rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4: Heterogeneity analysis**

||El|Weekly labor|
|---|---|---|
|Dependent variable:|mpoyment<br>status|income ($1,000<br>AUD)|
||(1)|(2)|
|_Panel A: Visa type (Onshore vs offshore)_|||
|Standardized mental health scores*Onshore|-0.002**|-0.041***|
||(0.001)|(0.014)|
|_Panel B: Time in Australia_|||
|Standardized mental health scores*Less than 1 year|-0.081***<br>(0.027)|-0.038***<br>(0.012)|
|_Panel C: Spent time in refugee camp before arrival_|||
|Standardized mental health scores*Spent time in camp|-0.004*|-0.044**|
||(0.002)|(0.022)|
|_Panel D: Country of origin (Reference: Middle East)_|||
|Standardized mental health scores*South-East Asia|0.025|0.001|
||(0.074)|(0.002)|
|Standardized mental health scores*Southern and Central|||
|Asia|0.044|0.000|
||(0.076)|(0.003)|
|Standardized mental health scores*Sub-Saharan Africa|0.113|0.006*|
||(0.089)|(0.003)|
|_Panel E: Major city vs regional Australia_|||
|Standardized mental health scores*Residence area|0.772|-0.141|
||(28.106)|(0.416)|
|_Panel F: Social network before arrival_|||
|Standardized mental health scores*No network|-0.098***<br>(0.031)|-0.031***<br>(0.008)|
|_Panel G: Government benefit received_|||
|Standardized mental health scores*Not received|-0.001***<br>(0.0002)|-0.0002*<br>(0.0001)|
|_Panel H: Local community support_|||
|Standardized mental health scores*No support|-0.011|-0.00005|
||(0.008)|(0.0006)|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). 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.

30
Beck et al. (2005) use the severity of access to finance as an obstacle to firms’ operations as perceived by the firms and show that the severity level is inversely correlated with firm growth; Djankov et al. (2003) show that a higher degree of judicial formalism is associated with lower perceptions of enterprises of courts’ fairness, honesty and consistency; Beck, Demirguc-Kunt and Levine (2006) and Barth et al. (2009) show that a more market-based supervisory approach and more efficient systems of credit information sharing are associated with lower financing constraints as perceived by the firms. Also see for example, Beck et al. (2010) and Safavian and Sharma (2007).

There are a number of advantages of using micro over macro data. First and at a broad level, using micro data provides an additional layer of robustness check for the existing results based on macro data. Qualitatively, are the findings based on macro data similar to the ones based on micro data? Second, the experience based measure we use is an overall summary measure of the quality of tax administration. That is, by its very nature, the measure covers all possible features of the tax administration system that may affect firms. In contrast, macro-level measures typically capture at best a few aspects of the phenomena under study. For example, the World Bank’s Doing Business project reports on the time it takes to prepare, file and pay three major types of taxes and contributions. We use this measure below to check the robustness of our main results. However, this macro-level measure is at best a partial measure as it does not include for example, the monetary cost or the hassle of complying with tax procedures. It is possible that two agents may take the same amount of time in complying with tax procedures but they may experience different levels of difficulty and costs in doing so. Our experienced based measure picks up any such difference. Third and somewhat related to the previous point, macro datasets have by and large failed to capture the enforcement of existing laws and barriers to 4
<!-- Start of picture text --> EU15 EU-10<br>-50(e) -50(e)as———<br>al -100 -100<br>a} 1993 1998 2003 2008 1993 1998 2003 2008<br>0 EU Candidate Countries EU Neighborhood Countries<br>be<br>[© ls (e)<br>-50ine -50<br>-100 -100<br>1993 1998 2003 2008 1993 1998 2003 2008<br>——_ Median Interquartile Range<br><!-- End of picture text -->
# 3.4 Who Works in Green Occupations?

