rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4 Real Exchange Rate and Inequality**

The relationship between differences in productivity and difference in prices illustrated above can have strong distributional consequences in a very unequal society, where consumption baskets a very heterogeneous across income groups. Inequality is the defining characteristic of the South African economy. Whatever measure of inequality is used, South Africa comes always at the top of any inequality index, which is a reflection of both its history and of the structural changes after the end of apartheid.

Although South Africa’s real exchange rate is in line with comparable countries, the economy is considered expensive for the majority of the population. This is often the main explanation for strong downward rigidity of wages even in the presence of an extraordinarily high unemployment rate. The affordability of the South African economy, defined as the ability of the majority of the population to access goods and services, given the prevalent income, is a combination of relative price distribution and the level of income inequality. The South African economy is very unequal and the dynamic of productivity, as will be illustrated in the next sections, increases the burden on the poorest part of the population, who are more dependent on good produced in the less productive part of the economy.

Following Leibbrandt et al. (2016), we investigate the expenditure categories that have promoted a gap between the inflation rates of poor and non-poor households. The methodology is explained in detail in the appendix. Focusing on the period 2009-2017 and using the 2014/2015 Living Conditions of Households in South Africa, we assess price changes from the CPI basket of goods and services to which poor households were overexposed. These are typically items from the CPI basket for which inflation rates for poor households exceeded those of richer households during the period considered. Our results are consistent with the findings of Leibbrandt et al. (2016) while using a different dataset and a different and more recent time period, demonstrating that this evidence is a constant distributional issues over the last 20 years. Figure 8 plots
Using Tariff Indices to Evaluate Preferential Trading Arrangements: An Application to Chile* Eric Bond Department of Economics Penn State University Park, PA 16802

*This paper represents the results of RPO 679-77 funded by the World Bank . I thank Kathleen Trask for research assistance. Helpful comments were provided by Alan Winters, David Tarr, and participants at seminars at the World Bank (IECIT) and the Midwest International Trade meetings.
# the subregion).

The fi rst column of the table shows that stricter enforcement of labor regulation strongly affects the demand for informal labor by fi rms.<sup>31</sup> Therefore, enforcement is a tool that can potentially be used by governments to effectively fi ght noncompliance with the law. We estimate that, on average, when the number of fi nes per 1000 fi rms in the subregion increases by 1, the share of informal workers falls by 3.5 percentage points. In our sample, the average number of fi nes per 1000 fi rms in the subregion is 0.77 and the average proportion of informal workers in a fi rm is 0.23. Based on these numbers we compute that an elasticity of illegal employment with respect to enforcement of -0.12. This number is comparable to those obtained in the crime deterrence literature using US data: for example, Levitt (1998) fi nds that the elasticity of crime with respect to the arrest rate is about -0.2, and Bar-Ilan and Sacerdote (2004) fi nd that the elasticity of driving violations with respect to fi nes is also about -0.2. However, average enforcement of labor regulation is low and the average amount of violations is relatively high in our sample, so that an elasticity of -0.12 corresponds to a large absolute effect of fi nes on illegal behavior.

Given the strength of the effect of enforcement on informality, we are likely to also fi nd effects on productivity as well. Columns (2) to (5) of table 2 show that weaker enforcement of labor regulation is associated with higher fi rm productivity as measured by value added per worker, average wages, sales per worker and pro fi ts per worker. Furthermore, it is interesting that weaker enforcement is also associated with higher investment in capital and technology. This suggests that labor productivity increases because the fi rm operates more efficiently and the capital stock is higher. We estimate that an
# **City statistical yearbooks**

This paper compiled sectoral value-added, foreign industrial firms’ total output and output share at the city level in 1990, 2000 and 2005 from city statistical yearbooks. Foreign firms’ output share is the key variable of interest in this paper. City statistical yearbooks also contain information on GDP per capita, share of rural population, output share of state-owned enterprises (SOEs) across cities used in the robustness checks section.

