rafmacalaba/data-use-mentions-extended huggingface dataset:
|**List of Acr**<br>**Spanish Acronym**|**onyms and Glossary**<br>**Spanish Name**|**English Name**|***English Acronym**<br>**(if used differently**<br>**than Spanish)**|
|---|---|---|---|
|ARV<br>|Anti-retroviral<br>|Antirretroviral<br>||
|ASAP<br>|Grupo de Trabajo del Banco Mundial<br>sobre Estrategia y Plan de Acción en SIDA<br>|World Bank Working Team on AIDS<br>Strategy & Action Plan<br>||
|CONASIDA<br>COPRECOS|Consejo Nacional de SIDA<br>Comité de Prevención y Control del|AIDS National Council<br>Armed Forces and National Police||
||<br>VIH/SIDA de las Fuerzas Armadas y<br>Policía Nacional|HIV/AIDS Prevention and Control<br>Committee||
|CRES|Comisión de Regulación en Salud|Health Regulation Commission||
|DTS|Direcciones Territoriales de Salud|Territorial Health Entities||
|ENDS|Encuesta Nacional de Demografía y<br>Salud|Demography and Health National Survey||
|EPS|Empresa Promotora de Salud|Health Promoting Company||
|ESE|Empresa Social del Estado|Social State Enterprises||
|ETS|Enfermedades de Transmisión Sexual|Sexually Transmitted Diseases|STD|
|FGSTM|Fondo Global de lucha contra el SIDA, la<br>Tuberculosis y la Malaria|The Global Fund to Fight AIDS,<br>Tuberculosis and Malaria||
|FOSYGA|Fondo de Solidaridad y Garantía|Solidarity and Guarantee Fund||
|HSH|<br>Hombres que tienen sexo con hombres|<br>Men who have sex with men|MSM|
|ICBF|Instituto Colombiano de Bienestar
shock. These are mostly the oil importers (Jordan and Lebanon) and the region’s developing oil exporters (Yemen, Iraq and Syria). In contrast, the Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) have ample budgetary resources to absorb higher costs, even though they are entirely dependent on the international markets for their wheat. Moreover, in these countries rising food import bills are frequently offset by rising export revenue, because of the positive correlation between energy and food prices. Egypt and Morocco do not have the luxury of ample fiscal space to absorb sustained higher prices, although their higher domestic production levels help cushion price shocks.

Despite the preponderance of consumer subsidies, evidence suggests that international food price shocks are nonetheless transmitted to various degrees to domestic food prices throughout the region (World Bank, 2011c). Over a five year period ending in February 2011, domestic food prices rose, on average, by more than 10 percent annually in Egypt, Iran, and Yemen, and by nearly 5 percent or more in Djibouti, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia and the United Arab Emirates.<sup>4</sup> Rising world food prices have been a major factor behind increases in domestic food prices, typically explaining some 20 to 30 percent of the

> 4 Average annual food price rates of increase are based on 60 months of data, with the following exceptions: 58 months of data was used for Lebanon, and the West Bank and Gaza; and 57 months were used for Yemen.

4
shown to be negatively associated with schooling in Appendix Tables C.7 to C.9.<sup>24</sup> Additionally, the differences in the estimated effect of each intensity measure can also be statistically tested. Using the estimates from the DHS in Appendix Table C.9, the survey that contains all age specific outcomes of interest, the null hypothesis that each combination of the three intensity measures has an equal effect on schooling is tested in Appendix Table C.16. Across the four tests, the null hypothesis of equality cannot be rejected, and no p-value is larger than 0.5.

In addition to testing the equality of each first stage coefficient, the combined intensity measure (∆ _Izy_ ) is also used to re-estimate the effect of years of schooling on number of children born, and all outcomes from Appendix Tables C.10, C.11, and C.13. The estimates using the combined intensity measure can be found in Appendix Tables C.17 to C.20; in each case, the combined intensity measure yields estimates similar to the model using five instruments. Most importantly, the estimated effect of an additional year of schooling

> 24The additive portion of ∆ _Izy_ does not double count years in which both FPE and MTI are available in the region. Both reforms making one additional year of schooling available can never increase schooling by more than a single year.

