rafmacalaba/datause-encoder-data huggingface dataset:
-63-

response to regionally unbalanced distribution of benefits. To the extent that extreme-poverty, as seen in section IV, is mostly a rural phenomenon, a strong case can be made to, without ignoring the urban extremely-poor, give preference to rural areas with the highest Pj(l,y) indices in locating PASSPA<sup>109</sup> . This case is strengthened to the extent that transportation costs associated with remoteness could preclude some of the rural extremely-poor from benefiting from PASSPA; in relative terms transportation costs are lower 11 for the urban extremely-poor o.

Location of PASSPA together with the type of benefits provided would be part of the targeting procedure. But to help insure that benefits reach extremely-poor children, the possibility of making eligibility partly a function of compliance deserves consideration: delivery of benefits to adults (say, the food coupons) could be made contingent on their bringing their children periodically for innoculations and other type of medical attention.

1<sup>09</sup> Ravallion and Chao (1989) develop a numerical algorithm to apply the Besley-Kanbur rule under the additional constraint that policy makers cannot identify within each region who is poor and who is not, so that all individuals within a given region must receive the same transfer. They point out that: "In many LDCs one finds that the incidence of poverty, P(O,z), is higher in rural than urban areas. However, the relevant shadow price for minimizing P(2,z) is directly proportional to P(l,z) rather than P(O,z). Thus a low (although positive) incidence of urban poverty does not mean that the poverty minimizing allocation of aid between urban and rural areas will give a higher per capita transfer to the latter. Similarly, although the incidence of poverty may be many times greater in rural areas, it does not follow that the poverty alleviation budget should ignore urban areas. The average depth of poverty may be greater in urban areas, even if the incidence is lower" (op. cit., p. 218; emphasis in the original). Nevertheless, in their numerical applications for Bangladesh, Sri Lanka, Phillipines and Indonesia they find that "All of the results support the case for targeting policies towards rural sectors" (ibid, p. 223). Similar computations for Mexico are yet to be performed (and are high on the research agenda), but the results of section IV, together with the need to reduce migration incentives to the urban areas support the conclusion of the text.

1100f course, a proper implementation of the Besley-Kanbur technique requires finer disaggregation in the computation of P (.) than the one presented in section IV. It is probably the case tha' even computations at the municipal level are insufficient to solve the locational problem. Thus, before this technique is implemented, there is a need for improvements in sampling design of th3 income-expenditure surveys, particularly in terms of coverage. The possibility of using the population census for this purpose could also be explored.
The analysis conducted also provides some concluding observations on methodology. First, in the absence of random placement of the intervention, definitive conclusions on impacts are much harder to reach. It is perhaps understandable that, in a program such as IKP, a full randomization may not have been possible given the intention to expand the program to all the rural poor. In the future, whenever program administrators are interested in a rigorous evaluation of impacts, they may want to consider an expansion design such that randomization may be effectively achieved and exploited to learn valuable lessons. This may be feasible particularly where resource constraints dictate phasing, which could be randomized.

Second, it may be worthwhile accompanying quantitative studies with careful participatory work throughout program implementation, as was done for some time in 2005-06. This would provide not only additional information on mechanisms and behaviors, but also a check on quantitative results, which are by nature sensitive to assumptions and methodological choices. Program authorities may want to consider a wider application of mixed methods in future studies.

Finally, the results presented here refer to 2008 and therefore do not capture the possible impacts of a number of interventions implemented later, that aimed at supporting productive activities— notably Community-Managed Sustainable Agriculture—and access to services, including nutrition centers. It may be worth considering a follow-up survey to assess the impacts of these later interventions.

25
consumption bundles as a result of depreciation-induced price changes. The second captures changes in household behavior in response to these price changes. A modest methodological contribution of this paper is to show how these substitution effects can be estimated easily given the (pseudo-) panel dimension of the data that I have for Egypt. I find that most of the compensating variation is captured by the direct effect, which averages 7.4 percent of initial expenditure, and is statistically significantly (although quantitatively modestly) higher in poorer households. I find that there is a great deal of heterogeneity across households in the estimated size of the welfare effect of the depreciation. Most of this heterogeneity is due to differences in consumption patterns across households. A policy implication of this heterogeneity is that it would be difficult to accurately target any kind of subsidy program to offset the effects of the depreciation.