# **Annual Survey of Industrial Firms (ASIF)**

The ASIF database is a nationwide mandatory survey administered annually by the National Bureau of Statistics (NBS) in China to firms in mining industries, manufacturing industries and utilities industries. NBS uses the ASIF database to compute China’s GDP, and the aggregate information is published in the official China Statistics Yearbooks. It contains many firms distributed across more than 600 four-digit industries and throughout China’s 31 provinces, autonomous regions, and municipalities. Firms in the sample account for 95 percent of total Chinese industrial output and 98 percent of total Chinese industrial exports (Eberhardt et al. 2011). The sample covers all SOEs and private companies with annual sales above 5 million RMB (about 600,000-660,000 US dollars based on exchange rate during that period). The number of firms covered in the sample increased steadily from around 165,000 in 1998 to 336,000 in 2007.

The database contains more than 100 variables covering firms’ basic information, financial and operational information. Basic information includes the unique identifier, firm name, legal representative, detailed address, year of incorporation, main product, industry affiliation, and ownership structure. Financial and operational information cover most items in firms’ balance sheet, profit and loss statement, as well as cash flow statement. We use the ASIF database in 2000 and 2005 to complement city statistical yearbooks in calculating foreign firms’ employment and output share at the industry and city level.

# **Tariff data**

We obtained China’s export
507 524 Note: ** significant at 5%; *** significant at 1%. Robust standard errors in parentheses clustered at school level. P- values are in brackets in the third column. Controls: (i) students characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his home), (ii) teacher characteristics (gender, age, years of experience, if she/he has post-graduate certificates), and (iii) school characteristics (school size, 7<sup>th</sup> size, 7<sup>th</sup> repetition rate, FEPBA score in Language, school district –or location). All the regressions exclude the classrooms where the Human Body Unit was not taught. Author’s analysis based on experimental data generated for this study **.**

# **Table A8.2. Results on Science learning according to student gender**

||Sequence vs.<br>Control|Coaches vs.<br>Control|Wald Test|
|---|---|---|---|
|_Dependent variable_|(1)|(2)|(3)|
||_Panel A: Female_|||
|Science score|0.519***|0.733***|1.93|
||(0.146)|(0.138)|[0.165]|
||544|545||
|Percent of correct answers|0.089***|0.121***|1.06|
||(0.030)|(0.025)|[0.304]|
||544|545||
|Percent of incorrect answers|-0.035|-0.049***|0.
# **4.2.1 Tetrachoric correlation catrix and eigendecomposition**

Table 5 presents the tetrachoric correlation matrices among the seven components of the job quality index for 2007, 2011, 2014, and 2021. As expected, the correlation coefficients are higher among the first four components, which assess a job’s regulatory and income dimensions. For example, in 2007, access to social security benefits exhibited a strong correlation (coefficient of 0.77) with minimum wage compliance, and this association remained consistent across the years. Similarly, minimum wage compliance and the incidence of relative underpayment showed a correlation coefficient above 0.85. On the other hand, compensatory moonlighting, job stability, and the incidence of overqualification demonstrated low levels of correlation with the other variables. Furthermore, most of the relationships between the components maintained similar magnitudes over the decade, suggesting a time-independent and structural correspondence between the components used to assess job quality.

Table 6 presents the results of the eigen-decomposition of the covariance matrix between 2007 and 2021. This table provides the eigenvalues associated with each principal component and allows for calculating the explained variance. Generally, it is observed that for almost every year, only the first three components had eigenvalues above one, which collectively accounted for 60 percent of the total variance. For further exploratory analysis, we present the rotated principal components (eigenvectors) for 2007, 2011, 2014, and 2021 in Table 7 (including only components with eigenvalues larger than one). Column (1) illustrates that across different years, the first principal component predominantly consists of variables such as access to social security benefits, minimum wage compliance, child labor, and relative underpayment. Consequently, the underlying factor behind this component could be interpreted as labor market formality or a rule of law dimension. Column (2) shows that labor stability and the incidence of overqualification have a higher weight in constructing the second component, suggesting a likely dimension related to career advancement and opportunities. Column (3) also reveals that the
tion, lower infant mortality, and more wealth, represented by the plotted circles. Therefore, the pooled regressions are essentially flat, potentially misleadingly showing no association between open defecation and infant mortality or a count if household assets included in the – DHS consistently with Deaton’s original result.