A.32
often for each parcel owned by any household member), there have been two major deficiencies in largescale survey data collection: (a) surveys have not systematically sought to identify exclusive and joint holders of specific rights, alongside reported and documented owners, and (b) individual household members have not been interviewed personally and it has been commonplace for selfidentified most knowledgeable household members to identify intra-household landowners and right holders, which resulted in biased measurements of men’s and women’s land ownership and rights (Kilic et al. 2021b).

Against this background, the first contribution of our paper is to provide cross-country, nationally representative insights regarding land rights among men and women in Sub-Saharan Africa, with a focus on non-residential (primarily agricultural) landowners. The analysis is informed by the nationally-representative household surveys that have been conducted in Ethiopia, Malawi, and Tanzania over the period of 2016-2020 and that have interviewed adult household members in private and collected self-reported, parcel-level information on each respondent’s _reported_ , _documented,_ and _economic_<sup>_6_</sup> ownership of land, as well as his/her right to invest in, use as collateral, rent out, bequeath, and sell that piece of land. Across these different ownership and rights constructs, respondents were asked whether they had exclusive ownership and rights or held them jointly with other individuals. Additionally, respondents were asked to identify individuals

> 5 Doss et al. (2015) provide a discussion on the nuances of landownership measures and their importance for genderdisaggregated statistics on land.

> 6 Economic owners are individuals that report themselves as having control over the proceeds of a land sale in the hypothetical event that a given  parcel would be sold.

3
<br>PYLL-diabetes<br>[52.67; 36.84]<br>PYLL-heart disease<br>[530.55; 306.48]<br>PYLL-cerebrovascular<br>[228.62; 134.89]<br>PYLL-Flu/pneumonia<br>[149.29; 178.54]<br>Aggregate outcome<br>[1923.51; 924.54]|Inpatient cases<br>[4914.95; 3021.12]<br>Outpatient cases<br>[2546.67; 4665.88]<br>Surgery cases<br>[423.23; 501.74]<br>Aggregate outcome<br>[7884.85; 6065.08]|
|Data sources|California<br>Department of<br>Education Data|PISA and WDI<br>Databases|OECD Health Data|Vietnam annual<br>hospital inventory<br>exercise|
mode of data collection for inequality and poverty. The use of surveys with both diary and recall interviews can yield improved measurements because these two collection modes work best for eliciting consumption in different segments of the distribution. Nevertheless, our calculations have shown that a fully fledged recall survey can yield inequality measurements that are not too different from those that would be obtained from the optimal mix of diary and recall interviews.

The first implication of our research is that the loss in accuracy from using recall questions is minimal compared to the higher costs of using diaries. This finding provides an empirical justification for considering a transition to recall modules in household surveys in developing countries. However, more research is needed to assess what makes a good recall module, given that the length of the recall list will affect the propensity of respondents to engage in the survey. The answer to this question could be given, for example, by randomizing households to recall modules of different length and using our research design. The decision between using diaries or recall modules is not confined to developing economies. For example, the problems associated with the Consumer Expenditure Surveys in the United States have intensified the call for a redesign of the survey, which is underway following the recommendations of a specially appointed panel (the Gemini project).

Diary surveys should collect information on the purchasing behavior of households to correct for the unwelcome effects of infrequent purchases on inequality measurement – which is the second implication

> 17Allocation based on proxies of _Y ∗_ , such as income or residence in areas with homogeneous compositions of household demographics, falls between this case and the one above.

28
# **B Data Appendix**

## **B.1 Computing Value Added Content**

As noted in the text, our measures of domestic content in foreign production and foreign content in domestic production can be motivated as an application of the ‘global value chain’ decomposition of final goods developed in Los, Timmer and de Vries (2015).<sup>60</sup> We briefly describe the computation here.

