rafmacalaba/data-use-mentions-extended huggingface dataset:
.028|0.099|
|Credit Bureau & Phone<br>Indicators|Logistic Stepwise|0.772|0.145|0.090|0.059|0.114|0.093|
|Credit Bureau & Phone<br>Indicators|OLS FE|0.645|0.054|0.025|0.010|0.046|0.099|
|Credit Bureau & Phone<br>Indicators|Random Forest|0.711|0.095|0.060|0.038|0.083|0.095|
|Credit Bureau & Phone<br>Indicators|Random Forest Weekly Ensemble|0.618|0.039|0.018|0.007|0.030|0.099|

_Note:_ All measures are out of sample. AUC represents the area under the receiver operating characteristic curve; H represents the H- measure (Hand, 2009). R2 measures aggregated over multiple fold draws. Mean Squared Prediction Error (MSPE) can be swayed by absolute differences, which are not relevant for the decision problem we consider: when a lender is setting an approval threshold only relative differences matter. An outlier observation was removed for computing the MSPE of the OLS Fixed Effect (FE) models. Source: authors’ analysis from telecom data.

10
had also been employed before the program deployment. We thus also estimate equation (7) for women continuously employed during the study period to examine if the results are influenced by changes in employment status. The results are reported in Appendix Table D9. One can see that the estimated coefficients are similar to those in panel A of Table A3. In Appendix Table D10, we cluster the standard errors at the district level and the significance levels of the estimated coefficients are similar to those in panel A of Table A3 and Appendix Table D9.

> 12The result in working hours may prompt inquiries about wage effect. We, therefore, also use the InP section data to examine the impact on employed women’s wages and find a statistically insignificant increase in treated states compared to control states following the scheme’s implementation, as shown in column 1 of Appendix Table D11. However, it should be noted the wage results presented in Appendix Table D11 are only suggestive evidence. We observe wages for 64 % of the employed women featured in Table A3. Most of this attrition is not due to selection bias (i.e., respondents refusing to answer the income survey). Instead, when matching the PoI and the InP datasets, we find that over 70 % of the unmatched observations result from interviewers being unable to reach the household within the available fixed period.

> 13Goel (2017) and Thomas (2011) classify skill levels by education levels. Asuyama (2012) categorizes “people who were illiterate or have only received education below the primary level” as individuals with the lowest skill level. Mehrotra, Gandhi and Sahoo (2013) also group people who have below primary or only primary level of education as individuals with low level of skill. In this paper, we follow previous literature and group skill level by education level, setting individuals who have not received education or have only attended primary school as low-skill individuals.

> 14These results are based on women’s skill levels before the implementation of the scheme
<br>0 0.0<br>1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> _Source:_ Calculation based on LFS data.

**Unemployment duration.** As in other transition countries, long-term unemployment is one of the most acute problems facing the Latvian labor market. Long-term unemployment generally refers to the unemployment spells which exceed one year. Appendix Table 5 shows the distribution of unemployed persons by education and duration of unemployment. Appendix Figure 6 shows that between 2000 and 2002 Latvia performed much better than other countries of Central and Eastern Europe in reducing longterm unemployment. In the second quarter of 2002 incidence of long-term unemployment in Latvia, 42 percent, was at the same level as in the EU-15. This was the first year since 1996 in which less than half of the unemployed (annual average 46.4%) were unable to find a job within the year. In 2003 incidence of long-term unemployment decreased to 43.8%.

