rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table A18: Impacts of mental health on weekly labor income – Inverse hyperbolic sine transformation**

|Dependent variable:|Weekly l<br>($1,0|abor income<br>00 AUD)|
|---|---|---|
||FE<br>(1)|IV-Logit-FE<br>(2)|
|Standardized mental health scores|-0.005***<br>(0.001)|-0.192***<br>(0.055)|
|_First stage of 2SLS (dependent variable is_|_standardized m_|_ental health scores)_|
|Trauma experience*Year after arrival||-0.039***<br>(0.009)|
|Kleibergen-Paap F stat.||19.635|
|AR 95-CIs||[-0.398, -0.123]|
|Dep. Mean|0.455|0.455|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|3,687|3,687|
|Number of individuals|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

50
WPS3820

# **How Important Are Financing Constraints?**

## **The role of finance in the business environment**

### **Meghana Ayyagari**

### **Asli Demirgüç-Kunt Vojislav Maksimovic***

**Abstract:** What role does the business environment play in promoting and restraining firm growth? Recent literature points to a number of factors as obstacles to growth. Inefficient functioning of financial markets, inadequate security and enforcement of property rights, poor provision of infrastructure, inefficient regulation and taxation, and broader governance features such as corruption and macroeconomic stability are all discussed without any comparative evidence on their ordering. In this paper, we use firm level survey data to present evidence on the relative importance of different features of the business environment.  We find that although firms report many obstacles to growth, not all the obstacles are equally constraining. Some affect firm growth only indirectly through their influence on other obstacles, or not at all. Using Directed Acyclic Graph (DAG) methodology as well as regressions, we find that only obstacles related to Finance, Crime and Policy Instability directly affect the growth rate of firms. Robustness tests further show that the Finance result is the most robust of the three. These results have important policy implications for the priority of reform efforts. Our results show that maintaining policy stability, keeping crime under control, and undertaking financial sector reforms to relax financing constraints are likely to be the most effective routes to promote firm growth.

#### **Keywords: Financing Constraints, Firm Growth, Business Environment**

#### **JEL Classification: D21, G30, O12**

#### World Bank Policy Research Working Paper 3820, January 2006

_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
output.

While both studies confirm the effectiveness of the interventions at the local level, they are limited by their respective data and corresponding research designs. Relying on the Annual Survey of Industries, the main results of Chaurey (2017) are based on formal companies of the manufacturing industries, which may bias the results by omitting services sectors and informal companies. The Annual Survey of Industries are not spatially granular either, which poses a challenge to Chaurey’s (2017) most stringent specification that relies on boundary discontinuity but compares district-level outcomes (Baum-Snow and Ferrira 2015). The findings of Shenoy (2018) depend on nighttime light intensity data, which are spatially much more granular, and more conducive to boundary discontinuity design. However, nighttime light intensity remains a proxy for economic activities. Additionally, nighttime light data measure aggregated economic output and are not conducive to investigate the specific ways that the interventions affect economic agents’ behaviors.

In this paper, we revisit the question on the effectiveness of the centrally sponsored interventions in Uttarakhand and address several limitations of previous studies. First, we use the 2013 round of the Economic Census to cover all modern economic activities (non-farm activities) in both the formal and informal sectors. Second, we follow a rigorous boundary discontinuity design and compare outcomes at the town and village level while controlling for distance to the border. Most importantly, we investigate outcomes at the town-sector level and explore the heterogeneity across industries in the incentives. Our results shed light on the specific channels through which these interventions affect employment and firm locations.

> 1 Alder et al. (2013) and Wang (2013) also find evidence supporting the effectiveness of the SEZs in China.

7
Among Vietnam’s wage workers,<sup>20</sup> the “informal wage” employers comprised the largest wage employment sector hiring about 6.8 million people in 2004.<sup>21</sup> Workers who worked for the state sector earned the highest hourly wage on average (VND 7,106) followed by workers working for the foreign (VND 6,783), private (VND 5,112) and informal wage sectors (VND 3,902).

