remoteness and dispersion would be based on data geo-coded at the individual household level.<sup>12</sup> In practice, for the Pacific Island countries, our calculations center around data for local-area communities. These are based on exogenously defined local-area (administrative, or survey catchment area) boundaries. The implications of these boundary definitions on the remoteness and dispersion measures is, we suspect, an empirical question, dependent on interior settlement patterns, and the boundaries themselves. One immediate direction for further refinement would be to re-estimate and compare the indicators using a range of geographical areas— from the finer to the coarser.

As a final note, it is worth highlighting that none of the measures we present in this paper explicitly accounts for the island-nature of PIC’s geography. However, they do reflect uninhabited areas of ocean in an implicit sense. Areas of uninhabited ocean lead to large measures of distance between communities (including between communities on outer islands and the national capital). This uninhabited distance results in high measures of remoteness, and relatedly, dispersion.

> 11 Part of the motivation for this work was a desire, for the purpose of empirical analysis, to apply a consistent approach to identifying ‘remote’, outer islands in different PICs. It became clear that it is not immediately obvious how to empirically measure remoteness of a particular community, within the broader geographical context of a country as a whole. This was part of a broader task of analyzing socio-economic characteristics of Pacific Island communities from a spatial geography perspective, with a particular interest in describing living conditions for people living on more remote islands (see World Bank, 2019). World Bank, 2019. ‘Socio-economic analysis: A spatial analysis of population and housing census data’, a background paper prepared for _A Review of Spatial Development Issues in the Pacific Islands Countries_ (P166346).

> 12 At the other extreme—where a country has no disaggregated data at all (that is, data analogous to just one ‘local area’)—our indicators would be completely insensitive to its internal settlement patterns.

27
Policy Research Working Paper 7796

# **Abstract**

This is the first study to investigate the short- and long-term causal effects of a child-labor ban. The study explores the law that increased the minimum employment age from 14 to 16 in Brazil in 1998, and uncovers its impact on time allocated to schooling and work in the short term and on school attainment and labor market outcomes in the long term. The analysis uses cross-sectional data from 1998 to 2014, and applies a fuzzy regression discontinuity design to estimate the impact of the ban at different points of individuals’ lifecycles. The estimates show that the ban reduced the incidence of boys in paid work activities by 4 percentage points or 27 percent. The study finds that the fall in child labor is mostly explained by the change in the proportions of boys working for pay and studying, and observes an increase in the proportion of boys only studying as a consequence. The results suggest that the ban reduced boys’ participation in the labor force. The study follows the same cohort affected by the ban over the years, and finds that the short-term effects persisted until 2003 when the boys turned 18. The study pooled data from 2007 to 2014 to check whether the ban affected individuals’ stock of human capital and labor market outcomes. The estimates suggest that the ban did not have long-term effects for the whole cohort, but found some indication that it did negatively affect the log earnings of individuals at the lower tail of the earnings distribution.

This paper is a product of the Impact Evaluation Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at caiopiza@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
show that grid-connected communes are better off than those without a grid. This result was to be expected because the extension of rural electrification to communes during that period was done based on a combination of factors, including economic factors, availability of local infrastructure, and population density. However, there may also be some impacts due to a community having electricity. Thus, as expected, households in communes with grid have higher cash income (in real terms) and better schooling outcomes than those in non-grid communes. And almost all outcomes improved from 2002 to 2005, except for farm and total income which dropped slightly.

The availability of the panel data for Vietnam makes possible three types of comparisons (Table 6). The first that we examine is the difference of outcomes between the communes with and without electricity in 2002 gives a with-and-without estimate (second column). The with-andwithout estimates show that households in communes with electricity are better than those in communes without electricity in 2002 in all economic measures and the differences are all significant. For example, total income and per capita expenditure in grid-connected communes are over 80 percent higher than those is communes without grid. Among the educational outcomes, only boys’ enrollment gain is significant, while girls’ is not. As indicated, this may be due to a combination of the way the communes were selected as part of the rural electrification plan and may also be the result of the impact of electrification as well.

The second method of analysis is made by examining the difference between 2002- and 2005-outcomes for communes that were without grid in 2002 but got electricity in 2005 gives a before-and-after estimate (third column). These effects are just as strong as the with-and-without electricity results for both economic measures and educational outcomes. For instance, school enrollment increased 17.1 percent for boys and 14.8 percent for girls in communes that received grid between 2002 and 2005.

