rafmacalaba/data-use-mentions-extended huggingface dataset:
about three times larger than that of equities.24 The relatively low level of the aggregate equity values in total asset holdings partly explains why asset operations by the official funds or any other measures in support of equity prices-aimed at assisting city banks to pass the BIS standard-could have only a marginal effect on macroeconomic activities.<sup>25</sup> Apart from the differential size effects and a slight difference in their channels of initial shock diffusion, sirnulated declines in asset prices created similar qualitative responses. These simulations single out the negative effects of asset price deflation on the Japanese economy, for both land and equities, in the following manner:

- 1) In both cases, declines in asset prices themselves would have depreciated the yen. The rapid appreciation of the yen during the first 8 months of 1993, which lengthened the business cycle downturn, was caused mainly by the interest rate differential between the Japanese and U.S. economies.<sup>26</sup>

- 2) GDP growth stagnates due to a slowdown in domestic absorption, despite the positive contribution from foreign sectors helped by currency depreciation.

- 3) Stagnation in domestic absorption reduces money demand and therefore interest rates. However. massive deflation causes increases in real interest rates after the third or fourth year.

- 4) Private consumption stagnates due to declines in disposable income and households' net worth. Movements in real interest rates turn hostile to consumption activities the third or fourth year following the shock. Declines in private consumption are generally larger than those in disposable income due to negative wealth effects, and the household saving rate increases when dissaving is preferable to stimulating an ailing economy. 27

24 For comparison purposes, the impacts can be size adjusted using SNA statistics (see Table 1). From April 1991 to October 1993, equity prices declined by 56.4 percent in real terms and the ratio of real capital loss to the GDP was 0.95. On the other hand, land prices declined by 30.9 percent during
<br>|-0.0130<br>|-0.00524<br>|
|||(0.0152)|(0.0168)|(0.0283)|(0.0230)|(0.0287)|
|**preneur**<br>**Model**|Ever been a business||0.325***|0.246***|0.334***|0.303***|
|**Entre**<br>**Role**|owner||(0.0752)|(0.0857)|(0.0738)|(0.106)|
|**e skills**|Digitspan score|||0.0251<br>(0.0234)|0.0571*<br>(0.0291)|0.0196<br>(0.0458)|
|s<br>**itiv**|Raven test score|||-0.0392*|-0.00342|0.00683|
|stic<br>**ogn**||||(0.0204)|(0.0171)|(0.0402)|
|teri<br>**n-c**|Personal intitative|||-0.289*|-0.574***|-0.433*|
|arac<br>**no**||||(0.169)|(0.126)|(0.226)|
|Ch<br>**and**|Error competence|||0.0182|0.0436|0.272|
|iner<br>**ive**||||(0.175)|(0.160)|(0.225
test however is applied only to a sample of students in each school.

An important issue related to using the ENLACE for this study is the possibility of cheating and, in particular, student copying on the ENLACE exams. When the government stopped applying the ENLACE tests, one of the stated concerns was over an increased incidence of suspected student copying. Unless the level of student copying was extremely high with the majority of students engaging in copying, the main way in which cheating might affect the results presented here is if there were differential rates of cheating between PROSPERA and nonPROSPERA students, e.g. that results on achievement tests would reflect both performance on the test and cheating. We are unaware of publicly available data either on the overall level of cheating detected by the Ministry of Education (SEP)/National Institute of Education Evaluation (INEE). Individual answer sheets would be necessary to directly analyze whether there are differential rates of cheating between PROSPERA and non-PROSPERA students based on standard statistical cheating detection methods developed in the education literature. (e.g. Wollak, 2003).

However, since the ENLACE exams are not used to calculate student grades, it is not obvious that PROSPERA and non-PROSPERA students would have different incentives for cheating on 9
Liabilities<br>*Self financing ratio<br>*Debtors and creditors ratio|
||**C -hiows**|

Source: Lemaitre (1997) Efficiency economic indicators are shown at Table _5.2._ These are classified into six distinct categories: overall costs performance, labor productivity, productivity of capital employed, revenue-generating performance, performance of commercial activities and overall profitability. In order to asses the economic efficiency of an airport through time, or to check whether regulated standards are being met, specific indicators are required. For example, we might need to explore labor and capital productivity if we wish to establish that resources are being used in the most efficient way. Alternatively, to determine the _49_
16.5|11.9|
|10 to 11|4.0|5.1|7.3|3.9|5.2|6.2|3.9|5.1|6.9|
|less than 12|87.8|77.2|65.1|88.2|80.5|62.2|88.0|78.9|64.0|
|12|5.2|8.7|24.6|6.0|9.5|24.8|5.6|9.1|24.7|
|13 to 15|2.4|4.2|6.1|2.4|3.7|7.4|2.4|3.9|6.6|
|more than 15|4.6|9.9|4.2|3.4|6.3|5.6|4.0|8.0|4.7|
|Age|31.2|28.6|28.3|32.2|27.8|30.7|31.7|28.2|29.2|
|N|4,008,334|159,712|694,206|4,288,508|172,192|461,990|8,296,842|331,904|1,156,196|