Appendix Table 6 describes how long-term unemployment has evolved in the Baltic countries since 1997. A decrease of this indicator by 12 percentage points between 2001 and 2002 is cause for optimism in Latvia. Still, the design of policies for fighting long-term unemployment requires a deeper insight into the following issues: (i.) Identifying individuals who are most likely to experience unemployment, and those who are most likely to experience long-term unemployment conditional on job loss; (ii.) Determining 16
in secondary education and tertiary education are 99% and 87%,<sup>17</sup> respectively), but Turkey still has large gender gaps in labor force participation, formal employment and entrepreneurship. The female-male gender participation gap, assessed at 46.6% in 2018 according to WEF data, indicates a significant gender-related policy distortion τG that limits female LFP. The World Bank SCD identifies this distortion as a binding constraint to growth in Turkey, giving it a score of 4 (see column 2 of Table 1), which suggests that this constraint is associated with a large λG and G≡Y. Therefore, lifting the distortions responsible for the gender participation gap is expected to produce a large positive direct welfare gain. In addition, failure to close this gap signals an inefficient policy stance<sup>18</sup> that may give rise to large negative interaction effects when policies

> 17 Source: WEF 2018, http://reports.weforum.org/global-gender-gap-report-2018/data-explorer/#economy=TUR. 18 Turkish women fare badly in terms of other workforce indicators. Compared to men, they are 43% more likely to be unemployed, twice as likely to be employed part time and three times more likely to be employed in unpaid work. Partly because of this gender workforce participation gap, but also due to lack of political participation, Turkey ranks 130<sup>th</sup> of 143 countries on the WEF’s Global Gender Gaps Report (2018).

13
police because of the fear of further attacks, stigma, and ostracism.

During episodes of communal violence, women are often targeted to create fear and also because they are viewed as transmitters of the culture of a particular community. Exposure to such physical and sexual brutalities can have deep physical and emotional effects on the victims, their families, their communities, and their marital decisions. The fear of sexual violence and stigmatization can lead to early marriages of women. Due to the communal conflict in Gujarat in 2002, we expect a relatively larger effect on Muslim women’s marital age since they were the primary victims of sexual violence. In this paper, we study the effects of the Gujarat riots of 2002 on women’s marital outcomes.

# **4 Data**

This study uses data from the fourth round of the National Family Health Surveys (NFHS-IV), based on the Demographic and Health Surveys (DHS), conducted in India during 2014-15. These are nationwide repeated cross-sectional surveys that are representative at 6
36

# The DPI compared to other databases

The DPI seeks to add to existing knowledge on political institutions, and to gather into one source information that had previously been scattered. Therefore, it is reasonable to ask how this database compares to other political databases, in terms of detail, coverage, and focus. Comparisons to two illustrate the potential contribution of the DPI to future empirical research in cross-country political economy. One of these is the Polity III database compiled by Gurr, et al. (1998), and the other are the objective assessments of government checks and balances created by Henisz (1997).

Polity III. Gurr, Jaggers, and Moore (1998) have compiled Polity III, covering a time period stretching from 1800 to 1994. Polity III covers 156 nations - all those with populations above 500,000 - and 20 areas that were later amalgamated into a single nation-state (Saxony, Hesse, etc. into Germany; Lombardy, Tuscany, etc. into Italy). By comparison, our database covers all sovereign nations with populations above 100,000 - in all, 177 countries, for the years 1975 - 1995.

Polity If includes numerous subjective indicators of the political and institutional environment in countries. However, these tend to be highly aggregated. For example, subjective measures of regulation, competition, and openness in executive recruitment incorporate information about the elections or their absence; fraud; coups; scope of participation in the selection of the executive; military involvement; the number of competing parties; and other relevant factors. The weights assigned to these different characteristics, all of which are appropriate to consider, are not clear, however. The Polity III variables “regulation” and “competitiveness” of participation, like most of the Polity III variables, exhibit similar highly aggregated characteristics. Implicit in their codings are evaluations of
analysis. These variables are available in the first round of the Peruvian Young Lives Survey (2002). Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. Mental health index 1 is the standardized average of the SRQ-20 items. Panel A presents statistics of mental health indexes. Mental health index 2 and 3 are standardized indexes estimated using principal components and factor analysis, respectively. Panel B presents the % of mothers reporting being exposed to any of the four shocks. Panel C shows correlations between the mental health indexes and shocks. *** and ** indicate statistical significance at 1% and 5%, respectively.

