rafmacalaba/datause-encoder-data huggingface dataset:
- <mark>[14] Gouriéroux C., Monfort A. (1997). Simulation based econometric methods. New York, NY: Oxford University Press</mark>

- [15] Handa S. (2002), “Raising primary school enrolment in developing countries: the relative importance of supply and demand”, _Journal of Development Economics_ , 69, 103-28

- [16] Handa S., Simler K. (2005), “Quality or quantity? The supply-side determinants of primary schooling in a poor rural economy”, _Journal of African Economies_ , 15 (1), 59-90

- [17] Hajivassilious V., McFadden D., Ruud P. (1995), “Simulation of Multivariate Normal Orthant Probabilities: Methods and Programs,” Journal of Econometrics, 72(1–2), 85–134

- [18] Heckman J. (1979), “Sample Selection Bias as a Specification Error”, _Econometrica_ , Vol.47 (1), 153-161

- <mark>[19] Jayachandran U. (2002), “Socio-economic determinants of school attendance in India”, Centre for Development Economics, Working Paper No. 103</mark>

- <mark>[20] Patrinos, H., Psacharopoulos G. (1997), “Family Size, Schooling, and Child Labor in Peru - An Empirical Analysis”,</mark> _<mark>Journal of Population Economics</mark>_ <mark>, 10, 387-405</mark>

- <mark>[21] Psacharopoulos G. (1997), “Child labor versus educational attainment: some evidence from Latin America”,</mark> _<mark>Journal of Population Economics</mark>_ <mark>, 10 (4), 377-86</mark>

- <mark>[22] Ravallion M., Wodon Q. (2000), “Does child labor displace schooling? Evidence on behavioural responses to an enrolment subsidy”,</mark> _<mark>The Economic Journal</mark>_ <mark>, 110, C158-C175</mark>

- <mark>[23] Ray R.(2000),</mark> **<mark>“</mark>** <mark>Analysis of child labor in Peru and Pakistan: a comparative study”,</mark> _<mark>Journal of Population Economics</mark>_ <mark>, 13 (1), 3-19</mark>

- <mark>[24] Schultz P. (1999), “Health and schooling investments in Africa”,</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>, 13 (3), 67–88</mark>

- [25] Tansel A. (2003), “Determinants of school attainment of boys and girls in Turkey: individual, household and community factors”, _Economics of Education Review_ , 21 (5), 455-70

- [26] UNDP (2007), Human Development Report 2007/2008 “Fighting Climate Change: Human Solidarity in a Divided World”, New York: UNDP/Palgrave MacMillan

- [27] Van de Ven W. P. M. M., Van Praag B. M. S. (1981), “The Demand for Deductibles in Private Health Insurance: a Probit Model with Sample Selection”, _Journal of Econometrics_ , 17: 229-252

- [28] Wahba J. (2006), “The influence of market wages and parental history on child labor and schooling in Egypt”, _Journal of Population Economics_ , 19 (1), 823-52

- [29] World Bank (2008), “Realizing Rights Through Social Guarantees: An Analysis of New Approaches to Social Policy in Latin America and South Africa”, Report No. 40047 – GLB, Social Development Department, February 2008

19
- 27 -

annual institutlonal borrowing by the household<sup>was utilized In the analysis, while</sup> for Informal credit the cumulative amount<sup>for the period was used. A positive</sup> coefficient Is expected If credit is a binding<sup>constraint.</sup> Two sets of estimates were obtained: OLS regresslons<sup>of crop-related</sup> capital stock, and Tobit estimates of<sup>Investment In housing.</sup> The capital regresslons, reported In Table 10, Indicate that the<sup>stock of capltal increases with</sup> farm size (parameters In all countles are slgnificantly<sup>greater than zero at a 95</sup> percent one talled confidence level). Only<sup>In the case of Gongzhuling does the</sup> capital/land ratlo increase with farm size,<sup>as the coefficient of land Is greater</sup> than 1. However, one cannot reject the hypothesis<sup>that the capital/land ratio Is</sup> fixed In Gongzhullng, ceterls paribus (i.e.,<sup>that the coefficient of land Is equal</sup> to 1). Similarly, In Tal county the hypothesis that<sup>the coefficient of land Is one</sup> cannot be rejected. In Jurong and Xiajlang counties, the coefficients<sup>of land</sup> are significantly less than 1, indicating that<sup>larger farms have lower capital/land</sup> ratios. This may be due to supplies of rationed<sup>variable Inputs (e.g. fertilizers)</sup> Increasing less than proportlonately with farm size,<sup>or due to less than full</sup> utilizatlon of capital ( a consequence of indivisibillty).<sup>The latter explanation Is</sup> compatible with the non-significant production<sup>elasticity of capital In Jurong</sup> county.

