rafmacalaba/datause-encoder-data huggingface dataset:
youth come at the expense of the untreated. To the extent that trained individuals crowd out nontrained individuals from jobs, we are understating the cost per job created and overstating the returns. Turkey has approximately 2.5 million unemployed people. The 3,000 individuals trained in this experiment are thus a trivial fraction of all the unemployed, and even the 210,000 individuals trained in total in 2010 constitute less than 10 percent of the unemployed. This makes it seem likely that any such spillovers or crowding out may be second-order effects in our context.

# **9. Conclusions**

This paper provides the first randomized evaluation of vocational training programs offered to the general unemployed population in a developing country setting. A large sample, containing both youth and older unemployed, as well as courses offered by the private and the public sector, enables us to examine the effectiveness of such training for a wider range of demographic characteristics and course types than existing literature. Linking participants to social security data enables us to trace the long-term trajectory of impacts on formal employment as well as avoid the attrition and measurement concerns that face existing studies in developing countries.

Our results show that the average impact of these vocational training programs is a very modest positive overall impact on employment and on the quality of employment, with the measured impact of the courses much less than expected by either course participants or government labor ministry staff. Despite the overall impact being close to zero, we find stronger and statistically significant impacts of vocational training in courses offered by private providers. Being selected for one of these courses results in a 4 to 6 percentage point increase in employment rates, relative to an employment rate of 37 percent for the control group. These returns persist when we control for observable differences in the characteristics of the courses and of their participants, but do not appear to last when it comes to impacts on formal employment over a 2.5 to 3 year period. Taken together the results suggest that there is some potential for vocational training to improve the short-term employment prospects of the unemployed, but that this potential will be best realized when courses are offered by providers that have both the incentives and ability to respond to market demands. However, overall the results suggest that this large-scale vocational training program struggles to meet a cost-benefit test. Given the renewed emphasis of these types of programs for many governments around the 25
(2022)). Specifically, firms responded by accelerating the use of existing digital technologies and investing in new digital solutions. This expansion in digitalization allowed firms to better reach consumers (or reach new ones) and organize production more flexibly and efficiently.

Figure 2 shows that nearly 45% of businesses had increased the use of digital platforms in the latest available data, compared to 32% in the first wave. Furthermore, 28% of firms had invested in new digital solutions, a 8 percentage points increase from wave 1. Although the intensity of these investments is not measured in these survey data, the number of firms taking actions to accelerate digitization has been unprecedented.

<!-- Start of picture text --> 60<br>45<br>44<br>40<br>28<br>23<br>20<br>0<br>Increase in digitalization<br>Increased use of digital platforms Have online sales<br>Invested in digital solutions Have remote workers<br>Share of firms<br><!-- End of picture text --> Note: Results from the latest round of available data.

Figure 2: Fraction of businesses by responses to the shock.

In addition, the increase in digitalization has been steady. For countries with all three rounds of survey data, the percentage of firms increasing the use of digital technologies has gone up from 28 percent to 41 percent, and 47 percent in the latest round (Figure 3). Moreover, the increase in digitalization has been widespread across the world. Even after controlling for the magnitude of restrictions on mobility, there was little difference in the uptake of digital technologies between countries at different levels of income. In fact, the advent of digitalization in response to the pandemic was lower in many high-income countries across Europe, compared with low- and middle-income countries across Asia and Africa. For example, the likelihood of firms starting or increasing their use of digital technologies in Hungary, Portugal, Italy, Slovenia, and the Slovak Republic was less than 20% during the first wave of the survey and did not exceed 30% during the second wave of the survey. In contrast, the corresponding share increased from around 50% to more than 80% in Kenya, from around 30% to more than 60% in Tunisia, and from around 30% to almost 60% in El Salvador between the two survey rounds. Nevertheless, pre-pandemic gaps in digitalization still remain. More firms in high income countries use some digital technologies but have not increased 7
level of employment. Revenga (1997) did not find any statistically significant relation between the level of employment and tariff liberalization in the case of Mexico. Márquez and Pages-Serra (1998) examined the relationship between trade liberalization and employment in Latin America and the Caribbean (LAC) and could not find any substantial effect. A comprehensive study by the IADB (2004), using household survey data for 10 LAC countries, did not find a statistically significant association between the two phenomena. De Ferranti, Perry, Lederman and Maloney (2003) confirm this result for several countries in LAC. In a similar study that also contemplates the effects of exchange rate appreciations, Haltiwanger et. al. (2004) did not find robust results on the relationship between trade liberalization and changes in net employment in the region. In their paper on the impact of trade liberalization on income distribution in Colombia, Attanasio _et al._ (2004) found no evidence of labor reallocation across sectors. Similarly small employment effects in Latin America are reported in Levinsohn (1999) for Chile, Moreira and Nakberg (2000) for Brazil and Gandelman _et al._ (2005) for Uruguay.

