rafmacalaba/datause-encoder-data huggingface dataset:
|2018|106|
|---|---|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

**Table 3: Factory count by evaluation cycle (number of visits)**

|**Year**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|---|---|---|---|---|---|---|---|---|
|2010|64|1|||||||
|2011|64|52|||||||
|2012|45|50|41||||||
|2013|19|42|48|30|||||
|2014|71|17|30|42|17||||
|2015|75|45|20|25|38|16|||
|2016|41|75|32|24|30|37|14||
|2017|62|37|68|24|24|22|39|10|
|2018|17|17|12|30|3|9|4|14|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Questions are grouped into compliance points, and sub‐grouped into clusters. The eight clusters are: child labor, discrimination, forced labor, freedom of association and collective bargaining, compensation, contracts and human resources, occupational health and safety, and working time. There are 39 compliance points across the eight clusters. Table A.1 lists the different compliance points under each of the eight clusters.

Moreover, not all questions in the Compliance Synthesis Report are compliance questions. For example, the Compliance Synthesis Report collections information on the number of staff employed by the factory. The data set on firm compliance was restricted to just questions that directly imply compliance. These non‐compliance questions were either saved as part of the firm characteristics when appropriate, or dropped. Table A.2 lists the variable names and their definitions available on the Compliance Synthesis Report.

The eight compliance clusters have not changed over time, however the compliance questions asked in the Compliance Synthesis Report have changed. Though changes to the monitoring instrument are needed to accommodate learning and shifting emphasis of working conditions, this creates difficulties in tracking compliance to individual questions over time. Moreover, question codes were also changed, and more frequently than the individual questions (that is, the same questions would be coded differently over time). To maximize comparability of compliance over time, careful attention was given to identifying and using only compliance questions that were available over the entire sample period. The individual compliance questions from each Compliance Synthesis Report were matched manually. The resulting data set contains 161 individual compliance questions that appear consistently throughout the sample period.<sup>6</sup> Better Work Vietnam identified 26 “critical issues” for which public disclosure would be assessed, listed in Table A.3. The analysis of whether transparency through public disclosure accelerates adoption of compliance requirements focuses on these critical issues. However, not all 26 critical issues have

> 6 Dropping questions that enter or leave the sample introduces potential bias, depending on why questions were added or removed. In some cases, multiple questions were replaced with a single question and vice‐versa. As an extension, the average across component questions could be used to measure compliance, following Robertson (2017).

13
# Table 16 Explaining Changes In Welfare In Terms of Income Components

|Income Components|IDue to Changes<br>InIncome Comp<br>Averages|Due to Changes<br> <br>hIIncome Comp<br>Distribution|Total Change |<br>due to Income I<br>Components|
|---|---|---|---|
||Change In|Welfare between 198|9 to 1991|
|Salary Income|0||<br>3.6 ||<br>3.6|
|Collective Farm Income|-3.2|1.3|-1.9|
|Govt Cash Benefts|12.3|-0.1|12.2|
|Smaliholding Income|3|-0.6|2.4|
|Other sources|1.8|0.5|2.3|
|Total Income|139|47|18.6|
||.<br>Change in|.<br>Welfare between 199|1 to 1992|
|Salary Income|-18.6|-2|-20.6|
|Collective Farm Income|4.7|0.9|5.6|
|Govt Cash Benefits|-17|-3.2|-20.2|
|Smaliholding Income|4.4|1.6|6|
|Other sources|-3.6|0.7|-2.9|
|Total Income|-30.1|-2|-32.1|

34
**Table 15. Explaining cross-country variation in efficiency, Single Input and Single output - Heterogeneous Slopes**

