rafmacalaba/datause-encoder-data huggingface dataset:
# 8. Discrimination beyond the screening point: Evidence from survey data

These experimental results are informative about gender discrimination in the job application process. However, discrimination in the labor market is not restricted to the hiring stage; a large share of discrimination occurs during employment, whereby the labor of men and women is valued differently by the market. To further examine gender differential beyond the point of hiring, this study examined the monthly wage differentials between men and women using the 2014 Tunisian Labor Market Panel Survey (TLMPS).<sup>14</sup> Table 10 presents the average differences in monthly wages between men and women at different disaggregation levels. It suggests a systematic tendency to value the labor of men over that of women and pay men higher wages than women. Indeed, the median wage for men is higher than the median wage for women in both urban and rural areas. Moreover, the raw gender gap in median wages persists at all age levels and for all education levels. In terms of sector of employment, the median wage for women is slightly higher than that of men in government jobs, a result consistent with the findings that the public sector tends to have favorable conditions for women’s employment. However, the median wage for men is higher in the private sector and in semipublic enterprises.

**Table 10 Raw wage differentials by area, education level, age, and sector of employment**

|Raw wage differential(m|onthlyin Tunisian din|ars)|
|---|---|---|
|Median, men|Median, women|P-value continuity<br>(median equality)|
|_Overall_<br>444|325|0.00|
|_Area_|||
|Urban<br>500|359|0.00|
|Rural<br>400|300|0.00|
|_Education level_|||
|Illiterate<br>315|260|0.00|
|Read and write<br>400|300|0.01|
|Under intermediate<br>418|300|0.00|

14 This section broadly complements the field experiment and substantiates the prevalence of gender wage differentials for workers at all education levels. It is not limited to the target group of the field experiment because the TLMPS is not sufficiently broad to allow a wage decomposition analysis for tertiary-educated workers only. Future research will help reveal the magnitude of the gender wage differential among those workers.

34
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**Table 3: Primary health care financing in Côte d'Ivoire**

||**GDP per capita**|**Level of sp**|**ending per ca**|**pita (US$)**|||**Share of sp**|**ending (%)**|||
|---|---|---|---|---|---|---|---|---|---|---|
|**Year**|**GDP per**<br>**capita, PPP**<br>**(current**<br>**international $)**|**Total PHC**<br>**expenditure**|**Domestic**<br>**general**<br>**government**<br>**expenditure**<br>**on PHC per**<br>**capita**|**External**<br>**expenditure**<br>**on PHC per**<br>**capita**|**Domestic**<br>**general**<br>**government**<br>**health**<br>**expenditure**<br>**as a share**<br>**of GDP**|**PHC**<br>**(government**<br>**and donors)**<br>**as a share of**<br>**GDP**|**PHC**<br>**spending**<br>**as a**<br>**share of**<br>**current**<br>**health**<br>**spending**|**Domestic**<br>**general**<br>**government**<br>**spending on**<br>**PHC as a**<br>**share of**<br>**total PHC**<br>**spending**|**Domestic**<br>**general**<br>**government**<br>**spending on**<br>**PHC as a**<br>**share of**<br>**domestic**<br>**general**<br>**government**<br>**expenditure**<br>**on health**|**External**<br>**spending**<br>**on PHC**<br>**as a**<br>**share of**<br>**total**<br>**PHC**<br>**spending**|
|2016|4650.67|52.1|13.5|9.6|0.9|1.2|78.5|26.0|79.4|18.4|
|2017|4750.94|53.6|15.7|8.5|0.9|1.2|78.1|29.4|79.9|15.8|
|2018|5067.86|57.1|16.5|8.2|0.9|1.1|80.4|29.0|80.2|14.4|
|2019|5343.00|59.8|17.2|10.7|1|1.2|80.9|28.8|80.2|17.9|

Notes: PHC means primary health care. The GDP per capita data are from the World Bank Development Indicators (WDI). The data on primary health financing are from the Global Health Expenditure Database (GHED).