# **4.3 Do short mothers cause short children?**

One candidate explanation for widespread stunting among Indian children that Deaton and – Dr`eze (2009) highlight is that Indian mothers are small. What if there were a direct effect – independent of genetics and of the rest of the environment of a too-small mother, causing restricted intrauterine growth, leading to lasting stunting? If so, then in principle Indian children could be short, on average, despite healthy modern environments and adequate nutrition, merely because their mothers were stunted by depravations of the past.

Both the possible existence of this mechanism and any plausible magnitude are debated in the medical and epidemiological literature. Discussion dates to Ounsted et al.’s (1986) classic paper, which matched data on the birth weight of 1,092 children born in Oxford, UK with self-reported birth weights of family members to show that very low or very high birth weight babies are likely to have parents that are low or high birth weight, respectively, with a greater correlation with mothers than fathers. From this they hypothesize that mothers’ own _in utero_ deprivations can restrict fetal growth of the children they have as adults. Although some later scholarship has criticized the conclusion that the constraint dates back to a mother’s _fetal_ development,<sup>24</sup> it is much more widely accepted that a mother’s size and net nutrition before and during pregnancy influence her child’s birth weight and subsequent growth (Martorell and Zongrone, 2012; Chiolero, 2010). For example, Ceesay et al. (1997)

> 24In 2008 the paper was republished for commentary in the _International Journal of Epidemiology_ ; discussion noted that clean causal identification
sample set includes 19,398 households, 8,469 of which are rural. The information was collected between February and March of 2010.

The LCMS VI also identifies beneficiaries of public spending programs, though not always precisely. The survey identifies individual and household beneficiaries of public health and education services and identifies whether a household received fertilizer and seeds from a public program, but not which one, FISP or FSP. In addition, the LCMS does not include information on beneficiaries of the Public Welfare Assistance Scheme (PWAS), either those receiving food subsidies or other cash transfers, nor does it record beneficiaries of school feeding programs or programs providing support to orphans and vulnerable children.

The benefit incidence analysis (BIA) presented here combines LCMS data on government expenditures in education, health care, and fertilizer subsidy programs at the provincial level. The latest available information for the education sector is for 2009 and covers both total spending and the number of beneficiaries enrolled in basic, secondary and tertiary institutions; enrollment data for 2010 are also available. For healthcare, the latest provincial-level information is from 2008 and includes expenditure data and the number of beneficiaries (counted as patients or recorded as services provided) at tier 1 and 2 healthcare providers. Finally, the 2010 budget allocations for all fertilizer and seed packages provided by the Fertilizer Support Program and/or the Fertilizer Support Program were used.

# **4.2. Allocation of Education Benefits**

The LCMS VI collects information about school attendance in 2010 and 2009 of each household member; additionally, it collects each household‘s aggregate expenditure in education<sup>15</sup> .The information about 2009 is not as complete as the 2010 one; mainly it does not contain information about the type of school attended (public or private). In order to calculate the allocation of school benefits we face data limitations and we need to make some assumptions explained in Table 8.

> 15 Appendix 3 provides detailed  information about the education related questions in LCMS VI
# **1 Introduction**

Global warming is expected to increase temperature and rainfall averages and their variability (IPCC, 2007).<sup>2</sup> Such changes are particularly worrying for developing countries because large portions of their populations rely on climate dependent activities, and have limited income to adapt.

In response, economic analysis has attempted to estimate the possible impacts of climate change in developing societies. One strand of literature focuses on the link between income and climatic factors often looking at cross-country data, with most studies finding a negative relationship between income and temperature (Dell et al., 2009; Bansal, 2009).