Consistent with international literature (Vona 2021; Valero et al. 2021, Doan 2022), we find that regardless of the definition (strict or broad), men are over-represented in green jobs. While women account for 44 percent of all workers, only 21 percent of strictly green jobs are held by women compared to 79 percent held by men (Figure 3). Women account for an even smaller share of broadly green jobs (19 percent). The underrepresentation of women in green jobs stems from gendered occupational segregation in the labor market, such that men and women sort into different occupations. Of the 20 occupations in Table 2, only three have a female share of more than 50 percent,<sup>29</sup> all of which employ a relatively small share of workers. The two-digit ISCO level occupations with the greatest number of four-digit green occupations are physical, mathematical, and engineering professionals (code 21) where the female share is only 22 percent. In contrast, office clerks (code 41) have zero GTI (strict or broad) though 70 percent of people working in the occupation are women. The green occupations where women are well represented, such as life science and health professionals (code 22), employ a relatively small proportion of South Africans.<sup>30</sup> Figure 3: Share employed in green occupations by gender <!-- Start of picture text --> Green strict Green broad All employed<br>100 100 100<br>21 19<br>80 80 80 44<br>60 60 60<br>40 79 40 81 40<br>56<br>20 20 20<br>0 0 0<br>Male Female<br>Green employment by gender<br><!-- End of picture text --> Source: authors’ calculations using PALMS A disproportionate share of white South Africans hold strictly green jobs as compared to the general employed population. As we have shown above, strictly green occupations are concentrated among highly skilled occupations. The continued gaps in educational attainment inherited from the apartheid era mean that white South Africans are better prepared for strictly green occupations. While Black South Africans dominate green and all jobs due to their high population share in South Africa, they are 60 percent of strictly green jobs but 74 percent of the working population. Instead, white workers hold 24 percent of strictly green jobs as compared to 13 percent of all jobs in the general labor market. There is little difference in representation between strictly green and non-green of the other racial or ethnic groups. The gaps by race between broadly green jobs and the general labor market are much narrower. White South Africans hold 9 percent of broadly green jobs while Black South Africans hold 78 29 Meteorologists (2112), biologists (2211) and chemists (2113).

30 For example, while the female share in the life science and health professionals is above 60 percent, this occupation only employs 3 percent of all women and 0.9 percent of all men (Mosomi et al. 2020).

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median size of 2,061 households. Although a few communes are rather small, almost all of them have sufficient population size to yield precise-enough poverty estimates.

The first task for the construction of a poverty map consists in making sure that the variables deemed common to both the census data and the household survey are really measuring the same characteristics. To this end, we first compared the questions and modalities in both the census and survey questionnaires to isolate potential common variables. We then compared the means of those (dichotomized) variables and tested whether they were equal using a 95% confidence interval<sup>2</sup> . Restricting ourselves to those variables ensures that the predicted welfare figures in the census should be consistent with the EIBEPbased poverty profile. This comparison exercise was done at the strata level (i.e., separately for each stratum). The two-stage sample design of the EIBEP survey was taken into account for the computation of the standard errors. The results are available on request.

The second step consists in estimating regressions to predict consumption per capita in the EIBEP. In order to maximise accuracy we estimated the model at the lowest geographical level for which the survey is representative. Specifically, we estimated regressions for each of the nine EIBEP sampling strata: Conakry, Lower Guinea, Middle Guinea, Upper Guinea and Forest Guinea. Except for the capital Conakry, all strata were further broken down into urban and rural areas for the estimation. Following Elbers et al. (2002; see also Mistiaen et al. 2002 from which the presentation below is adapted), denote by _ych_ the household consumption per capita of household _h_ in location _c,_ by **xch**<sup>a set of</sup> explanatory variables, and by _uch_ a residual. We have: The locations represent clusters as defined in the first stage of typical household sampling design. The explanatory variables need to be present in both the survey and the 6
To investigate the importance of the quality of economic environment in a given exporting ACP country an _indicator of economic freedom_ was used ( _Freedomindex_ ). The index was obtained from the Freedom House "Freedom in the World Countries" database.<sup>13</sup> This is a database containing an annual comparative assessment of the state of political rights and civil liberties in 192 countries and 18 related and disputed territories. The lower is the index the more economic freedom the country has. We expect that countries with greater economic freedom are more open and more likely to trade.

To capture _historical linkages_ between trading partners two zero-one type dummy variables were included in the regression. _FrenchExcolonyij_ and _NonFrenchExcolonyij_ take the value of 1 if the exporting country (i) was a colony of France or other partner country(j). Colonial links often reflect not only historical ties but also that the traders of the two partner countries can speak the same language. If a country was an ex-colony of its trading partner trade between the two countries would probably necessitate lower transaction costs thus more trade. A separate dummy is included for non-French ex-colonies and French excolonies because we expect that there might be differences in the intensities of the trade links for French ex-colonies.<sup>14</sup> In many aspects, such as size of the economy or the level of development, South Africa differs from most of the other countries in our sample. In order to avoid having specificities of South Africa drive our results, a dummy taking the value 1 if the exporting country is South Africa is included in the regressions. _DUMijk_ are a set of _k_ dummy variables for agriculture, textiles, clothing, footwear, machinery and mineral products.<sup>15</sup> _Quotak_ is a dummy which takes the value 1 if the product was eligible for quota preferences and zero otherwise. The dummy for quota is included in the regression because it is likely that the circumstances for products entering under a preferential quota are different than for products entering without quota preferences; for example traders using the quota preferences are likely to be better informed. Furthermore, we include a dummy (called _difference_ ) for agricultural products for which the difference between preferential and MFN tariffs exceeds 30%. These agricultural products benefit from seasonal preferential duty reduction. Since we have yearly data it was not possible to exactly calculate the tariff

> 13 For details on the index see: http://www.freedomhouse.org/research/freeworld/2003/methodology.htm. 14 Only four countries were not colonies in our sample.

