rafmacalaba/datause-encoder-data huggingface dataset:
obtained by updating the $1.45 line at 1993 PPP for inflation in the US; and (iv) $2.50, twice the $1.25 line, which is also the median poverty line of all except the poorest 15 of countries in the RCS data set of national poverty lines. The range $1.00 to $1.45 is roughly the 95% confidence interval for our estimate of the mean poverty line for the poorest 15 countries.

To test the robustness of qualitative comparisons, we also estimate the cumulative distribution functions up to a maximum poverty line, which we set at $13 per day, which is about the official poverty line for the US (at average household size and composition).<sup>16</sup> We use the same PPPs to convert the international lines to local currency units (LCUs). Three countries were treated differently, China, India and Indonesia. In all three we used separate urban and rural distributions. For China, the ICP survey was confined to 11 cities.17 We treat the ICP’s PPP as an urban PPP and use the ratio of urban to rural national poverty lines to derive the corresponding rural poverty line in local currency units. For India the ICP included rural areas, but they were underrepresented. We derived urban and rural poverty lines consistent with both the urban-rural differential in the national poverty lines and the relevant features of the design of the ICP samples for India; further details can be found in Ravallion (2008b). For Indonesia, we converted the international poverty line to LCUs using the official consumption PPP from the 2005 ICP. We then unpack that poverty line to derive implicit urban and rural lines that are consistent with both the ratio of the national urban-to-rural lines for Indonesia and the fact that the national PPP from the ICP is based on expenditure-weighted prices.

**_Comparison with our old international poverty line._** Recall that the international poverty line for 1993 proposed by Chen and Ravallion (2001) was $1.08 a day ($32.74 per month). If one adjusts only for inflation in the US one obtains $1.45 a day at 2005 prices, which is well above the average poverty line for the poorest counties in Figure 1. However, it will be recalled that the $1.08 figure at 1993 PPP was based on the RDV data set of national poverty lines for the 1980s. If we use the new data set on national poverty lines provided by RCS but evaluated at 1993 prices and converted to $’s using the 1993 PPPs we would obtain a considerably higher poverty line.  Figure 2 plots both the new (RCS) and old (RDV) data on national poverty lines, both at 1993 PPP. The relationship between the RCS national poverty lines and consumption per capita

> 16 See http://aspe.hhs.gov/poverty/05poverty.shtml. We used the poverty line for a four member household at 2005, which is $416 per person per month.

> 17 Although the survey included some surrounding rural areas, it cannot be considered representative of rural China; evidence on this point is provided by Chen and Ravallion (2008a).

11
We find that reforms are associated with increases in domestic stock market capitalization, trading, and capital raising, contrary to the claim that they are not effective. However, we also find that reforms are associated with increased internationalization, and that some of the reforms seem to have been followed by a higher share of activity in international markets. This runs contrary to the view that a poor domestic environment prompts firms to access international markets and that reforms reduce internationalization.

Most of the results are robust to controlling for domestic and international macroeconomic variables. These controls are important because capital market reforms can be contemporaneous to other policy changes (such as macroeconomic stabilization programs, trade liberalization, and the easing of exchange rate controls) or may occur at high points in the domestic and/or international business cycle. Since many countries implemented several capital market reforms in a short period of time, when analyzing each reform we also control for other reforms clustered around that time. We find that our results remain mostly unchanged when including this control, suggesting that the reforms under analysis tend to have a positive marginal effect on domestic stock market development and internationalization. We also present some robustness tests controlling for time effects in the regressions of domestic stock market development variables and find that some of the reform dummies remain statistically significant and positive.

The rest of the paper is structured as follows. Section 2 describes the data and the reforms under analysis. Section 3 presents the empirical results on the impact of reforms on domestic stock market development and internationalization. Section 4 discusses some potential interpretation problems and presents robustness tests. Section 5 summarizes the main results and concludes.

# **2. Data**

This section presents the data used in the paper. We first describe the data on stock market activity, both in domestic markets and abroad, and then discuss in detail the reforms we analyze.