Another strand focuses on the effects of climate-related disasters on macroeconomic indicators or country-level variables, including GDP or its growth. Studies differ in their techniques, data and findings: Some encounter negative effects (Auffret, 2003), while others encounter positive effects (Skidmore and Toya, 2002). Loayza et al (2009) reconcile the seemingly contradictory results estimating the medium-term effects of climate-related disasters such as droughts, floods, storms (separately and simultaneously) on economic growth using a model with three main sectors (agriculture, industry, and services) and with the whole economy. Severe disasters have adverse effects regardless of type, but moderate floods, for instance, do increase industrial growth.

All these studies of climate change effects or climatic disasters focus on local and economy-wide effects on output. However, it is known that output measures are an imperfect way to gauge society’s overall well-being. Therefore, this paper will focus on the effects of climate change on poverty, as opposed to economic growth.

A few studies have analyzed the possible impact of climate change on poverty in Mexico, some modeling the channels and the heterogeneity of impacts across income groups while others concentrate on poverty at the municipal level. Hertel et al. (2010)<sup>3</sup> use a global Computable General Equilibrium model (the Global Trade Analysis Project or GTAP) to mimic
Table B.3b: Changes in extreme poverty for the countries using <u>phone surveys</u>

|Ct|2019|2020 (co|unterfactual)|2020 (|COVID-19)|Net COVID-19|
|---|---|---|---|---|---|---|
|ounry|Rate,%|Rate,%|Change, pp|Rate,%|Change, pp|impact,pp|
|Burkina Faso|30.00|28.90|-1.10|31.50|1.50|2.60|
|Bulgaria|0.90|0.80|-0.10|1.00|0.10|0.20|
|Central African Rep.|67.70|66.90|-0.80|67.80|0.10|0.90|
|Congo, Dem. Rep.|62.30|62.10|-0.20|62.60|0.30|0.50|
|Gabon|2.40|2.40|0.00|2.70|0.30|0.30|
|Ghana|22.20|21.20|-1.00|22.00|-0.20|0.80|
|Guinea|13.40|12.50|-0.90|14.20|0.80|1.70|
|Gambia, The|10.30|9.80|-0.50|11.00|0.70|1.20|
|Guatemala|6.90|6.80|-0.10|7.40|0.50|0.60|
|Honduras|12.70|12.00|-0.70|15.60|2.
born from January to June of the following year. Children born in January are one month younger than those born in December. Panel A of Figure 3 presents a graph using data from the pooled sample of children. Other panels of the figure present graphs for different age groups. For example, for children age 1-2, panel B of the figure shows the percentage of childcare attendance of children age 2, born in July to December, and of children of the age of one and born from January to June. Older children have a higher rate of childcare attendance. However, there is an obvious, large gap in the incidence of childcare attendance between children born in December and those born in January.

> 11 The VHLSS contain data on age (year and month), but not an individual’s full date of birth. Thus, we cannot use the date of birth as the conditioning variable. But we also offer robustness checks in Section 5 where we vary the width of the months around the cut-off threshold.

10
*|**1989-94**|**5.19**|**1991-94**|**6.99**|**9.97**|
|Chile|**1988-91**|**1987-94**|**4.17**|**1989-94**|**10.42**|**12.75**|
|Colombia|**1989-91**|**1988-94**|**5.93**|**1990-94**|**10.87**|**18.29**|
|Mexico|**1987-89**|**1986-94**|**6.16**|**1988-94**|**4.10**|**19.06**|
|Peru|**1990-92**|**1989-93**|**6.98**|**1991-93**|**4.21**|**8.23**|
|**Uruguay**|**1987-92**|**1986-94**|**6.14**|**1988-94**|**7.93**|**12.83**|
|Venezuela|**1989-91**|**1988-94**|**1.69**|**1990-93**|**7.15**|**6.26**|

**Note: The change in the openness index is measured as the average of the changes in the ratio between the year prior to the initiation of the liberalization episode and 1994. The change in export and import growth is measured as the average of the changes between the year following the initiation of the liberalization episode and 1994. Source: World Bank data.**
different for that treatment. Given that consulting is twice as expensive as insourcing and outsourcing, and the impacts on firm growth outcomes are similar, it may take twice as long to recoup the value of these services. But overall, all three business services appear to have returns that could pass a cost-benefit test, but with a large degree of uncertainty still.