Columns 2 and 3 of Table 2 report summary statistics by gender. The wage differential between men and women, known as the “gender gap”, is relatively low as women earn on average an 11 percent lower wage relative to men for the whole sample. At first glance, women working in foreign firms appear to be experiencing the greatest degree of gender wage inequality as women are found to be paid about 40 percent lower wages relative to male counterparts whereas the gap is narrowest in the state sector (8 percent).<sup>22</sup> Columns 4 and 5 of Table 2 disaggregate statistics by skill levels which are measured by whether or not the workers have upper-secondary educational attainment (12 grades completed with upper-secondary education diploma). The wage premium for skilled workers, defined as the relative wage of skilled to unskilled workers, is 176 percent for the whole sample. The skill premium is largest in the foreign sector (where skilled wages are 183 percent of unskilled wages) and lowest for those who work for the informal wage sector (132 percent).

# **3.2. Model Specification**

> 20 About 31 percent of Vietnam’s workers held a wage job as the first job in 2004. The remaining 69 percent were self-employed of which about 73 percent worked in agriculture/fishery/forestry activities.

> 21 The figure is computed using the household weights available in the VHLSS 2004. The corresponding figures are 0.7 million for “foreign”, 4
largely neglected in the literature on international banking. We use new survey data on the minimum capital requirements for opening banks across countries, as well as recently available information on the general costs (fees, costs of procedures and forms, fiscal stamps, legal and notary charges, etc.) of starting up a business to study their importance in driving the share of local to total foreign claims.

Also, since banking is an informationally intensive industry, we reinterpret the role of variable transportation costs in the manufacturing trade/multinational firm literature as information costs that arise in the international banking context. However, contrary to the case of transportation costs, which have an unambiguously negative effect on the share of exports to FDI, the impact of informational costs on international banking is harder to measure and a priori unclear. On the one hand, there are the informational costs of screening and monitoring foreign clients, which tend to be higher the larger the “distance” (geographic, cultural, legal, etc) between the borrower and the bank (Petersen and Rajan, 1994, 2002; Berger et al., 2002; and Bonaccorsi and Gobbi, 2001). Because extending claims through overseas affiliates allows banks proximity to its foreign clients, local claims are expected to carry lower informational costs of screening and monitoring borrowers than cross-border claims. On the other hand, extending claims through overseas affiliates raises the costs of information and oversight faced by international banks’ CEOs and/or shareholders in trying to monitor the actions of managers and loan officers at these affiliates. In other words, extending claims through affiliates might increase information costs by augmenting the “distance” between bank CEOs, managers, and loan officers (Mian 2004). Thus, as a result, the net impact of information costs on the share of local claims is largely an empirical question, which we hope to address.

5
constraints, leaving little space for optimizing behavior that characterizes economic migrants.

As suggested by the regression analysis in Table 3, refugees eventually choose to go to places with higher economic opportunities, and those who have more assets are able to travel farther. Refugees with larger household size make fewer moves within the country because each movement is plausibly costlier for these households. Qualitative interviews suggest that households with social networks in the host country move significantly more often than households without social networks.<sup>10</sup> In Lebanon, families with higher educational attainment move more frequently than families with lower educational levels, probably because more job opportunities are available to educated individuals.

# **_C. Human Capital Profile_**

Before considering welfare after displacement, we provide evidence on basic demographics and, especially, the education and work-experience profiles of Syrian forced migrants in neighboring countries. We first consider absolute rates of educational attainment in the UNHCR data. Then we use the data on the pre-crisis Syrian labor force and our survey to provide some evidence of selection (on observables) into migration to Lebanon, Jordan, and Kurdistan with respect to education and previous work experience.

There are very few highly educated refugees in any of these countries, according to UNHCR registration data. Less than 1 percent of refugees have completed university in any of these countries, and only about 10 percent have completed high school (results not shown). The majority of refugees in each country in these data have finished between basic and middle school. Our survey suggests that the number of individuals out of school with a graduate degree (or more) might be understated in the UNHCR data, but the main conclusions about the educational profile of Syrian refugees remain qualitatively unchanged. Data from the Syrian Labor Force Survey in 2010 reveal how the average migrant compares to Syrians participating in their country’s labor market before the crisis.

10 Focus group participants in the qualitative survey corroborated the strong influence of networks in determining current location choice in Lebanon and Jordan. Refugees
br>Rwanda (2014) Rwanda (2017) Rwanda (2020)<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>5 20 100 5 20 100 5 20 100<br>Average firm size Average firm size Average firm size<br>All firms Informal firms<br>Fraction of firms Fraction of firms Fraction of firms<br>Fraction of firms Fraction of firms Fraction of firms<br><!-- End of picture text --> _Source_ : Own calculations based on census data from Burkina Faso (2016), Cameroon (2009), Ghana (2014) and Rwanda (2014, 2017 and 2020). The x-axis reports log firm size to ease visibility. Informality definition according to each country’s criteria. Red lines are size thresholds of 5, 20, and 100 workers.