**Source:** For migrants to the US, the 2000 US Census and within Mexico, the 2000 Mexican Census.

29
sults are very similar to those in equation 4-1. The morality-ability correlation remains highly significant for this smaller sample.

In equation 4-3 we measure ability (schooling), morality, and the morality-ability correlation using only the 1995-1998 WVS surveys. For this sample, the mean year for surveys used is 1996. This change not only limits the sample size – by dropping countries included only in the later survey waves – but arguably introduces more random measurement error for most of the remaining countries, by throwing away information from the subsequent survey waves. Nevertheless, schooling, morality and the morality-ability correlation are all significant with the expected signs in equation 4-3.

Equation 4-4 includes all 74 countries, but measures the morality variables using only the earliest available WVS wave for each country. Equation 4-5 is similar in other respects but measures the morality variables using only the most recent available WVS. (For 38 countries these are the same values in both tests.) If causation runs mostly from income to morality, the morality variables should have more explanatory power in equation 4-5 than in 4-4. However, the R<sup>2</sup> is marginally higher in 4-4 (.77) than in 4-5 (.76). The coefficient for the initial-wave morality-ability correlation is also somewhat higher (in 4-4) than for the final-wave. The reverse is true for the morality proxy: its coefficient is larger when measured using final-wave data.

In results not reported in Table 4 for space reasons, we include both sets of morality variables together, measured using the initial-wave and final-wave WVS. For the morality proxy, only the final-wave variable is significant (at the .10 level). For the morality-ability correlation, only the initial-wave variable is significant (at the .05 level). These results, and those in Table 4, are consistent with the view that any causal relationship between income and the morality-ability correlation runs from the latter to the former. For the morality proxy, on the other hand, the results are 24
# **2.2. Road data**

In the original formulation of the Rural Accessibility Index, the “all-seasonality” of rural roads is based on the surface type and roughness of each road. The RAI only considers the roads serviceable if they are paved roads in ‘good’ or ‘fair’ condition, and unpaved roads in ‘good’ condition.

National road agencies typically have a database of their road network with some information on paving and condition. However, in many low-income countries, these databases are incomplete and rarely updated (Espinet Alegre et al., 2018). An alternative to these databases is OpenStreetMap (OSM), a collaborative project to create a free map of the world. OSM allows any user to edit the map anywhere in the world, without restriction. It represents perhaps the largest map in the world, though completeness varies from region to region. OSM users classify roads based on their function, using their knowledge of the local area or satellite imagery. We reduce this classification down to four categories (primary, secondary, tertiary, and tracks, see Annex 2 for the detailed classification).

Unfortunately, OSM does not allow users to provide information on the type of pavement or the quality of the road; and yet information on road quality is essential for the calculation of rural accessibility. To overcome this issue, the RAI is calculated three times, assuming that all-season roads can be loosely defined from the OSM ‘highway’ tag as (i) primary or secondary roads only; (ii) primary, secondary and tertiary roads; (iii) all roads. We then compare the results with previous RAI estimates (Figure 2) and with the fraction of paved roads provided by the International Roads Federation<sup>2</sup> and conclude that the best proxy for all-season roads are primary and secondary roads.

> 2 https://worldroadstatistics.org/

5
.7|14.5|78|-4.4|
|Latvia|1999|20.4|16.0|78|9.7|
|Lithuania|1999|27.7|16.6|60|12.0|
|Romania|1999|32.8|29.8|91|18.5|
|Macedonia|2002|46.4|44.9|97|7.3|
|**Bulgaria**|**2002**|**34.8**|**34.0**|**98**|**21.2**|

Source: Own calculations based on national trade statistics reported to the UN COMTRADE database.

Bulgaria became part of TC value chains much later than other CEEC-10, but it has caught up very quickly. Since 1996 Bulgaria has been very successful in tapping opportunities offered by outward processing mostly by EU firms of garments. With the average annual rate of growth of 21 percent, it recorded the fastest growth over 1996-2003 among CEEC-10 and SEE-4
control variables at the firm and country level.