# **2.1. Stock market activity data**

As measures of stock market activity, we use three variables: market capitalization, value traded, and amount of equity capital raised. For all, we need data for both domestic and international activity. While there are several sources on domestic stock market capitalization and value traded that comprise a large number of countries, there is no comprehensive database on capital raised domestically. There are even less data available growth during the 1990s. In contrast to these cross-sectional findings, their within-country evidence shows that trade liberalization has a robust positive impact on growth and investment rates.

4
increasing over time as more sophisticated survey instruments may require hiring a better educated labor force.

This study assumes a constant annual increase of 2.5 percent in costs of survey implementation and technical assistance. It is worth noting that even if the annual growth rate of costs is constant, total survey costs may differ depending on the timing of survey implementation. For example, the total cost of implementing five surveys within a country is much smaller if all surveys are conducted in the first five years of the 15-year period between 2016 and 2030 than in the last five years. Even if the five surveys were to be conducted every three years, costs would vary depending on when the first survey starts.

In our analysis, we assume that all the 78 countries carry out surveys every three years, with the implementation of the first surveys uniformly distributed between 2016 and 2018. Under this assumption, 28 household surveys will be implemented every year. This assumption appears to be strong; however, the global World Development Indicators database of poverty estimates shows that the total number of household surveys conducted each year remained more or less the same in the last 10 years. Under these assumptions, the total survey cost over the 15-year period is estimated to be 20 percent higher than the total cost if there is no increase in costs of the survey implementation and technical assistance.

To appreciate the impact of the assumption of 2.5 percent annual growth in costs, we further estimate the increases in the total survey costs by assuming different growth rates such as 2 and 3 percent. The analysis suggests that a 1 percentage point increase in the growth rate increases the total cost estimate by approximately USD 70 million, which accounts for about 7.5 percent of the total costs if there is no growth in the survey cost.

# **IV. Total survey implementation and technical assistance costs**

Building on our discussion in the previous section, a baseline total survey implementation cost estimate is calculated assuming that 1) each country implements a multi-topic household survey every three years, allowing for the estimation of official poverty and inequality statistics; 2) a sample of 7,000 households is interviewed per survey; 3) each country’s estimated survey implementation cost per household is identical to the regional average derived for that country from our database of survey implementation costs; and 4) the survey implementation costs per household grow at an annual rate of 2.5 percent. We will refer to these as our _preferred implementation cost assumptions._ Under these assumptions, an estimated **USD 692 million** in 2014 prices would be needed for conducting multi-topic household surveys every three years in the 78 countries of interest over the 15-year period between 2016 and 2030.

13
- Deshingkar, Priya and Sven Grimm (2005). Internal migration and development: A global perspective. 19. United Nations Publications.

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42
in which borrowers create a loan listing with unverified personal information and pictures in order to request funding. The authors find that signals about the lenders’ age, race, and gender from the advertised profile significantly affect the likelihood to receive loans and the interest rates paid by borrowers.

The paper proceeds as follows. Section 2 reviews the evidence on labor market discrimination and (undocumented) immigration in the South African context. Section 3 summarizes data collected from an online labor market for domestic workers. Section 4 discusses the identification strategy, reports the main results and offers model extensions that explore the role of the applicant pool composition and market thickness. Section 5 explores mechanisms and Section 6 tests if spatial sorting of job seekers is linked to employer preferences. Section 7 concludes.

# **2 Background: Immigration and discrimination in the South African labor market**

Racial discrimination has played an important role in South Africa’s history, both before and during _apartheid_ . The post- _apartheid_ regime implemented legislation to improve the economic situation of previously disadvantaged groups, most notably the Employment Equity Act (Act 55 of 1998) and the broad-based Black Economic Empowerment (BEE) Act (Act 53 of 2003). Yet, the legacy of half a century of racial segregation and discrimination under apartheid has persistent effects even more than 20 years after the democratic transition.<sup>9</sup> Firstly, while BEE created a small class of highly successful workers, the majority of businesses are still owned by the white minority. Secondly, racial tension and discrimination exists between previously disadvantaged population groups, partly as the result of the apartheid’s policies of racial division and the selective lifting of restrictions for Coloureds<sup>10</sup> and Asians in the 1980s (Seekings and Nattrass (2008)). Thirdly, it has been argued that apartheid’s racist immigration laws, class division, de-sensitization to violence, and attitude of superiority towards the rest of Africa are partly to blame for South Africa’s high degree of xenophobia that resulted in violent riots in 2008 and 2015 (Crush (2008)).

