rafmacalaba/data-use-mentions-extended huggingface dataset:
# **7. Conclusions and Suggestions for Future Work**

The last few years have seen rapid growth in the number of randomized trials evaluating business training programs, providing a growing body of evidence in an area with large policy interest. However, to date a number of challenges have hampered how much we can learn from these studies, with methodological concerns and heterogeneity in both training content, and in the characteristics of who is trained, making comparisons across studies difficult. Many of the key questions needed to justify largescale policy interventions in this area remain unanswered. Researchers have learned more in the interim about how to do firm experiments better, suggesting these difficulties are not insurmountable.

In order to learn more from the next generation of studies, we believe the following elements are needed.

1. **Much larger samples or more homogeneous firms** . Rather than more studies with 100 to 500 individuals in each treatment or control group, we ideally need studies to move to samples of several thousand or more. This would increase the power of studies, and allow more consideration of what types of people training works for best. An alternative to large crosssectional samples is to reduce the heterogeneity of the sample by focusing on firms within one industry and size category, and collecting much more frequent time series data on these firms (McKenzie, 2011, 2012).

2. **Better measurement of outcomes** . Measuring firm profits and revenues has proved a real challenge for many studies, and even less evidence is available on how exactly training changes the firm’s production process. Further efforts to improve the measurement of financial information (and to ensure that there is not just a measurement effect of training) are needed, while focus on a specific industry or sector may allow more detailed production level monitoring of physical outputs and inputs.

3. **Designing experiments to measure spillovers** . This could include more use of GPS measurement to measure local spillovers (Gibson and McKenzie, 2007), and randomizing
Bank's Approach to Poverty Reduction. _AEA Papers and Proceedings_ 87(2): 32-37. The World Bank.

Blanchflower, David G., Oswald, Andrew J. 1998, What Makes an Entrepreneur? _Journal of Labor Economics_ 16(1), 26-60 Buainain Ant6nio Marcio, Jose Maria da Silveira, Hildo Meireles Souza, and Marcelo M. Magalhaes, Communitybased land reform implementation in Brazil: A new way of reaching out to the marginalized? Paper presented at the GDN Launch Workshop Bonn, Dec 5-8, 1999 (www.gdnet.org).

Carter, Michael and Julian May 1999, Getting Ahead or Falling Behind: The Dynamics of Poverty in Post-Apartheid South Africa, University of Wisconsin, mimeo Case, Anne and Angus Deaton 1996, Large Cash Transfers to the Elderly in South Africa, NBER Working Paper No. W5572, Cambrige MA Cousins, Ben 1995, Common Property Institutions and Land Reform in South Africa, _Development Southern Africa_ 12 (4), pp. 481-507 Cousins, Ben 2000, Does land and agrarian reform have a future and, if so, who will benefit? In: B.Cousins (Ed.), At the Crossroads: Land and Agrarian Reform in South Africa into the 21 **'t** century, Cape Town Deininger, Klaus, and Lyn Squire. 1998. "New Ways of Looking at Old Issues: Inequality and Growth." Journal of Development Economics 57 (2), 257-285.

Deininger, Klaus, Francisco Lara, Miet Maertens, and Agnes Quisumbing 1999, Agrarian Reform in the Philippines: Past Impact and Future Challenges'. Paper presented at the GDN Launch Workshop Bonn, Dec 5-8, 1999 (www.gdnet.org).

Development Bank of South Africa, 1998: _Development Report, 1998: Infrastructure, A Foundation for Development,_ Development Bank of Southern Africa, Midrand.

Duflo, Ester 1999, Child Health and Household Resources in South Africa: Evidence from the Old Age Pension Program, MIT, Cambridge,
level, with the latter managed by Ministries of Education. Many of the national training systems employ a significant degree of decentralization in order to strengthen training center responsiveness to local industry needs. Decentralization strategies, however, have encountered implementation difficulties where adequate preparation of decentralized units has not been undertaken.

xxii. Such systems may be under development in current projects in a limited number of countries in Sub-Saharan Africa. However, most of the early investments in these countries have been small projects supporting a few vocational schools; in later years these have supported nonformal training centers. Sustained investment in systems development has so far been comparatively rare.

# Performance

xxiii. Data on economic outcomes of training are absent from project evaluations. Reliable unit recurrent cost data is almost entirely lacking, although the data that are available confirm that secondary vocational schools are more expensive than general secondary schools. Thus assessment of investment performance is largely limited to criteria of implementation success.

xxiv. The exception is found for the investments in Asia. There information on graduation rates, placement rates and employer satisfaction was available for a sufficient proportion of the components to justify inferences. On all three criteria, the institutions supported in these components performed very well. These included all modes of training.

