rafmacalaba/datause-encoder-data huggingface dataset:
vulnerability line as the upper bound income level for this population.<sup>3</sup> These approaches offer a simple but theoretically grounded way to define vulnerability that, to the best of our knowledge, has not been attempted elsewhere.

Furthermore, the vulnerability lines derived within our framework possess several novel features. First, by directly considering the risks of falling into poverty, and abstracting away from one or other specific poverty level, we can compare the same level of vulnerability for a country over different time periods as well as across different countries. This advantage clearly does not exist with the arbitrary scaling of the poverty line, at least for multi-country comparison where each country may use a different scaling factor. Our approach avoids the arbitrariness and indirectness of scaling up the poverty line by a certain factor by starting first, with what policy makers might deem and declare as an acceptable level of vulnerability (say, 10 percent); and then working backward to identify the appropriate vulnerability line associated with that acceptable vulnerability level.

Second, our vulnerability lines can be straightforwardly estimated from either panel or cross sectional household survey data. Our estimation approach is non-parametric, and involves relatively simple estimation procedures that make only “light” demands of underlying data. In particular, in contexts where true panel data are not available, our vulnerability lines can be estimated using only two rounds of cross sections with relatively parsimonious modelling assumptions. As such, they can be presented alongside standard poverty lines, yielding an expanded analysis of economic welfare for poverty and vulnerability in a variety of country settings including data-scarce environments.

> 3 The first approach is perhaps more relevant for the purpose of measuring prosperity, and the second approach is more closely linked to poverty measurement. Which approach should be applied depends on the specific context and objective under consideration; and the order we present them in later sections is merely for convenience of presentation purposes.

4
# <u>Table A2. Analysis of differential attrition (Panel A)</u>

||<br>N|<br>(1)<br>**Full data**<br>Mean[SD]|N|(2)<br>**No skills measures**<br>Mean[SD]|N|(3)<br>**Full attrition**<br>Mean[SD]|t-test<br>Diff<br>(1)-(2)|t-test<br>Diff<br>(1)-(3)|Total obs.|
|---|---|---|---|---|---|---|---|---|---|
|**Location - 2018**||||||||||
|Original village|4391|0.67<br>[0.47]|565|0.17<br>[0.38]|395|NA|0.50***<br>(0.02)|NA|4956|
|Within district|4391|0.19<br>[0.39]|565|0.15<br>[0.36]|395|NA|0.04**<br>(0.02)|NA|4956|
|Within country|4391|0.13<br>[0.34]|565|0.29<br>[0.46]|395|NA|-0.16***<br>(0.02)|NA|4956|
|Outside country|4391|0.01<br>[0.12]|565|0.39<br>[0.49]|395|NA|-0.37***<br>(0.02)|NA|4956|
|**Individual - 2018**||||||||||
|Age|4391|23.62<br>[3.64]|565|24.96<br>[3.23]|395|NA|-1.34***<br>(0.15)|NA|4956|
|Years of schooling|4391|8.56<br>[4.96]|564|8.40<br>[4.33]|395|NA|0.16<br>(0.20)|NA|4955|
|Ever married|4391|0.45<br>[0.50]|565|0.48<br>[0.50]|395|NA|-0.04*<br>(0.02)|NA|4956|
|Age at first marriage|1974|20.89<br>[3.39]|274|21.40<br>[3.69]|395|NA|-0.51**<br>(0.25)|NA|2248|
|Has children|4391|0.33<br>[0.47]|565|0.35<br>[0.48]|395|NA|-0.02<br>(0.02)|NA|4956|
|Working|4391|0.42<br>[0.49]|565|0.64<br>[0.48]|395|NA|-0.22***<br>(0.03)|NA|4956|
|Main work is farming|4391|0.04<br>[0.19]|565|0.01<br>[0.08]|395|NA|0.03***<br>(0.00)|NA|4956|
|HH has toilets on premises|4391|0.96<br>[0.19]|13|0.85<br>[0.38]|395|NA|0.12<br>(0.09)|NA|4404|
|HH has access to electricity|4391|0.98<br>[0.14]|13|0.92<br>[0.28]|395|NA|0.06<br>(0.07)|NA|4404|

