rafmacalaba/data-use-mentions-extended huggingface dataset:
## **Figure 2. Monitoring progress toward the SDGs**

<!-- Start of picture text --> A. Extreme poverty B. Human capital index, by country group<br>Percent Millions Index, frontier=1<br>50 Share of population 2000 1.0<br>Number of people (RHS)<br>40 1600 0.8<br>0.6<br>30 1200<br>0.4<br>20 800<br>0.2<br>10 400<br>0.0<br>0 0 Low Lower Upper High<br>income middle middle income<br>1990 1993 1996 1999 2002 2005 2008 2010 2011 2012 2013 2015 income income<br><!-- End of picture text --> <!-- Start of picture text --> A. Extreme poverty<br><!-- End of picture text --> Source: PovcalNet <u>(http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx), World Bank Human Capital</u> Index.

A. Lines show incidence of poverty ($1.90 per day at 2011 purchasing power parity) at the global level. B. Bars show median of index of each country group. The human capital index calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of a child born today relative to the benchmark of full health and complete nutrition.

# **B. Partnerships with international agencies**

The WBG has partnered with other agencies on initiatives in several areas directly related to the SDGs. In 2018, the heads of the WBG and the United Nations jointly signed the _UN-WBG Strategic Partnership Framework_ to implement and finance the 2030 Agenda for Sustainable Development, among three other focus areas. The WBG has partnered with UN agencies on inequality
0.053]|
|EU-10 country dummy times CAB||-0.262|||-0.245|
|||[0.094]|***||[0.101] **|
|EU candidates country dummy times CAB|||-0.133||-0.124|
||||[0.055] **||[0.062] **|
|EU neighborhood country dummy times CAB||||0.476|0.485|
|||||[0.127]|***<br>[0.135] ***|
|Common intercept|20.640|20.456|21.139|20.272|20.159|
||[3.654]|***<br>[3.608]|***<br>[3.731] ***|[3.648]|***<br>[3.627] ***|
|Number of observations|584|584|584|584|584|
|Number of countries|88|88|88|88|88|
|Number of instruments|30|35|35|35|45|
|Hansen test for over-identifying restrictions|0.064<sup>[B]</sup>|0.153|0.059<sup>[B]</sup>|0.164|0.204|
|Arellano-Bond AR(1) test (p-value)|0.000|0.000|0.000|0.000|0.000|
|Arellano-Bond AR(2)test(p-value)|0.812|0.790|0.847|0.749|0.769|

Notes:<sup>[A]</sup> GDP data are adjusted
# Appendix 5: Impacts on Individual Management Practices

Table A5.1 shows the breakdown of significant improvements in management practices within the Anexo K index:

**<mark>Table A5.1: Summary of Impacts at the Sub‐Index and Individual Practice Level</mark>**

||#|**Sub‐In**<br># sig. Ind.|**dices**<br># sig. Group|#|**Individual Prac**<br># sig. Ind.|**tices**<br># sig.<br>Group|
|---|---|---|---|---|---|---|
|Finances|8|6|5|29|17|15|
|HR|7|3|2|20|11|6|
|Logistics|7|5|2|31|8|9|
|Marketing|5|3|3|22|9|13|
|Production|8|6|8|39|22|30|
|TOTAL|35|23|20|141|67|73|

<mark>Note: lists number of practices that are statistically significant at the 5% level post‐intervention.</mark> Table A5.2 details the individual management practices that have treatment effects of 0.8 or more (on a 5-point scale).

|**Table A5.2: Practices that increase by 0.8 or more from at least one‐treatment**|||
|---|---|---|
||Individual|Group|
|_Finance Practices_|||
|System of monitoring and control of financial goals in place|**0.827*****|0.666***|
||**(0.175
**Figure 5: Total job content of exports as a share of total employment 2004-2011**

<!-- Start of picture text --> 1995 2000 2005 2010<br>Year<br>ZAF BRA RUS COL<br>PER THA TUR<br>50<br>40<br>30<br>20<br>Export jobs in total employment (%)<br>10<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

# **4. Labor and Jobs Content of Exports across Sectors**

# **a. Within South Africa**

The aggregate trends mask a wide heterogeneity across sectors. In particular, we show in this section how commodity exports versus manufacturing and services exports contribute differently to jobs and wages, which depends on whether we consider the direct versus indirect content as well as the forward versus backward linkages. A sectoral perspective also shows how the composition of South Africa’s exports are driving the above aggregate results.