-xv. On the criterion of proportion of enrollment targets achieved at completion, performance varied directly with the income level of the country. About 40% of targets were achieved in low income countries; 90% in lower-middle income countries, and 109% in upper-middle income countries. Overall, there was little varlition across modes. However, secondary vocational schools performed best overall, and slightly better across income levels than the more complex nonformal institutions. The data indicate that it is difficult to establish any institutional mode in low income countries (including secondary vocational schools), with weak implementation capacity and financial constraints. The opposite is true for middle income countries.

xxvi. This criterion, of course, does not capture the eventual enrollment performance of project institutions, many which suffered from significant delays
# **1. Introduction**

The global food and fuel price spike in 2007-08 motivated many researchers to examine the welfare effects of these aggregate shocks on households in various developing countries. These price spikes were found to have a significant net negative impact on food security and poverty, with studies estimating that between 141.2 million and 155.6 million people could have fallen under the $1.25 per day poverty line due to the price increase (de Hoyos & Medvedev, 2011; Ivanic & Martin, 2014). However, there is widespread heterogeneity in the results of food price welfare incidence analysis, with the effects varying with location (both across and within countries), the temporal scale of the analysis, price levels as well as their volatility, and the degree of price transmission to local markets (Headey & Martin, 2016). Given this heterogeneity, the appropriate policy responses to these price rises are not always clear.

While global agricultural prices have been relatively low, especially after 2011, many individual countries have continued to experience sudden rises in prices. A case in point is Mozambique, which saw steep food price inflation during 2016-17, going up as high as 40%. With a higher risk of natural disasters, a large share of the population dependent on agriculture and unequal progress in development across regions, the likelihood of negative welfare impacts of food price inflation in Mozambique cannot be ignored. The context specific nature of the impacts of price hikes and their policy responses necessitates a deeper understanding of the welfare incidence of the price spikes in Mozambique to better target the segments of the population who are likely to require support.

This paper performs food price incidence analysis in Mozambique, combining data from household and farmer surveys on consumption and agricultural activities collected from Mozambique in 2014 and 2015. We exploit the spatial location information of households across two different data sets to match household consumption data with production data from the set of nearest neighbors. In addition, we also incorporate disaggregated, market-level price data on
distance from the attacks. The difference between exposed and control households is the timing of their interview in relation to when an attack occurred. However, fieldwork of Wave 2 is likely to have followed a geographical pattern or strategy when conducting interviews, due to logistical considerations. Thus, exposed and control groups and unlikely to be random but determined by the data collection schedule, which implies that households interviewed before and after the incidents are likely to be different. Yet, we find that they do not differ much in terms of observable characteristics. Exposed and control groups are relatively comparable or balanced on key observable dimensions (Table 14 in the Appendix).

Table 6: OLS estimates for the effect of terrorist attacks against civilians considering Wave 2 households from Mogadishu interviewed before and after the incidents

|Log of core|consumption (<br>|per capita defla<br>|ted)<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Exposed/control|-0.215*<br>(0.107)|-0.458***<br>(0.117)|-0.458***<br>(0.142)|-0.458***<br>(0.124)|
|District fixed effects|Yes|Yes|Yes|Yes|
|Characteristics of household & head|Yes|Yes|Yes|Yes|
|Dwelling characteristics|Yes|Yes|Yes|Yes|
|Standard errors|Sampling<br>weights|Clustered<br>by district|Clustered<br>by PSU|HAC|
|Observations|180|180|180|180|

Source: Authors’ calculations based on the SHFS and ACLED. Note: Estimated coefficients from an OLS regression. Characteristics of household refer to size and
>3 3<br>2.5 2.5<br>2 2<br>1.5 1.5<br>1 1<br>0.5<br>0.5<br>0<br>0 1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent India percent India<br>7 25<br>6<br>20<br>5<br>4 15<br>3<br>10<br>2<br>5<br>1<br>0 0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>Stock: imports subject to any imposed TTB in effect All trading partners' exports under any TTB in effect<br>Stock: imports subject to AD only in effect China's exports under any TTB in effect<br>Flow: imports subject to any newly initiated TTB investigation Other emerging economies' (non-China) exports under any TTB in effect<br>Flow: imports subject to newly initiated AD investigation only High income countries' exports under any TTB in effect<br><!-- End of picture text --> Notes: Shares of nonoil imports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

19
Policy Research Working Paper 9238

# **Abstract**

In many regions of the world, the persistent, and growing, proportion of young people who are currently not in employment, education, or training is of global concern. This is no less true of Morocco: about 30 percent of the Moroccan population between ages 15 and 24 are currently not in employment, education, or training. Drawing from various rounds of Moroccan labor force surveys, this paper contributes to understanding the complex dynamics of labor markets in developing countries. First, it identifies the socioeconomic determinants of Morocco’s young population not in employment, education, or training. Second, employing a synthetic panel methodology in the context of labor market analysis, the paper describes how the conditions of individuals in this group has changed over time. One striking, and worrisome, pattern that emerges from the 2010 synthetic panel data is that, even after 10 years, a majority of the young population not in employment, education, or training remained outside the labor market or education, with very little chance of moving out of their situation. Their chronic stagnancy confirms the powerful effect that initial conditions have on determining young people’s future outcomes.