> 15 From the 23,685 observations, 9,015 are not covered by any sectoral dummies. These observations belong to sectors such as metals, vehicles, optics, chemicals, plastics, stones, and glasses.

12
local warehouses,<sup>23</sup> this will provide some insulation against seasonal and other price movements. This may go some way to explaining the relatively low estimated price elasticity for rural households. LSMS‐ISA breaks down rural households into three groups: net food purchasers, net food sellers and an intermediate group. The two bottom panels of Table 5 report estimates of equation (10) for the net buyer and net seller sub‐categories. The number of observations becomes very small, in particular when considering further division into asset rich and asset poor categories. The net buyers are the largest group within the rural classification and the estimates are very similar to those for the entire group. However, the net food sellers are associated with much greater price responsiveness, apparently irrespective of asset level. These findings are consistent with those reported by Khandker (2012) for Bangladesh, i.e. seasonality in food consumption increasing with seasonality in income, with no clear difference in this pattern between poor and non‐poor households. Net sellers derive more of their income from crop sales (around 50 percent of total income for rural net sellers versus 35 percent for the rural net buyers), which turns out to be mostly sold in the months immediately after the harvest (Palacios‐Lopez, Christiaensen, Galindo‐Pardo, 2015), especially within the first three months after harvest.<sup>24</sup> Their incomes are more seasonal as a result. For Ethiopia, Bellamare, Barrett and Just (2013) also find that the willingness to pay for price stabilization is highest among the larger net food sellers.

# **5 Conclusion**

While any knowledgeable observer knows of seasonality in African food markets and consumption, it is much less clear what it is that is actually known. There proves to be surprisingly little systematic evidence across crops, markets and countries about the extent of food price seasonality, how it differs across these crops and markets, how it compares to reference markets, and how it affects household welfare and health outcomes. Moreover, most available studies date from the 1980s and 1990s and they have typically been conducted on

> 23 Most maize‐producing households retain grain for about six months for food consumption purposes in most cases, After 10 months it is 90 percent depleted (Kaminski and Christiaensen, 2014)..

> 24 Richer rural net sellers manage to spread their sales more evenly over a longer period after the harvest. They also show a lower price elasticity of caloric intake than poor rural net sellers.

22
145-186.

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Kashyap, A. K. and C. J. Stein. (2000). "What Do a Million Banks Have to Say About the Transmission of Monetary Policy?," _American Economic Review,_ forthcoming.

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those found in the empirical macroeconomic literature (Bernanke and Gurkaynak, 2001; Gollin, 2002). They are also highly significant and robust to the various experiments we perform.

We also find little evidence of heterogeneity across countries in the output elasticities of the inputs of the aggregate production function. Specifically, the output elasticity of infrastructure does not seem to vary with countries‘ level of per capita income, their infrastructure endowment, or the size of their population.

The rest of the paper is organized as follows: Section 2 describes the dataset. Section 3 lays out the methodological approach. Section 4 describes the empirical results. Finally, section 5 concludes.

# **2. Data**

Our goal is to estimate the contribution of infrastructure capital to output in a large panel data set using an infrastructure-augmented aggregate production function framework, in which aggregate output is produced using non-infrastructure physical capital, human capital, and infrastructure. The data set is a balanced panel comprising annual information on output, physical capital, human capital, and infrastructure capital for 88 industrial and developing countries over the period 1960-2000, thus, totaling 3,520 observations. The Appendix lists the sample countries used in the analysis.<sup>8</sup> Real output is measured by real GDP in 2000 PPP US dollars from the Penn World Tables 6.2 (Heston, Summers, and Aten, 2006). The data on physical capital was constructed using the perpetual inventory method. To implement it, the initial level of the capital stock was estimated using data on the capital stock and real output from PWT 5.6 for those countries for which such data is available. We extrapolate the data for countries without capital stock information in PWT 5.6 by running a cross-sectional regression of the initial capital-output ratio on (log) real GDP per worker.<sup>9</sup> As a robustness check, we also constructed an alternative ̳back-cast‘ projection of capital per worker. Specifically, to construct an initial capital stock for the year 1960, we assume a zero capital stock in the distant past. Using the average in-sample growth rate of real investment

> 8 Country coverage is dictated by the availability of information. In particular, time coverage is limited by the human capital indicator, which is not available after 2000.

> 9 The regression used for extrapolation is: K/Y = -1.1257+0.2727*ln(Y/L), where K is the capital stock, Y is real GDP, and L is the labor force. The depreciation rate employed in the perpetual inventory calculations is 6%.