Natural resource sectors – which tend to be capital intensive – generate most of South Africa’s labor value added contained in its exports. Figure 6 shows that in 2011 the leading export sectors in terms of total labor content were minerals, metals, machinery and equipment, and ferrous metals. Minerals contributed over $6 billion in wages, by far the largest contributor to wages among export sectors, reflecting the large value of gross exports and a relatively high – and growing - labor intensity of exports vis-à-vis other sectors (more on this below). Together these four sectors contributed 51% of South Africa’s total labor contained in its exports and 52% of South Africa’s gross exports.

Consistent with its enclave-type characteristics, the minerals sector primarily generates domestic labor value added directly within the sector’s exports, as opposed to its backward linkages with input sectors. On the other hand, around three-quarters of the total labor contained in
To our knowledge, this is the first paper that analyzes and presents a comprehensive assessment of how the distribution of household income is affected by each type of tax or social transfer and by the whole system in Serbia. The paper advances on the previous literature as follows. Earlier work has looked at the impact of direct taxes and direct transfers using SRMOD, a version of the EUROMOD model (Randjelovic and Zarkovic, 2013) and estimated the effects of fiscal instruments on income inequality using a decomposition approach (Krstic and Zarkovic, 2017). However, these studies did not include indirect taxes, which are a major source of revenue collection, and did not look at poverty impacts. This paper incorporates these aspects and, in addition, includes inkind transfers such as education and health, which are major components of social spending, in assessing impacts on inequality.

This paper follows the Commitment to Equity Assessment (CEQ) approach<sup>3</sup> and, to our knowledge, is the first to apply the methodology and generate new findings for Serbia. Compared to other approaches, using the CEQ has the benefits of assessing the comprehensive fiscal system as a whole, assessing impacts for various types of households, and comparison with the many countries where this same approach has been applied. The paper uses the household survey data available at the time of analysis, namely the 2017 Survey of Income and

> 2 World Bank (2020). Serbia Systematic Country Diagnostic Update.

> 3 The Commitment to Equity Assessment (CEQ) toolkit provides a solid methodological framework for answering these questions; see <u>https://commitmentoequity.org/publications-ceq-handbook. This approach has been</u> implemented in many countries across different regions of the world. For methodological details, see Lustig, Nora (editor). 2018. _Commitment to Equity Handbook. A Guide to Estimating the Impact of Fiscal Policy on Inequality and Poverty_ . Brookings Institution Press.

3
), this implies likely NCME in fertilizer, labor, seed, and other inputs that have been less well studied. Second, simplifying mental models naturally generate correlated measurement error, which further complicates econometric correctives, as replacing an erroneous self-reported variable with an accurate measure of the same variable can aggravate rather than reduce bias in regression coefficient estimates due to correlated NCME (e.g., Abay et al., 2019).

We run several robustness checks to probe the robustness of our main results. Among these, we limit our sample to those cases where we have the same respondent in both the postplanting and post-harvest surveys. We re-estimate for these restricted sample involving the same respondent. Appendix Tables A6-A9 show adjustment in plot size self-reports in the post-harvest survey round. These estimates confirm those in Tables 7-10, confirming the already-discussed 28
creating the best possible mask for our period of analysis. And for 2015, the two approaches result in very similar light data. Clusters are identified by removing outlier observations, averaging cells over time, and clustering areas based on their nighttime light intensity. In practice, this approach amounts to setting to zero cells that are distant from homogenous bright cores. We present results based on data cleaned with the second method, but our results are robust to both cleaning methods. For this paper, cleaned monthly data are aggregated to the district and city levels and standardized by area. Nighttime light is measured in Nanowatts/cm2 /steradian.