* p<.10  ** p<.05  *** p<.01

_Notes_ . Columns (1) to (3) display the means for the group that answered all parts of the survey, the group surveyed that did not complete the cognitive and socioemotional assessments (either because they were surveyed over the phone or information was collected indirectly), and the group who attrited fully. Standard deviations are shown in brackets. Column (4) is the difference between the mean of respondents with complete data and those who do not have skills measures. Column (5) is the difference between the mean of respondents with complete data and the mean of attritors. Panel A shows differences in variables measured in this latest round of data collection, in 2018, so we cannot report data on full attritors. Panel B reports data measured during the tracking exercise of 2016. Panel C reports data measured in 2003 at the individual level. Panel D reports data measured in 2003 at the household level. Differences in means are computed by OLS regressions. All standard errors in parentheses are clustered at the village level.

76
20 Table 1: Utilization and out‐of‐pocket spending, by subsector

|Variable|Mean per<br>person|Aggregate<br>figure for<br>Vietnam|Concentration<br>index (_CI_)|
|---|---|---|---|
|_Visits/admissions per person p.a. (qk) _||||
|Outpatient: CHC|0.30|24,934,564|‐0.1926|
|Outpatient: polyclinic|0.04|3,360,418|0.0200|
|Outpatient: hospital|0.38|31,737,412|0.1972|
|Inpatient: hospital|0.07|6,124,170|0.0649|
|_Out‐of‐pocket spending per person p.a. (Fk) _||||
|Outpatient: CHC|7.25|598,103|‐0.1410|
|Outpatient: polyclinic|2.85|235,070|0.3939|
|Outpatient: hospital|71.34|5,884,149|0.4237|
|Inpatient: hospital|106.96|8,822,251|0.3955|

Notes: The survey includes 39,071 cases, each with an average sampling weight of 82,480,853. Spending figures are in thousands of Vietnamese Dong. CHC stands for ‘commune health center’. The means and the concentration indices are computed from the 2006 VHLSS; sampling weights are used in their computation. The concentration index is computed by ranking individuals according to their per capita household consumption, inclusive of food consumption (whether domestically produced or purchased out of the home), but exclusive of health expenditures.
# **Business Environment and Dual-Track Private Sector Development:**

# **China’s Experience in Two Crucial Decades**<sup>**1**</sup>

Cheryl Xiaoning Long Xiamen University Lixin Colin Xu World Bank Jin Yang Southwestern Univ. of Finance and Economics

**Key words** : business environment, China, private firms, dual track, innovation. **JEL codes** : H77, O10, O17, O53, P27.

> 1 The paper has benefited from support from the RSB of the World Bank, and comments of Martin Raiser . Daniel Xu offered excellent editorial service. The views here are the authors own and do not implicate the World Bank or its member countries.
is higher is due to the exclusion of China, which has both a large population and low rate of extreme poverty. Nonetheless, the data in our analysis is representative of nearly 5.9 billion people or more than 3/4<sup>th</sup> of the world’s population. The coverage of people estimated to be living in extreme poverty, the population that is relevant for this analysis, is much larger – the data cover 98 percent of this population.

For each country, the surveys have been conducted in different years and the data are reported in local currency units in current prices. Following the methodology used to report on SDG 1.1, we convert all income and consumption data into 2011 constant local prices using Consumer Price Indices from each country, and then convert the resulting vector into an internationally comparable US dollars using 2011 purchasing power parity exchange rates (PPPs).<sup>9</sup> The CPIs are used to estimate real changes in income and consumption over time, while the PPPs account for relative price differences across countries. In addition to using the same CPI and PPP data as used by the World Bank for global poverty monitoring, we also use the same population and national accounts data. For more details on how the World Bank estimates poverty, see World Bank (2020a).

In the second part of the analysis, we use covariates of poverty status to investigate the reliability of the square-root allocation rule of household consumption in identifying the poor, relative to the per-capita allocation rule. For this analysis, we use detailed micro-level data from the latest surveys of a subsample of eight countries, namely Nigeria, Mali, India, Pakistan, Colombia, Tajikistan, Indonesia, and the Republic of Yemen.<sup>10</sup> We wanted this analysis to have

> 9 For more details on the CPI series, see: <u>https://worldbank.github.io/PIP-Methodology/convert.html#CPIs.</u> For more details on the PPPs, see: <u>https://worldbank.github.io/PIP-Methodology/convert.html#PPPs</u> . [Both last accessed on July 23, 2022.]