Since data on the whole firm size distribution is scarce, our strategy first defines a statistical 10
# General Equilibrium E¤ects of Land Market Restrictions on the Labor Market: Evidence from Wages in Sri Lanka

## M. Shahe Emran<sup>1</sup>

George Washington University and IPD, Columbia University Forhad Shilpi World Bank JEL Codes: O10, J31, J61 Key Words: Policy Restrictions in land Market, Sales Restriction, Rental Restriction, Labor Market, Wages, General Equilibrium E¤ect, Interaction E¤ect, Travel Time, Malaria, Sri Lanka

> 1We would like to thank Joseph Stiglitz, Stephen Smith, Will Martin, Atonu Rabbani, Quy-Toan Do and Kazi Iqbal for helpful discussions and/or comments on earlier drafts. We are grateful to Dr. Galappathy and Chaitri Hapugalle for help with malaria data used in this paper. Somik Lall, Claus Astrap and Nobuo Yoshida also helped in getting access to di¤erent datasets for the study. The standard disclaimers apply.
(0.00501)|
|Dependency ratio in household|-0.0231<sup>_∗∗_</sup>|-0.0306<sup>_∗∗∗_</sup>||-0.00851|-0.0302<sup>_∗∗_</sup>|
||(0.00934)|(0.0102)||(0.0169)|(0.0152)|
|Female head of household|-0.00442|-0.00354||0.00176|0.0294|
||(0.0201)|(0.0222)||(0.0336)|(0.0320)|
|Asset index|0.00428|0.00473||0.0172|0.00562|
||(0.00644)|(0.00700)||(0.0142)|(0.0121)|
|Food insecurity index (HFIAP)|0.0297<sup>_∗∗_</sup>|0.0402<sup>_∗∗∗_</sup>||0.0381|0.0486<sup>_∗_</sup>|
||(0.0133)|(0.0151)||(0.0332)|(0.0263)|
|Observations<br>_R_<sup>2</sup>|6664<br>0618|6671<br>0636|6671<br>0872|6671<br>0389|6671<br>0542|
|Fixed efects|.<br>Country|.<br>Neighbourhood|.<br>HH|.<br>Country|.<br>Neighbourhood|

Notes: All specifications consider the full cross-country sample of host respondents and include the full set of control variables described in Table A.3
) and slightly less rapid growth for low-income households (since the transfers that they receive are lower), leading to a marginally higher Gini coefficient. However, if transactions costs were significantly higher, the distributional consequences of the transfer scheme would also be more significantly weakened. This points to the importance of considering administrative costs and means of keeping them in check when transfer schemes are considered. This observation also applies to untargeted food subsidies since they typically also impose administrative costs.

Figures 3.6-3.8 compare selected MDG outcomes for the policy scenarios to the scenario with the same increase in food import prices but without any policy response ( _pwm+_ ). For MDG 1 (headcount poverty; Figure 3.6), the scenarios with targeted transfers dominate. Among the food subsidy scenarios, financing from grant aid dominates as it does not require either higher 25 In the net importer archetype database, the base-year savings rates (average and marginal) are considerably lower for rural and urban low-income households (6.6 and 1.8 percent, respectively) than for high-income households (with rates of 12.5 and 16.4 percent for the rural and urban high-income households, respectively). 26 To verify that the growth difference between the _trn+tax_ and _sub+tax_ scenarios is driven by these two differences (in savings and labor-intensity, the two scenarios were rerun with alternative assumptions for savings (scaling of household savings rates so that the private investment share in absorption is the same as under the _base_ scenario) and a fixed (base-year) unemployment rates for the full simulation period. When these two changes were combined, the growth and absorption outcomes for the two scenarios were practically identical. 27 However, these results are based on national relationships that do not consider likely positive effects of income redistribution on undernourishment and the other HDs as low-income households with relatively low HD are in a better position to buy goods and services food or other) and make other decisions (like reducing child labor), all of
cell level, , which can be acquired either with population censuses or through global population data sets such as WorldPop or Landscan<sup>17</sup> for example. ii aa Using this reduced form approximation and using the model’s own prediction of the spatial distribution of households in the urban area , we estimate annual and aggregate land value creation from resilience investments to be respectively US$ 8.5 million and US$ 425 million. These numbers are reasonably close ii aa to our fully fledged model results of US$ 7.6 million and US$ 379 million but they overestimate them by 12% in both cases. There is always a tradeoff between the benefits and the drawbacks of model complexity. Given the simplicity of producing estimates for the reduced form expression in terms of data and modeling requirements, we consider this to be a promising result and approach that could be used in locations where data is scarce so as to produce quick estimates of land value creation potential from resilience investments.