Informal credit does not affect Investment In<sup>crop-related capital In any</sup> of the counties (none of the estimated parameters<sup>are statistically significant at</sup> the 95 percent confidence lovel). This Is<sup>compatible with the segmentation anu</sup> lack of fungibility characterizing this source<sup>of funding.</sup> Formal credit significantly and positively affects Investment<sup>In farm capital In Gongzhuling county,</sup>
provides detailed information about this sample across countries and years. After this, we drop firms for which we cannot identify the sector in which they operate. This give us a sample of 131,347 observations.

If we further restrict this sample to manufacturing industries, which is the focus of our analysis, we end up with a sample of 74,723 observations corresponding to 59,820 firms. Of these firms, 79.4 percent appear only once in the database; 17.0 percent appear twice; 3.0 percent appear three times; 0.5 percent appear four times; and 0.1 percent appear five times. Table 7 in section A of the Appendix displays detailed information about this sample across countries and years. A common feature of many firm-level databases from developing countries is the presence of missing values for variables needed to measure firm performance (e.g., labor, sales, capital, and materials, and investment). For example, in our sample, labor is the variable with the least proportion of missing values (2.3 percent), followed by sales (14.2 percent), materials (31.8 percent), capital (32.8 percent), and investment (58.2 percent).

To maximize sample size, correct selection in misreporting, and gain efficiency, we impute data for sales, labor, capital, materials, and investment using the largest WBES database available, which contains 131,347 observations, and a pseudo-Gibbs sampler (Lee and Carlin 2010; Van Buuren, Boshuizen, and Knook 1999).<sup>4</sup> The explanatory variables used for imputation include email adoption, website adoption, export status, managerial experience proxied by a dummy variable that identifies firms with managers with above-median years of experience. It also controls for country, industry, and survey year. We do not impute data for email adoption, website adoption, export status, and managerial experience as we are interested in understanding their effect on TFPR. Table 8 in section A of the Appendix presents summary statistics of the main variables with and without imputation. As can be observed, the imputation method performs well, as there are not statistically significant differences in the statistics across sample groups.

> 4. The only observations that were not included in the imputation method were those that did not report any sector activity.

10
<!-- Start of picture text --> 4500 45%<br>4000 40% 40%<br>3500 35%<br>§ 3000 30%<br>: 2500 25%<br>© 2000 20%<br>> 1500 15%<br>1000<br>500 5%10%<br>0 0%<br>Non-Fully Liberalized Non-commodity Fully Liberalized Non-commodity<br>m Pre-Policy Period (2019-2021Q1)Sectors m Post Policy Period (202103-2022Q3)Sectors @% Change<br><!-- End of picture text --> <!-- Start of picture text --> 4000<br>3500 I<br>3000 |<br>20002500 -<br>1500 i<br>1000<br>500 I<br>aee |<br>0 Qtr1 Qtr2Qtr3 Qtr4 Qtr1Atr2 Qtr3Qtr4QAtr1Atr2 Qtr3 Qtr4 Qtr1 Qtr2 Qtr3<br>2019 2020 2021 2022<br>—®—40-59 % Liberalized —@=60-79 % Liberalized<br>—®=80-99 % Liberalized -@=100 % Liberalized<br><!-- End of picture text -->
# **Mental Health Patterns and Consequences: Results from Survey Data in Five Developing Countries**<sup>**#**</sup>

**Jishnu Das (Centre for Policy Research, New Delhi and World Bank, Washington DC) Quy-Toan Do (World Bank), Jed Friedman (World Bank), David McKenzie (World Bank)**

_Development Research Group, The World Bank_ _JEL codes:_ O12, I10, I32, O15.

