rafmacalaba/datause-encoder-data huggingface dataset:
_Table 14: The effect of socioemotional skills for women and men, conditional on applicant CV clicked_

|||Women only|||Men only||
|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
|SE skills|-0.050**|-0.049**|-0.046**|-0.002|-0.001|-0.003|
||(0.023)|(0.023)|(0.023)|(0.022)|(0.022)|(0.022)|
|Signaled SE skill required in vacancy|-0.004|-0.004|-0.010|0.014|0.013|0.010|
||(0.028)|(0.028)|(0.028)|(0.025)|(0.025)|(0.025)|
|SE skills * Signaled SE skill required in|||||||
|vacancy|0.071**|0.069**|0.066**|0.027|0.028|0.029|
||(0.029)|(0.029)|(0.029)|(0.028)|(0.028)|(0.028)|
|Experience (in months)||-0.0014|-0.0020||0.0034|0.0033|
|||(0.0022)|(0.0022)||(0.0022)|(0.0022)|
|Age||-0.0147|-0.0110||-0.0524**|-0.0534**|
|||(0.0250)|(0.0247)||(0.0233)|(0.0233)|
|Objective beauty||-0.0075|-0.0029||0.0373|0.0256|
|||(0.0517)|(0.0519)||(0.0315)|(0.0311)|
|Subjective beauty||-0.0231|-0.0092||0.0316*|0.0292|
|||(0.0188)|(0.0186)||(0.0189)|(0.0191)|
|Worked in one firm only||-0.0019|-0.0062||-0.0040|-0.0021|
|||(0.0210)|(0.0212)||(0.0193)|(0.0192)|
|Constant|0.201***|0.670|0.558|0.134***|1.323**|1.384**|
||(0.023)|(0.588)|(0.584)|(0.020)|(0.558)|(0.560)|
|Vacancy char.|No|No|Yes|No|No|Yes|
|N.obs.|1813|1813|1813|1656|1656|1656|
|R-squared|0.003|0.005|0.022|0.002|0.008|0.016|

**Notes** : Models 1 to 6 report the results from OLS regressions. Models 1 to 3 include women only, and models 4 to 6 include men only. Cluster-robust standard errors at the vacancy level are shown in parentheses. Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant is invited for an interview. Variable SE skills takes on the value 1 for treatment CVs, 0 otherwise. Vacancy characteristics include total number of applications for the vacancy, occupational clusters and location. Symbols ***, **, and * indicate significance at the 1%, 5% and 10% level, respectively.

# **4 Discussion and Conclusions**

Economics literature has clearly demonstrated that socioemotional skills are important in determining earnings, but it is not obvious whether socioemotional skills matter during hiring. Focusing on the hiring stage, this study answers whether and how these skills should be signaled for male and female candidates. Our results suggest that signaling a socioemotional skill increases the probability of receiving a callback only if the skill is specifically solicited in the vacancy text. There is also a penalty for female applicants: if they signal a skill not asked for, their probability of receiving a callback decreases; the same penalty does not apply to men.

Our unique data set allows us to open the black box of candidate screening, and we investigate the importance of gender in all three stages of employer screening: making the longlist, clicking on a candidate’s CV, and inviting a candidate for an interview. We find that some employers indicate a preference for women when making the longlist, and that gender discrimination does not exist conditional on candidates making it to the longlist. Interestingly, this result suggests that candidate’s beauty does not play a role in gender
Our data is subject to some caveats. First, our results on small firms are subject to the limitation that the Enterprise Surveys sample only the formal sector in each country and exclude the informal sector. Some of the developing countries in our sample have large informal sectors which implies that we are underestimating the importance of the SME sector in those countries. In addition, since the sampling frame is restricted to 5 employees or above, our results do not speak to the micro enterprises.

Second, our data is only on the continuing/surviving firms and hence we have no data on job destruction by firms which were liquidated over the sampling period. On a related note, the surveys are stratified only by industry, firm size, and geographical location and so we may not have a completely representative sample of firm ages, though the firms within the strata are randomly sampled. Hence we present all our results on firm age as being conditional on firm size.