Three major qualifications regarding these results should be kept in mind. First, a significant limitation of this paper is that I am only able to study the welfare effects of depreciation-induced changes in consumer prices. The depreciation is likely to have had heterogenous effects on the incomes of different households as well. With imperfect labour mobility, for example, it is plausible that households employed in exporting sectors would have seen increases in earnings, while household employed in import-competing industries would have seen declines in earnings, as a result of the depreciation. Unfortunately, however, the Egyptian household survey data that I use provide only very limited information on the economic sector of employment, and so I cannot investigate these kind of effects, and their distributional consequences, in any detail.<sup>3</sup> A second limitation is the fairly coarse level of aggregation at which I am able to estimate the exchange-rate induced component of price changes. As discussed further below, by working at this coarse level of aggregation, I am likely to be underestimating the scope that households have for adjusting their expenditure patterns in response to price changes. This in turn means that I am likely to be overestimating the adverse

> 3 See Ravallion and Chen (2004) for an effort to look at the effects of relative price changes on household consumptions and incomes, in the case of China, and Ferreira et. al. (2004) for the case of Brazil.

2
Table A3: **News sentiment and migrants’ movement: Other groups of migrants**

||Sha<br>|re of migrants who sta<br>|y in FMP<br>|
|---|---|---|---|
||Less than 2 weeks|Less than 3 months|Between 3 and 6 months|
|Tone of the news at destination|0.0099<br>(0.0119)|0.0262<br>(0.0216)|0.0312<br>(0.0237)|
|Other controls|Yes|Yes|Yes|
|FMP FEs|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|
|Num. Obs.|1,568|1,568|1,568|

_Notes:_ OLS regression results. Standard errors clustered at the FMP level in brackets. Variables are defined in Tables A1 and A2. _Other Controls_ includes thew same controls as in Table A11 column 3, namely: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ .. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Table A4: **News in Europe and preference for returning home**

||Returnee cou<br>|ntries among preferred destinations<br>between m and m+1<br><br>|
|---|---|---|
||Change (1 =|Yes)<br>Diference|
|Tone of the news at destination|0.0641<br>(0.0639)|0.2285<br>(0.1485)|
|Country of destination fxed efects|Yes|Yes|
|Year-month fxed efects|Yes|Yes|
|Number of Observations|1,165|1,165|

_Notes:_ OLS regressions results. Standard errors clustered at the FMP level in brackets. In column 1, the dependent variable is a dummy which takes value one if there is a difference in the number of returnee countries indicated among the preferred destinations in FMP _i_ between month _m_ and _m −_ 1, and zero otherwise. In column 2, the dependent variable is equal to the difference in the number of returnee countries indicated among the preferred destinations in FMP _i_ between month _m_ and _m −_ 1.The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ . *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

34
# **The value of vocational education: High school type and labor market outcomes in Indonesia**

**Daniel Suryadarma**

**David Newhouse Daniel Suryadarma** World Bank Research School of Social Sciences Washington, DC Australian National University <u>dnewhouse@worldbank.org daniel.suryadarma@anu.edu.au</u> ~ <u>http://econrsss.anu.edu.au/ dcjs</u>

Keywords: Vocational education; Indonesia; labor market effects. JEL Classifications: I21, J24, O15.

> * The findings, interpretations , and conclusions expressed here are personal and should not be attributed to the World Bank, its management, its Executive Board of Directors, or any of its member  countries. This study is a background paper for the Indonesia Jobs Report. We thank Vivi Alatas and Andrew Leigh for their support and encouragement, and Dandan Chen, Wendy Cunningham, Eric Edmonds, Edgar Janz, Daan Pattinasarany, and seminar participants at World Bank Office Jakarta and the Institute for the Study of Labor for helpful comments and suggestions. All remaining errors are our own.
# **7.2 Poor versus rich households**