22
<!-- Start of picture text --> Counterfactual PPP (in logs) against ICP 2011 PPP (in logs)<br>Lim] - te<br>#6 3°<br>ard<br>sb * #¢#<br>* .- -<br>« % “ _*<br>TM * «t* aon<br>+ * ae ad<br>+ i<br>oa *<br>Pad * 3<br>* <* *<br>1 a<br>et . 7<br>—<br>= a«*<br>-4 -2 0 2 ; 4 6<br>2071 log ICP PPP (consumption)<br>+ Log counterfactuals §=§5£— Linear prediction<br>———-—- ¥=X line<br><!-- End of picture text --> <!-- Start of picture text --> Per-capita annual GDP (in logs) evaluated at<br>counterfactual PPP against that evaluated at ICP 2011 PPP<br>=<br>+ ae<br>ol_ * a. =t Pani eo+<br>o + + * 4 a .* * *<br>oo<br>oo<br>*<br>=r<br>qPer-capita; annual 10GDP (in logs) evaluated17 at ICP 2011 12consumption PPP13<br>* Per-capita annual GDP {in logs) evaluated at counterfactual PPP<br>— Linear prediction<br>—-—-—-- Y=Xline<br><!-- End of picture text -->
<!-- Start of picture text --> 6000<br>5000 Mee ee<br>4000 ao<br>3000<br>2000<br>+--+} --——+-.<br>7 TR OO<br>1000 a,<br>1994 1885 1936 i9s7 = 1958 1995 2000 2001<br>woke GONNections "TU _+— Lapex Km<br>—e— Riconn —a— Rican after PPI<br><!-- End of picture text --> <!-- Start of picture text --> National average cost/connection Year 1995 Year 2001<br>(Rand current)<br>Urban 2170 2674<br>Rural 3568 2622<br><!-- End of picture text -->
Table 6: Current and estimated probabilities for flood damages of a given size

|**Damage**|**1°C of**|**2°C of**|**3.7°C of**|
|---|---|---|---|
||**warming**|**warming**|**warming**|
|**0**|91.233%|82.467%|43.034%|
|**1.04%**|5.000%|10.000%|32.490%|
|**1.69%**|2.000%|4.000%|12.996%|
|**2.17%**|1.000%|2.000%|6.498%|
|**2.78%**|0.400%|0.800%|2.599%|
|**3.02%**|0.200%|0.400%|1.300%|
|**3.49%**|0.100%|0.200%|0.650%|
|**3.65%**|0.067%|0.133%|0.433%|

Source: UNISDR (2015), Myhre et al. (2019) and author’s calculations

## _Model modifications_

Damages are given in terms of the reconstruction cost of the damaged capital. For example, if a flood destroys a bridge and the reconstruction cost of the bridge is 10 million USD, then the official damaged caused by the destruction of the bridge is 10 million USD. However, the economic damage caused by the destruction of that bridge might be far higher than 10 million USD (Hallegatte & Vogt-Schilb, 2016) because the productivity of existing capital is higher than the productivity of marginal capital, which determines the replacement cost of existing projects.<sup>24</sup> To accommodate this reality, the modeling must evaluate the productivity of investment to replace damaged capital goods differently from the productivity of new investment at the margin. In the model, it is assumed that the productivity of damaged capital is equal to the average productivity of capital. This is equivalent to assuming that the productivity distribution of damaged capital is the same as the productivity distribution of the existing capital stock or assuming the projects damaged are randomly selected from the total capital stock.

# 3.8.7 Tracking unrepaired damages

As modeled, capital destroyed by a flood has average capital productivity, while newly constructed capital has marginal productivity. In the modeling we distinguish between two types of capital: damaged capital and normal capital . The economic impact of a unit of damaged capital is equal to the average productivity of capital times the damaged capital . The level of damaged capital at time and is tt tt DDSS KK YY determined according to the following formula tt KK ∗DDSS SS , DD where is new damage in time , is the stock of unrepaired damage and tt tt−1 tt tt is investment into DDSS = DDSS + DD −II repairing damage. Note that this form keeps the memory of the capital stock that would have depreciated DD tt tt tt DD SS DDSS II if was not destroyed. In the modeling the amount of total investment the government can undertake is determined by the fiscal rule. Given that the capital destroyed by floods has an average capital productivity, it would be tt II optimal to direct all investment to reconstruction. However, only a share of total government investment

> 24 This is a natural outcome of diminishing marginal productivity, which implies that average productivity of existing capital is greater than the productivity of new investments.