Third, our analysis is at the establishment level and not at the firm level since the sampling unit in the Enterprise Surveys is the establishment.<sup>10</sup> While this has the advantage that our job creation measures are well defined and capture actual new jobs at the establishment rather than changes from mergers, acquisitions, and divestitures, we are not able to measure firm size accurately for multi-establishment firms. However we are helped to some extent that the establishments in our sample report whether they are part of a larger firm or whether they are stand-alone. While most of the establishments in our data are stand-alone establishments (86%) and hence can be treated as firms, for robustness, we repeat the analysis on the sub-sample of firms that report that they have a single establishment and find that all our results about relative

> 10 In the Enterprise Surveys, the establishment is defined as a physical location where business is carried out and where industrial operations take place or services are provided. In addition, an establishment must make its own financial decisions, have its own financial statements separate from those of the firm, and have its own management and control over its payroll.

8
regions at different points of the per capita GRDP distribution. The relative mean deviation, which we use to check for a few extreme deviations, is significantly different for Pakistan, Russia, Indonesia, Philippines, Uzbekistan and Vietnam.

# **3.3 Federal vs. unitary countries**

We have seen above that, in general, federal countries are less unequal in terms of per capita GRDP than unitary countries. In this sub-section, we look at this relationship between decentralization and regional inequality. We regressed three weighted measures of inequality on a dummy variable representing whether a country is unitary or federal.<sup>5</sup> The value of the dummy is and 1 if the country is unitary and 0 if the country is federal. For each measure of inequality we carried out two regressions - the first just on the unitary dummy and the second with the natural logarithm of population as the control variable. The measures of inequality used in the regression were from 1997 or the latest year available before 1997.

We obtained positive coefficients on the unitary dummy in all the regressions, signifying that unitary countries tend to be more unequal. However, except in the case of weighted CV as the dependent variable, the R-square value was too small or the coefficients were not statistically significant. Table 3.3a shows the results of the regression with weighted CV as the dependent variable and the natural logarithm of the country population as a control variable. The coefficient on the unitary dummy is positive and statistically significant at the 5% level with the two-tailed test. This provides some evidence for the proposition that centralization leads to greater regional disparities. The coefficient on the log of population also has a positive sign and is statistically significant at the 5% level with the two-tailed test. This is not surprising as, ceteris paribus, we would expect countries with larger populations to have greater regional inequalities.

15
A Duration Analysis of CONALEP

**Table 6.  Cox Regression Model. Job Search (time) after finishing CONALEP**

|**Covariate****|**Survey 94**|**Survey 98**<br>**Difference**|
|---|---|---|
|Male|**4.0**|**2.8**<br>1.2|
|Female|4.9|3.0<br>1.9|
|Age (mean=21.5 years)|**4.0**|**2.8**<br>*<br>1.2|
|Age (22.5 years)|4.0|2.8<br>*<br>1.2|
|Center Area|**4.0**|**2.8**<br>1.2|
|South-East Area|2.9|1.9<br>1.0|
|Center-South Area|2.9|1.9<br>1.0|
|North-East Area|2.0|1.9<br>0.1|
|North-West Area|2.8|1.9<br>0.9|
|Center-North Area|2.8|1.9<br>0.9|
|Pacific Area|2.8|1.9<br>0.9|
|91 Cohort|**4.0**||
|92 Cohort|4.0||
|93 Cohort|5.0||
|94 Cohort||4.9|
|96 Cohort||2.8|
|97 Cohort||2.9|
|None – Primary HH|**4.0**|**2.8**<br>1.2|
|Lower-Secondary HH|4.0|2.8<br>*<br>1.2|
|Upper-Secondary HH|4.0|2.8<br>*<br>1.2|
|University - + HH|4.0|2.0<br>*<br>2.0|
|Don't know HH|4.0|2.0<br>*<br>2.0|
|GDP per capita (mean=38.8)|**4.0**|**2.8**<br>*<br>1.2|
|GDP per capita|3.0|2.0<br>1.0|
|Worked when studying|**4.0**|**2.8**<br>1.2|
|Did not work when studying|5.0|3.0<br>2.0|

** The other covariates are in the base category or at the mean. Base categories: men, Center area (D.F. and Mexico), 91 cohort, none or primary school education of household head (HH), and had worked when he was studying at CONALEP. Means: 21.5 years old, 38.8 thousands of 1998 pesos GDP per capita. *Not significant at 5 percent.

graduates, the probability of working is 16 percent higher than for females in 1998. Also, the probability of searching for a job is slightly higher for the 1994 graduates than it is for 1998 graduates. The probability that a graduate continues to study is three percent higher in the 1994 than in 1998 Survey. For female graduates the probability of working at home as homemakers is 16 percent higher in the 98 cohort (Table 7). Controlling for relevant variables, we found that the 94 Survey respondents earn higher hourly wages than the 1998 cohorts do (Tables A1.3).