For Argentina, in particular, Galiani and Sanguinetti (2003) only found a small correlation between trade liberalization and the rate of employment in the 1990s. Pessino and Andres (2005) attribute the negative effects of trade liberalization on employment to the distortions and rigidities of Argentina’s labor market rather than to trade liberalization. Sánchez and Buttler (2002) point to other explicative factors beyond trade liberalization, such as labor costs, access to credit finance, financial and real shocks, informality, etc.

Other studies, such as Altamir and Beccaria (1999), Beckerman (2000), and Damill, Frenkel and Mauricio (2002) point to the accelerated process of trade liberalization combined with exchange rate appreciation, as the main culprits of the net employment loss suffered by the Argentinean manufacturing sector in the last decade. In sum, the evidence presented in these studies is not conclusive. In this paper, we are not concerned about which policies may have been the cause of that decline, but rather on whether imports from the two rapidly growing Asian economies can explain part of this trend.

3
|**moonlight**|878|0.011|830|0.000|913|0.036|802|0.009|859|0.037|690|0.009|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**jobdissati~d**|878|0.065|830|0.028|913|0.122|802|0.041|859|0.162|690|0.061|
|**apprentice~p**|878|0.115|830|0.102|913|0.194|802|0.171|859|0.256|690|0.216|
|**Mentoring**|878|0.000|830|0.002|913|0.005|802|0.004|859|0.013|690|0.006|
|**Trnewtech**|878|0.002|830|0.000|913|0.019|802|0.004|859|0.026|690|0.007|
|**Trother**|878|0.883|830|0.895|913|0.782|802|0.822|859|0.705|690|0.771|
|**Union**|878|0.015|830|0.008|913|0.103|802|0.065|859|0.142|690|0.101|
|**size_5less**|878|0.076|830|0.043|913|0.164|802|0.050|859|0.221|690|0.075|
|**size_5t9**|878|0.013|830|0.006|913|0.064|802|0.025|859|0.068|690|0.029|
|**size_10t19**|878|0.003|830|0.054|913|0.033|802|0.103|859|0.035|690|0.141|
|**size_20plus**|878|0.006|830|0.002|913|0.050|802|0.041|859|0.106|690|0.055|
|**Gsocontrib**|878|0.158|830|0.134|913|0.117|802|0.130|859|0.127|690|0.091|
|**Omone**|878|0.261|830|0.058|913|0.313|802|0.064|859|0.302|690|0.070|
|**Gfamily**|878|0.179|830|0.445|913|0.183|802|0.536|859|0.244|690|0.596|
|**Gworkexp**|878|0.063|830|0.045|913|0.058|802|0.031|859|0.035|690|0.028|
|**Gmeaning**|878|0.067|830|0.076|913|0.060|802|0.055|859|0.045|690|0.058|
|**Gselfest**|878|0.156|830|0.129|913|0.127|802|0.096|859|0.116|690|0.075|
|**Rural**|878|0.369|830|0.384|913|0.426|802|0.353|859|0.469|690|0.432|
|**poors_Az1**|878|0.228|830|0.224|913|0.174|802|0.196|859|0.164|690|0.191|

Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan.

# **Table A4. Descriptive statistics by** **<u>gender, the employed</u>**

|||**All**<br>||||**Wome**<br>**n**<br>|||||**Men**<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|Obs|Mean|Std.<br>Dev.|Min|Max|Obs|Mean|Std.<br>Dev.|Min|Max|Obs|Mean|Std.<br>Dev.|Min|Max|
|**Log of total monthly labor income (in th.**<br>**of old Manats)**|1896|5.9355|0.5171|2.3026|8.8537|658|5.8693|0.5647|2.30258<br>5|7.82404<br>6|1238|5.9706|0.4865|3.9120|8.8537|
|**Women**|1896|0.3470|0.4762|0|1|658|1|0|1|1|1238|0|0|0|0|
|**Log of weekly hours of work**|1896|3.6412|0.3126|0.6931|4.4308|658|3.6070|0.3325|0.69314<br>7|4.43081<br>7|1238|3.6594|0.3001|0.6931|4.4308|
|**Primary education or below (including**<br>**vocational education)**|1896|0.0601|0.2378|0|1|658|0.0502|0.2184|<br>0|<br>1|1238|0.0654|0.2474|0|1|

49
Policy Research Working Paper 6617

# **Abstract**

This study uses a newly compiled database of women’s property rights and legal capacity covering 100 countries over 50 years to test for the impact of legal reforms on employment, health, and education outcomes for women and girls. The database demonstrates gender gaps in the ability to access and own property, sign legal documents in one’s own name, and have equality or non-discrimination as a guiding principle of the country’s constitution. In the initial period, 75 countries had gender gaps in at least one of these areas and often multiple ones. By 2010, 57 countries had made reforms that strengthened women’s economic rights, including 28 countries that had eliminated all of the constraints monitored here. In the cross-section and within countries over time, the removal of gender gaps in rights is associated with greater participation of women in the labor force, greater movement out of agricultural employment, higher rates of women in wage employment, lower adolescent fertility, lower maternal and infant mortality, and higher female educational enrollment. This paper provides evidence on how the strengthening of women’s legal rights is associated with important development outcomes.