Finally, as recalled by Chauvet and Ehrhart (2018), the statistical bias resulting from the attempt to measure the effect of macro variables on micro-units was underlined by Moulton (1990).<sup>7</sup> Therefore, as in Chauvet and Ehrhart (2018) and Bambe et al. (2022), the standard errors are clustered at the country level (as we have country level control variables such as economic growth, bank concentration, inflation, etc.).<sup>8</sup>

# **4.2 Data and variables**

In this analysis, we consider 31,406 manufacturing firms in 84 developing countries from 2006 to 2019, 30 of which are in Sub-Saharan Africa (AFR), 8 in East Asia and Pacific (EAP), 14 in Europe and Central Asia (ECA), 20 in Latin America and the Caribbean (LAC), 6 in the Middle East and North Africa (MNA), and 6 in South Asia (SAR).

## **4.2.1 Firm-level data**

We mainly use the World Bank Enterprise Survey (WBES) data in this analysis. The strata of the enterprise surveys are firm size, industry and geographical region within a country. The firm size levels are 5-19 (small), 20-99 (medium), and 100+ employees (large). It is important to note, however, that these surveys

> 5In the regression step, we additionally control for the covariates used in the first step. This is equivalent to including control variables in a randomized experiment to increase the efficiency of the estimation.

> 6We also include region and income group fixed effects in our econometric specification.

> 7Random disturbances in the correlated regression within the groupings that are used to merge the aggregate data with the micro data can lead to a downward bias in the ordinary least squares standard errors (Moulton, 1990).

> 8Clustering the standard errors
are presented here only for the group of Lima wage employees. And for them, because the inclusion of lower-level certificate variables results in nonsignificant regression coefficients and also renders the coefficients for the corresponding years-of-education variables nonsignificant owing to the collinearity between years of education and certification, the Model III equation includes only the university degree certificate variable.

Selectivity Bias. We do not have a sufficient number of theoretically relevant background variables to test for self-selectivity in an individual's choice between VTE and general education, given that this decision could have been made far in the past and the PLSS data set is cross-sectional rather than longitudinal. We do, however, test for selectivity in the present-day choice between wage employment and selfemployment. Economic theory says that an individual will choose whichever sector of employment promises the highest net marginal returns. OLS (ordinary least squares) estimates of the earnings equations specified above will be biased to the extent that there are variables omitted from an earnings equation that determine the probability of being in either the wage or self-employed sector. Their omission implies that the error terms of the earnings and labor supply equations will be correlated.

To take into account the choice between wage employment and selfemployment, i.e., to adjust for selectivity bias, we use the standard econometric procedure introduced by Heckman (1976; 1979). We first estimated a set of probit equations to determine, separately, for all males in Lima, and then for all males in OUAs: (a) the probability of being employed in the private wage sector versus all other options (including, but not restricted to, 3elf-employment), and (b) the probability of being self-employed versus all else (including, but not restricted to, wage employment). The following right-hand variables were used in estimating the probit functions: age, age squared, the five splines representing each level and type of schooling, the three educational certificate variables, the training and marriage vEriables, years in the current place of residence, annual unearned income, father'
<br>states states: states: excl. small 0<br>Commodity Commodity states LAC small EAP small SSA small<br>exporters importers states states states<br><!-- End of picture text --> _Sources_ : Black et al. (2023); Cantu-Bazaldua (2021); GFS (database); Herrera et al. (forthcoming); WDI (database); World Bank.

_Note_ : EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean; LICs = Low-Income Countries; SS = Small states; SSA = SubSaharan Africa.

A. Panel shows the Human Development Index (HDI) score for 2021 and average health and education expenditures as a percent of GDP for 30 small states and 104 EMDEs excluding small states for the period 2011-21.

B. Bars show average efficiency score for up to five different methodologies for 35 small states for health, 6 small states for education, and 8 small states infrastructure investment for the period 2010-20 from Herrera et al. (forthcoming). Sample includes 32 advanced economies and up to 111 EMDEs excluding small states.

44
The data shows that deindustrialization has permeated Central America. Except for Panama, the industrial employment shares have contracted in each regional economy, with an average decline of 2.5 percentage points (pp). In the first step of my study, I uncover the fundamental drivers of industrial change in each country. Specifically, I use the sectoral data on employment, value-added, consumption, and trade to identify the importance of three channels in accounting for the dynamics. First, a decline in the industrial labor allocation can be demand-driven, i.e., due to a fall in the industrial share of final consumption. Second, a rise in industrial imports substituting domestic production may lead to trade-induced deindustrialization. Finally, an intensification in the barriers, or labor market _wedges_ (Hsieh & Klenow, 2009), to the movement of resources into the industry can have the same qualitative effect on labor allocation.