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Low-skilled 0.92 0.70 0.76 0.67 0.58 Note: Real weekly wages in USD and prices of 2011. Author's estimates using labor force survey data from India.

We complement these results by computing district and industry premiums for Bangladesh. First, state and industry premiums can be indicators of segmented labor markets. A lack of labor mobility across sectors and districts would result in premiums. If labor is perfectly mobile, wage premiums after any shock should decline over time since workers with the same characteristics would move to districts and industries offering higher wages. Furthermore, the correlation of the premiums over time should then be small and decline.

This is, however, not the case. Figure 9 compares Bangladesh and India in terms of regional wages over time. On the one hand, this figure clearly shows that the wages are not equalized across states in India. If anything, the states with the highest wages early in the sample have experienced the largest wage increases. For instance, weekly average real wages in Mizoram are more than three times larger than those in Tripura and Chhattisgarh. On the other hand, earnings dispersion across districts is not only much lower in Bangladesh but also seems to have been decreasing during the last decade. These findings suggest that Bangladeshi faces less labor mobility restrictions than India, spreading more rapidly the benefits form trade shocks over time.

**Figure 9. Average Real Wages Across Regions a) India**

<!-- Start of picture text --> 2500<br>2000<br>1500<br>1000<br>500<br>0<br>States<br>1999 2004 2007 2009 2011<br>Average weekly real wage (rupees)<br>Mizoram Nagaland Delhi Chandigarh Lakshadweep Sikkim Manipur Haryana GOA Uttaranchal Meghalaya Assam Jharkhand Kerala Maharashtra Daman & Diu Pondicherry Punjab Rajasthan West Bengal Karnataka Dadra and Nagar... Uttar Pradesh Andhra Pradesh Bihar Tamil Nadu Gujarat Orissa Madhya Pradesh Tripura Chhattisgarh<br>Arunachal Pradesh Jammu & Kashmir Himachal Pradesh<br>Andaman & Nicobar...<br><!-- End of picture text -->

**b) Bangladesh**

18
# **Table 3 Unit Root Test Results for Various Income Measures, Exchange rate, Interest Rate, Oil Price, and MUV**