As in the main text, let _i, j ∈{_ 1 _,_ 2 _, . . . , C}_ denote countries and _s ∈{_ 1 _,_ 2 _, . . . , S}_ denote industries. The World Input-Output Database includes an input shipments matrix, _IIt_ , with ( _S × S_ ) dimensional block elements _IIijt_ ( _s, s_<sup>_′_</sup> ) that record input shipments from sector _s_ in country _i_ to sector _s_<sup>_′_</sup> in country _j_ . These matrices can easily be re-written in share form. Let _Aijt_ be a ( _S × S_ ) dimensional matrix with elements _Aijt_ ( _s, s_<sup>_′_</sup> ) = _IIijt_ ( _s, s_<sup>_′_</sup> ) _/Yj_ ( _s_<sup>_′_</sup> ), which record the share of inputs from sector _s_ in country _i_ used by sector _s_<sup>_′_</sup> in country _j_ as a share of gross output in sector _s_<sup>_′_</sup> in country _j_ . Then assemble blocks _Aijt_ into the global input-output matrix _At_ . The Leontief inverse of the global input-output matrix, [ _I − At_ ]<sup>_−_1</sup> , times any ( _SC ×_ 1) vector of final goods output equals yields the ( _SC ×_ 1) vector
# **1 Introduction**

The strong relationship between financial development and economic growth is well documented in the literature (see, for example, King and Levine, 1993; Beck _et al_ ., 2000; Demirgüç-Kunt and Maksimovic, 1998; Beck _et al_ ., 2004; Levine, 2005; Klapper _et al_ ., 2006; Demirgüç-Kunt _et al_ ., 2008). In more recent years the debate expanded to include the notion of financial “exclusion” as a barrier to economic development and the need to build inclusive financial systems (Beck _et al_ ., 2008).

Recent empirical evidence using household data indicates that access to basic financial services such as savings, payments and credit can make a substantial positive difference in improving poor people’s lives (Caskey _et al_ ., 2006; Dupas and Robinson 2009). For firms, especially small and medium enterprises (SMEs), access to finance is often the main obstacle to growth (Schiffer and Weder, 2001; Cressy, 2002; IADB, 2004; and Beck _et al_ ., 2005, 2006, and 2008).

Accordingly, financial sector reforms that promote financial inclusion are increasingly at the core of the international development agenda for policy makers and development institutions at the global level. The United Nations (UN) has declared 2005 the Year of Microfinance, and the recent Pittsburg and Korea G-20 communiqués increasingly underscore the importance that this topic has gained in the international arena. The Nobel Institute awarded the Nobel Peace Prize to the founders of microfinance, Muhammad Yunus and the Grameen Bank, in 2006. New international bodies, such as the Alliance for Financial Inclusion (AFI) have emerged whose primary objective is to advance financial inclusion for the world’s poor. The International Monetary Fund (IMF) and the International Finance Corporation (IFC) also increasingly pay attention to this debate. The IMF has launched a new database on financial inclusion,<sup>1</sup> and the IFC together with Consultative Group to Assist the Poor
higher among food crop farmers than among export crop farmers (table 9). This is somewhat surprising because there was a large decline in real export crop prices, particularly cocoa, which declined over 50 percent between 1988 and 1993 (table 10). In contrast, real staple food prices declined between 20 percent to 30 percent between 1988 and 1993 (table 11), not quite as much as real export crop producer prices. So all other things being equal, we would have expected food crop farmers to have been relatively more insulated from the recession than export crop farmers.

A partial explanation for the finding may be that the decline in producer prices had already been absorbed in the 1988 survey results. There is some evidence that farmgate cocoa and coffee producer prices were lower than official prices before the prices were officially lowered in 1989 (Azam, 1994). Food crop farmers may have also suffered disproportionately to the extent that they provided wage labor for export crop farmers, and wages or employment fell. There is, however, little information about the behavior of the rural labor market available.

The decline in real staple food prices merits some comment. Several factors may have been responsible. One factor is that as the real returns to cocoa, coffee and cotton production fell in the second half of the 1980s, farmers may have shifted some resources out of export crop production into the production of staple food crops, thus increasing the supply of staples and putting downward pressure on staple food prices. As the recession intensified and incomes fell, demand for staple foods fell as well (except perhaps for staples such as maize and cassava that are inferior goods). Finally, the decline in the real price of rice--due in part to government's pricing policy--also likely placed downward pressure on the real price of other staple food crops.