27
_Prudential measures_ <mark>include temporary relaxation of certain key regulatory and supervisory requirements,</mark> including changes in capital requirements, limits on exposure, concentration, loan-to-value ratios, minimum reserve requirements, and cancelation of stress tests. By slowing down the decline in banks’ regulatory capital ratios, these measures reduce the rate at which banks draw buffers down, spreading the recognition of losses and allowing a given amount of equity to support a larger lending volume.

_Borrower assistance_ include measures to supply funds to firms and households due to the loss of revenue/income from the extended lockdowns. These include government liability guarantees for newly issued or existing wholesale financing, direct credit lines to strategic sectors, state support for interest-free loans and fully replenishable working capital financing. Other measures include enhancement of deposit protection schemes and simplified programs of loan restructuring.

_Monetary policy_ are interest rate decisions (e.g., benchmark rate, repo rates, etc.), central bank purchases of government securities ( _Quantitative Easing_ ), and credit easing, which typically consist of purchases of private sector debt in secondary markets. Given the different nature of conventional and unconventional monetary policy measures, we further split this category into _Policy Rate_ announcements and _Asset Purchases_ (both public and private bond buying programs and purchases of asset-backed securities).

Our final sample contains policies in 17 developed and 28 developing countries, for which we have stock market and policy data (Table 4). Some European countries such as, Austria, Finland, and Netherlands, did not have domestic financial policy initiatives. We include banks of these countries in days of announcements by the European Central Bank. Other Euro countries, for example, France, Germany, Italy, and Spain have both financial support policies taken at the 16
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**Establishment-Level Productivity Measurement** Total factor productivity is measured as the log of sales minus the logs of labor, capital, and materials, each multiplied by their output elasticities in the production function. We estimate production functions in each case using the control function approach with refinements by Ackerberg et al. (2015) in the longitudinal establishment panel compiled from the Economic Census by Busso, Fentanes T ́ellez and Levy Algazi (2019).<sup>17</sup> Specifically, for each treatment and control industry we estimate a distinct Cobb-Douglas production function for sales as a function of labor, capital, and intermediate inputs. The factor shares are not restricted to sum to one, and indeed are consistent with decreasing, constant, and increasing returns to scale across different specifications. To account for potential omitted variable bias when estimating the production function, materials are included in a control function to stand for the component of productivity that is unobserved to the econometrician, but was forecast by the firm when it made its decision to fix capital assets in the previous period, as in Levinsohn and Petrin (2003). In the control function we also include the log of the firm’s specific wage rate, which is set equal to zero if the firm has no remuneration; a dummy for whether the firm has no remuneration; and a dummy indicating whether the antitrust decision has occurred. These choices follow the advice of De Loecker and Syverson (2021) to control for factors that could affect firms’ input decisions or the competitive environment. The estimation also includes a selection function to account for potential attrition bias as in Olley and Pakes (1996), though in practice this made little difference to the results, nor did including investment instead of materials in the control function.

Distinct production functions are estimated for each treatment industry and all control industries in each case. When estimating the production function for control industries, fixed effects for each six-digit control industry are included in the control function. Total factor revenue productivity
of the phone survey sample comparable to the full sample. These weights were applied in all our analyses to address potential biases due to systematic sample attrition and non-response (Wooldridge, 2007; Korinek et al., 2007). Nonetheless, to the extent that our results fail to control for this bias, our results may be taken as lower bounds of the actual impacts of the conflict.

# **2.3 Measuring Key Outcomes**

The paper focuses on three broad categories of outcomes that are observed both in the pre-war and post-war-onset phone surveys. These are: (i) the household food insecurity experience, (ii) Households’ access to food markets, and (iii) participation in labor market and livelihood activities. The 2019 face-to-face survey collected a wealth of information on household food security, employment and labor market participation, consumption, and other socio-economic characteristics that serve as baseline for the phone surveys. The phone survey also covered these topics which are of interest in this study, particularly household food security and participation in various livelihood activities. Because phone surveys cover a few modules, the surveys implemented similar questionnaires across rounds but followed a modular approach – some modules were dropped, and others kept or added in different rounds. Thus, we observe food security and labor market outcome indicators across multiple pre-war and post-war-onset rounds but not necessarily across all twelve rounds.

