rafmacalaba/data-use-mentions-extended huggingface dataset:
if the values found were coming from less labor market productivity or from employer discrimination against Roma males or females. Considering this limitation, in the policy experiments we one increased male and female labor parameters to simulate if there is a reduction in discrimination, or if Roma wives’ productivity increases over Roma husbands. We then calculate spouses’ optimal time allocation, labor supply, and wages for these scenarios.

**The presence of any source of nonlabor income changes the marginal utility of labor income against leisure and home production; thus, the behavior of couples with this income might differ significantly from couples without it.** In their analysis, Flabbi and Mabli (2018) include the effects of two levels of nonlabor income (or the absence of it) for U.S. couples’ labor decisions. Even in a labor market closer to Roma conditions, labor and nonlabor income of other household members is an important factor in determining female labor participation (Hafez and Ahmad 2002; Andres et al. 2017).

**Health status may also be highly correlated to employment status and labor force participation.** The Roma are often not able to invest in their health due to lack of access to medical care. In all countries, the prevalence of self-reported unmet need for medical care is high among the marginalized Roma, ranging from 16 percent in North Macedonia to as high as 36 percent in Albania. Self-perceived health is also lower among the Roma than the neighboring non-Roma, possibly indicating lower objective health outcomes among the marginalized Roma.<sup>38</sup> Health status is a condition that changes

> 38 Subjective health measures cannot be constructed using the 2017 RRS.

33
evidence that the financing patterns of large firms in financially developing countries are consistent with the pecking order theory of capital structure.

When we examine data from the latest round of Enterprise Surveys, conducted from 2006-2010, we find that debt finance and bank finance in particular, is the most common type of external finance for firms of all sizes across countries. Although bank financing is the most prevalent type of external finance, a large proportion of firms, especially small and medium firms, do not have any bank loans. The firm survey results suggest that this reflects both firms being refused bank loans as well as a lack of demand for bank loans either because of other financing sources or lack of good projects to finance. Some of the common reasons why firms are excluded from bank finance include high interest rates, collateral requirements, corruption in banking, and cumbersome paperwork.

# **_5.3 Cash Holdings and Liquidity Management_**

Most of the cross country studies of capital structure focus on debt levels and there have been relatively few studies of cash holdings and overall liquidity management of firms in developing countries. From the studies of US firms, we know that under capital market imperfections, there are two views on the optimal amount of cash holdings: According to the precautionary motive for liquidity, firms hold cash so as to not have to raise funds when external capital is expensive (Myers and Majluf, 1984; Almeida, Campello, and Weisbach, 2004; Acharya, Almeida, and Campello, 2007; and Gamba and Triantis, 2008); by contrast, according to the agency view, management and controlling shareholders may not use cash in the best interests of the firm, such as on excessive spending and value destroying acquisitions (Jensen (1986), Blanchard, Lopezde-Silanes, and Shleifer (1994), Harford (1999), Opler et al. (1999), Dittmar, Mahrt-Smith, and Servaes (2003), Mikkelson and Partch (2003), Pinkowitz, Stulz, and Williamson (2006), Dittmar
**Table 1: Overview of National Surveys Used in Analysis**

||**Ethiopia**|**Tanzania**|**Cambodia**|
|---|---|---|---|
|Survey|2018/2019<br>Ethiopia Socioeconomic<br>Survey|2019/2020<br>Tanzania National Panel<br>Survey|2019/2020<br>Cambodia LSMS+|
|Implementing Agency|Ethiopia Central<br>Statistical Agency|Tanzania National<br>Bureau of Statistics|National Institute of<br>Statistics of Cambodia|
|Fieldwork Period|9/2018 – 8/2019|1/2019 – 1/2020|10/2019 – 1/2020|
|Household Sample|6770 Households|1184 Households|1512 Households|
|Scope of Household<br>Questionnaire|8 modules|16 modules|10 modules|
|Adult Respondent<br>Sample for Individual<br>Questionnaire|7235 Men<br>8153 Women|1407 Men<br>1506 Women|1845 Men<br>2095 Women|
|Scope of Individual<br>Questionnaire|7 modules|8 modules|11 modules|
|Individual Questionnaire<br>Modules on Asset<br>Ownership|Non-residential (primarily<br>agricultural) and<br>residential land, financial<br>accounts, mobile phones,<br>livestock|Non-residential<br>(primarily agricultural)<br>and residential land,<br>financial accounts,<br>mobilephones|Non-residential (primarily<br>agricultural) and residential<br>land, financial accounts,<br>mobile phones, livestock,<br>consumer durables|
|Other Individual<br>Questionnaire Modules<br>Notes: LSMS+ data are pub|Employment, non-farm<br>enterprises, education,<br>health, savings<br>licly available. More informa|Employment,
||4.716<br>(13.76)|
|Region Code = 3, LCN|||4.613<br>(7.408)|
|Region Code = 4, MEA|||41.25|
||||(28.46)|
|Region Code = 7, SSA|||103.1***|
||||(18.60)|
|Constant|182.0***|167.0***|78.99*|
||(47.69)|(46.97)|(44.66)|
|Observations|48|48|48|
|R-squared|0.345|0.364|0.603|