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 mhallward@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
Figure 1: _GIC_ US 1995–2007 <!-- Start of picture text --> GIC o<br>Counterfactual GIC<br>Average<br>Counterfactual Average<br>o<br>o<br>o<br>o<br>o<br>o oo o o o<br>ooooooooooooooooooooooooooooooooooo o oooo ooo ooooooooooooooooo o ooooooooooooooooooooooooooooooo o oooo o oo o oooo oo ooooooooooo o oooooooooooooo oo oooooo ooo oooooooo o oooooooooooo o ooooo oo ooooooooo oo ooooooooooo oo ooo oo ooooooooooo oooo ooooooooooooooooo<br>0.0 0.2 0.4 0.6 0.8 1.0<br>quantiles<br>0.4<br>0.3<br>GIC 0.2<br>0.1<br>0.0<br><!-- End of picture text --> reject the null hypothesis, which is in line with the heterogeneity observed across quantiles in Figure 1.

The second interesting finding from our analysis is that the counterfactual growth incidence curve, _GIC_<sup>_∗_</sup> , lies everywhere between the no-growth line at zero, and the actual _GIC_ , and its shape is very similar to that of the latter. This implies that both changes in (broadly defined) economic structure - encompassing changes in returns to observed worker attributes, as well as changes in both the distribution of and returns to unobserved characteristics - and changes in the joint distribution of age, gender and education contributed to the modest increase in US wages during the study period. Since the _GIC_<sup>_∗_</sup> is also flat until _τ_ = 0 _._ 8 or thereabouts, and then sharply increasing, we can conclude that the rise in wage inequality is not driven by changes in the gender, age and educational make-up of the workforce. It is driven instead by changes in economic structure and by their impact on the remuneration structure of various worker attributes.

This finding is confirmed by an inspection of the wage inequality measures for the US counterfactual distribution, _FY_ (1) _|T_ ( _·|_ 0), in Table 3 above. All five measures lie strictly between the actual wage inequality levels in 1995 and 2007, but are all much closer to the higher 2007 levels. Taking the mean log deviation as an example, the decomposition indicates that changes in economic structure between 1995 and 2007 shifted the measure from 0 _._ 218 in 1995 to 0 _._ 250. Changes in the joint distribution of covariates - i.e. the age, gender and

28
much less—as the program excludes textiles. Brazil, China, India and Korea Rep. also operate LDC preference schemes, which are considerably more restrictive than those offered by developed economies (Laird, 2012).

The average tariff rates LDCs face in developed country markets generally average about 2 percent, about half those facing other developing countries. Tariffs in other developing country markets against LDC products are much higher, 14.5 percent (WTO 1997).

The low average tariff rates in developed country markets reflect primarily the fact that the bulk of LDC exports are primary commodities where tariffs are low or non-existent and less the extensive preferences they obtain in these markets—which on average offer no more than few percentage points of advantage.

On the other hand, the dutiable products in which LDCs tend to have comparative advantage, namely agriculture, textiles, and footwear are subject to tariff peaks and tariff escalation. For a number of products (beef, asparagus, cigarettes, processed wood, clothing and footwear) are amounting to about 10 percent of their total exports, tariffs in the Quad, Australia, Norway and Switzerland ranged between 5 and 15 percent (WTO 1997).

In these cases DFQF can be meaningful as preference margins may be substantial. But for LDCs to exploit these they need to have relatively simple qualification requirements regarding rules of origin. The requirements for such rules can be prohibitive if they require substantial value added in the country benefitting from the preferences. LDCs typically do not have enough industrial capacity or the investment climate to attract the upstream suppliers needed to produce sufficient domestic value added or to transform inputs into a processed product (Francois and Hoekman et al 2009, p.7). The evidence from DFQF programs which offer both a significant 45
These data distinguish farm households’ places in the wealth distribution, commodity sources of farm income, and detailed information on off-farm income so that changes in total income and welfare can be calculated in the wake of agricultural reforms. Keeney (2005) uses these data to analyze the distributional consequences of stylized WTO scenarios, representing the only previous analysis of US farm household impacts of a Doha agreement. The ARMS data have served as the source for other disaggregate analyses (most notably Hanson and Somwaru’s (2003) work on the WTO acceptability of counter-cyclical payments) but in these cases the distributional character has been focused on farm structure rather than the welfare focus of Keeney (2005), and global reforms have not been considered.