The accounting reveals that shifts in labor market wedges are essential in explaining the region's deindustrialization. The channel causes industrial employment to contract in each regional economy. Its impact on industrial share varies across countries, ranging from a more than 5pp decline in Panama and El Salvador to less than one pp decay in Costa Rica. These wedge-driven contractions are higher than the actual share loss in three of the five economies that have experienced deindustrialization. The channel plays a secondary role in Costa Rica, explaining only 15 percent of the country's massive 6.2 pp drop in industrial share.

Global trade has flourished over the last two decades. Trade, therefore, can potentially explain deindustrialization in countries having a comparative advantage in non-industrial sectors. Instead of engaging in production to satisfy their industrial demand, it might be economical for these economies to import these goods. Surprisingly, I do not find evidence supporting this oft-cited trade-led deindustrialization hypothesis for Central America. Instead of creating headwinds, trade has usually fostered industrial expansion in the region. Nicaragua and El Salvador are the two economies where the channel has induced deindustrialization. Still,
however, several exceptions (Tovar and Martinez 2011), including one of the first instances of survival analysis of exporter data (Sabuhoro, Larue and Gervais 2006). This kind of analysis is expected be more relevant in light of the short export spells and significant levels of experimentation encountered in developing countries.

The main analysis is carried out with a discrete-time survival model that takes into account unobserved heterogeneity among exporters. The choice of survival analysis method follows a recent push to break away from the basic (and extended) Cox survival analyses that have generally been used in the literature. Instead of relying on the rather simplistic assumptions of continuous-time, proportional hazards and no unobserved heterogeneity (all three of which can be shown to be fallacious) the case has been made for using binary outcome models with random effects (Hess and Persson 2011a).

Following Besedes and Prusa (2011), the thrust of the argument hinges on the belief that what matters for trade deepening and the export growth of developing economies is the survival of new exports and not just entry into foreign markets. The survival of specific trade links is a further refinement of exporter survival and allows for 3
poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children.

3
Policy Research Working Paper 9769

# **Abstract**

Do larger interventions improve longer run outcomes more cost effectively? And should poverty traps motivate increasing intervention size? This paper considers two approaches to increasing intervention size in the context of temporary unconditional cash transfers—larger transfers ( _intensity_ ), and adding complementary graduation program interventions ( _scope_ ). It does so leveraging 38 experimental estimates of dynamic consumption impacts from 14 developing countries. First, increasing intensity decreases cost effectiveness and does not affect persistence of impacts.

This result can be explained by poverty traps or decreasing marginal return on investment in a standard buffer stock model. Second, increasing scope increases impacts and persistence, but reduces cost effectiveness at commonly evaluated time horizons and increases heterogeneity. In summary, larger interventions need not have more persistent impacts, and when they do, this may come at the expense of cost effectiveness, and poverty traps are neither necessary nor sufficient for these results.

This paper is a product of the Development Impact Evaluation Group, Development Economics. 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 fkondylis@worldbank.org and jloeser@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
aim at improving youth’s employability by strengthening their cognitive and non‐cognitive skills as well as helping them identify and follow a life plan.<sup>8</sup> This paper explores the role that cognitive and personality traits (a facet of non‐cognitive skills) play in labor market outcomes in Peru. It contributes to the existing literature in two ways. First, it adds a new data point to the very limited evidence about whether non‐cognitive skills are valued in developing economies. More importantly, though, it builds on the methodology presented in Heckman, Urzúa, and Stixraud (2006) to estimate of the effect of ability, as proxied by latent (rather than measured) skills, on wages and labor supply to more robustly measure both cognitive and non‐ cognitive skills, thereby permitting cleaner correlations than those estimated in the other developing country literature. Second, as a contribution to the more general skills literature, it explores the degree to which measurement error biases standard OLS estimates. The paper compares OLS estimates derived from measured skills and MLE estimates that employ latent skills measures, where the latter intends to correct for problems of measurement error emerging from written tests and multi‐collinearity, which affect the precision of estimates derived from more standard methodologies.

# **II. Data and Definitions**

This paper uses the National Skills and Labor Market Survey ( _Encuesta Nacional de Habilidades_ , ENHAB), collected in January through March 2010, which is based on the standard Peruvian household survey (ENAHO), augmented by modules measuring cognitive skills and personality traits. These cross‐sectional data were gathered from a random sample of youth and adults in 11 cities (57 districts) in the three regions of Peru (coast, Andes and jungle).<sup>9</sup> The cognitive and non‐ cognitive test batteries were applied to a randomly selected adult at home, resulting in a sample of 1,394 individuals aged 18‐50. Additional variables that provide early