Note: Results from OLS regressions of the absolute gap between survey estimates and administrative figures of vaccination coverage on indicators of statistical performance. To avoid extreme leverage at low values of coverage, the sample is constrained to those survey waves where administrative data coverage was at least 5% of the adult population. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 We find lower statistical performance scores to be significantly associated with higher gaps between the survey and administrative data in our sample of LMICs (Table 9). In a bivariate regression, the coefficient estimate suggests that a 1 percentage point increase in the total SPI score is associated with a 1.6 percentage point reduction in the percent gap between both data sources. Further, the availability of Civil Registration and Vital Statistics (CRVS), the SPI’s metric for administrative data quality specifically, is associated with a 55.4 percentage point smaller gap. When additionally controlling for GDP per capita, we find that only the SPI indicator of administrative data quality is significantly and negatively associated with the percent gap between 25
11</sup> coal, gas, oil, hydro, solar, and wind. The electricity generation mix follows the results of an Electricity Planning Model, see (Chattopadhyay, de Sisternes, & Oguah, 2018).

> 11 Although Türkiye does not have an active nuclear power plant as of 2018, a new plant is currently being built. To be able to incorporate nuclear into the model baseline and scenarios, we introduced a very small nuclear power activity in the base year based on an average cost structure from the GTAP power database and followed the expected growth of share of nuclear in power mix in the baseline and scenarios.

8
Assuming a relatively open economy, a key determinant of whether resources are likely to shift from agricultural and other resource-based products into manufactures and services is the relative rates of technical change. Many economists, including Matsuyama (1992) follow a tradition dating back to Adam Smith and assume that productivity growth in agriculture is very slow. However, more recent empirical studies (eg Bernard and Jones 1996; Martin and Mitra 2001) suggest that the average rate of total factor productivity growth in agriculture has been higher than in manufacturing. This appears to represent a change from results from earlier periods surveyed by Syrquin (1986), in which there was no consistent tendency for total factor productivity in agriculture to grow more rapidly than productivity in manufactures. This apparent change may reflect the substantial investments in international research and dissemination of rural technologies during recent decades.

Key results from the Bernard and Jones and the Martin and Mitra studies are presented in Table 5. The Bernard and Jones analysis is based on data from OECD countries over the period 1970 to 1987, while the Martin and Mitra study is based on data collected by Larson and Mundlak (see Larson, Butzer, Mundlak and Crego 2000) for 1966 to 1992. While this evidence is somewhat limited as a basis for judgement, further support for the proposition that agricultural TFP has been more rapid than that in manufacturing is provided by a number of single-country studies, including Jorgenson, Gollop and Fraumeni (1987). The Bernard and Jones estimate of a small, negative rate of TFP growth in mining is surprising given the manifestly rapid changes in the technology used for mining, and may reflect resource depletion in some OECD countries.

26
The wage differentials tend to be higher in rural as compared to urban areas, and are higher among women than among men. The wage differential also tends to be higher for low-skilled workers. Moreover, there is considerable evidence of an increase in the wage differential between 1993-94 and 1999-00. The public sector wage advantage has increased for rural male and female workers according to all three sets of estimates. However, in urban areas the trend is more ambiguous. For male workers, two sets of estimates show an increased differential, while one method shows a slight decline in the differential. For female workers, the wage advantage of public sector has declined slightly according to two sets of estimates, and increased according to one.