|Independent<br>Variable|Gross<br>Primary<br>Enrollment|Net<br>Primary<br>Enrollment|Gross<br>Secondary<br>Enrollment|Net<br>Secondary<br>Enrollment|Literacy of<br>Youth|Average<br>Years of<br>School|First<br>Level<br>Complete|Secondary<br>Level<br>Complete|Life<br>Expectancy|Immuni-<br>zation<br>DPT|Immuni-<br>zation<br>Measles|
|---|---|---|---|---|---|---|---|---|---|---|---|
|WAGE|-.00006|.00076|-.00035|-.00228|-.00056|-.00200|-.00120|-.00419|-.00306***|-.00079|-.00241|
|GOVEXP|-.00363*|-.00255***|-.00377*|-.00727***|-.00552*|-.00595***|-.00453|-.00611***|.00337**|.00168***|.00221|
|PUBTOT|--|--|--|--|--|--|--|--|-.00162*|-.00162*|-.00097|
|GDPPC|-.00002*|-.00002*|-5.4e-6|.00003*|-.00002***|.00004*|.00003***|.00003***|.00002**|-.00002*|-.00001|
|URBAN|.00179*|.00132**|.00193*|.00139|.00212*|.00566*|.00601*|.00593*|-.00080|-.00117*|.00021|
|AIDS|-.03866***|-.06603**|-.03153|.01010|-.02177|.05491|.06656|.06464|-.02321|-.04147**|-.00826|
|GINI|-.14230|-.42098*|-.14976|-.29395|-.13107|-.09995|-.15463|-.24762|-.12865|-.38851*|-.42162**|
|WAGG|-4.4e-6***|-1.2e-6*|-4.6e-7***|-9.4e-7|-4.5e-7|-8.1e-7|-8.8e-7|-2.4e-7|8.9e-7**|6.95e-8|5.1e-7|
|GOVG|-8.6e-8|-5.2e-7***|4.3e-8|3.6e-7|4.0e-7|-4.3e-7|-4.4e-7|-5.3e-7|-1.4e-6*|-5.4e-7*|-9.4e-7*|
|GINIG|.00003|.00011*|-2.4e-6|-.00003|2.0e-6|-.00006|-.00005|-.00006|.00001|.00009*|.00006***|
|CONS|1.0156*|1.1036*|1.0098*|.74603*|1.0365*|.60371*|.53977*|.68648*|.82665*|1.0119*|.93820*|
|# of Obs|82|47|82|36|75|74|74|74|120|121|121|
|(# ofCountrs)|(52)|(31)|(52)|(21)|(47)|(46)|(46)|(46)|(70)|(71)|(71)|
|Wald Chi2(6)|87.32|93.98|62.74|105.34|58.40|94.00|69.32|82.38|74.33|450.54|52.71|
|(Prob>Chi2)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|

Note: * 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise 35
- 14 -

rental payments. This cash deposit can be increased by the landlord during tenancy and must be refunded in its entirety by the landlord when the tenant vacates the dwelling. The interest foregone by the renter is expected to cover the implicit rental value of the dwelling. Instead of raising rents when the rental market becomes tigp, or operating costs increase, landlord increase the amount of chonsei.29. This pervasive practice developed spontaneously in response to earlier economic and financial market conditions when inflation was high, regulated interest rates were negative. Urban housing was in extremely short supply due to the destruction of the housing stock during the war and to high rates of urbanization. Chonsei is the most conspicuous aspect of the financial repression imposed on the household sector in Korea. The financial magnitudes involved in this housingrelated financial claim are very large both for an individual tenant and in comparison with the institutional housing finance system.

30. Household surveys in the mid-1980s show that chonsei represents a very high share of the capital cost or market value of the rented space or unit. Typical chonsei ratios range between 35 percent and 40 percent of unit market value, these ratios are higher in large cities. For modern apartment units they can go as high as 70 percent of the unit's value. Thus the financial claim can be startlingly large. For instance, the Office of National Tax Administration (ONTA) reported in 1987 that a Hyundai apartment in Apkujong-dong a quality district of Seoul was posted at 243 million won for tax purposes, or 3 million per pyong (one pyong is 3.? m2). For a typical 55-pyong Hyundai apartment the Chonsei deposit can therefore vary between US$75,000 and US$133,000 when per capita income in Korea was $2,800. Because, it is not often possible to accumulate such contracts exist such as partial or declining chonsei, which large financial savings for housing, various forms of rental include blends of cash deposits and monthly payments. Such large financial claims also explain why room rentals and dwelling sharing are so common.