||**‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐ Leve**|**ls ‐‐‐‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐**|**‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐ First Di**|**fference ‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐**|
|---|---|---|---|---|---|---|---|---|
||**‐‐‐ No**|**Trend ‐‐‐**|**‐‐‐ With T**|**rend ‐‐‐**|**‐‐‐ No**|**Trend ‐‐‐**|**‐‐‐ With**|**Trend ‐‐‐**|
||**ADF**|**PP**|**ADF**|**PP**|**ADF**|**PP**|**ADF**|**PP**|
|**GDP measured at Ma**|**rket Prices, Co**|**nstant US$ 2**|**010, total**||||||
|World|‐3.19**|‐4.37***|‐3.04|‐2.85|‐3.83***|‐4.25***|‐5.01***|‐5.30***|
|High income|‐3.90***|‐6.10***|‐2.14|‐1.99|‐3.49***|‐3.87***|‐5.34***|‐5.50***|
|Middle income|‐0.60|‐0.03|‐2.15|‐1.32|‐3.24**|‐3.66***|‐3.21*|‐3.61**|
|Low income|3.37|2.89|0.97|1.66|‐2.60*|‐3.49***|‐4.04***|‐4.11***|
|**GDP measured at Pur**|**chasing Power**|**Parity (PPP)**|**, total**||||||
|World|‐2.19|‐2.56|‐3.01|‐2.59|‐3.82***|‐4.32***|‐4.31***|‐4.82***|
|High income|‐3.75***|‐5.65***|‐2.38|‐2.24|‐3.46***|‐3.92***|‐5.07***|‐5.39***|
|Middle income|‐0.42|0.40|‐2.05|‐1.11|‐3.53***|‐3.73***|‐3.48**|‐3.64**|
|Low income|3.41|2.98|0.99|1.73|‐2.54|‐3.30**|‐4.03***|‐4.00***|
|<br>**GDP measured at Ma**|**rket Prices, Co**|**nstant US$ 2**|**010, per capi**|**ta**|||||
|World|‐2.37|‐3.05**|‐3.20*|‐3.12|‐4.47***|‐4.81***|‐4.95***|‐5.24***|
|High income|‐3.55***|‐4.98***|‐2.04|‐1.69|‐3.82***|‐4.27***|‐5.37***|‐5.55***|
|Middle income|0.32|1.04|‐1.26|‐0.54|‐3.03**|‐3.39**|‐3.07|‐3.41**|
|Low income|2.88|3.63|0.97|1.80|‐2.40|‐3.19**|‐3.91**|‐3.87**|
|**GDP measured at Pur**|**chasing Power**|**Parity (PPP)**|**, per capita**||||||
|World|‐1.21|‐1.39|‐2.43|‐2.21|‐4.18***|‐4.65***|‐4.23***|‐4.73***|
|High income|‐3.41**|‐4.63***|‐2.28|‐1.96|‐3.78***|‐4.31***|‐5.12***|‐5.45***|
|Middle income|0.54|1.58|‐1.04|‐0.21|‐3.16**|‐3.29**|‐3.26*|‐3.35*|
|Low income|3.04|3.79|0.97|1.87|‐2.33|‐3.00**|‐3.89**|‐3.77**|
|**Other Variables**|||||||||
|Exchange Rate|‐3.07**|‐2.30|‐3.32*|‐2.53|‐4.17***|‐4.52***|‐4.13***|‐4.48***|
|Interest Rate|‐2.30|‐2.66*|‐2.55|‐2.80|‐6.36***|‐8.15***|‐6.32***|‐8.09***|
|MUV|‐1.58|‐1.53|‐1.29|‐0.96|‐3.78***|‐4.09***|‐4.01***|‐4.24***|
|Oil Price|‐0.97|‐0.98|‐1.85|‐2.04|‐5.13***|‐7.44***|‐5.07***|‐7.37***|

**Notes** : The lag length of the ADF test is selected by the BIC criterion. One (*), two (**), and three (***) asterisks indicate significance at 10%, 5%, and 1% levels, respectively.

_18_
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Figure A-4: Estimates of Changes in TFP, Capital Stock and Human Capital (Mahajan, 2003)**

<!-- Start of picture text --> Cote d'Ivoire: Growth of GDP. Capital Stock, Human Capital and Total Factor Productivity<br>20<br>15<br>10<br>5<br>Cote d'Ivoire<br>GDP growth<br>0<br>Cap Stock Growth<br>Human Capital Growth<br>-5<br>TFP growth<br>-10<br>-15<br>-20<br>Annual Growth, 196--2000<br>Constant CFA 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000<br>Yearly Percentage Growth GDP and Capital in<br><!-- End of picture text -->

**Figure A-5: Indices of TFP, Physical & Human Capital (Mahajan, 2003)**

<!-- Start of picture text --> Cote d'Ivoire: Indices of Real GDP, Capital Stock,<br>Human Capital, and Total Factor Productivity<br>1000<br>900<br>800<br>700<br>600 TFP growth<br>GDP<br>500 Capital Stock<br>Human Capital<br>400 TFP<br>300<br>200<br>100<br>0<br>Years: 1960 to 2000<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br>Index Base Year 1960+1<br><!-- End of picture text --> 27
# **_V._ Fiscal Institutional Reforms: A Bird’s Eye View**<sup>**15**</sup>

The design, reform or creation of fiscal institutions has been a major challenge for transition economies where defining the boundaries of the state has been and remains a continuing challenge. The point of departure in the reform process across countries differed substantially depending on the length of time each country spent under socialism and the type of socialism it practiced. All countries faced severe political and institutional fragmentation, which led to the emergence of soft-budget constraints with noted fiscal consequences that delayed the transition process (Kornai et al. (2003), World Bank 2002) The efforts to address these challenges included the corporatization of productive and financial enterprises and their privatization as well as setting the institutional frameworks for social security, and introducing fiscal systems for local and regional governments. The fiscal institutional building agenda focused on fundamentals, such as taxation, accounting, treasury and the establishment of budgetary procedures. These changes have happened in a fluid and fragmented political situation. Not surprisingly, design and implementation of these agendas has taken time and proceeded in spurts often linked to external events. The efforts by transition economies to close institutional gaps that existed with respect to market economies provide valuable experimental information on the process of change and the role of fiscal institutions in reducing fragmentation and increasing transparency, the importance of political fragmentation, and the contribution of economic events.

