rafmacalaba/datause-encoder-data huggingface dataset:
point increase in the probability that a firm will have access to credit, a 5 percentage points decline in the interest rate charged on loans, a 7 month extension in loan maturity, and a 4 percentage point increase in the fraction of working capital financed by banks. On the other hand, the establishment of a credit registry does not have a robust effect on firm financing except for a 3 percentage point increase in the fraction of fixed assets financed by banks.

There are a number of reasons why the introduction of a credit registry might not have a significant impact on firm financing. First, credit registries are often used for supervisory purposes and, hence, might have high minimum loan limits. Second, they might not provide positive and negative information, which is most useful to financial institutions. Third, to the extent that they are run by the government, in countries with bad bureaucracies, they might not function effectively and, hence, might not be used often. Given that we do not find registry reform to affect firm financing, in the rest of the analysis, we concentrate solely on credit bureau reforms.

# **_Differences in CB scope and quality_**

In our baseline estimations for CB reform, we identified the impact of the introduction of a credit bureau, while treating all CBs in the same way. However, CBs may differ substantially depending on the breadth of coverage and quality of data. Countries that introduce a CB with more dimensions of information that, for example, covers more firms, conveys both positive and negative information, includes information from non-financial institutions, maintains a longer history etc, could differentially impact the effect of CB reform on firm financing. We test this hypothesis in our empirical analysis by introducing an interaction term of CB reform with the Doing Business _depth of credit information index_ and, separately, with the _CB coverage_ 13
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- f) Excessive influence activities in the agricultural sector to induce (possibly, bribe) power utility officials to increase agricultural supply, and to reduce illicit use (including direct power losses). Such activities are socially costly, and could in addition involve increased externality costs as noted under point d). Similar influence activities can be exerted by illicit electricity consumers, to increase illicit use. Such activities are directly countervailing to the former. A “zero-sum game” situation may easily arise, where different types of influence activities have impacts in opposite directions, with no overall major change in outcome.

- g) Inefficient incentives of the power utility to stem losses, when neither the overall utility (it bottom line) nor its employees are directly rewarded for this; see also d) and e).

- h) Insufficient incentives of the power utility to provide good and reliable service to the agricultural sector. Such lack of incentives is exacerbated by the lack of payments for electricity delivery (as also stressed by Strand (2011)).<sup>10</sup> Above, this loss takes the form of directly reduced utility of agricultural users, for a given power supplied.

In sum, these various misallocations make up a major, and somewhat confusing, set of overall distortions, when the entire situation for this system is compared to a (hypothetical) “firstbest” situation. Not all distortions go in the same direction; and some of the distortions tend to partly eliminate the adverse effects of other distortions.

An important next step in this analysis would be to obtain a better empirical understanding of the quantitative distortions caused by inefficient electricity pricing and supply, in situations such as those facing Indian farmers. Initiating that empirical work in turn requires additional effort to gather the disaggregated empirical data necessary for estimating the various relationships, which is an important issue for future research. This requires additional surveys to be done of farmers’ water use and pumping practices; willingness to pay e.g. in terms of higher water prices for better service, including the value of water meters; surveys of additional features such as how crop choice might depend on effective water prices; and various features of pumping equipment. It would also be important in such surveys to include groups of farmers with water metering, wherever feasible. Such data should also include supply side data, and data for water table depth and magnitude of groundwater extraction externality, which are likely to vary significantly by location.

> 10 See also e g Bardhan and Mookherje (2006), Muhairwe (2009), and Walker et al (1999).

15
52

*ahl _I_

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||Implied Priv<br>PRiMary|ete lessra by Ue<br>seinior<br>Nish|b.Lim<br>Uwe?<br>9mi.sity|
|---|---|---|---|
|Without fixed-efoect<br>controle|t. <sup>1 2</sup>b<br>(21.1)|11Sb<br>(43.4)|12 .1<br>(27.6)|
|Withcomunityand<br>familyfixed-Offects|S.Zf<br>(1.32)|.6Zb<br>(35.3)|10a0<br>(24.?)|
|Hales:<br>Without fixed-eftect<br>controls|.<br>7<br>b<br>(2.64)|*.2Xb<br>(10.6)|*.22b<br>(6.41)|
|With com_nity and<br>familytixod-effecte|6.12 b<br>(1.43)|.<sup>2 </sup>4b<br>(10.3)|*4.t b<br>(5.23)|

aA logaritbhic monthly ***aenin&c** function s *estimated pelns _im MA_ _ _ that includes dumy variables for nine levels of sehoolias md qyeetatle la **age.** Parameters are estimated for the differeace between **ual m* femole** coefficients for all varishbles inluding the iatereept. ith ee*- it% *A housebold tixed effecte are believed to eontrol fez pooeable sael qualty variation and the *ftfot of tfamiy background an earninas **toe** **_test)._ hft** - tanately the ordinary resrosalon esti&ates and these t eluiima the tfied effects are for diftereat samples as notod ln tbe test, raising the pesebility that the diffteences reported may be due to the different **sampes Mtd** not due to the introduction of the tixed-offtet sontsels.