10 Unfortunately, the aggregate data file we obtained for the 1985-88 LSMS data does not include the socioeconomic group variable so we cannot reproduce Grootaert's results using the
estimates (for example, from 2010 to 2011) to the corresponding panel component within the data.

The paper is organized in six sections. The first is the current Introduction. The second section profiles NEETs, drawing comparisons in different countries around the world. Section 3 summarizes research and findings related to the NEET phenomenon. Section 4 presents data and the methodology we applied, while section 5 displays results. Section 6 draws conclusions.

> 1 Using a synthetic panel approach to study the German labor market during the 1993-2014 period, Burda and Seele (2013) document the vital role played by part-time employment in reallocating a modest increase in total hours worked over a large number of new workers, leading to net employment growth. Beblavy et al. (2013) undertake a synthetic panel analysis of adult learning for cohorts aged 25 to 64 in 27 European countries, using the European Labour Force Survey and studying the factors affecting educational attainment, participation in education and training.

3
- _Family background:_ Parental education level, the family’s religion, and the primary language spoken at home (as a proxy for ethnic background which is not directly recorded).

- _Location characteristics_ : The province in which the student attended junior secondary school and whether the town in which the respondent lived at age 12 was a village, a small town, or a big city.

- _Type of elementary school_ : The type of elementary school attended (public secular, public Madrassah, private secular, private Madrassah, private Muslim

non-Madrassah, or private other)

- _Student characteristics_ : Whether the respondent is female, and whether the

respondent worked while attending junior secondary school.

- _Type of junior secondary school_ : Whether the junior secondary school

attended was a vocational school, and the type of junior secondary school attended (public secular, public Madrassah, private secular, private Madrassah, private Muslim non-Madrassah, or private other) Regressions that use the junior secondary school sample include the following three time-varying characteristics of the household:

- _Household income/wealth_ : Household per capita expenditure and type of floor

in the dwelling.

- _Student health:_ General health status (healthy, somewhat healthy, or

unhealthy).<sup>6</sup>

> 6 This question was not asked in 1993 and is therefore only available for half of the sample. Missing observations are grouped as a separate category.

14
034)|(0.034)|(0.034)|(0.034)|
|**NACE dummies**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Country dummies**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**R-squared**|0.1318|0.1318|0.1327|0.1321|0.1324|0.136|0.1115|0.1115|0.1115|0.1115|0.1115|0.1115|
|**N. obs**|16,800|16,800|16,800|16,800|16,800|16,800|28,360|28,360|28,360|28,360|28,360|28,360|

> ~~1~~ (10-49) is the omitted size category.

2 (1-5) is the omitted age category.

3 Purely domestic is the omitted ownership category.

4 var0308_instock_gdp(var0308_outstock_gdp) is related to the stock of inward (outward) FDI in the manufacturing industry. var0308_instock_gdp(var0308_outstock_gdp) is related to the stock of inward (outward) FDI in the services industry.

_Note 1_ : The variables for infrastructure and stock of outward FDI were excluded due to multicollinearity.

_Note 2_ : All PCA indices of business regulation (all_DB, DB_business_startup, DB_business_operations, and DB_institutional_environment) were included in the analysis.

However, only all_DB and DB_business_operations were statistically significant. The sub-indicators for DB_business_operations (permit, tax, trade, and employment) are also included in the results above.

Significance: *** 1%, ** 5%, * 10%.

16
The results show that mobility variables, when selected by LASSO, are generally powerful predictors of income and employment changes. When examining the initial impact, they explain between 27 percent (for farm income declines) and 40 percent (for work stoppage) of the explained variation. For the subsequent evolution, they explain between 34 percent (Nonfarm income decrease) and 46 percent (current employment) of the explained variation.