> 15 This section is drawn from “Fiscal Institutions in ECA countries” Eckhardt and Islam (2010) draft.

Page | 18
more salient in treatment villages, causing a change in beliefs about the importance of the disease in these areas, which resulted in an increase in private health investments.

The data used in our study come from an experimental evaluation of the impact of an IRS program organized by the Government of Eritrea in the most malarious region of Eritrea (Gash Barka). Fifty-eight (58) villages were randomly assigned to treatment and 58 villages were randomly assigned to control. Between June and July 2009, before the start of the malaria season, households in treatment villages were visited by government workers carrying IRS equipment and were offered free IRS<sup>3</sup> . Households in control villages did not receive publicly provided IRS and, at the same time, IRS is not privately provided in this market. A household survey and malaria rapid diagnostic tests (RDT) were administered during the malaria season that followed (October 2009).

The prevalence of malaria parasite infection was found to be very low in this area, and our data show that IRS had no detectable impact on it (Keating et al., 2011). However, it led to higher ownership and use of ITNs, in a setting where IRS and ITN are more likely to be perceived as substitutes rather than complements. In addition, households in treatment villages became more aware of (and concerned with) malaria than in control villages. In particular, they were more likely to mention mosquitoes as a malaria vector and to mention children as one of the groups most affected by malaria, than households in control villages.

A large literature debates the extent to which a variety of public programs discourages (or crowds-out) private investments in those goods or services that are provided by the public sector. Two examples (among many) are Peltzman (1973), who discusses the case of higher education in the US, and Cutler and Gruber (1996), who study health insurance in the US. Examples of the importance of crowding-out effects for health programs in developing countries are much less common in the literature than for developed countries, perhaps because of lack of data. Some examples include Das et al. (2011), who analyze education subsidies in Zambia and India, and Bennett (2012), who studies the negative effect of the provision of piped water on household sanitary behavior in the Philippines.

The standard presumption in these papers is that there is substitutability between pri-

> 3Teams visiting villages for IRS treatment were composed by social workers. It is unlikely in this situation that IRS teams provided information about malaria, in addition to offering IRS treatment, to the households living in treated villages. Within the National Malaria Control Program, information campaigns are managed by a communication team, which did not participate in the IRS campaign.

3
Based on the surveys, we construct two measures of access to banking services. The first measure is a dummy variable indicating whether an individual has a bank account. The second is another dummy indicating whether an individual has a loan from a bank. Table 3 shows that the proportion of individuals having a bank account in Kenya increased considerably in the sample period, going from 14 percent in 2006 to almost 23 percent in 2009. The proportion of individuals having a loan from a bank showed a more modest increase from 3 percent to 4 percent. While 95 percent of the individuals that had a loan from a bank also had a bank account, the remaining 5 percent did not. Table 3 also reports descriptive statistics for the other individual and household-level variables that we employ from the FinAccess survey, and these will be used as controls in our tests below.

# **III. Methodologies**

With data on bank branching and household surveys, we examine two related questions. Does Equity Bank pursue different branching strategies from other commercial banks in Kenya? And if so, do these different expansion strategies lead to different outcomes on households’ access to banking services? For the first question, we relate the number of branches by districts to a number of district-level characteristics. Our baseline specification takes the form: where _Branches_ is the number of bank branches in district _d_ at time _t_ for Equity Bank; for other banks it is the number of branches over the total number of banks in each group (based on ownership types) in district _d_ at time _t,_ to put them on a comparable basis with Equity Bank branches at the district level. _μ_ t is a dummy variable for 2009, and _Xd_ is a set of district level control variables for 2006; these variables indicate whether the district is a rural or arid and semi-arid district, the population density of the district and the proportion of the population speaking a minority language or Swahili. We estimate this model by ordinary least square (OLS), and cluster standard errors by district for four types of banks: foreign (private) banks, government-owned and government-influenced banks, domestic private banks ( _excluding_ Equity Bank) and Equity Bank. Furthermore, to explore if the branch expansion of Equity Bank between 2006 and 2009 targeted more under-served districts, we augment our baseline specification with an interaction term between the district level proportion of people speaking a minority language and the 2009 indicator.

8