bthe absolute value of t ratios are repoeted in Parentheses boma%th the coot ficients in the ease ot tamle returns, and fot th* ditlefe i*e beftwe Oh male and female a*grest"s coe ffcients beneath th maet Reurms. Thuas a fte* siticant t ratio undeg a "a*le &surn e.g. *eseadaey. suests that **Ut** *OItL of return on schoolin got **mno and** weme difest by a StetIUSU*AX& s&isaitlet amount in this pooled _earnings repression._ Source: sebhsea and *eoLalihas, lobbstable A.
31 Among men, quarter-by-quartereamingsestimates reported by Bloom (1990:163) indicate that WAD participants were reemployed sooner than would have otherwise been the case. But ultimately, as seen in Table 5, the employment opportunities of male participants were no better than for members of the control group. The Houston program also allows the dffferential effect of Tier I versus Tier l/il services to be calculated. Despite the higher costs of Tier II services (which were almost exclusively CT), the evidence suggests that essentially no additional gains accrued from adding Tier II services to job search assistance.

Authors of the evaluation reports for each of these three demonstrations take care in interpreting their Inconclusive or even negative results for skill training. Caveats mentioned include the difficulty of drawing reliable inferences from small sample sizes; the problem that program participants undergoing skill training have relatively little time left to receive placement assistance (given demonstration periods of fixed length); the scarcity of training providers capable of putting together highquality, short-duration training courses on short notice; and the possibility that the CT provided is either not saleable in the local labor market or not of particular interes **t** o the client population. On the last point, Bloom (1990:139) notes forthrightly that the Tier II training programs In blue-collar skills were not well matched to the backgrounds and interests of the mostly white-collar participants in the Houston WAD project.

For the New Jersey Ul Demonstration, Corson and others (1989) present quarterly net impact estimates of treatment effects for the first year following the date of filing the initial Ul claim. Table 5 indicates that by the second quarter after filing, participants receiving JSA-only and JSA/reemployment bonus services were enjoying eamings that are significantly higher than those measured for the control group. By the fourth quarter, however, the eamings effect of both treatments tailed off to essentially zero. Looking at Ul benefits, the difference between the JSA-only and JSA/reemployment bonus estimates is large and statistically signiticant Indicating a sizable incremental effect of the bonus in speeding up reemployment. For JSA plus skill training, however, there Is no evidence of either a permanent increase
# **3.2. Comparisons of establishment characteristics and formality across data sets**

We then focus on our universe of interest, comparing the formality of private non-agricultural establishments with 1-24 workers across the three data sets, using various definitions. We start by examining the distribution of firms along various characteristics across data sets to ascertain if they represent similar firms. Subsequently, we examine the patterns of formality across the common characteristics and data sets. We present our results descriptively and also test whether there are significant differences using regressions. Specifically, we pool the three data sets and run a logit model for probability of being formal, with all the common characteristics fully interacted with a categorical variable indicating the data source, to understand whether there are significant differences in formality across data sources.

# **3.3. Predictors of formality**

A key focus of our research is investigating what factors predict formality and whether formal and informal firms are comparable or different. We run logit models to predict formality. In “Model 1” we include only the few characteristics that are common across all three data sources. “Model 2” includes additional firm characteristics and can only be run on the EcC and ELMPS (with a somewhat different set of variables in each survey; see above). “Model 3,” which adds the owner’s characteristics, is only possible for the ELMPS.

# **3.4. Comparing Distributions of Predicted Probability of Formality by Formality Status**

After running the various models mentioned above, we predict the probability of being formal using each model and data source. We compare the distribution of the predicted probability of formality by whether or not firms are actually formal. We take the size of the overlap in the distribution functions of predicted probability across formal and informal firms as a test and metric of whether informal firms are similar to formal firms and thus might potentially formalize. We acknowledge this overlap is, in part, driven by the number of covariates included in the model. We also look at the proportion of informal firms in different quartiles of the predicted probability to ascertain the proportion of informal firms that are most eligible for formalization.

# **3.5. Grouping Analysis**

We undertake grouping of firms, by characteristics that were particularly important in our regression models, as one approach to creating profiles of firms that vary in their potential to formalize. We specifically group together informal firms with common characteristics and explore the proportion in the different quartiles of the predicted probability of formality. We undertake this work only for the ELMPS (model 3) and EcC (model 2), since the EsC has a very limited set of covariates.