The importance of different Google search terms varies considerably by indicator. When predicting initial impacts in the first wave, searches related to food, money, and jobs account for 80 percent of the explained variation. When examining the same variable (the prevalence of total income declines) over time, important variables include food, exercise, and religion, which combined account for over 90 percent of the explained variation. When modeling work stoppage in the initial wave, search related to food, money, and religion contribute 36 percent of the variation. When modeling variation over time in current employment, searches related to food and religion contribute 27 percent of the variation, while jobs and stopping school contribute 5 percent each.

In general, Google mobility and search terms explain a much larger share of the variation than the satellite-based measures. Night-time lights, although included in the candidate set of variables, was not selected by lasso for any of the 18 models listed in Table 8. There are a couple of cases however where EVI and NO2 contribute significant variation, particularly EVI in the case of stopping school and not eating all day. NO2 is a significant determinant in particular of non-farm enterprise income gains, which is suggestive of a particularly strong association between household enterprises and mobility. Combined, the Google mobility measures and NO2 explain 51 percent of the variation in the prevalence of non-farm income losses and 59 percent of the prevalence of non-farm income gains.

# **5. Conclusion and Discussion**

This paper assesses the ability of real-time indicators to predict household survey-based wellbeing measures. Reference outcomes are provided by high-frequency phone surveys administered
Policy Research Working Paper 8749

# **Abstract**

This paper studies the effects of a large increase in the minimum wage on the destruction of formal firms, and the associated impacts on employment, wages, and informality in a developing economy. It examines the ramifications of a 33 percent nominal increase in the minimum wage that took effect in Turkey in 2016. It utilizes an exceptionally rich, employer-employee-linked dataset that shows the wage distribution within firms, which enables to measure the degree of firms’ minimum-wage exposure, and to estimate causal effects using a difference-in-differences approach. The paper shows that raising the minimum wage substantially increased the destruction of formal firms, leading to a fall in the number of formal firms in the economy. Effects are concentrated among small firms with low levels of productivity; highly productive firms are unaffected. The minimum-wage increase had negative effects on formal employment that largely originated through firm destruction, rather than through cuts in formal employment within surviving firms. Among workers who lost jobs in firms that ultimately exited, only a minority had obtained new employment a year later. The minimum-wage increase was accompanied by a rise in inactivity and unemployment, rather than growth of informal employment, suggesting that workers who lost formal employment mostly failed to find new jobs one year after the minimum wage hike, even informally. While the higher minimum wage had large, positive effects on the wages of formally employed workers, limited effects on the wages of informal workers are reported.

This paper is a product of the Social Protection and Jobs 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/research. The authors may be contacted at lbossavie@worldbank.org; mmakovec@eorldbank.org; or aacar@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage
receive the transfer (complying with its condition of sending children to school) are left out of the program. Neither does it take into account other types of implementation flaws, including “inclusion errors” of non-eligible households receiving the transfers, lack of compliance with the conditions of the transfer, or schools being unable to accommodate increase in enrollments. Secondly, due to the nature of the BFL model, the _ex ante_ AIT estimator does not take into account any time trend effects on outcomes on which impacts are measured. Given this, it is best to compare the _ex ante_ AIT estimate with _ex post_ AIT estimates obtained using doubledifference or difference in difference (DID) method whenever possible – recognizing that this _ex post_ method removes the time trend effect from the estimated impact.

# _The impact of PROGRESA_

We compare the simulated _ex ante_ impact of _PROGRESA_ , applying the BFL model on the baseline survey (from 1997 – see section IV for details), with _ex post_ results from Skoufias and Parker (2001). To make this comparison possible, all income variables from the baseline survey are converted into November 1999 pesos, and transfer amounts corresponding to the second half of 1999 (see Appendix, Table A-1 for the amounts) are applied in the simulations.

The selected sample comprises only children who were of age 8-17 years at the time of the baseline survey in 1997. Given how the BFL model is set up, the data from the follow-up survey on the same children, which is to say the panel data, is not utilized for the simulation. Out of the 33,609 children in the baseline sample the simulation was applied to, around 30 percent were not attending school, while only around 4 percent of children were combining work outside the household with school. Children not attending school were on the average older and with higher education level than those who were in school and not working. Households with higher dropout rates were not necessarily poorer, indicating the importance of child income for the household. Child earnings increased with