Our review of wage differentials (estimated using similar methodologies) across the world shows that India has one of the largest differentials between wages of public workers and workers in the formal private sector.<sup>15</sup> Differentials as high as in India were found in only two African countries (Ghana and Côte d’Ivoire) and in regions of Brazil.

There are several qualifications and possible caveats to our results. First, it is quite possible (even likely) that our sample missed the most skilled workers in India’s labor market – managers, lawyers, and accountants – those who tend to work in the internationally competitive private sector. In particular, the 75<sup>th</sup> (90<sup>th</sup> ) percentile of the distribution of private-formal wages in the 1999-00 NSS sample is Rs 800 (Rs 1,500). The several highest weekly wage points are in the range of Rs 17,000 - 23,500. (The highest observed wage in public sector is Rs 21,500). At the same time, the median weekly wage of a “fully qualified professional” employed in multinational corporation operating in the financial or industrial sectors in India or working for a “non for profit” international organizations is
***|0.92***|1.00***|
||(1.09)|(1.54)|(0.27)|(0.03)|(0.05)|
|Food consumption|40.39***|6.48***|2.54**|0.27***|0.29***|
||(0.69)|(0.26)|(0.08)|(0.01)|(0.01)|

Source: “Pauvreté et Structure Familiale” (PSF) survey, N=1762, authors’ calculations.

Note: The first panel presents inequality measures computed using per capita household consumption as a measure of individual consumption. The second panel presents inequality measures computed using per capita cell consumption. Bootstrap standard errors (250 replications) between parentheses. *,**,***: difference with per capita household consumption inequality measure is significant at the 10%, 5% or 1% level (see Note 16 and Appendix S2 for details).

39
Hill.

- Meadows, D.H., D.L. Meadows, J. Randers, and W.W. Behrens III. 1972. _The Limits to Growth: A Report for the Club of Rome’s Project on the Predicament of Mankind_ . New York: Universe Books.

- Nappi, C. 1990. “The Food and Beverage Container Industries: Change and Diversity.” In _World Metal Demand: Trends and Prospects_ , ed. J. E. Tilton. Resources for the Future Press, New York.

- Newell, R.G., A.B. Jaffe, and R.N Stavins. 1999. “The Induced Innovation Hypothesis and Energy‐Saving Technical Change.” _Quarterly Journal of Economics_ 114(3): 941‐974.

- Olmstead, A.L., and P. Rhode. 1993. “Induced Innovation in American Agriculture.” _Jour‐ nal of Political Economy_ 101(1): 100‐118.

- Pei, F., and J. E. Tilton. 1999. “Consumer Preferences, Technological Change, and the Short‐Run Income Elasticity of Metal Demand.” _Resources Policy_ 25: 87‐109.

- Pesaran, M.H. and R. Smith. 1995. “Estimating Long‐Run Relationships from Dynamic Heterogeneous panels.” _Journal of Econometrics_ 68: 79–113.

- Pesaran, M.H. 1997. “The Role of Economic Theory in Modelling the Long Run.” _Economic Journal_ 107(440): 178–191.

- Pesaran, M.H., Y. Shin.1999. “An autoregressive distributed‐lag modelling approach to cointegration analysis.” In _Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (Econometric Society Monographs, pp. 371‐413)_ , ed. S. Strøm. Cambridge University Press, Cambridge.

- Pesaran, M.H., Y. Shin, and R. Smith. 1999
mm)|246|
|Manager age (years)|41.9|Long term mean flowering season rainfall (|211|
|Manager years of education|6.18|||
|Manager completed primary education°|0.39|||
|Manager received extension services°|0.31|||
|Primary occupation is agriculture°|0.80|||

Notes: N = 840; ° Binary variable; + Road network distance to nearest FEWSNET market