52
29 agricultural research and adapting and adopting as appropriate the new technologies it generated, and with the number of farms and farmers declining steadily to lower the labor intensity of the sector even as output expanded.<sup>25</sup> Within that context, the removal of the anti-agricultural policy bias over the past 30 years has boosted, with a not-unexpected delay, the rate of growth of farm productivity substantially. Multifactor farm productivity (MFP) growth increased following the international price hikes in 1973-74, but then plateaued during the next decade until the reforms from the mid-1980s began to have their effect (Figure 7). In the 1983-93 period, farm MFP grew at just 1.4 percent per year, but during 1993-2000 its growth rate was 4.1 percent (Productivity Commission 2005, p.121). Similar results are reported in Parham (2004): less than 1.5 percent during 1974-88, then 2.6 percent in 1988-93 and 4.3 percent in 1993-98.<sup>26</sup> His estimates show that even that earlier rate of 1.5 percent compares favorably with that for the other sectors of Australia’s economy, the 25 For example, Australia’s dairy industry saw the following changes over the 25 years to 2004: the number of dairy farmers more than halved (from 22,000 to 10,000), the average herd size increased 2.5 times (from 85 to 210), average annual yield per cow nearly doubled (from 2850 to 4900 litres), and average annual milk production per farm quadrupled, from 0.25 to 1.05 million litres (Productivity Commission 2005, p.131; Harris 2005a). As well, the dairy processing industry has become significantly more productive (Balcombe, Doucouliagos and Fraser 2007). Overall, the number of farmers in Australia has fallen by more than one-third over the past half-century (ABARE 2007).

26 See also the results since the mid-1970s in Fleming (2007), who shows that MFP growth on Australia’s farms enabled producers to cope with the fact that, over the past three decades, the prices they paid for their inputs grew 1.6 percent more per year than the prices they received for their products.
WPS3760

# **Evaluating Training Programs for Small and Medium Enterprises**

## **Lessons from Mexico**

**Hong Tan Gladys Lopez Acevedo**

### World Bank Policy Research Working Paper 3760, November 2005

_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 World Bank World Bank Institute and Latin America and the Caribbean Region Poverty Sector Unit**
|**City**|**Country**<br>**Top T**|**Country Income Group **<br>**en Cities ‐ GDP Growth**|**GDP Dif **|**Employment Dif **|**GVA Dif**|
|---|---|---|---|---|---|
|**Makhachkala**|**Russia**|**Upper middle income**|**0.0443**|**0.0232**|**0.0331**|
|**Sharjah**|**UAE**|**High income: nonOECD**|**0.0411**|**‐0.0102**|**0.0283**|
|Abeokuta|Nigeria|Lower middle income|0.0386|0.0141|0.0108|
|Benin City|Nigeria|Lower middle income|0.0319|0.0275|0.0057|
|Ibadan|Nigeria|Lower middle income|0.0311|0.0196|0.0072|
|**Kigali**|**Rwanda**|**Low income**|**0.0305**|**0.0591**|**0.0102**|
|**San Jose‐Sunnyvale‐Santa Clara, CA**|**United State**|**s High income: OECD**|**0.0299**|**0.0117**|**0.0263**|
|Penza|Russia|Upper middle income|0.0297|0.0002|0.0239|
|Ogbomosho|Nigeria|Lower middle income|0.0296|0.0409|0.0069|
|**Onitsha**|**Nigeria**|**Lower middle income**|**0.0294**|**0.0450**|**0.0052**|

|**City**|**Country**<br>**To**|**Country Income Group **<br>**p Ten Cities ‐ Employme**|**GDP Dif **<br>**nt Grow**|**Employment Dif **<br>**th**|**GVA Dif**|
|---|---|---|---|---|---|
|NayPyi Taw|Myanmar|Low income|‐0.1636|0.4083|‐0.0840|
|Lusaka|Zambia|Lower middle income|‐0.0022|0.0885|‐0.0580|
|Ouagadougou|Burkina Faso|Low income|‐0.0050|0.0810|‐0.0422|
|**Kigali**|**Rwanda**|**Low income**|**0.0305**|**0.0591**|**0.0102**|
|Pondicherry|India|Lower middle income|0.0062|0.0554|‐0.0319|
|Abuja|Nigeria|Lower middle income|0.0149|0.0533|‐0.0036|
|Kabul|Afghanistan|Low income|0.0190|0.0500|‐0.0310|
|Lilongwe|Malawi|Low income|‐0.0020|0.0496|‐0.0258|
|Bujumbura|Burundi|Low income|0.0075|0.0471|‐0.0208|
|**Onitsha**|**Nigeria**|**Lower middle income**|**0.0294**|**0.0450**|**0.0052**|

Note: Cities that are in the top ten ranking outperforming their national average in more than one category are highlighted as follows:

- Orange and green for those that outperform in GDP per capita en Employment growth,

- Orange and blue for those that outperform in GDP per capita and Productivity growth,

- None of the cities outperform in both Employment and Productivity growth.