> 10 The latest survey year varies across these countries. The surveys years are 2018.75 for Nigeria, 2009.89 for Mali, 2011.5 for India, 2018.5 for Pakistan, 2015 for Tajikistan, 2017 for Indonesia, 2014 for Yemen,

9
**Table 1 - Mexican Macroeconomic Indicators during the Economic Liberalization**

|**Year**|**GDP Growth**<br>**(% per year)**|**Inflation**<br>**(% per year)**|**Observed nominal**<br>**exchange rate**<br>**(pesos/USD)**<sup>*****</sup>|**CETES 28 days**<br>**interest rate**<br>**(average annual**<br>**rate)**<sup>******</sup>|
|---|---|---|---|---|
|1981|8.5|28.7|0.0262|N.A.|
|1982|-0.5|98.8|0.0963|44.408|
|1983|-3.5|80.8|0.1436|58.741|
|1984|3.4|59.2|0.1920|N.A.|
|1985|2.2|63.8|0.3683|61.590|
|1986|-3.1|105.8|0.9151|87.372|
|1987|1.7|159.2|2.2097|96.048|
|1988|1.3|51.7|2.2810|69.534|
|1989|4.1|19.7|2.6410|44.993|
|1990|5.2|29.9|2.9454|34.759|
|1991|4.2|18.8|3.0710|19.278|
|1992|3.5|11.9|3.1154|15.622|
|1993|1.9|8.0|3.1059|14.931|
|1994|4.5|7.1|5.3250|14.098|

*Nominal exchange rate to face foreign currency obligations (end of period). **CETES 28 days interest rate for 1982 was based only on the September-December average; in the case of 1983, the January-July average was taken into account and for 1985, the February-December average was considered. Source: INEGI, Banco de México.

It was in this context that the Mexican government decided to unilaterally liberalize its economy as the only way to increase its competitiveness and thus break the vicious circle of inflation and stagnation. The trade liberalization process began in 1983, but was strengthened from 1985 onward; maximum tariffs, as high as 100%, were lowered to 20% in only three years. The weighted average tariff dropped from 13.3% to 6.2%.<sup>7</sup> In 1985, of a total 5,219 tariff items subject to import permits at the beginning of the year, 3,064 were exempted from such a requirement. The value of imported goods subject to licenses dropped from 83% to 37%. The impact of such phasing out of trade protection mechanisms during the period was

> 7 Broadly speaking, 5 tariff levels were established based on the following criteria: 20% for consumer goods; 15% for intermediate goods; 10% for raw material and 5% or 0% for products either insufficiently produced or not manufactured at all at the domestic level or by agreement with the private sector, SECOFI, 1993.

2
# **3. Evidence on interventions addressing constraints to young women’s employment**

This section reviews the evidence on the effectiveness of programs to increase young women’s employment in Sub-Saharan Africa. The review aims to comprehensively cover the available rigorous (experimental or quasi-experimental) evidence in Sub-Saharan Africa, with selective inclusion of relevant studies from Latin America and South Asia. Interventions are organized by the constraint they try to alleviate and by the strength of the evidence in support of the intervention. Programs to develop skills (both vocational and business skills) have the largest number of evaluations, but the resulting evidence is weak at best. Relatively few evaluations have addressed the other constraints to young women’s employment, such as care responsibilities, limited occupational choice, and safety. Instead, we include interventions that were not designed to increase employment but rather may influence it indirectly, such as schooling interventions which delay family formation and child care interventions that free up women’s time. We did not find any stand-alone interventions to expand young women’s networks, although the safe space programs reviewed at the end of this section address networks as one of many constraints.<sup>17</sup> To date, the evidence favors interventions that address multiple constraints simultaneously, but these comprehensive programs remain a “black box”, with limited insight as to which program elements are most effective at addressing which constraints. Relative to the rest of the world, the evidence base and effectiveness of various interventions differs for Sub-Saharan Africa. For an overview of findings from the global evidence base, see Box 2.