# **3.2.AFSIS**

Geospatially-derived soil data is more widely available to researchers and policy makers than plotlevel soil sampling linked to household surveys. Yet, geospatial data in this realm is of coarser granularity than what would be observed at the plot-level. In order to understand the implications of relying on geospatial soil data in cases where plot-level sampling is unavailable, we complement the analysis using MAPS-based soil properties with that using soil data drawn from one of the premier, publicly available geospatial soil databases – the Africa Soil Information Service’s AfSoilGrids250m data product.<sup>10</sup> The AfSoilGrids250m product, henceforth referred to simply as _AFSIS_ , utilizes multiple inputs to construct a map of more than 15 key soil properties at 250-meter resolution across the entire African continent. Inputs to the product, including the Africa Soil Profiles database (Leenars et al.,

> 10 AfSoilGrids250m is a product developed by the World Soil Information (ISRIC) in collaboration with the World Agroforestry Centre (ICRAF), The Earth Institute (Columbia University), and the International Centre for Tropical Agriculture (CIAT).

8
.518||0.516|0.319|0.330|
|First-stage_R_<sup>2</sup>||0.628||||
|First-stage_F_ test of excluded instruments||19.07||||

_Notes:_ The dependent variable is the unweighted (count based) Duranton-Overman _K_ -density cdf at 50 kilometers distance.<sup>_a_</sup> ,<sup>_b_</sup> and<sup>_c_</sup> denote coefficients significant at the 1%, 5% and 10% levels, respectively. Our measures of input and output distances are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. Model 11 replicates our preferred Model 7 but the standard errors are bootstrapped because of the generated regressor. Model 12 instruments the ‘Ad valorem trucking costs’ using costs constructed from us price indices. Models 13 and 14 use the Lewbel (2012) methodology to instrument input-output distances and trade shares. In Model 13, only a subset of the import shares is instrumented, while all trade shares are instrumented in Model 14. See Appendix C for additional details. A constant term is included in all regressions but not reported. All industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with postsecondary education; In-house R&D share of sales) are included but not reported. All trade share variables are included in the regressions, but we do not report detailed results to save space.

variables, we use the Lewbel (2012) estimator with internal instruments for the input-output distances and the trade shares (see Lewbel, 2012, and Appendix C for more details on the
implementation (such as for 2012 versus 2016). These figures are available in the appendix (figures A.1 to A.4).<sup>9</sup> For this purpose, we rely on individual, bank-level balance sheet data from Bankscope. For the analysis, we consider all the banks available in Bankscope for the countries in our sample. Consequently, for 2012, the data set consists of 389 banks from the treated group and 402 banks from the control group. The corresponding figures for 2016 are 408 and 306 banks for the treated and control group countries. As the data set is limited to only the balance sheet data, we do not conduct this analysis for the same variables as our banking system indicators studied earlier. Consider the results in table 8 first.

Table 8: Higher moments of bank capitalization: Treated versus control group

||**Treated**<br>|**group**<br>|**Contro**<br>|**l group**<br>|**Treated %**<br>|**Control %**<br>|**DiD in**|
|---|---|---|---|---|---|---|---|
|**Variable**|**2012**|**2016**|**2012**|**2016**|**change**|**change**|**p.p.**<br>**(%)**|
|Mean|21.25|21.69|20.46|20.46|1|0|1|
|Median|13.80|14.54|15.02|14.07|2|-3|5|
|Std. dev.|30
* significant at 5%; *** significant at 1% a/ fixed effects by importer  product pair

# **2.3   The China effect**

Considering the rising importance of OECD trade with China over the last decade, we must control for the role that China may play in that re-concentration. Figure 3 shows the evolution of the Theil index for extra-OECD imports, both with and without China. The figures show that China is indeed driving the observed re-concentration. Further evidence is provided in Table 5, which shows that the coefficient on time squared loses its significance when China is excluded from the sample.

Figure 3 Theil index for OECD imports excluding China, 1963-2006 Extra-OECD imports Extra-OECD imports excluding China <!-- Start of picture text --> 3.3<br>3.2<br>3.1<br>3<br>2.9 Theil<br>2.8<br>2.7<br>1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005<br><!-- End of picture text --> <!-- Start of picture text --> 3.3<br>3.2<br>Theil<br>3.1<br>3<br>2.9<br>2.8<br>2.7<br>1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003<br><!-- End of picture text --> 12
# **Table S1.2: Equality in divorce access**