5
Schady (2006), exploiting the cut-off score used to determine eligibility for Cambodia’s Japan Fund for Poverty Reduction (JFPR) scholarship, find a 30 to 43 percentage point increase in the enrollment and attendance of girls. Chaudhury and Parajuli (2006; 2008) use repeated school censuses in conjunction with a district-level eligibility cut-off and find that Pakistan’s female student stipend program in Punjab between 2003 and 2005 increased enrollment by six female students per school (a nine percent increase in female enrollment in terms of relative change).

In the context of gender-targeted transfers such as these, a natural question arises: when these girls go to school, who takes care of the work within the household they had been doing? Answers to this question are absent in the literature because often analysts do not have access to the right kind of data. The evaluations described above have either had to rely on administrative (Filmer and Schady, 2006) or school level (Chaudhury and Parajuli, 2006; 2008) data or on household data where no information is typically available on time use (Khandker et. al. 2003). An answer to this question is, however, particularly relevant as new programs are brought online and existing ones are expanded. Skoufias and Parker (2000 and 2001) shed some light on the time use of recipient households under _Progresa_ . Their insights on the issue were, however, necessarily limited because time use data was only available to them for one post-program year.<sup>3</sup> Consequently, this paper has a modest goal: to build on this earlier work and to provide a more complete picture of the effects of a gender-targeted conditional cash transfer program on the time allocation of rural households.

> 3 See also the evidence in George and Schechter (2009) on issues of data quality in the _Progresa_ data.

5
|oss Investment<br>(5)<br>(6)<br>-0.030*<br>-0.008<br>(0.018)<br>(0.017)<br>48,687<br>48,709<br>0.694<br>0.692<br>-0.014<br>-0.010<br>(0.0191)<br>(0.016)<br>0.693<br>0.692<br>48,687<br>48,709<br>-0.107<br>-0.0197<br>(0.065)<br>(0.044)<br>48,687<br>48,709<br>**king Age Pop.)**<br>0.288***<br>0.328***<br>(0.023)<br>(0.025)<br>151.57<br>446.21<br>48,687<br>48,709<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>No<br>Yes<br>nd i) armed attacks by illegal armed groups<br>ent transfers to health, education, and other<br>00 GDP share in agriculture, services, and<br>are reported in parentheses. _∗∗∗_signifcant|
|---|
|Gr<br>(4)<br>-0.027**<br>(0.013)<br>48,725<br>0.691<br>-0.012<br>(0.0143)<br>0.691<br>48,725<br>-0.075**<br>(0.036)<br>48,725<br>**DP (% Wor**<br>0.353***<br>(0.013)<br>711.12<br>48,725<br>Yes<br>Yes<br>No<br>No<br>No<br>No<br>ear dummies a<br>entral governm<br>n 1995, viii) 20<br>t the frm level|
|nt<br>(3)<br>-0.040<br>(0.025)<br>23,669<br>0.710<br>-0.004<br>(0.026)<br>0.710<br>23,669<br>-0.0942<br>(0.062)<br>23,669<br> **Share of I**<br>0.328***<br>(0.025)<br>446.21<br>23,669<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>nteractions of y<br>s in 1995, v) c<br>ction offces i<br>andard errors a|
|Net Investme<br>(1)<br>(2)<br>-0.053***<br>-0.070**<br>(0.019)<br>(0.027)<br>23,680<br>23,655<br>0.707<br>0.711<br>0.001<br>0.013<br>(0.0229)<br>(0.0296)<br>0.707<br>0.711<br>23,680<br>23,655<br>-0.146***<br>-0.240**<br>(0.055)<br>(0.104)<br>23,680<br>23,655<br>**Dep. Variable:**<br>0.353***<br>0.288***<br>(0.013)<br>(0.023)<br>711.12<br>151.57<br>23,680<br>23,655<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>Yes<br>No<br>No<br> Additional controls include i<br>s 1995, iv) public expenditure<br>1995, vii) number of tax colle<br>density in 1995. Clustered st|
|**Dependent Variables (in logs)**<br>**Panel A. Reduced Form**<br>Predicted Infows<br>Observations<br>R-squared<br>**Panel B. OLS**<br>Share of IDP (% Working Age Pop.)<br>Observations<br>R-squared<br>**Panel C. 2SLS**<br>Share of IDP (% Working Age Pop.)<br>Observations<br>**Panel D. First Stage**<br>Predicted Infows<br>First Stage F-statistic<br>Observations<br>**Controls (for all panels)**<br>Year FE<br>Firm FE<br>Year_×_Department FE<br>Homicides Rates<br>Confict Controls<br>Additional Controls<br>_Notes:_ Each coeffcient corresponds to a separate regression. <br>in 1995, ii) fatal victims of attacks in 1995, iii) homicide rate<br>expenditures in 1995, vi) number of fnancial institutions in<br>industry, ix) municipal tax income in 1995, and x) night light<br>at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%.|

38