## **3.3 Other variables**

Data on quarterly economic activity measured as gross value added (GVA) is from the Central Statistics Office. Temperature data is collected from the Average Daily Temperature Archive from the University of Dayton (2020) and based on recordings of the National Climatic Data Center. We generate an India aggregate by weighting the daily temperatures recorded in Chennai, Delhi, Kolkata, and Mumbai by population.<sup>11</sup> Data on Indian holidays is from different online sources. The information about registered COVID-19 infections at the state and district level is from Covindia (2020), the data on previous short-run inmigration and outmigration at the state and district level from the Development Data Lab (2020), and the share of employment in manufacturing and services at the state and district level is from the South Asia Spatial Database (Li, Rama, Galdo, and Pinto 2015).

# **4 Electricity, nighttime lights, and economic activity**

Intuitively, electricity consumption and economic activity are closely related since most economic activity needs electricity. Plenty of studies analyze the relationship of the two over longer periods, often with a focus on the direction of causality. Chen, Kuo, and Chen (2007), for example, study 10 newly industrializing and developing Asian countries and find a bi-directional long-run causality between real GDP and electricity consumption
13.See, for example, Beleva (1995), Earle (1995), Commander, et al (1995). For an opposing view, see Boeri (1994).

14.There is some evidence from other transition countries that those who have exhausted their benefits tend to stop reporting to labor office. See Boeri (1994) for Eastern Europe, and OECD (1993) for Poland.

15 .These trends are similar, albeit at smaller magnitudes, for the rest of Mongolia, consistent with the notion that the informal sector is primarily an urban phenomena. Indeed, it should be noted that Ulaanbaatar is home to 27% of Mongolia's population, 37% of the "not employed," and only 19% of the officially unemployed.

I 6.The relationship between the three groups Employed, Not-Employed, and Able-bodied People of Working Age is not exact. Students over the age of sixteen are not included in either the employed or the not-employed categories, and the number of people employed may include working children and working pensioners. Indeed the - number of working pensioners and working children has nearly doubled since 1990 if they were netted out of total employment, the decline in officially employed people would be even larger.

1 7.For Mongolia as a whole the numbers are: unemployment as registered at the EROs was about 55,400 at the end of 1996, whereas the census of December 1996 counted 227,100 as not-employed, 128,500 of which "without any reason." 1 8.It should be noted that such a drastic change in labor force participation in such a short period of time would be - highly unusual the period during which the US labor force participation rate changed by an equal percentage was thirty years.
rather than just the U.S.

Global (food) commodity price inflation equaled 43% during the 12 months ending on March, 2008. While this rate is high, it is not unprecedented. Similar increases in (food) commodity prices were observed between 1971 and 1974 and between 1994 and 1996 [52]. While biofuels were unique to the recent crisis, other (important) factors were common to one or both crises. Furthermore, each of the three periods of peak prices has been marked by a below-normal ratio of stocks to use. An IMF report assessing the impact of rise in food and fuel price on macroeconomic indicators such as balance of payments, overall inflation, and poverty also concludes that biofuels are one among several factors, which coincided to cause the food commodity price inflation [38]. This report also contends that restrictive trade policies were the major reason for the run-up in the price of rice.

**Table 1. Quantitative estimates of impact of biofuel on food commodity prices**

|**Source**|**Estimate**|**Commodity**|**Time period**|
|---|---|---|---|
|Mitchell[47]|75%|global food index|Jan 2002 to Feb 2008|
|IFPRI[59]|39%|corn|2000to 2007|
||21-22%|rice and wheat|2000 to 2007|
|OECD-FAO [51]|42%|coarsegrains|2008to 2017|
||34%|vegetable oils|2008 to 2017|
||24%|wheat|2008 to 2017|
|Collins[15]|25-60%|corn|2006 to 2008|
||19-26%|U.S. retail food|2006 to 2008|
|Glauber[25]|23-31%|commodities|Apr 2007 to Apr 2008|
||10%|global food index|Apr 2007 to Apr 2008|
|
B. 2008a. The role of noncognitive skills in explaining cognitive test scores. _Economic inquiry_ , 46(1), pp. 2-12.