_Keywords:_ Mental health; Poverty; Shocks; Social Protection; Subjective Welfare.

> # Special thanks to Alison Buttenheim and Le Dang Trung for excellent research assistantship and to the valuable comments of the editor and three anonymous referees. The views expressed in this paper are those of the authors, and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

- 1 -
# **C- Western countries and Japan**

<!-- Start of picture text --> Norway: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Japan: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>UK: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Netherlands: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Spain: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> 29
definition of an urban area has not changed during this period. As we discuss further below, some sub-units of districts move from being rural areas to being urban areas with the 2001 Census compared to the 1991 Census when the sub-units begin to satisfy the urbanization criteria. This change can influence our measured urbanization levels, and we discuss below the robustness of our documented patterns to these reclassifications.<sup>5</sup> It is important to note that India uses a more demanding set of criteria than most countries to define what is ‘urban’ (e.g., Bhagat, 2005; United Nations, 2001). For instance, substantial parts of U.S. metropolitan areas like Atlanta or Phoenix would be classified as rural in Indian statistical analyses because their population densities fall below 1000 persons per square mile. Thus, our measured urbanization for India will be lower than many international standards. This consideration does not affect, however, the longitudinal consistency of our trends for India.

We primarily measure urbanization of manufacturing activity for a district through the share of manufacturing employment contained in establishments classified to be in an urban location. This choice most closely corresponds to the prior literature and the central concerns of Indian policy makers. We also consider urbanization of plant and outputs for comparison. Table 1 provides basic descriptive statistics on districts, further discussed below, that begin with the average of these measures for 2001.

Table 2 lists the 17 major states from our sample and their urban shares, combining the organized and unorganized sectors. Figure 3a provides a graphical depiction. These states are a subset of India’s 35 states/union territories. Exclusions were due to three potential factors: 1) the state was not sampled across all of our surveys, 2) the small sample size for the state raised data quality concerns, or 3) persistent conflict and political turmoil existed in the region. We discuss below in greater detail the explicit criteria for a district’s inclusion in the regression sample. These exclusions are minor in terms of economic activity.

The most urbanized states in terms of manufacturing employment are Delhi and Chandigarh at over 90% in 2000, with Gujarat, Haryana, Maharashtra, and Punjab also above 60%. These states account roughly for 35% of urban employment and 47% of urban output for India in 2000, with these shares fairly stable over time. The larger states of Bihar, Orissa, Uttar Pradesh, and West Bengal have below-average urbanization rates. Bihar, Orissa, and Himachal Pradesh have urbanization rates of less than 20% for manufacturing employment.

> 5 Kundu  (2011a) suggests that reclassification between the 1991 and 2001 Censuses is a minor factor. Bhagat and Mohanty (2009) calculate that reclassifications account for about 12% of the total change in urbanization during the between the 1991 and 2001 Censuses.

6
**Table 8.  Estimates of model (8), using 50% quantile regression**

||**Dep. var: C**<br>**A**|**PIA clusters**<br>**& B**|**Dep. var.:**<br>**Freedo**|**Economic**<br>**m Index**|
|---|---|---|---|---|
|**Independent variables**|**Coeff.**|**Robust**<br>**s.e.**|**Coeff.**|**Robust**<br>**s.e.**|
|∑���<br>�<br>A�<br>�.�|0.0500|0.0137***|0.443|0.183**|
|Cumulated length of engagement|‐0.00180|0.00082**|‐0.0128|0.0094|
|GDPper capita at PPP|0.0000179|5e‐06***|0.000062|0.000073|
|Aid/GDP|‐0.00357|0.149|‐13.3|4.74***|
|Political Rights|‐0.0512|0.0330|‐0.786|0.268***|
|Country&year fixed effects|Yes||Yes||
|N|2280||2518||
|Number of countries|145||154||

Note: * = significance level of 10% or less; ** = 5% or less; *** = 1% or less. Standard errors are adjusted for 145 clusters.