> 9For investigations of labor market discrimination between South African race groups see Burger and Jafta (2006).

> 10The term ‘ _Coloured’_ refers to a heterogeneous ethnic group with ancestry from Europe, local tribes, West Africa, Mozambique and various places in Asia including India, Indonesia, and Malaysia. Coloureds tend to have lighter skin than the black population group and make up 9% of the population in South Africa and are mainly concentrated in the Western Cape (Seekings and Nattrass (2008)).

5
# **4 Concentration of resource, labor and capital intensive industries**

As the economy has developed, the production structure has gradually changed in Guangdong. The ratio of the primary, secondary and tertiary industries changed from 29.8 %, 46.6 %, 23.6 % in 1978, to 5.5 %, 51.6 %, 42.9 % in 2008, and to 4.9 %, 47.3 %, 47.8 % in 2013. Nine major industries are particularly competitive in the domestic market and gaining shares in the global arena, including the three major traditional industries, food and beverage, textiles, and building materials; the three major new pillar industries, electronic communications, electrical machinery, and petrochemicals; and the three emerging industries, forestry and paper, pharmaceutical, and automotive. The Pearl River Delta has become one of the world's major manufacturing bases (for example, electronic information industry), attracting investments from many multinational corporations and international consortia.

Compared with that in the Pearl River Delta, the production structure in the rest of the province, is less developed, with a significantly higher share of the primary sector and lower levels of urbanization and export-orientation. To understand the different production structure at the industry level within the province,<sup>5</sup> we group the 43 industries listed in the Guangdong industrial economic statistical yearbooks into three large categories (called “sectors” hereafter): resource intensive sector, labor intensive sector and capital intensive sector. We measure the relative concentration of sector _i_ ( _i_ =1, 2, 3) in region _j_ in time _t_ ( _t_ =2006, 2007...2013) by an indicator of specialization, noted as _IS_ , as follows: where _mi_ stands for the number of industries that belong to sector _i._ In other words, _vai_ , _j_ , _t_ is the sum of the value added of all industries in the sector _i._ In a relative sense, the more specialized a region _j_ is in the sector _i_ in time _t_ , the higher is the value of this indicator.

We calculate the _IS_ in two ways (or two levels). Table 2 presents the results of _IS_ by region (grouping the 22 cities into four regions: Pearl River Delta, East Wing, West Wing, and Mountain area).<sup>6</sup> The results show a sharp difference in the patterns of concentration of industries: capital intensive industries largely clustered in Pearl River Delta, labor intensive industries mainly located in East Wing (followed by the Northern Mountainous area), and resource intensive industries highly concentrated in the West Wing and Northern Mountainous Area. The high specialization of the capital intensive and labor intensive industries in the PRD and East Wing, respectively, reveals their comparative advantages.

> 5 The empirical results of specialization at the industry and city levels are available upon request. For readability, we present in this paper the results of specialization of capital-, labor- and resource- intensive industries at the four regional level.

> 6 In annex 3, we consider each of the ten cities in Pearl River Delta and consider all other cities as the rest of the province, and calculate the _IS_ by these 12 entities.

9
**Figure 4. Reduction in Expenditure: Because of the Reduction in Medical Expenditures**

# **c. Income gains because of increased years of employment**

The cost of working life lost because of tobacco consumption is estimated based on the assumption that there is a direct impact of lower tobacco use on health and thus work-generated income. The impact on the income of each income decile is calculated using the age pattern of mortality and estimating the years of life lost. The welfare effect is then estimated using the lower-, medium-, and upper-bound elasticity by decile variation.

The 218,658 deaths attributed to tobacco consumption are distributed using the occurrence of mortality profile.<sup>14</sup> For each death, the number of potential years of work are calculated, and the lost working years are divided across the deciles according to tobacco consumption. Using equation (6) and tables 3 and 8, one may calculate the impact of the tax increase on the increase in the years of employment. For example, in the first decile, assuming the upper-bound elasticity, the income increase would be 0.01 percent of income. Table 11 shows the results for all deciles using the three elasticity scenarios.