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 vmolini@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
. In some cases, the geographic representation is at the level of “urban”, or “rural”. In these cases, subnational areas in the household survey are aggregated to levels that can be appropriately represented by boundaries. Several sources of geospatial files were leveraged to construct the GSAP: GADM, GAUL, NUTS, and customized spatial files. The choice of spatial files is based on more disaggregated availability and geographic alignment with household surveys. For example, NUTS spatial files are used prominently for the European countries in GSAP, since these files are developed and regulated by the EU.

Building on the cross-sectional GSAP database, we construct a new database on poverty statistics based on almost 500 available household income/expenditure survey data in the Global Monitoring Database (GMD)<sup>13</sup> for 139 economies, with more than 90 percent of the survey data ranging from 2010 to 2019. This database consists of panel data that are representative at 1,650 subnational areas. The number of countries across regions and over time are presented in Figures B1 and B2.

As both country boundary and survey representativeness can change over time, constructing a panel data of poverty at area-level is not a simple task. When there is a change

> 13 The Global Monitoring Database (GMD) is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country, and then compiled, processed, and harmonized. The process is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice. Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) also contributed historical data from before 1990, and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared
# **Table 7. Robustness: Information Complexity and Contractual Completeness**

||L|og of Export Value p|er|||
|---|---|---|---|---|---|
||Logof # of Exporters|Exporter|Exporter EntryRate|Exporter Exit Rate|Exporter Turnover Rate|
||(1)|(2)|(3)|(4)|(5)|
|||_Panel A: IV_|_Estimates for the Baseline S_|_pecification_||
|Foreign Bank*ExtFin|-2.258***|-0.131|0.184**|0.196**|0.295*|
||(0.716)|(0.869)|(0.084)|(0.095)|(0.150)|
|_First-stage_||||||
|Entry barriers (t-5)*ExtFin|0.103***|0.100***|0.096***|0.097***|0.096***|
||(0.025)|(0.025)|(0.025)|(0.025)|(0.025)|
|Kleibergen-Paap F stat|17.05|16.39|14.33|14.52|14.47|
|Cragg-Donald F stat|757.27|705.64|593.56|607.68|602.14|
||_Pa_|_nel B: Alternative M_|_easure of Country-Level Cre_|_dit Information Coverage_||
|Foreign Bank*ExtFin|-0.060|-0.667***|0.021|0.033*|0.064**|
||(0.110)|(0.188)|(
baseline, the women who self-select into our sample are poorer than the average rural Ugandan household.<sup>5</sup> Upon graduating from the program, treated women are 16pp more likely to have an active business than women in the control group. They own 0.22 more businesses, on average, at endline. The program is also effective at promoting productive entrepreneurship: treated women are 10pp more likely to generate positive profits (off a control mean of 59%) and they are 8pp more likely to have positive profits in additional businesses (off a control mean of 16%). Although individual effects on profit levels in both the main business and all other businesses are noisy, our index of intensive-margin business performance shows positive, significant effects at both midline and endline. Importantly, our effects represent gains in women’s employment, not simply switching between salaried and self-employment: treated women are 8pp more likely to be employed at midline (over a control mean of 72% of women being either employed or self-employed).

We exploit the unanticipated market shock of the first COVID-19 lockdown in Uganda to test for women’s ability to cope with negative economic shocks. High-frequency SMS data shows that the program is highly effective at enabling women to navigate the first COVID lockdown. Treated women experience no decrease in revenues relative to the period immediately preceding the lockdown. By contrast, the control group only recovers to pre-lockdown revenue levels a month before the lockdown was fully lifted. We interpret this as promising evidence that skills-based programs can increase women’s economic resilience.