> 17 One study of microfinance groups in India demonstrates the monetary returns to social interaction: Feigenberg et al. (2013) found that a higher frequency of social interaction with a self-help group increased individuals’ willingness to take risks and reduced their likelihood of default dramatically.

18
little evidence consistent with dramatic negative impacts of China’s growing exports to third markets on LAC exports. On the contrary, LAC exports were positively correlated with the growth of Chinese and Indian exports to third countries. These results are at odds with industry-level studies cited in the introduction, but can be explained by interindustry effects captured by the aggregate merchandise trade data, which could be due to increasing production-sharing around the world. More importantly, the few negative elasticities pale in comparison with the large Chinese and Indian demand elasticities for LAC exports, which were presented in Tables 1 and 2. Therefore, the preponderance of the evidence makes it difficult to conclude that the threats posed by the growth of China and India in world markets have outweighed the opportunities offered to LAC exporters.

# **3. Concluding Remarks**

China and India’s rapid economic growth over the last decade is seen with envy by many observers. The growth of their internal markets is undoubtedly an opportunity for exporters from throughout the world, but their accompanying growing presence in world markets can be either a threat or an opportunity. It can be a threat because it may have displaced exporters from third markets, and it can be an opportunity because the availability of a growing variety of Chinese and Indian products at cheaper prices in world markets opens production possibilities for exporters in third markets through different channels, linked to the availability of cheaper imported inputs at home that increase the efficiency of home exporters, the presence of production networks, and learning by exporting for firms selling to the growing Chinese and Indian markets.

This paper assessed the importance of the opportunity that the growth of China and India’s markets represented for LAC exporters during 2000-2004. It also explored the extent to which China and India’s growing presence in world markets affected LAC exports to third markets, aiming at disentangling the net impact through four different channels, which are associated with the two Asian economies’ exports to third markets, their imports from third markets, and their bilateral imports and exports with LAC countries. The preponderance of evidence suggests that the opportunities offered by the growth of China and India easily outweigh any potential threats, which might not have 14
- 10 -

Table 2

|S<br>Subsector|ector and_Sik_<br>C<br> <br>Number of<br>Enterpri|_of_Entwprise<br>S F R<br>Average Book<br>Value<br>(MiUllon<br>Kca)|Offered in th<br>CZEC<br> <br>Number of<br>Enterpriea|e Fit Wave(1<br>H REPUBLIC<br>Average Book<br>Value<br>(Million Kcs)|991)<br>SLOV<br>Number of<br>Enterprie|AK REPUBLIC<br>Average Book<br> <br>Value<br>(Milloan Kca)|
|---|---|---|---|---|---|---|
|ASriculture|61|165.9|55|<br>170.0|6|128.4|
|Baking|6|5,629.0|4|6421.3|2|4,044.4|
|BuUding and civil engineerIg|224|115.6|132|<br>141.0|92|<br>79.2|
|Buildig**materials**industy|61|254.6|35|319.6|26|<br>167.0|
|Chemical and rubber industries|27|1,198.5|20|804.0|7|2,325.8|
|Clothing iJustry|2|378.5|1|1.0|-|721.4|
|_Communication_|4|16S.0|4|165.0|-|-|
|Caisucti<br>activity|I|S11.2|I|S11.2|-|-|
|CooliS, ho spring and tobacco indus|<br>8|140.5|3|193.1|_5_|08.9|
|Cultul service|4|40.3|3|36.8|1|50.8|
|Desp Activity|87|S5.S|38|88.0|49,|<br>30.7|
|Domeatle trade|60|136.1|47|140.7|13|119.5|
|Ecotchnical<br>indusuy|52|327.0|38|300.2|14|<br>399.7|
|<br>Engineering|234|482.2|174|540.7|60|312.3|
|Food Indutry|148|216.2|87|275.9|61|131.0|
|Foripgntrade|34|1,121.1|31|1,180.2|3|S10.9|
|Forestry|2|93.5|I|26.7|1|160.3|
|Fuel indwtzy|IS|876.4|14|673.0|1|3,723.3|
|Fundamentalscience|2|146.9|2|146.9|-|-|
|Gologcal activity|30|66.6|14|88.2|16|47.7|
|Gla, china and stoneare industries|18|614.2|16|667.9|2|184.4|
|Hotelindustry|4|56.2|4|56.2|-|-|
|Houding|2|127.2|2|127.2|-|-|
|Iurance|1|1,841.9|1|1,841.9|-|-|
|Iron nd sted industi|11|3,499.6|7|2,234.2|4|S,714.1|
|Leather, boot-and-shoe and furrier lad|<br>7|315.9|6|223.8|1|868.4|
|Modial and other halth seIc|2S|134.0|2S|134.0|-|-|
|Melworking indu-try|21|S97.6|5|1,134.9|16|429.7|
|Non-ferrous metallurgy|4|1,72S..1|3|217.5|1|6,248.2|
|Other industrial tvies|8|193.6|S|277.0|3|54.7|
|Other producion activity|5|228.8|5|228.8|-|-|
|Other sevices|29|124.1|_15_|149.1|14|97.4|
|Personal ervic|9|62.6|9|62.6|-|-|
|Printing industry|9|181.1|8|152.7|1|408.2|
|Productlo of eloctric powwr ad hea|17|3,836.2|17|3,836.2|-|-|
|Publising activiUes|S|73.4|2|83.6|3|66.6|
|PulpandpaperIndustry|14|1,187.1|8|1,481.2|6|794.9|
|RocreoalS srvices|7|224.3|S|288.6|2|63.6|
|Rsarch and science ervices|2|47.S|2|47.S|-|-|
|Resatch &dovlopaientnbabllindus|2S|84.9|9|77.1|16|89.4|
|Rsarch & developmeant<br>In building|7|26.0|2|?3.3|5|7.1|
|Reearch & developmet in commercie|<br>2|23.5|1|45.2|1|1.5|
|Resarc A developmnat In cosumer|7|142.0|1|3.5|6|16S.1|
# **Table 4 –** SME lending volume and SME definition