Borghans, Lex and Duckworth, Angela and Heckman, James J. and ter Weel, Bas. 2008b. The economics and psychology of personality traits. _Journal of Human Resources_ 43(4), pp. 972-1059.

<mark>Broda, M., Yun, J., Schneider, B., Yeager, D. S., Walton, G. M., & Diemer, M. 2018. Reducing inequality in academic success for incoming college students. A randomized trial of growth mindset and belonging interventions.</mark> _<mark>Journal of Research on Educational Effectiveness, 11</mark>_ <mark>, pp. 317-338.</mark> <mark>CAF. 2017.</mark> _<mark>Encuesta CAF 2017: Trayectorias Laborales y Productivas en América Latina</mark>_ <mark>. Retrieved from</mark> <u><mark>http://scioteca.caf.com/handle/123456789/1400</mark></u> <mark>Carlana, M., 2019. Implicit stereotypes: Evidence from teachers’ gender bias.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>, 134(3), pp.1163-1224.</mark> <mark>Cohen, G.L., Garcia, J., Purdie-Vaughns, V., Apfel, N. and Brzustoski, P., 2009. Recursive processes in self-affirmation: Intervening to close the minority achievement gap.</mark> _<mark>Science,</mark>_ <mark>324(5925), pp.400-403.</mark> 27
Servén (2009). Details are given in the Appendix. In brief, given the limited availability of survey-based poverty data, we construct the headcount ratio, P0, the poverty gap, P1, and the squared poverty gap, P2, using a lognormal approximation on the basis of the observed per capita income levels and Gini coefficients, which are available much more widely (López and Servén, 2006). Similar approaches have been used by Dollar and Kraay (2002), Sala-i-Martín (2006) or Pinkovsky and Sala-i-Martin (2013; 2014). We experiment with alternative and widely-used poverty lines: US$ 1.25, US$ 2 and US$ 4 per person and per day, in 2005 PPP US$.

Our final database is an unbalanced panel of non-overlapping five-year periods, containing 804 observations covering 158 countries and spanning the years 1960-2010. This sample is fairly large

> 14 Our sample mainly contains economies with headcount poverty rates between 0.10 and 0.60, as well as rich economies with negligible poverty, as discussed in the next section.

9
4. Basic patterns for the relationship between FDI and female corporate leadership**

How do we define female entrepreneurship? Ideally, we want to capture firms with women being the primary owner and running the firm; that is, women act as both owners and chief executives. Female-owned firms may not be a good indicator for entrepreneurs since family ownership is prevalent and important in both developed and developing countries

> 2 Thus, wholly state-owned firms are not in the sample.

> 3 See http:\\www.enterprisesurveys.org for a more detailed description of the WBES.

> 4 Because of the missing values of other variables, for each dependent variable in the regressions, the number of observations differs. In the baseline regressions, the number of observations when the dependent variable is female-manager and female-entrepreneur are 50,472 and 47,702 respectively.

8
– intuitively when the baseline data have little predictive power for future

> 1 Bloom et al. (2010) provides an example of very large T, with 114 weekly observations on firm output, quality, and inventory levels.

> 2 Gibson and McKenzie (2010) provide an example where multiple rounds of follow-up data are averaged to get more precise measurements of consumption and income in a matched difference-in-differences impact evaluation of a seasonal migration program. One key difference with non-experimental evaluations is that the presence of a baseline becomes more valuable to allow one to control for baseline differences across individuals; whereas randomization ensures these baseline differences are balanced on average. In addition, multiple rounds of pretreatment data are also of additional use in non-experimental studies to show parallel trends in difference-indifferences analysis, or to rule out or control for the presence of pre-program effects like an Ashenfelter dip. Multiple pre- and post-treatment data can also be combined to conduct overidentification tests to distinguish between different data generating processes (Ashenfelter and Card, 1985).