13
# **Table Sup 2: Cell level robustness checks**

This table presents robustness checks for our main result presented in Table 1, column 1. Robustness checks are: Column 2: as in (1) but excluding the 10 major oil producing countries for which economic production is expected to be significantly less affected by weather. Column (3): as in (1) but excluding China and USA. Column (4): as in (1) but including a WB region Year FE instead of Year FE. Column (5): as in (4) but without country year trends. Column (6): as in (1) but without Year FE. Column (7): as in (1) but with a linear instead of quadratic country time trend. Columns 8 and 9: as in (1) but including one lag and three lags of growth as in a Arellano Bond approach.

||(1)<br>Base|(2)<br>No Oil|(3)<br>No<br>US/China|(4)<br>ContYr<br>FE|(5)<br>ContYr FE<br>+no Trend|(6)<br>No<br>YrFE|(7)<br>Linear<br>Time|(8)<br>LDV 1<br>Lag|(9)<br>LDV 3<br>Lags|
|---|---|---|---|---|---|---|---|---|---|
|Rainfall|1.261***|1.322***|1.456***|1.430***|1.696***|1.403***|1.573***|1.157***|0.947**|
||(0.325)|(0.339)|(0.391)|(0.361)|(0.378)|(0.390)|(0.334)|(0.308)|(0.343)|
|Rainfall<sup>�</sup>|-0.211***|-0.220***|-0.225**|-0.256**|-0.250**|-0.237**|-0.251***|-0.189**|-0.165**|
||(0.064)|(0.066)|(0.070)|(0.078)|(0.078)|(0.078)|(0.069)|(0.058)|(0.062)|
|Temperature|0.574**|0.650*|0.685**|0.600**|1.020***|0.493*|0.693**|0.363+|0.330|
||(0.213)|(0.283)|(0.265)|(0.229)|(0.250)|(0.202)|(0.225)|(0.216)|(0.216)|
|Temperature<sup>�</sup>|-0.015*|-0.019*|-0.017*|-0.027***|-0.036***|-0.017**|-0.021**|-0.012+|-0.010|
||(0.006)|(0.008)|(0.007)|(0.007)|(0.007)|(0.006)|(0.006)|(0.007)|(0.007)|
|Observations|869,447|642,439|725,697|869,447|869,447|869,447|869,447|833,226|760,784|
|R-squared|0.359|0.366|0.261|0.379|0.314|0.336|0.319|0.364|0.329|

Clustered standard errors at the Administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1 16
It is also interesting to see that transition economies had a much stronger correlation of their job flow pattern by industry and size class with the United States in the sample that covers the entire 1990s than in the sample focusing on the 1996-2001 period. This could be surprising, since the early phases of the transition were characterized by massive job reallocation and the unique need to change the structure of the economy. One working hypothesis that we develop later in the paper is that after the initial phases of transition, these countries have moved towards the flow patterns observed in EU countries, with whom they share several policy and institutional factors.

# **4 Analysis of Variance**

In the previous section, we explored the different dimensions of the job flow data across countries, industries and size classes. The next logical step is to assess the relative importance of these different dimensions in explaining the overall variance in our dataset. Tables 9 and 10 present the analysis of variance of job flows, for the unbalanced total economy<sup>8</sup> and manufacturing samples, respectively. We consider industry, size, country and industry*size effects separately, and, in addition, differentiate the analysis of variance by region (OECD, transition, Latin America).

It is noticeable that technological and market structure characteristics that are reflected in the industry-specific effects explain only 6.8 percent of variation in overall crosscountry gross job reallocation (Table 9), although they account for a higher share in Latin America (23.3 percent). By contrast, differences in the size structure of firms explain as much as 40.0 percent of the total variation in cross-country gross job reallocation in all regions, and play an even more important role in transition countries at the beginning of the 1990s. This fact is again in accordance with the characteristics of transition, as already mentioned in the previous section. Even country effects explain more of the variation in gross job reallocation than the industry effects, except in Latin America, so even though there are similarities among countries within a region, there is still variation between them. Overall, the combined industry*size effects can explain the bulk of the variation in gross job reallocation: 55.6 percent overall, 55.8 percent in OECD countries, 73.3 percent in Latin American countries and 72.3 percent in transition countries (66.9 percent, if we look only at the second half of the 1990s).

Gross job reallocation consists of job creation and job destruction, so we now turn

> 8The total economy sample is unbalanced in the sense that it covers manufacturing only for United Kingdom, Brazil, Chile and Colombia - see Table 1 for details.

20
In this study, Nairobi and Dar es Salaam were found to have relatively medium levels of clustering in all three land classes, while Kigali and Addis have a wide range of dispersion (see <mark>figure X)</mark> . Commercial and Industrial appear to be the most clustered in Dar es Salaam and Nairobi, which is not surprising given the large industrial park that traverses Dar es Salaam, and the large commercial area in the center of Nairobi. In Addis Ababa, the commercial/Industrial clustering is quite low, and much lower than its irregular residential land. This is likely due to the high percentage of irregular residential land found in the city center, and the small pockets of industrial/commercial land spread throughout the city. In Kigali, the clustering of the commercial and irregular residential land is quite low, which is reflective of the lack of any large central irregular residential neighborhoods or commercial/industrial land.