6 Robustness of the analysis and driving forces Systematic sensitivity analyses of model results are useful for two reasons. First, whereas most parameter values are the result of a careful calibration procedure, there nonetheless remains irreducible uncertainty, and it is useful to explore the consequences of deviations from the central estimations. Second, aggregate land value creation from resilience investments materializes across a long-time horizon, when housing and land markets have adjusted, new construction has occurred, and households’ locational choices have evolved.<sup>18</sup> When we look into the future, even assuming that the static parameters informing the model are perfectly calibrated, there are uncertainties pertaining to their stability over time as various agents’ behaviors can vary, for example as a response to exogenous changes in construction techniques.

To evaluate the robustness of the results, we recalculate aggregate land value creation not only using the calibrated model parameters, but also across various combinations of parameterizations. In particular, we recalculate land value creations across 3,000 scenarios in
to the question of the overall effectiveness of providing collective conditional conservation payments. While we cannot provide formal proof of additionality, it is very likely that our study’s outcomes are substantially better than absent financial incentives. As discussed in more detail below, we observe an average survival rate of about 34 percent, and this is substantially higher than the survival rates that are typically obtained in such dry agro-ecological conditions (Carey, 2020).<sup>9</sup> We assess the impact of the threshold versus the linear group payment scheme on both intermediate and ultimate measures of within-management-group cooperation. Theory suggests that, unless uncertainty is too high, threshold group payments should induce higher survival rates through stricter coordination between participants. Our endline survey included several questions that intended to measure the (self-reported) extent to which maintenance groups managed to cooperate. These measures are (i) how often members of a maintenance group organized and/or attended group meetings to discuss and plan maintenance activities, (ii) their assessment of their fellow group members’ trustworthiness, and (iii) the extent to which they feel that all group members contributed more or less equally to the maintenance effort.

These three variables constitute our intermediate measures of cooperation; the actual tree survival rate is the key output measure – and hence also our ultimate measure of cooperation. We employed independent tree survival verification teams to measure the number of trees still alive at endline. To be able to accurately determine the number (and share) of trees surviving we georeferenced all planted trees at baseline in every reforestation plot. Accurate estimation of tree survival rates was facilitated because, in line with standard reforestation practice, all saplings were planted in hand-dug holes of about 20 centimeters in diameter and about 10 centimeters deep. Still, it proved to be very challenging to systematically verify the health status of each individual sapling. Obviously, there is GPS measuring error when the newly planted trees were georeferenced, and again when we revisited the trees nine months later. We solved this
Policy Research Working Paper 7966

# **Abstract**

Economists and other social scientists are increasingly using big data analytics to address longstanding economic questions and complement existing information sources. Big data produced by online platforms can yield a wealth of diverse, highly granular, multidimensional information with a variety of potential applications. This paper examines how online job-portal data can be used as a basis for policy-relevant research in the fields of labor economics and workforce skills development, through an empirical analysis of information generated by Babajob, an online Indian job portal. The analysis highlights five key areas where online job-portal data can contribute to the development of labor market policies and analytical knowledge: (i) labor market monitoring and analysis; (ii) assessing demand for workforce skills; (iii) observing job-search behavior and improving skills matching; (iv) predictive analysis of skills demand; and (v) experimental studies. The unique nature of the data produced by online job-search portals allows for the application of diverse analytical methodologies, including descriptive data analysis, time-series analysis, text analysis, predictive analysis, and transactional data analysis. This paper is intended to contribute to the academic literature and the development of public policies. It contributes to the literature on labor economics through application of big data analytics to real-world data. The analysis also provides a unique case study on labor market data analytics in a developing-country context in South Asia. Finally, the report examines the potential for using big data to improve the design and implementation of labor market policies and promote demand-driven skills development.

This paper is a product of the Education Global Practice 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 authors may be contacted at snomura@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of