|_Equal access to divorce_|_Inequality in access to divorce_|
|---|---|
|Colombia|Azerbaijan|
|Haiti|Bangladesh|
|Honduras|Burkina Faso|
|Kyrgyz Republic|Cambodia|
|Mozambique|Cameroon|
|Sao Tome and Principe|Democratic Republic of Congo|
|Ukraine|Dominican Republic|
||Gabon|
|UK<sup>*</sup>|Ghana|
|US<sup>*</sup>|India|
||Jordan|
||Kenya|
||Liberia|
||Malawi|
||Mali|
||Pakistan|
||Peru|
||Philippines|
||Rwanda|
||Tajikistan|
||Tanzania|
||Timor Leste|
||Uganda|
||Zambia|

_Source:_ OECD 2019 Social and Institutions Gender Index, https://www.genderindex.org/ _Notes:_

1. Equality in access to divorce is defined in the SIGI as "Women have both the same rights to initiate divorce and the same requirements to finalise divorce or annulment as men, without negative repercussions on their parental authority. This applies to all groups of women. Customary, religious and traditional laws or practices do not discriminate against women’s rights regarding divorce or parental authority after divorce".

2. * Not in sample; included for comparative purposes.
**Figure 15. Simulated change in earnings as a result of the minimum wage increase a.  Low labor demand elasticity**

<!-- Start of picture text --> 3.2<br>2.0 1.8<br>1.3<br>1.1<br>0.50.3 0.50.3 0.60.4 0.80.5 0.50.3 0.30.2 0.20.1 0.7 0.70.4 0.20.1<br>Total Gender Age Education<br>Short-run Long-run<br>b. High labor demand elasticity<br>0.9<br>0.4<br>0.1 0.1 0.2 0.2 0.1 0.1 0.0 0.1 0.1 0.10.0<br>-0.1 -0.1 -0.1 -0.3 -0.4 -0.2 -0.1 -0.1 0.0 -0.2<br>-0.6<br>Total Gender Age Education<br>Short-run Long-run<br>Male Female 15-19 20-24 25-29 30-34 35-54 55-64 Primary or less Secondary Post-secondary<br>Male Female 15-19 20-24 25-29 30-34 35-54 55-64 Primary or less Secondary Post-secondary<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

**Figure 16. Simulated long-run change in earnings as a result of the minimum wage increase by sector**

<!-- Start of picture text --> 0.56<br>0.05 0.05<br>0.00<br>-0.02<br>-0.20<br>-0.3 -0.7 -0.9<br>long-run elasticity for skilled
.

<u>Source: Teravaninthorn and Raballand 2009.</u> The comparatively slow effective velocity of freight in East Africa can be explained by lengthy customs clearance processes, administrative delays at ports, and delays at the border. The situation seems to have worsened since 2009. According to the LPI survey, customs clearance time in the region is up to seven times less predictable than in other parts of the world. Associated border delays are also lengthy (World Bank 2010b). Based on estimates from 2008, East African trucking companies have indicated that their trucks have to wait between one and two days at Malaba (the border post between Kenya and Uganda). Congestion at the posts due to limited parking availability, limited space in customs yards, poor cargo documentation, and duplication of administrative processes in Uganda and Kenya have also contributed to the delays. Ugandan and Kenyan authorities are now working on an initiative that would oblige trucks to pass through only one stop instead of two. Once fully launched this effort should significantly reduce delays (Teravaninthorn and Raballand 2009).

Recent reports indicate that roadblocks and weigh stations significantly increase transport costs. A 2008 survey of the East African Business Council found that random police checks that impose costs and delay trucks are pervasive. Similar problems have been documented in West Africa. Truckers traveling west from Mombasa or Dar es Salaam report an average of 19 roadblocks and 4.4 weigh stations per trip, resulting in 12 hours spent on diversions. Truckers traveling between Kigali and Mombasa experienced a combined total of 47 roadblocks and weigh stations. Although the value of the individual bribes sought at roadblocks and weigh stations is not large, their cumulative total is daunting—around $8 million per year in the East African Community alone (USAID 2009).

East Africa has four landlocked countries, which use Dar es Salaam and Mombasa as their gateways to the sea. Ethiopia uses Djibouti as its gateway, and Sudan has access to Port Sudan. Rwanda and Burundi have the option of using either Mombasa