_Distributional Impacts for Poor Country Households:_ Winters (2002) and Winters et al. (2004) provide an analytical framework and evidence on tracing the effects of trade policy through to individual households and poverty. Hertel and Reimer (2005) develop this framework in the context of CGE modeling. We believe that the impact of trade reform on individual households will vary widely depending on their sector of primary employment, their endowments, and their consumption patterns. Therefore for each of our 15 focus developing countries we utilize household survey data to divide households into seven classes (strata) according to their principal income source and estimate factor-specific poverty elasticities for each country and stratum combination. These elasticities are incorporated directly into our global CGE model and embody information about the shape of income distribution and income sources in the neighborhood of the poverty line. When combined with estimates of consumption behavior at the poverty line, those estimates allow for accurate assessments of how 6
percent). The last two columns of table 1 show baseline characteristics for the sample of individuals interviewed at follow-up. The characteristics are very similar to the full sample and we do not find any statistically significant differences between control and treatment group means in the follow-up sample. This shows the difference in attrition rates between the two groups is not leading to imbalance on observable characteristics.

# **6.2 Administrative Data**

We obtained administrative data on saving account balances and credit card outcomes from our partner institution. Due to confidentiality reasons, our partner cannot disclose individual level data, but they offered to generate summary statistics at the treatment and control group level. This was going to be straightforward with the sample we screened through the mail survey since the list of individuals in this sample came from our partner financial institution, meaning that they could easily find these individuals in their records. The low response rate to this survey implied, however, that our sample now almost entirely consists of individuals who were screened through the street or branch surveys. About half of the current sample (1,034 individuals) reported being clients of our partner financial institution and 470 of these individuals could be found in the institution’s records based on name, address and phone number.<sup>16</sup> The last two columns of table 1 show our baseline statistics for this sample of 470 clients. Overall, this group is similar to the full sample. Also, the characteristics of the treatment and control group clients who were matched with administrative data were not statistically different at baseline.

# **6.3 Estimating Treatment Impacts**

We estimate the impact of the financial literacy training with the following intention-to-treat (ITT) equation where yi,s,m is a follow-up survey measure of the financial knowledge, behavior, or outcome of individual i, in randomization strata s, who was surveyed in month m. The variable TrainingInvitei,s,m indicates whether an individual was invited to the course or not and is thus equal to one for the treatment group and equal to zero for the control group. We control for randomization strata dummies ds , as well as month of follow-up interview dummies dm . The main coefficient of interest is β, which represents the treatment effect of being invited to a

> 16 For ethical reasons and given the security concerns in Mexico City, we did not ask for date of birth or national ID numbers in the survey.

20
characteristics.<sup>12</sup> Many studies discussed in section 2 indicate that the burden of inflation is not equally distributed across households from different parts of the income distribution and that it differs based on household characteristics. This literature motivates our empirical specification: where _Li_ is the inflation rate experienced by household _i_ , **_X_** _i_ is a vector of household characteristics (including household size, household size squared, the share of children and elderly in the household, whether female heads the household, and whether a household member is unemployed); **_D_** _i_ is a set of dummy variables indicating the decile of consumption expenditure distribution a household belongs to; **_R_** _i_ are regional variables, including urban/rural and, for some countries, regional dummies; **_β_** , **_π_** , and **_γ_** are the estimated coefficients, and _ε_ i is an error term. For this analysis, we rely on microdata on expenditure consumption in 19 EMDE ECA countries.

<u>Figure 7</u> displays the magnitudes and significance levels of the regression coefficients estimated separately for each country. The estimations of (4) reveal that household size is a statistically significant determinant of inflation for most countries in our sample. In 16 of 19 countries, larger households appear to face lower inflation than smaller households, holding total consumption expenditure constant. In most countries, the magnitude of this effect appears to be non-linear and to decrease with household size, indicating that the more substantial effect may be evident when comparing single-person households with two- or three-person households. This effect should not be interpreted as evidence that larger households pay lower prices for the same good than smaller households do; in our analysis, prices for a given good or service are the same for all households. Rather, this effect indicates that larger households allocate proportionally more spending to goods and services for which inflation is lower than do smaller households.

The location of residence also affects the levels of inflation households face. In 13 of 16 countries for which estimation was possible, urban households seemed to experience lower inflation than rural households, with a median difference of 0.3 percentage points. Exceptions include Moldova, North Macedonia, and Poland, where urban households appeared to face inflation 0.4 to 0.7 percentage points higher than that faced by rural households.

> 12 A similar approach to identify the groups differentially affected by high inflation was used by Michael (1979) and <u>Hagemann (1982).</u>

12