# **Figure A2: Clustering Index (Moran’s I)**

<!-- Start of picture text --> Level of Clustering by Land cover Type<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Addis Ababa Dar es Salaam Kigali Nairobi<br>c. 2012 c. 2011 c. 2012 c. 2012<br>Regular Residential Irregular Residential Commercial/Industrial<br><!-- End of picture text --> _Note: high clustering = high Moran’s I value_ Data Source: Land cover Classification Calculations made by Author

# **Euclidian Distance from Residential to Commercial/Industrial**

To study the spatial relationships between land cover classes, an average neighborhood distance indicator was developed. Using the land cover 0.5x0.5km grid, the distance from each residential grid cell to each commercial/industrial area was calculated. The distances were then averaged, producing a single value for each grid cell which represented the average distance between residential areas and commercial/industrial areas. Finally, all distances for residential regular and irregular neighborhoods were summarized as two values, showing the difference between the locations of regular and irregular residential areas relative to commercial activity.

34
# should be controlled for?

Our work is based on a set of developing countries in Africa where there is a literature focused on push-pull issues in the urbanization process. These are reviewed in Henderson and Kriticos (2018) or Gollin et al. (2016), with analysis of classical push arguments in Schultz et al. (1968), Matsuyama (1992), Gollin et al. (2007), and Bustos et al. (2016) and an analysis of pull arguments in Lewis (1954), Hansen and Prescott (2002), and Galor and Mountford (2008). While we know climate deterioration or conflict may also push people into cities (Henderson et al. (2017), Fay and Opal (1999), Barrios et al. (2006), Br ̈uckner (2012)), we want to see if in Africa there is the appearance of strong income pull factors. That is motivated by a literature which argues that, while African cities have high population densities, they remain unproductive for the development of traded goods such as manufactured goods because they have low economic density (Collier and Jones (2016), Venables (2018), and Lall et al. (2017)). Low economic density arises if firms and economic activity more generally are not clustered but spread throughout the city, potentially because of the high costs of commuting within cities inducing firms to locate nearer to residents (Fujita and Ogawa, 1982). We will explore whether African cities have a lower degree of clustering of economic activity relative to the rest of the world and we will take a detailed look at patterns of clustering within Nairobi and Kampala.

In the paper, Section 1 starts with how we define cities and what are the advantages and disadvantages of the LandScan (2012) dataset on economic activity that we use. We then try to ground-truth LandScan (2012) data using Nairobi and Kampala as test cases. Section 2 defines various measures of economic density for urbanized and rural areas, decomposing them into first and second moment components. Section 3 compares the extent of economic density and its attributes for African cities versus other cities worldwide. Section 4 looks at the relationship between different measures of economic density and income differentials across the whole spatial hierarchy. Section 5 looks specifically at cities and examines issues such as the optimal rate of spatial discount for De La Roca and Puga (2017) measures of neighbor effects; the role of the second moment aspects of economic density measures; and how important local density measures are within cities, as well as location within the city. Section 6 looks in detail at how the productivity of firms within Kampala is related to the 3
<!-- Start of picture text --> 15.0%<br>10.0%<br>5.0%<br>0.0%<br>-5.0%<br>-10.0%<br>Current Account Deficit GDP growth<br>Source: Authors’ calculations using data from the IMF World Economic Outlook and Haver Analytics<br>Figure 3: Current account balance, Turkey and comparator groups<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>Turkey Non-NR UMICs NR UMICs UMICs<br>Source: Authors’ calculations using data from Haver Analytics<br>Notes: NR: Natural resource; “Non-NR” defined as a country less than the average natural resource rents as a<br>percent of GDP within that group in that year.<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

**Turkey’s CAD has been roughly comparable to the average of all upper middle-income countries (UMICs), and in most years, smaller than the average for non-natural resource dependent UMICs.** At an average of 4.5 percent of GDP per year, Turkey’s CAD since 2002 has similar to the average of (today’s) UMICs of 4.2 percent and less than the average for non-natural resource dependent UMICs (-6.9 percent) of which Turkey is one (Figure 3).