# **_Food Insecurity_**

Food insecurity is measured using the Food Insecurity Experience Scale (FIES), an experiencebased food insecurity metric developed by the Food and Agriculture Organization (FAO) of the United Nations that is widely applied to measure prevalence of food insecurity (FAO, 2014a).<sup>15</sup> The FIES relies on respondent’s direct responses to an eight-question survey module, referring to experiences of difficulties to access sufficient and nutritious food in the last 30 days. The FIES elicits responses based on whether the respondent or another household member (1) was _worried_ about having enough food to eat, (2
# **IV. A GLOBAL COMPARISON OF MALAYSIA’S PATTERNS OF GROWTH, PRODUCTIVITY, AND TRADE**

In this section we document the nature of Malaysia’s progression to achieve middleincome status over the past several decades. In particular, we focus our analysis on the sectoral patterns of growth in productivity and the growth and sophistication of Malaysia’s exports to the rest of the world. The first section presents a series of growth accounting calculations that separates Malaysia’s recent economic growth into the contributions from increased employment, capital, labor productivity, and a residual total factor productivity measure. We also compare these estimates to several other comparable countries to place the Malaysian experience in an international context. The second section reviews the performance of Malaysian exports in recent years, and presents our calculations on the sophistication of exports separately for goods and services. Again, we place Malaysia in an international context through a series of cross-country regressions on the interaction of trade and economic growth. An accurate exposition of what propelled Malaysia into a middle-income status will be indispensable for charting the future path to high-income status.

## **Growth Accounting Calculations**

We present a series of growth accounting calculations for Malaysia for the period 19902007. The growth accounting framework originates from the concept of an aggregate production function that relates output to the contributions of factor inputs (namely, capital and labor) and a shift component normally associated with an adjustment of the inputs for quality. Assuming competitive markets where the factors are paid their marginal products, we can derive a simple index number formulation that relates changes in output to changes in the factor inputs plus a residual term typically referred to as total factor productivity (TFP).<sup>13</sup> For the case of Malaysia, we use measures of output (value added) from the national accounts as published by the Department of Statistics (DOS), and employment estimates are obtained from the labor force surveys published by DOS. The labor input is adjusted for quality using a
3orroving<br>Rate<br>Nominal<br>(C)|45.8<br>|45.3<br>|53.1<br>|58.5<br>|58.4<br>|
|3orrowing<br>Rate<br>Nominal<br>Compounded (D)|54.28<br>|53.59<br>|64.64<br>|72.63<br>|72.48<br>|
|:ompensating<br>Balances<br>(E)|0.38|0.29<br>|0.32|0.33|0.3<br>|
|Interest<br>Rate on CD's (F)|0|<br>0|5.1|7.5|14.6|
|Effective<br>Cost<br>of Loans to Borrower||||||
|Nomjnal((C-(E.F))/(1-E))<br>|73.87<br>|63.80<br>|75.69<br>|83.62<br>|77.17<br>|
|Nominal Compounded ((D-(E.F)))I(l-E))|87.55|75.48|92.66|104.71|97.29|