We examine dynamic treatment effects on savings, investments, and household consumption spending to understand how women allocate their earnings. We find no significant effects on savings or investments at midline, right after program completion. However, treated women invest 97% more in other businesses at endline and our index of savings and investment outcomes shows positive, significant effects. Together, results on savings and investments indi-

> 5Half of the sampled women have an active
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<mark>statistical offices in each country, and then compiled, processed, and harmonized.</mark><sup>13</sup> <mark>The data are nationally representative, and hence represent the demographics of the entire country. Since our survey targeted individuals and not households, we use the individual surveys from GMD for comparison. While the GMD survey years do not match our Internet survey years in all countries, they are not older than the online surveys by more than a few years. Since demographic characteristics, such as age and gender are unlikely to vary within a few years, the GMD is a valid comparison.</mark><sup>14</sup> <mark>Specifically, we used the GMD survey from Brazil in 2019, Egypt in 2015, Indonesia in 2018, Kenya in 2015, Sri Lanka in 2016, and Türkiye in 2018.</mark><sup>15</sup> <mark>The details are presented in panel B in table 3.1.</mark> <mark>Third, we compare labor market indicators from the online survey with probabilistic samplebased surveys through the pandemic depending on their availability. In Brazil, we use the National Household Sample Survey – PNAD, a nationally representative survey of 193,000 households per month, conducted during the pandemic in December 2020 and May/June 2021. In Indonesia, we use the labor force surveys conducted in August 2020 and February 2021, each comprising 793,542 and 203,464 households respectively. In Kenya, we use the Kenya Continuous Household Survey comprising 11,997 households conducted in the last quarter of 2020 (October to December 2020). We use labor force indicators from the fourth quarter of 2020’s Quarterly Report of the Sri Lanka Labor Force Survey covering up to 25,750 households, and from the Türkiye Household labor force survey covering 58,560 households for each quarter.</mark><sup>16</
338)|61 (0.366)|66 (0.296)|143 (0.028)|45 (-0.070)|
|b. Decomposition:|Sector-Occu|pation-Formal|Status|||
|Total Variance|-|100 (0.604)|100 (0.450)|-|100 (-0.154)|
|Variance in sector-occupation-formal status|-|25 (0.153)|22 (0.099)|-|35 (-0.054)|
|Variance in skill-gropus|-|10 (0.060)|8 (0.038)|-|15 (-0.022)|
|2*Cov(sector-occupation-formal status, skill-groups)|-|9 (0.055)|10 (0.042)|-|8 (-0.013)|
|Residual|-|56 (0.337)|55 (0.271)|-|42 (-0.065)|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng).

_Note:_ Table above is based on available information from Argentina, Bolivia, Brazil, Costa Rica, Mexico, Nicaragua, Peru and El Salvador. Total wage variance is the variance of the log hourly wage in the household surveys data set. Variance in skill-groups is the variance of the log real hourly wage that is explained by a full set of gender, education, and experience dummies, and their interaction. The variance in sector-occupation is the variance of the log real hourly wage that is explained by a full set of sector-occupation dummies. Similarly, the variance in sector-occupation-formal status is the variance of the log real hourly wage that is
of taxes and government expenditure items - comparable to other countries- capturing a comprehensive view of the fiscal system.** This analysis covers 60% of the total expenditure and 69% of the total revenues for Bulgaria in 2020. It uses a CEQ scenario where pensions are treated as a deferred income rather than a direct government transfer (See Lustig, 2023).<sup>15</sup> This analysis includes the following fiscal interventions: direct taxes (personal income tax), contributions to social security, social protection transfers, subsidies, indirect taxes, and in-kind transfers (health and education). The analysis used the 2021 EU-SILC for the main income components and the 2021 HBS, which provides expenditure details of the goods subject to indirect taxes and consumption.

# **This analysis encompasses implicit subsidies associated with electricity and gas consumption, as in**

**Vaughan and Cabrera 2022.** First, we estimate the electricity and gas expenditure per household from the 2021 Household Budget Survey (HBS). Then, the current residential tariff structure, including all associated taxes, is employed to underpin household kilowatt-hour consumption. Subsequently, to quantify the direct impacts of the electricity subsidy, the subsidy is estimated as the product of the consumed kilowatt-hours multiplied by the difference between the price and the production cost per unit. The estimates of the production cost stem from the International Monetary Fund's (IMF) Fuel Subsidies

> 14 The 2021 Survey on Income and Living Conditions (SILC) used 2020 as the reference year. However, the 2020 Household Budget Survey was not available from the National Statistics Office. Therefore, the 2021 Household Budget Survey was used to approximate spending in 2020.

> 15 In the CEQs, there are two contrasting scenarios: 1. Pensions as Deferred Income (PDI) - This is the baseline scenario, where pensions are considered part of pre-fiscal income, and contributions are seen as individual savings; and 2) Pensions as Government Transfer (PGT) - In this scenario,