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|DependentVariable||SME loa|ns/GDP|||SME loans|/total loans||
|Income per capita, log|0.045**|0.031|||0.001||-0.018||
||(0.017)|(0.020)|||(0.016)||(0.023)||
|Maximum number of employees,|||||||||
|<br>log|-0.000||0.025||0.027|0.027|||
||(0.022)||(0.020)||(0.019)|(0.017)|||
|Maximum sales volume, log||0.013||0.029**|||0.022|0.010|
|||(0.017)||(0.014)|||(0.016)|(0.010)|
|Constant|-0.272**|-0.353|-0.005|-0.334|0.032|0.046|0.007|0.029|
||(0.133)|(0.223)|(0.096)|(0.214)|(0.136)|(0.080)|(0.173)|(0.169)|
|N|44|33|46|34|44|46|33|34|
|Adjusted R<sup>2</sup>|0.155|0.117|-0.002|0.109|-0.015|0.009|-0.002|-0.006|
|Robust standard errors in parentheses|, *** p<0.|01, ** p<0.|05, * p<0.1||||||

25
the wage distribution, but this part was null for most workers and even negative for workers at the top of the distribution.

**Figure 16: Quantile Decomposition of Change in Hourly Income between 2007 and 2012**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>Decil 1 Decil 2 Decil 3 Decil 4 Decil 5 Decil 6 Decil 7 Decil 8 Decil 9<br>Total Explained Unexplained<br><!-- End of picture text --> Source: EEEI (2007) and ECVMAS (2012)—authors’ calculations. Note: Total gap refers to the total difference in mean real log wages between 2007 and 2012. The OB decomposition is performed for each quantile using RIF-regressions. The estimated model includes explanatory variables dummies for region of residence, type of work (agriculture, public sector and NGOs, private informal, and private formal), household size, gender, age group, level of education, and 10 economic sectors of employment.

Higher returns for individuals in their prime working age and for informal workers were the main drivers of labor income increase in the period. Figure 17 provides a more detailed breakdown of the unexplained or returns component. Across the distribution, the two driving forces of higher income are the demographic component, which reflects higher returns for individuals in their prime age (especially in their 30s), and the type of work, with sizeable increase in returns of individuals working informally. The returns to specific economic sectors/industries are smaller on average, but reinforce the message of a reconstruction-driven labor market: the small gains observed in the lower end of the distribution are associated with increasing returns in the construction and hotels/restaurants sectors. The negative effects for the higher end, on the other hand, relate to decreases in returns for the wholesale/retail trade and education/health sectors. As presented above, the effects of changes in endowments are very small—a detailed breakdown of sectors for the explained component is presented at Table A.3 in Annex 4.