It is also evident from this table that cities often do not perform well in all three variables. Nonetheless, some examples for cities that outperform their nation in GDP and another category can be pointed out.

10
> approved or rejected for pension fund investment purposes, although investment limits are weighted **by** an ownership concentration _factor._

Table **I**

||InvestmentLimitsper Instrg<br>**(%**ofvalueoffund)|ment||
|---|---|---|---|
|**a.**|Statesecurities||_45%_|
|**b.**|Liabilitiesofbankinginstitutions||**_50%_**|
||ofwhich short-termliabilities|**30%**||
|**c.**<br>|Mortgagebonds||**80%**|
|**d.**|Corporatebonds||**_50%_**|
|e.|Commercialpaper||**10%**|
|f.|Quotasofotherpension funds||20%|
|**g.**|Corporateequities<br>ofwhich<br>||**30%**|
||"Chapter12"companies<br>"|**30%**||
||non-Chapter12"companies|**10%**||
||realestatecompanies|i0%||
|h.|<br>Sharesofinvestment funds||**10%**|
||ofwhich venturecapital companies|**_5%_**||
|i.|Foreignsecurities*||2%|

- The limit is set **by** central bank subject to a maximum 2% until September **1992**

# Source: Habitat **(1991)**

> Investments are subject to limits **by** instrument (or class of instruments) and **by** issuer. Limits on investments **by** instrument aim to contain pension fund exposure to the risks of particular instruments. Limits **by** class of instruments are required because some instruments have similar or

> even identical risks. Failure to group instruments that are subject to similar or identical risks would

> undermine the objective of risk diversification that these limits aim to achieve.

The limits **by** instrument in force in **1990** as a proportion of the value of the fund are shown in Table **1.** Limits on investments **by** issuer are determined **by** several different formulas, depending on the issuer, with the lowest limit being the binding one. The main objective of limits **by** issuer is to avoid undue exposure of individual pension funds as well as of the whole pension fund sector to particular issuers or to particular liabilities of different issuers. The following limits are imposed:

- a. Limits expressed as a share of the value of the fund. This applies to investments in the liabilities or equity capital of an issuer as a share of the value of each pension fund. Thus, pension funds are not allowed to invest more than **15%** of the value of their funds in the liabilities of an individual financial (banking) institution; or more than **7%** of the value of the fund in the securities (bonds, commercial paper and

**11**
WPS3945 ways. Our fi rst strategy is to instrument for migration. To do this, we need instruments correlated with the migration decision but uncorrelated with unobserved attributes of the household or the community.

A number of recent papers have used a measure of the migrant network to instrument for migration. Migrant networks are seen as reducing the costs of migration for potential migrants via two channels First, they constitute an information network which can educate potential migrants about conditions in speci fi c migration destinations as well as potential hazards and costs, both at home and in migration destinations (Massey 1988; Orrenious (1999)). Second, they serve to relax credit constraints (Genicot and Senesky (2004)). A number of studies have also shown that networks increase the economic returns to migration (Munshi (2003); McKenzie (2005)). All of this implies that the probability of migration should be higher for households residing in communities with signi fi cant migration experience.<sup>13</sup> The PRHS 2001-02 includes a complete census of all village households which ascertained the household’s current migration status.<sup>14</sup> Using this, we can construct a measure of the migration network for each village in our sample as the proportion of households in the village with a current migrant. While this census based measure of migrant networks is unlikely to be correlated with household speci fi c unobservables in ηjv, this is of course not the case for any number of village level unobservables which could well be corrrelated with both the propensity to migrate at the village level and village average child outcomes. In order to deal with this problem, we need an instrument which varies at the household level.

Fortunately, a feature of migration that is particular to the context we study, provides us with such household level variation in the opportunity to migrate. Mobility and seclusion restrictions on women typically require the presence of an adult male in the household. Indeed, households without an adult male are a rare feature among non-migrants in rural Pakistan (less than 3%). Households with a single adult male are therefore much less

> 13 This is indeed borne out in several empirical studies. For example, Winters et. al. (2001) show that the probability of migration to the United States is higher for households living in Mexican communities which have greater experience with migration. Banerjee (1991) and Caces(1986) have shown the importance of networks in the rural-urban migration decision in the Indian and Philippine context respectively. Ilahi and Jafarey (1999), have shown the importance of extended family networks in fi nancing migration costs in Pakistan.

> 14 In the census,14% of households report a current migrant. This is signi fi cantly below the migration incidence we get from the household survey since the latter is not restricted to current migrants. It is worth noting that the number of households in the sample with a current migrant is just above 13%, as we would expect from the census.

12