3
opportunities. We use a (pseudo-) Tobin's Q (TOBINQ), which is a measure of the market's assessment of a firm's investment opportunities, to determine the market's perception of future growth.'<sup>0</sup> We find that firms that filed for bankruptcy had lower (pseudo-) Tobin's Q, which may have effected their inability to access credit during financial distress and increased their likelihood of entering bankruptcy.

We also include two dummies to account for differences in corporate ownership. FAMILY is equal to 1 (0 otherwise) if the firm is a member of a family of firms. BANK is equal to 1 (0 otherwise) if the firm is directly owned by a bank or belong to a family that also owns a bank." Only 10% of the bankrupt firms are owned by banks or owned by a group that also owns a bank, compared to 40% of the firms which do not file. Bankrelated firms thus appear significantly less likely to file for bankruptcy.

In addition to firm-specific variables, we include country-specific indicators. These measures allow us to test the effect of variations in institutional features, macroeconomic performance, and judicial efficacy on the likelihood of filing for bankruptcy. We include country dummies to identify differences in institutional characteristics. We use the growth of GNP in 1998 as an ex-post measure of economic activity and degree of general financial distress in each country following the crisis. Furthermore, we use our index of creditor rights to study the influence of legal protection on the decision to file. Alternatively, we use dummies to identify the legal-origin in each country, as in La Porta et al. (1998). Finally, we use the interaction term between

> **10** Tobin's Q is approximated by the book value of debt plus the market value of equity divided by total assets.

11 This is an improvement on previous literature that examined bank-related firms in Japan that only took account of
# 6. Empirical Results

## 6.1. OLS Results

We start with results from OLS estimation of the wage equation which controls for urban and estate dummies and indicators of sectoral composition along with land endowment, but no human capital or productivity controls are used.<sup>23</sup> The results are presented in column 1 of Table 2. Consistent with the theoretical model in section (2) above, the coe¢ cient of percentage of area under LDO restrictions is negative, and the coe¢ cient of its interaction with travel time to urban center is positive. Both of the coe¢ cients are signi...cant at 1 percent level. All standard errors reported in this paper are corrected for clustered sampling design of the HIES 2002 survey.

The second column in Table 2 reports the estimation results when we include a set of measures of human capital both at the individual and DSD levels. The DSD level variables are percentage of labor force with primary or more schooling, and a measure of current malaria parasite infection rate (Plasmodium Vivax, the most common malaria parasite for human infections in Asia). The individual level controls include education, age (as a measure of experience and cohort e¤ect), gender dummy, and etnicity/religion dummies.<sup>24</sup> The gender dummy may capture possible gender based division of economic activities, ethnicity/religion is used as a control for cultural norms regarding work ethic, and marital status is used as a proxy for di¤erences in motivations and preferences. The estimated coe¢ cient on LDO restrictions become numerically smaller (from -0.49 to -0.37) which is consistent with the idea that the estimate in column (1) of Table 2 might have partially captured the negative correlation between human capital and LDO restrictions. The interaction e¤ect has the right sign. Both of the coe¢ cients are statistically highly signi...cant (at 1 percent or lower
causing the imbalance by sustaining a large undervaluation of its real exchange rate only since 2003. Nevertheless, China‘s trade surplus did not become large until 2005, and its global trade surplus in 2003 was smaller than that in 1997 and 1998 (Figures 27 and 28). Interestingly, in 1998, at a time when the RMB was still pegged to the U.S. dollar, China‘s currency was viewed as substantially _over_ valued (Lardy 2005).

<!-- Start of picture text --> Figure 25: Global Trade of China<br>1.8 9.0<br>1.6 8.5<br>8.0<br>1.4<br>7.5<br>1.2<br>7.0<br>1.0<br>6.5<br>0.8<br>6.0<br>0.6<br>5.5<br>0.4<br>5.0<br>0.2 4.5<br>0.0 4.0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Exports Imports Trade Balance Chinese Exchange Rate (right axis)<br>Source: International Monetary Fund, International Financial Statistics.<br>RMB per US$<br>US$, trilions<br><!-- End of picture text --> 32