# **_Driven by import expansion, particularly of intermediate goods_**

**The CAD expansion was primarily driven by a growing deficit in the balance of trade in goods, led by rising imports, of which part was due to rising oil prices.** The net balance of trade in goods has been negative in almost all of the last 35 years and the trade deficit expanded post 2002 (Figure 4). Both export and import flows grew markedly over the past four decades as the country has moved from a closed, import-substitution model to an open, export-oriented growth model. Exports in Turkey over the period have a relatively high foreign content, so we would expect to see growing imports to support an increasing base of gross exports, and this seems to be the case in the

6
To account for other potentially confounding factors in the IV specification, our first stage includes additional conditioning variables. First, the expansion in school construction over the last three decades implies that younger mothers had systematically greater exposure to a school at the time of their enrollment decision. Since other changes in the environment affecting enrollment are also time-varying, we control for age directly in the first-stage with a full set of age dummies—one for each year—for all mothers in the sample. Second, it could be that schools were built in a targeted manner in specific villages. We therefore also control for a full set of _tehsil_ dummies—a _tehsil_ is an administrative unit one level below the district, equivalent roughly in size to a US county. The province of Punjab, where our sample is drawn from, has 34 districts and 104 _tehsils_ in the latest (1998) census. Since roughly 50 percent of our mothers were born in a different village, there are 72 different _tehsil_ fixed effect dummy variables. Given the sample size, including a full set of birthvillage dummies (over 400) leaves us with too little within-village variation to achieve identification.

The second stage regressions are therefore specified as follows.

_MotherOutcomesi_ = γ0 + γ1 _MotherEducatedi_ + γ2 _MotherAgei_ + γ3 _BirthTehsili +_ ξj (2a) _ChildOutcomesij_ = α0 +α1 _MotherEducatedi +_ α2 _Agei_ + α3 _BirthTehsilii +_ α4 _ChildAgej_ + α5 _ChildGenderj_ + _εij_ (2b) Equation (2a) is a mother level equation. The variable _MotherEducated_ is instrumented using the first stage regression (1) and is estimated using 2SLS. The variable _SchoolPresent_ , which captures the presence of a school in the birth-village at the time of the enrollment decision, is the excluded variable from Equation 2a. _MotherAge_ and _BirthTehsil_ are the same variables as in the first stage and are in the second stage regression because of their potential direct effect on child level outcomes. All specifications cluster for the standard errors at the village-level. Equation (2b) is run at the child level and adds a full set of indicator variables for child age and a dummy variable for child gender to the set of explanatory variables. The subscript _i_ refers to the mother and _j_ to the child. In the 2SLS estimation, all child-related controls are also included in the first stage regression.

Finally, we also present bivariate probit results with the same specifications as above for all the discrete variables used in the analysis. Although the linear IV specifications are unbiased in terms of the Local Average Treatment Effects (LATE), the efficiency of the IV estimator is low at the sample sizes that we are 14
years (close the average mean remaining maturity of bonds and loans of 2.31 and 4.27 years for the firms in our sample, calculated from Capital IQ data), and we assume that firms repay the outstanding debts in equal annual payments, i.e. they repay a third of their debts each year. Using these assumptions, we calculate the mean dMVD/Book assets to be 5.95 basis points for a sample of 111 firms.

We also consider the increase in the market value of equity relative to the sum of the increases in the market values of equity and debt, i.e. dMVE/(dMVE + dMVD) in case both dMVE and dMVD are positive, which is the case for 66 firms. The mean of dMVE/(dMVE + dMVD) is 0.879, i.e. on average 87.9% of the total valuation gains accrue to shareholders. Alternatively, we compute dMVE/(dMVE + dMVD) conditional on the sum of dMVE and dMVD being positive, yielding 84 observations with a mean of 0.812.

We relate the various dependent variables reflecting share price and CDS spread changes to a range of independent variables that capture (i) the likely relative impact of the PEPP on the firm as indicated by the firm’s credit rating and reliance on bond finance, (ii) other firm characteristics that potentially proxy for credit constraints and debt overhang that could be alleviated by the PEPP, (iii) whether a firm belongs to an industry that is highly affected by COVID-19, and, finally, (iv) the strength of pertinent national fiscal policy measures to counter the pandemic that potentially affect the implications of monetary policy for the firm.

The PEPP is restricted to purchasing corporate debt securities that are investment grade, which suggests that corporate issuers with an investment grade rating are more directly affected by the PEPP. To test this, we construct the Investment grade dummy variable, which signals that the firm had a long term issuer credit rating of at least BBB-/Baa3 issued by either Moody’s, S&P or Fitch on March 17, 2020. We use both domestic and foreign currency credit ratings to 12