**Figure 17: Decomposition of Changes in Hourly Income (2007-2012)—Unexplained Components**

<!-- Start of picture text --> 2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>-0.50<br>-1.00<br>Quantile 1 Quantile 2 Quantile 3 Quantile 4 Quantile 5 Quantile 6 Quantile 7 Quantile 8 Quantile 9<br>Geographical Type of Work Demographic Education Economic Sector<br><!-- End of picture text --> Source: EEEI (2007) and ECVMAS (2012) – authors’ calculations. Note: Total gap refers to the total difference in mean real log wages between 2007 and 2012. The OB decomposition is performed by each quantile using RIF-regressions. The estimated model includes explanatory variables dummies for region of residence, type of work (agriculture, public sector and NGOs, private informal, and private formal), household size, gender, age group, level of education, and 10 economic sectors of employment.

18
- 15 -

Table 2. Import. of Goode and Nonfector Servicee In Current Prices **as** a Percentage of OOP for Selected Country Croupings, 1UO-66 (unwe ghted **average)**

|S|aple<br>group|1960|1911|1962|1963|1964|<br>196|196|1W97|1968|Percontag<br>1994-6U/<br>1980-82|e chango<br>1985-87/<br>1981-83|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|10 I<br>lo<br>26<br>re|ntensivo<br>trde<br>anreciplints<br>Trade loan<br>cipients|32.7<br>84.8|83.8<br>84.7|29.4<br>J1.0|80.1<br>29.7|81.1<br>80.6|82.0<br>80.9|29.6<br>29.2|31.1<br>31.7|83.1<br> 80.7|-2.9*<br>-9.3*|-0.100<br>-3 8**|
|40<br>r|Trade loan<br>ecipient.|33.0|83.4|81.6|$0.3|30.7|30.6|29.0|80.3|80.6|-7.7**|-5.6**|
|43|Nonreciplints|S.61|40.4|86.8|35.6|34.1|83.5|$3.1|32.2|32.3|-14.6|-14.0|
|68|Developing countrioe|30.1|87.2|3.56|33.2|32.5|32.3|31.2|31.3|31.4|-41.6*|-10.5|
|21 I|ndustrial<br>countries|35.6|85.7|35.1|84.5|36.5|86.9|33.2|32.6|31.5|0.8|-2.5|
|c|The difterence<br>inm<br>significant<br>at the|ansb<br>10-perc|etween**t**<br>ent con|**he**trad<br>fidence|e adjus<br>Interva|tment<br>l.|loan rec|iplents|and n|onreciplen|ts<br>is||
|**e**<br>**d**|<br>The difference<br>In m<br>eignificent<br>at**the** <br>Preliminary<br>estima|oens b<br>5-perce<br>tes.|etween**t**<br>nt conf|**he**trad<br>idence|e adjus<br>Interval|tment<br>.|loan rec|iplonts|and n|onreciplen|ts<br>is||

Source: World Bank etimates.

Table 3. Composition of Nonfue!. Imports at Current Prices in the Trade Adjustment Lending Countries, 1980-87 (percentage shares of total nonfuel imports)

|Component|1980|1982|1983|1984|1985|1986|1987|
|---|---|---|---|---|---|---|---|
|Consumer goods|22.4|20.4|20.2|19.7|18.0|17.9|16.5|
|Capital goods|31.0|32.6|33.7|32.7|33.1|32.2|32.2|
|Intermediate goods|46.6|47.0|46.1|47.6|48.9|49.9|51.3|
|Total nonfuel imports<br>-----------------------------------------------------------------|100.0|100.0|100.0|100.0|100.0|100.0<br>__----------__-|100.0|
|Average value (US$ million)|4,260|<br>3,871|<br>3,492|3,568|<br>3,517|<br>3,818|<br>4,379|

**Notet** Data are averages for the thirty-seven countries for which data were available.

Source: World Bank data.