The coefficients retrieved by quantile regression with the CPIA as the dependent variable are similar in size and significance to those retrieved by least squares (as reported in Table 5, p. 15). The coefficient on policy actions is 0.0500, which is within two standard errors of the coefficient estimated by least squares. The coefficient on the cumulated length of the engagement is statistically significant, and at ‐0.00180 it is less than a standard deviation away from the estimate by least squares. An exception is the variable Aid/GDP which is now statistically insignificant. There does not seem to have been much if any contamination of the sample, to judge by these results.

Similar comments may be made of the coefficients with the Economic Freedom Index as the dependent variable. The coefficient on policy actions is 0.443 which is statistically different from zero but is much smaller than the coefficient from least squares of 0.994 (see Table 7) and more than two standard deviations distant from it. The coefficient on the length of the engagement is negative, as before, but now it is not significant at conventional levels (p=0.174), which may be due to some measurement error in the dependent variable.

Since our dependent variables are censored – CPIA between 0 and 6 and Economic Freedom Index between 0 and 100 – we have estimated a Tobit model as a second robustness test. Results from this estimation are presented in Table 9, for both CPIA and Economic Freedom as the dependent variable. Table 9 indicates that censoring does not affect the main results: coefficient estimates and significance levels for policy actions and length of engagement are similar to those from the base model.

20
Aydemir, Abdurrahman, and Murat Kirdar. 2017. “Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition.” _European Economic Review_ 98: 282-315.

Baez, Javier E. 2011. "Civil Wars beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees." _Journal of Development Economics_ 96(2): 391-408.

Balkan, Binnur, and Semih Tumen. 2016. “Immigration and prices: quasi-experimental evidence from Syrian refugees in Turkey.” _Journal of Population Economics_ 29:657–86.

Balkan, Binnur, Elif Ozcan Tok, Huzeyfe Torun, and Semih Tumen. 2018. “Immigration, Housing Rents, and Residential Segregation: Evidence form Syrian Refugees in Turkey.” IZA Institute of Labor Economics Discussion Paper Series No. 11611.

Bodvarsson, Örn B., Hendrik F Van Den Berg, and Joshua J. Lewer. 2008. “Measuring Immigration's effect on Labor Demand: A Reexamination of the Mariel Boatlift.” Labor Economics 15(4): 560–74.

Borjas, George J. 2017. “The Wage Impact of the _Marielitos_ : A Reappraisal.” _Industrial and Labor Relations Review_ 70(5): 1077–110.

Borjas, George J., and Joan Monras. 2017. “The Labor Market Consequences of Refugee Supply Shocks.” _Economic Policy_ (July), 361–413.

Braun, Sebastian Till, Anica Kramer, and Michael Kvasnicka. 2017. “Local Labor Markets and the Persistence of Population Shocks.” IZA Discussion Paper Series No. 11077.

Braun, Sebastian, and Henning Weber. 2016. “How do regional labor markets adjust to immigration? A dynamic analysis for post-war Germany.” Deutsche Bundesbank Discussion Paper No 05/2016.

Braun, Sebastian, and Michael Kvasnicka. 2014. “Immigration and structural change: Evidence from postwar Germany.” _Journal of International Economics_ 93: 253–69.

Braun, Sebastian, and Toman Omar Mahmoud. 2014. “The employment effects of immigration: Evidence from the mass arrival of German expellees in post-war Germany.” _The Journal of Economic History_ 74 (1), 69-108.

Calderón-Mejía, Valentina, and Ana María Ibáñez. 2016. “Labour market effects of migration-related supply shocks: evidence from internal refugees in Colombia.” _Journal of Economic Geography_ 16: 695– 713.

Card, David. 1990. "The Impact of the Mariel Boatlift on the Miami Labor Market." _Industrial and Labor Relations Review_ 43 (2): 245-57.