For the ELMPS, we pool 2012 and 2018. We group on firm size, firm age, current capital, whether the firm hired a non-relative outside of the household, quartile of the log of revenue per worker, and owner’s education level. We use a collapsed set of categories for these covariates to have a manageable number 8
# **IV.2 Credit Allocation**

Portfolio composition data for privatized, provincial, and private banks in June, 1996 are presented in Table 9. While time series data on portfolio composition would show the evolution of privatized provincial bank credit allocation over time, the snapshot presented here is also telling.33 In general, regardless of the industry, the percent of total credits rated good was highest among private banks. The better established privatized provincial banks (those that privatized prior to April, 1996) eclipsed the private banks on one measure -- the percentage of good credits to the government services sector (99.9 versus 99.6%). The earliest privatizers approached the private bank percentages on two other measures -- family loans (77.2 versus 84.9% good credits) and construction (83.3 versus 91.8%). It is important to note that the private bank sample contains only the ten largest in Argentina, presumably among the best banks in the country.34 The percentage of good credit in other sectors (primary production, manufacturing, utilities, trade, and other services) was somewhat lower for the early privatizers than for private banks as of 1996.

In general early privatizers had higher good credit percentages than either public provincial banks, recent privatizers (since June, 1996), or those that had begun but not completed the privatization process ("beginners"). Public provincial banks had slightly higher good credit percentages than early privatizers in only two categories -- primary production and utilities. Further, there are two reasons why the good credit percentages for the public provincial banks are likely to be significantly overstated. First, several measures of performance (nominal assets, net worth, and NW/TL) of privatized banks appear to have declined quite remarkably during the

> We did also have credit allocation data for December, 1995. However, for the five categories of banks in Table 9 the qualitative differences between that data and the 1996 data were not great. As a result, we present only the 1996 data.

> 34 Of course, because all new loans are good loans, it will more telling to observe privatized provincial banks' portfolio quality in coming years. They do, however, appear to be starting on very solid footing.

20
A Duration Analysis of CONALEP to those of linear regression, but they are not symmetrically distributed around zero. Another method that was used to verify the assumption of proportional hazards between the groups of interest was the Kaplan-Meier estimate of the survival function for each group. For this estimate, we plotted log (λˆ (<sup>_t_</sup> _i_ ) ) against log(<sup>_t_</sup> _i_ ) which yielded parallel curves across the different groups, thereby providing evidence that the assumption of proportional hazard was correct.

Multinomial models were estimated to assess if the reformed program increased individuals’ probability of studying further after completing CONALEP. They also permitted estimation of the probability of having a certain status in the job market. The multinomial models estimated have a response variable with categorical outcomes 0, 1, 2, ..., J. In this analysis, these variables are status in the labor market and the type of occupation. The model also has K explanatory variables, Χ _i_ = [ _xi_ 1, _xi_ 2,..., _xiK_ ] such as age, region, schooling, gender, cohort, and GDP<sup>4</sup> . There are K parameters of the model for the outcome j, Β ( _j_ ) = [β1( _j_ ) ,β2( _j_ ) ,...,β _K_ ( _j_ ) ]' . In the multinomial logit model, the set of coefficients Β(0) , Β(1) ,...Β( _J_ ) corresponding to each outcome category is estimated. Assuming that Β(0) = **0** , where _y_ = 0 is the category base, the probability that the variable _y_ takes the value “j” is: The above-estimated equations provide a set of probabilities for the J+1 choices faced by an individual with characteristics Χ _i_ . The marginal effects of the characteristics on the probabilities are obtained by differentiating (6):

> 4 There is a question about whether the changes that are observed can be attributed to the change in the program rather than the external factors such as the state of the economy. For this reason, in the Kaplan-Meier and Cox models, we include control variables, such as GDP. Also, the reform period is not a recession or crisis period.

12
requiring immediate rehabilitation or reconstruction, or unpaved roads neading reconstruction and major drainage works.

The sample countries for which the required data was available are listed in ^he Appendix. Naturally, the information on the total length of paved and unpaved road networks is deemed more reliable than the percentages in good, fair or poor condition. Information on road condition from different countries is often collected by different methods and on the basis of different definitions.

# Cross-Section Analysis

We employ an empirical approach to explore the association between road infrastructure and economic development. Different regression analyses were carried out using GNP/capita as dependent variable and selected indicators of magnitude and condition of road networks as independent variables. Independent variables used in the analyses included: a) spatial road density (i.e., road length per land area) of paved and unpaved roads classified in good, fair or poor condition; and (b) road density or per capita length (km/million population) of paved and unpaved roads in good, fair or poor condition.