Revealed preference then offers another view of whether firms benefited enough from these services to pay for them themselves after the program is over. Table 9 shows that firms which received training are not more likely to subsequently go and hire expertise than the control group. By contrast, firms in the insourcing, outsourcing and consulting treatments are more likely to be using business service providers relative to control firms. This continues to be true in the second follow-up survey, so that well over a year after the program has stopped subsidizing these services, firms which received treatment are 14 to 19 percentage points more likely to be paying for outside HR, accounting, marketing or consulting services on their own. This remains significant after multiple testing corrections (Appendix Table 9.2).

We also asked firms directly in the second follow-up survey (over a year after subsidies ended) whether they still had the worker or professional hired through the GEM project. Insourcing firms report still keeping 37 to 48 percent of the workers hired through the program.<sup>13</sup> Moreover, 84 percent of those who left quit, rather than were fired. And unlike wage subsidy programs for young workers (Groh et al. 2016) or for microenterprises to hire non-specialist workers (de Mel et al. 2019), data on when workers left show no spike when the subsidy ended but rather steady churn throughout the year. Outsourcing firms report still retaining the original service provider in 29 to 36 percent of cases. Here there is more of a spike in dissolution rates at the time the subsidy ended, with 46 percent of the churn occurring in the three months after the subsidies ended
- 4 -

changes. This paper exploits stop-by-stop freight shipping data collected by the Asian Development Bank (ADB) over the period 2010-2019.<sup>1</sup> The data allows to estimate granular changes in transport costs and times at the individual road level, suggesting that transport connectivity was changed substantially over the last decade.

On the firm data side, the paper takes advantage of Kazakh comprehensive firm registry data with location identifiers, balance sheets and income statements, covering all formal sectors for the period of 2010-18. By matching this with the above connectivity data, panel data regression is performed, which is a powerful way to construct valid IVs systematically and avoid the endogeneity problem.

One of the disadvantages of the partial equilibrium approach may be that the bigger picture, i.e., what happens to the overall economy, might be misunderstood. To mitigate such a risk, the paper considers both direct and indirect effects of transport connectivity in the model and examines non-firm activities by applying the same methodology to the agriculture sector, which can help to understand the overall effects of improved regional corridors.

The remaining sections are organized as follows: Section II provides an overview of the country and sectoral context related to transport connectivity in Kazakhstan. Section III develops our empirical strategy. Section IV describes our data, and Section V presents the main estimation results and discusses policy implications. Section VI discusses the impacts of improved connectivity on agriculture. Then, Section VII concludes.

> 1 See ADB (2020) for more details. We would like to express our special thanks to the Central Asia Regional Economic Cooperation (CAREC) Corridor Performance Measurement and Monitoring (CPMM) team for sharing relevant raw data.
Its response is split half for each The main issue in the supply of innovators appears to have been a prevailing culture in Singapore that until recently may have discouraged creativity, risk-taking, and failure. Although a few companies report the need for people with further training and greater expertise, the majority point to the lack of innovative and entrepreneurial spirit in society as a whole. Based on the annual surveys of the Global Entrepreneurship Monitor (GEM), Singapore has ranked relatively low in the propensity of her adult population engaging in starting up new businesses; in 2003, only 5% of Singapore’s adult population engaged in new start-up activities, significantly below that in the US (11.9%) or China (11.7%), and ranking 21<sup>st</sup> out of 31 countries covered (Wong et. al. 2004). A major factor contributing to the relative lack of entrepreneurial participation appears to have been the high opportunity cost, given that Singapore had enjoyed full-employment for many years, and a generation of young educated people had experienced rapid career advancement and steady and secure income growth working in subsidiaries of global MNCs or the public sector. In this sense, Singapore is a paradox of her past success in creating rapid economic growth through leveraging foreign MNCs (Wong, 2002).

39