Carrington, William J., and Pedro J. F. de Lima. 1996. "The Impact of 1970s Repatriates from Africa on the Portuguese Labor Market." _Industrial and Labor Relations Review_ 49(2): 330–47.

Ceriani, Lidia and Paolo Verme. 2018. “Risk preferences and the decision to flee conflict.” The World Bank _Policy Research Working Paper Series_ No 8376.

Ceritoglu, Evren, H. Burcu Gurcihan Yunculer, Huzeyfe Torun, and Semih Tumen. 2017. “The Impact of Syrian refugees on natives' labor market outcomes in Turkey: Evidence from a quasi experimental design.” _IZA Journal of Labor Policy_ 6(5): 1-28.

Clemens, Michael A., and Jennifer Hunt. 2016. “The Labor Market Effects of Refugee Waves: Reconciling Conflicting Results.” NBER Working Paper Series 23433.

20
**Figure 26: Herfindahl-Hirschman Index for domestic air travel in the Caribbean countries (2013)**

<!-- Start of picture text --> Haiti 10,000<br>Belize 3,820<br>Bahamas 4,194<br>Jamaica 4,945<br>Trinidad and Tobago 10,000<br> - 2,000 4,000 6,000 8,000 10,000 12,000<br>Herfindahl Index<br>Country<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ The intra-regional market is also highly concentrated, and shows three monopolies, with LIAT, in St. Kitts & Nevis, St. Vincent & the Grenadines and Dominica (Figure 27). In two cases these are perhaps state mandated through bilateral agreements, since St. Vincent has an important ownership stake in LIAT, and Dominica has also just invested. This theory is supported by the fact that Caribbean Airlines also has turboprop aircraft that could easily fly into Dominica and St. Kitts, though not perhaps into St. Vincent, yet the airline does not as per 2013 scheduling data.

57
jobs created per megawatt of energy generation capacity, similar to the work by Kammen and others mentioned previously, drawing on India’s National Action Plan on Climate Change. Such estimates are likely to show a bigger contrast between renewable and traditional energy than do estimate of jobs per dollar spent, because the cost of an extra megawatt of generating capacity is likely to be higher with the former. Unlike Strand and Toman, where the focus is on evidence from advanced industrialized countries, they conclude that low-carbon employment is one of the key co-benefits of promoting the renewables sector in India. They find that solar power is more labor intensive than wind power and better able to meet India’s requirements for small-scale, off-grid power; biomass, ‘green’ transport and public works in water and forest management are also seen as attractive ways of achieving both employment and environmental objectives. One serious problem with Upadhyay and Pahuja’s study is that the costs of different renewables scenarios are barely considered; the perspective is more of a quantitative planner. It is also interesting to note that the study explicitly rules out job losses in fossil-fuel power generation, arguing that “investment in fossil fuels is going to be a mainstay” of the Indian economy, at least in the nearer term, given the need to expand availability and reliability of electricity supplies as well as to pursue environmental objectives. This, together with an implicit assumption of surplus labor, allows the authors to dismiss job displacement as an issue.

Upadhyay and Pahuja’s study is a contribution towards the Global Climate Network (GCN) report _Low Carbon Jobs in An Interconnected World_ (GCN, 2010), which also covers China, South Africa, Brazil and Nigeria, as well as some developed countries. The study of China emphasizes the potential employment losses from the planned sharp reduction in the energy intensity of Chinese industry, but notes that this could be outweighed by increased employment in renewables and – quantitatively, much more important – the shift of the Chinese economy towards services and away from heavy industry. But the latter shift cannot be seen as a specifically ‘green’ objective. The study of South Africa focuses on direct job creation potential in renewables and draws the conclusion that “significant opportunities for employment lie in clean-energy sectors and can be harnessed if the South African government scales up its renewable energy ambitions.” But again, this result flows from the fact that renewable energy production is more labor intensive than traditional energy sources. The question of the labor productivity and costs of renewables compared with traditional energy is not tackled.

18