A sample of the relationships resulting from analyses of the data described above, with per capita GNP as the dependent variable, is given in Table 1. The most significant relationship was between per capita GNP and density of paved road network. Figure 1 shows this relationship for 98 developed and developing countries in 1988, along with the scatter diagram. The resulting correlation equation is:

# PGNP = 1.39 x LPR

where PGNP is per capita GNP ($/inhabitant) and LPR is the per capita length of paved roads (km/million inhabitants). The R squared value is 0.76, the number of degrees of freedom is 97, and the t-statistic of the coefficient is 20.7. By changing units in the above equation, one can show that there exists on the average $1.39 of per capita GNP for each millimeter of paved road in a country. A less significant regression equation (with an R squared value of 0.50) was obtained between per capita GNP and the spatial density of paved roads (Figure 2): LGNP = 2.25 + 0.49 x LD, where LGNP is the logarithm of per capita GNP ($Iinhabitant), and LD is the logarithm of spatial density of paved roads (km/l,000 square kn of land area); the number of degrees of freedom is 96 and the t-statistic of the coefficient is 9.6.

The methodological, conceptual, and statistical problems over cross-country studies of growth are well described by Levine and Renelt (14). Notwithstanding these problems, the relationship described above seems quite reliable because of the relatively large sample size (98 countries) and its statistical significance. It is also relatively consistent with the results of a time-series analysis of U.S. data, as shown later in the paper.

4
# Gender Differences in Household Coping Strategies for COVID-19 in Kenya

Yuanwei Xu<sup>*</sup> , Antonia Delius<sup>†</sup> , Utz Pape<sup>‡</sup>

**JEL-Codes** : I31 (General Welfare, Well-Being), J16 (Economics of Gender), Z13 (Economic Sociology, Economic Anthropology, Social and Economic Stratification ) **Keywords** : Coping Strategy, Poverty, Social Network, COVID-19

> *Xu: Leibniz University Hannover, Ruhr University Bochum, and the World Bank. Email: yuanweii.xu@gmail.com.

> †The World Bank. Email: adelius@worldbank.org.

> ‡The World Bank and Georg-August-Universit ̈at G ̈ottingen. Email: upape@worldbank.org.
of the global antidumping database, and the limited data that are available for them from the WTO. Section 5 describes the detailed data on countervailing duty use for three countries that we were able to collect. Section 6 discusses some additional caveats to using the antidumping data and concludes.

# **2 Countries and Data Included in the Global Antidumping Database**

The basic contribution of the data set is to provide a standardized set of information for as many users of the antidumping policy instrument as possible. To that end, we have collected a reasonably consistent set of detailed data for 15 different user countries, listed in table 2.1.

**Table 2.1 : Antidumping User Countries with Detailed Data in the Database**

|**Country Code**<br>**(3-letter CTY)**|**Country**<br>**Name**|**Excel Workbook**<br>**File Name**|**Spreadsheet Names within**<br>**Country Workbook**|**Available**<br>**Years of**<br>**Data**|**Current**<br>**Version**<br>**(last updated)**|
|---|---|---|---|---|---|
||||AD-ARG-Master ,|||
|ARG|Argentina|AD-ARG-v1.0.xls|AD-ARG-Products ,<br>AD-ARG-Domestic-Firms ,<br>AD-ARG-Foreign-Firms|1996-2004|v1.0<br>(8/2005)|
||||AD-AUS-Master ,|||
|AUS|Australia|AD-AUS-v1.0.xls|AD-AUS-Products ,<br>AD-AUS-Domestic-Firms ,<br>AD-AUS-Foreign-Firms|1989-2004|v1.0<br>(8/2005)|
|BRA|Brazil|AD-BRA-v1.0.xls|AD-BRA-Master ,<br>AD-BRA-Products|1988-2003|v1.0<br>(8/2005)|
||||AD-CAN-Master ,|||
|CAN|Canada|AD-CAN-v1.0.xls|AD-CAN-Products ,<br>AD-CAN-Domestic-Firms ,<br>AD-CAN-Foreign-Firms|1985-2005|v1.0<br>(8/2005)|
|COL|Colombia|AD-COL-v1.0.xls|AD-COL-Master ,<br>AD-COL-Products|1991-2004|v1.0<br>(8/2005)|
||||AD-EUN-Master ,|||
|EUN|European<br>Union|AD-EUN-v1.0.xls|AD-EUN-Products ,<br>AD-EUN-Domestic-Firms ,<br>AD-EUN-Foreign-Firms|1987-2005|v1.0<br>(8/2005)|
|IND|India|AD-IND-v1.0.xls|AD-IND-Master ,<br>AD-IND-Products ,<br>AD-IND-Domestic-Firms ,<br>AD-IND-Foreign-Firms|1992-2004|v1.0<br>(8/2005)|

7