**<u>Table 7.2A Employment in manufacturing and services by income</u>**

||Poo|r|Ric|h|
|---|---|---|---|---|
||manufacturing|services|manufacturing|services|
||(1)|(2)|(5)|(6)|
|**Panel A: Change in“ratio of number of em**|<br>**ployed members to**|<br>**number of memb**|<br>**ers in working ages”**||
|Change in provincial tariffs|-1.663<sup>***</sup>|-1.042<sup>***</sup>|-0.999<sup>***</sup>|-0.421|
||(0.302)|(0.328)|(0.119)|(0.293)|
|Share of manufacturing workers-1|-0.464<sup>***</sup>||-0.398<sup>***</sup>||
||(0.025)||(0.016)||
|Change in share of provincial|0.030||0.011||
|manufacturing workers between 2002 and<br>1998|||||
||(0.108)||(0.110)||
|Share of service workers-1||-0.415<sup>***</sup>||-0.353<sup>***</sup>|
|||(0.016)||(0.009)|
|Change in share of provincial service<br>workers between 2002 and 1998||0.006<br>(0.047)||0.011<br>(0.042)|
|Constant|-0.132<sup>***</sup>|<br>-0.091<sup>***</sup>|-0.062<sup>***</sup>|<br>-0.038|
||(0.026)|(0.028)|(0.014)|(0.029)|
|_N_|9187|9187|9770|9770|
|adj_R_<sup>2</sup>|0170|0181|0200|0180|
|.<br>**Panel B: Changes in working hours as shar**|.<br>**es of total potential**|.<br>**working hours by**|.<br>**members in working**|.<br>**ages**|
|Change in provincial tariffs|-1.880<sup>***</sup>|-0.985<sup>*</sup>|-1.211<sup>***</sup>|-0.842<sup>*</sup>|
||(0.328)|(0.566)|(0.136)|(0.443)|
|Share of hours of manufacturing workers-1|-0.476<sup>***</sup>||-0.416<sup>***</sup>||
||(0.030)||(0.017)||
|Change in share of provincial|<br>0.013||<br>0.031||
|manufacturing workers between 2002 and|||||
|<br>1998|(0.091)||(0.091)||
|Share of hours of service workers-1||-0.356<sup>***</sup>||-0.360<sup>***</sup>|
|||(0.021)||(0.012)|
|Change in share of provincial service<br>workers between 2002 and 1998||<br>0.017||<br>-0.008|
|||(0.050)||(0.050)|
|Constant|-0.166<sup>***</sup>|<br>-0.087<sup>*</sup>|-0.083<sup>***</sup>|<br>-0.086<sup>**</sup>|
||(0.029)|(0.046)|(0.016)|(0.040)|
|_N_|9187|9187|9770|9770|
|adj._R_<sup>2</sup>|0.171|0.135|0.220|0.178|
|Notes: - Standard errors in parentheses.<sup>*</sup> _p_<|0.10,<sup>**</sup> _p_< 0.05,<sup>***</sup>|_p_< 0.01|||

- Control variables are Household head (HH)’s ethnicity, HH age, HH gender, HH vocational education dummy, HH high school dummy, HH middle school dummy, HH primary school dummy, spouse vocational education dummy, spouse high school dummy, spouse middle school dummy, spouse primary school dummy, household size, and number of working age members. All of these household characteristics are reported in 2002. Standard errors are clustered at province level.

32
# <u>Table 3. Logistic regression of project playgroup sustainability on finance characteristics</u>

||<br>(1)<br>Playgroup is open in<br>2016 (1=Yes)|<br>(2)<br>Playgroup is open in 2016 with<br>two trained teachers (1=Yes)|
|---|---|---|
|**Finance**(units: 100 million IDR)|||
|Amount from government in 2009-2013|0.28|0.03|
||(0.28)|(0.06)|
|Amount from non-government in 2009-2013|0.70|0.08|
||(0.48)|(0.92)|
|Amount from parents in 2009-2013|0.33|0.04|
||(0.35)|(0.14)|
|Proportion of annual expenditure on teacher|0.48*|-0.18|
|salaries in 2010|(0.28)|(0.52)|
|Change in proportion of annual expenditure<br>on teacher salaries from 2010-2013|0.40*<br>(0.21)|0.73*<br>(0.38)|
|Log-likelihood|-62.63|-154.29|
|Observations|245|245|

* p<0.10,  ** p<0.05,  *** p<0.01

Note: Robust standard errors clustered by center. Coefficients are marginal effects.

Figure 2 shows the relationship between the playgroup remaining open and the change in proportion of annual expenditure on teacher salaries. The predicted probability that a playgroup remained open and open with two trained teachers are presented as solid and dashed lines, respectively. The predicted probability of a playgroup remaining open and open with two trained teachers (dashed line) is 90% and 32%, respectively for a center that made no change in teacher expenditures during the project period. Playgroups that increased their annual expenditure on teacher salaries by 50% between 2010 and 2013 is estimated to have a 98% probability of remaining open and a 70% chance of being open with two trained teachers in 2016.<sup>18</sup> These results suggest the importance of financial commitment to teachers for project sustainability. It is noteworthy that the amounts of funding from various sources are positively associated with sustainability – yet these estimates are imprecise and indistinguishable from zero (controlling for other variables in the model).

> 18 See appendix figure 8 for further details on how playgroups chose to pay teacher salaries.

14
**Figure 1.** Trends in the categories of body mass index in adult persons between 20 to 24 years of age by gender. Mexico 2000, 2006 and 2012.