SOURCE: Central Bank of Turkey. Capital Markets Board. Institute of Bankers. World Bank Staff Estimates
Obstacle (cell average)|1.016|0.715|0|3.955|43198|
|Crime Losses Y:1 N:0|0.178|0.383|0|1|43173|
|Firm Inspected Y:1 N:0|0.598|0.49|0|1|44070|
|How Much Of An Obstacle: Tax Rates|1.768|1.322|0|4|43885|
|How Much Of An Obstacle: Labor Regulations?|0.963|1.132|0|4|44003|
|GDP per capita (logs)|8.598|1.005|6.462|10.264|44539|
|Population (logs)|16.266|1.35|13.329|19.345|44539|
|Urbanization (%)|48.113|19.99|0|95.045|44539|
|<br>WBL Index|0.691|0.164|0.176|1|44539|
|Women can work the same night hours as men|0.825|0.38|0|1|44539|
|Women can work in the same industries as men|0.463|0.499|0|1|44539|
|Ratio of Female to Male Population|50.251|5.534|0|56.84|44539|
|Fertility Rate|3.296|1.693|1.23|7.57|44539|
|<br>Starting a Business (DTF)|67.956|20.73|0|95.81|44539|
|Inflation (%)|7.028|7.885|-8.975|81.138|44539|
|Labor Productivity (2009 USD, logs)|9.738|2.145|-5.089
labor/immigration literature has noted that migrants often have a greater likelihood of being self-employed at destination than natives, and has sought to explain why this is the case (e.g. Portes and Yiu, 2013), and to estimate their contribution to the economy of the receiving region (e.g. Fairlie and Lofstrom, 2015). The development literature has, in contrast, focused on the role of remittances in funding self-employment activities for remaining family members (e.g. Woodruff and Zenteno, 2007), and on the tendency of return migrants to engage in self-employment activities (e.g. McCormick and Wahba, 2001; Mesnard, 2004). In both cases, the direction of interest has been from migration to self-employment outcomes, complementing our focus on the link from self-employment to migration decisions. The second is literature examining the impacts of social assistance programs like cash transfers, pensions, and job guarantees on migration. Adhikari and Gentilini (2018) provide a recent review of these studies, noting that unrestricted cash transfers to poor people have tended to increase migration, by overcoming credit constraints and providing some insurance against risk. In contrast, they note that place-based programs such as India’s rural public employment guarantee studied by Imbert and Papp (2019) have reduced (seasonal) rural to urban migration. We add to this literature by showing the causal impacts of a range of self-employment and entrepreneurship programs on migration.

The remainder of this is structured as follows: section 2 discusses what paper migration theory predicts for the relationship between self-employment and migration; section 3 uses different panel data surveys to answer the descriptive question of whether the self-employed are more or less likely to migrate than wage workers and the not employed; section 4 then analyzes different randomized experiments to causally examine whether interventions that increase self-employment in turn reduce migration; and section 5 concludes.

# **2 What does economic theory predict for the relationship between self-employment and migration?**

A economic model of has individuals or households the
needed to telework and pay for telecommunication services such as the internet and the proportional cost of electricity used for telework purposes, among other benefits.<sup>8</sup> One benefit of telework is the possibility that workers can better balance the demands of paid employment and unpaid household work. However, digital skills and access to technological equipment are not an option for many employees in Mexico, in particular women. Women and men do not have the same opportunities, uses, and access to technology. According to the 2020 ENDUTIH survey,<sup>9</sup> 19 percent of women did not know how to use a computer in 2020 compared with 15 percent of men in the same situation. Moreover, not all Mexicans can afford the cost of a computer. For instance, only one-eighth of women knew how to use a computer but do not have access, unlike one-ninth of men.<sup>10</sup> In this paper, we aim to contribute to understanding women’s labor force and workplace decisions in emerging economies by studying the characteristics of teleworkers in Mexico during the COVID-19 pandemic, distinguishing between being an employee or being self-employed. We use data from the National Survey on Availability and Use of Information Technology in Households (ENDUTIH) carried out in 2020 to estimate the extent to which telework can influence labor supply decisions. We estimate the probability for men and women to choose between five states: out of the labor force, working on-site salaried, working on-site self-employed, teleworking salaried, or teleworking self-employed using a multinomial framework.

Our paper differs from earlier empirical work on workplace choice<sup>11</sup> as it focuses on telework, adopting a narrower definition of working at home, which considers using computers for work at home and discriminates between self-employed and salaried workers. Moreover, this paper adopts a micro perspective to understand what characteristics determine telework. We argue there are two factors at play: first, the existence of fixed costs to working on-site,