**Table 11. Years of Working Life Lost and Income Increase (%)**

|_Price shock scenario_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|0.0005|0.0004|0.0004|0.0003|0.0003|0.0003|0.0003|0.0003|0.0002|0.0002|
|Medium elasticity|0.0008|0.0007|0.0007|0.0006|0.0006|0.0005|0.0005|0.0005|0.0004|0.0004|
|Upper-bound elasticity|0.0010|0.0010|0.0010|0.0009|0.0008|0.0007|0.0008|0.0008|0.0007|0.0007|

_Source:_ Proportion of household budget. Based on data of the 2013 household budget survey. _Note:_ The table shows the share of total household budget for each decile.

> 14 Numbers based on 2013 data.

16
their own network of private financial intermediaries that borrow from them to lend to their end customers. These customers can then use the funds to finance new business development, capital enhancement, training for staff, interest rate subsidies, IT equipment, etc. The scope and performance of advisory services provided by DBs could be the topic of a separate study.

# **5. Asset Quality and Return**

A common criticism against DBs is they are institutions that are prone to experience asset quality problems. DBs are often portrayed as institutions with a limited capacity to assess borrowers‟ capability and willingness to repay their loans. They are also portrayed as having a limited capacity for debt collection on loans, resulting in a high amount of non-performing loans, debt forgiveness programs, and financial losses that are ultimately borne by taxpayers.

The survey collected data on the non-performing loan (NPL) ratios of DBs. As shown in Figure 9, at the end of 2009, 55% of DBs reported NPL ratios of less than 5%; 30% of DBs reported NPL ratios between 5% and 30% and 15% of DBs reported ratios of more than 30%. This distribution remained almost unchanged over the 2006-2009 period.<sup>8</sup> Second-tier-only DBs reported lower NPL ratios than “retail-only DBs”. Indeed, all “second-tier-only” DBs fell within the less than 5% bracket while only 27% of “retailonly” DBs were in this bracket.

A comparison of the NPL ratios of DBs with the NPL ratios of their respective banking systems<sup>9</sup> shows that 39% of DBs exceeded the national average in 2009, while the other 64% of DBs was below the national average. For the years 2006 and 2008, the percentage of DBs with NPL ratios below the average in their banking systems was 57%, and in 2007 it was 58%.

> 8 Rules for the definition and classification of NPLs differ across countries. Therefore, one should be cautious when interpreting and comparing them.

> 9 Average NPLs of banking systems were obtained from the International Financial Statistics online database.

17
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ceteris paribus, the export response to bilateral depreciations among differentiated products is 27 percent lower than to bilateral appreciations. Instead, there is no difference in export responses to appreciations or depreciations for homogenous products.

Our third finding relates to the role of supply constraints in limiting the ability of exporters to fully react to improved relative price conditions. We look at supply constraints through three sectorspecific angles. First, by exploiting sectoral differences in access to Pakistan’s largest export finance schemes run by the State Bank of Pakistan (the export finance scheme (EFS), and the longterm finance facility (LTFF)), and second, by exploiting sectoral structural differences in terms of financial dependence. We rely on Rajan and Zingales (1998) measures on external finance needs of U.S. firms, which due to size and access to a deep capital market isolate needs derived from the sectoral technological production process. Third, by exploiting sectoral variation in the extent to which a sector intensively uses the country’s most abundant factor – labor. We find that responses to bilateral exchange rate depreciations vary alongside sectoral characteristics. Sectors with higher access to finance, lower structural financial dependence, and higher labor intensity are associated with larger exchange rate elasticities to depreciations, relative to those sectors with lower access to finance, and higher structural financial dependence or capital intensity. We estimate that, on average, an additional 10 percent value of loans per export is related to a 5 percent larger export elasticity with respect to the BRER. The other two structural characteristics appear to cut sectors’ responses more sharply. We find that a 10 percent rise in finance dependence implies a 22 percent decline in the responsiveness of exports to BRER depreciations, while a 10 percent increase in labor intensiveness is associated with a 30 percent increase, respectively. These two channels – information costs and supply constraints – are not only statistically significant but also economically significant. A back-of-the-envelope calculation using our estimates suggests that if 6