<!-- Start of picture text --> Females Males<br>100% 100%<br>0.08<br>90% 0.14 0.14 0.22 90% 0.14 0.17<br>80% 80%<br>0.29<br>70% 0.29 0.32 70% 0.3 0.27<br>60% 0.32 60%<br>50% 50%<br>40% 40%<br>30% 0.57 0.53 30% 0.63 0.56 0.56<br>0.46<br>20% 20%<br>10% 10%<br>0% 0%<br>2000 2006 2012 2000 2006 2012<br>Normal Sobrepeso Obesidad Normal Sobrepeso Obesidad<br><!-- End of picture text --> <!-- Start of picture text --> Normal Sobrepeso Obesidad<br><!-- End of picture text --> <!-- Start of picture text --> Normal Sobrepeso Obesidad<br><!-- End of picture text --> Source: Authors’ estimates based on data from the National Health Survey 2000 (ENSA 2000), National Health and Nutrition Survey 2006 (ENSANUT 2006) and the National Health and Nutrition Survey 2012 (ENSANUT, 2012).

When performing the same analysis on the population group relevant to PROSPERA, the lowest income quintile, the increase in the prevalence of obesity was even more pronounced. As shown in Figure 2, between 2000 and 2012, the percentage obese of those in the lowest income quantile increased from 8% to 17% for females and 3% to 19% for males, which corresponds to a 113% and 533% increase in obesity, respectively.

It is important to highlight that while the prevalence of overweight and obesity has increased in recent years, the population prevalence of these conditions in Mexico was already high in 2000. In other words, the growing trend of overweight and obesity precedes PROSPERA and, in this sense, is part of the context in which the program was developed and is part of the ongoing epidemiological transition in Mexico.

6
15 through coefficient smaller than 0.8 in times of rising prices is likely to suggest attempts to keep domestic prices artificially low, or else domestic prices were very high initially and price stability was maintained against volatile world prices (as seems to be the case with LPG in Japan).

The starting point for computing pass-through coefficients is collection of retail prices. Retail prices of gasoline, diesel, kerosene, and LPG were collected in January 2009, January 2012, July 2012, and January 2013. Figure 5 shows the distribution of the prices in January 2013. The prices in U.S. dollars are given in Table B.2 in appendix B. The prices of gasoline across the countries varied by a factor of 190, diesel by a factor of 250, kerosene by a factor of 10, and LPG by a factor of 74. The large differences between the minimum and maximum prices are because of exceptionally low prices of gasoline and diesel in República Bolivariana de Venezuela, and very low LPG prices in the Arab Republic of Egypt and República Bolivariana de Venezuela. Without these countries, the price differences are reduced to a factor of about 10. Of the four months for which retail prices were collected, these variations were largest in January 2013. When distribution curves from the three months in 2012 and 2013 are overlaid, they match between the 20<sup>th</sup> and 95<sup>th</sup> percentiles for gasoline, 25<sup>th</sup> −95<sup>th</sup> for diesel, virtually the entire range for kerosene, and 0−45<sup>th</sup> for LPG. World prices in U.S. dollars were comparable in the three designated months for gasoline, diesel, and kerosene, but the price of LPG fell by one-third in the Americas between January 2012 and January 2013.

Figure 5: Distribution of Fuel Retail Prices in January 2013 in U.S. Dollars <!-- Start of picture text --> 4.50<br>4.00 Gasoline, $/liter<br>3.50 Diesel, $/liter<br>3.00 Kerosene, $/liter<br>LPG, $/kg<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>Percentile<br>$/liter or kg<br>0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100<br><!-- End of picture text --> _Sources_ : See Table A1.2 in Kojima 2012.

Table 3 provides median prices by income, oil trade status, and region. For gasoline and diesel, high-income OECD countries had the highest median prices on account of high fuel taxes and provision of no price subsidies. At the opposite end of the spectrum, lower-middle-income countries had the lowest median prices. As seen in all earlier publications, net oil exporters had much lower prices than net oil importers. Median gasoline prices were comparable across the regions, with the exception of the Middle East and North Africa, a region dominated by net oil exporters. The median prices of other fuels showed greater variation across the regions.
Policy Research Working Paper 5980

# **Abstract**

The ability of consumers to make informed financial decisions improves their chances of having sound personal finance. This paper uses a panel dataset from Russia, where consumer loans grew at an astounding rate—from about US$10 billion in 2003 to over US$170 billion in 2008—to examine the importance of financial literacy and its relationship with behavior. The survey asked questions on financial literacy, consumer borrowing (formal and informal), and spending behavior. The paper studies the consequences of greater financial literacy on the use of financial products and financial planning. Even though consumer borrowing rose rapidly in Russia, only 41 percent of the survey respondents understood how interest compounding worked and only 46 percent could answer a simple question about inflation. Financial literacy is positively related to participation in financial markets and negatively related to the use of informal sources of borrowing. Individuals with higher rates of financial literacy are significantly more likely to report having more unspent income at the end of the month and higher spending capacity. The relationship between financial literacy and the availability of unspent income is more evident during the financial crisis, suggesting that better financial literacy may better equip individuals to deal with macroeconomic shocks.

This paper is a product of the Finance and Private Sector Development 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 author may be contacted at lklapper@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