31. As a market, chonsei is considerably larger than the outstanding bank lending for housing. The chonsei system was estimated on the basis of simple ratios to be about 5.5 times larger than the housing finance system in 1980. The results of the KRIHS survey of household assets also indicate that chonsei funds used in the purchased of housing were five to six times larger that the volume of mortgage lending provided by KHB in (see Table 2).

32. As a savings instrument, chonsei reflects the poor quality or absence of household-oriented financial claims generated by the regulated financial sector. On one hand, the tenants build financial assets with the periodic increase in the deposit amount, generally every six months. They receive a real rate of return on these financiai asset which is positive but in kind in the form of housing services. This return has been found to stay close to the UMM interest rate. Tenants rent less space than they would under a monthly rent system.

_ll/_ See Renaud (1985) and World Bank (1986).
Households residing in more arid areas may have reacted differently from those living in villages with more vegetation, either because the direct impact of spraying is different across areas or because the role of information and perceptions varies. We analyzed this possibility for the case of the malaria awareness. Tables B11 report in Column 1 the estimates of heterogeneous treatment effects obtained from OLS regressions where the treatment status is interacted with dummy variables indicating the Normalized Difference Vegetation Index category<sup>9</sup> . Our estimates in Table B11 suggest that malaria awareness did not change on average in any vegetation area.

Workers may have been impacted by IRS campaign differently compared to unemployed adults<sup>10</sup> , because the marginal cost of being infected might be higher. Similarly, for malaria awareness, Columns 2-4 in Table B11 report estimates of heterogeneous treatment effects obtained from OLS regressions where the treatment status is interacted with the employment status of the respondent<sup>11</sup> . Estimates show a significant 13% increase among workers (Column 2). In Columns 3 and 4, we restrict the sample to men and women and we find similar estimates and particularly significant for women.

In addition we present heterogeneous treatment effects estimates on net ownership looking at other individual characteristics: unemployment and literacy status, religion and tribe, gender of household head, household size. Table B12 shows that households with unemployed respondents did not significantly differ from the ones with an employed respondent. When looking at literacy, literate respondents<sup>12</sup> acquired more nets than those with an illiterate head (even if the difference is not statistically significant). We don’t observe significant difference among muslim and among tribes different than the Tigre 9Normalized Difference Vegetation Index (NDVI) is a vegetation index computed using satellite images. We divided villages in the following categories: 0 hints to “very limited vegetation”, 1 stands for “some vegetation” and 2 means “with significant vegetation”. A detailed presentation of the index is presented in the Online Supplementary Material.

> 10Our definition of unemployed includes those adults ( _>_ 20 years old) who are out of the labor force as well as those who are enrolled in National Service, which is compulsory for some years for all young people of the country, for men and women alike. The salary provided to people in National Service is very low.

> 11The variable _work_ is an indicator variable equal to 1 if the respondent is employed or self-employed and zero otherwise.

> 12This information is available for all respondents, but not for all households heads. 62% of respondents were household heads and 34% of respondents were partners of the head. We replicated these regressions including and excluding respondents who are not the head or the spouse. Their inclusion does not affect the estimates, so we use the unrestricted sample.

14
# **References**

- **Agarwal, Sumit, Chunlin Liu, and Nicholas S. Souleles** , “The Reaction of Consumer —

- Spending and Debt to Tax Rebates Evidence from Consumer Credit Data,” _Journal of Political Economy_ , 2007, _115_ (6), 986–1019.

- **Ali, Merima, Abdulaziz B. Shifa, Abebe Shimeles, and Firew Woldeyes** , “Building Fiscal Capacity in Developing Countries: Evidence on the Role of Information Technology,” _National Tax Journal_ , 2022, _74._

- **Arango, Carlos and Varya Taylor** , “Merchants’ Costs of Accepting Means of Payment: Is Cash the Least Costly?,” Technical Report Winter 2008-2009, Bank of Canada Review 2008.

- **, Kim P. Huynh, and Leonard Sabetti** , “Consumer payment choice: Merchant card acceptance versus pricing incentives,” _Journal of Banking & Finance_ , 2015, _55_ , 130–141. Global Governance and Financial Stability.

- **Banerjee, Abhijit, Esther Duflo, Clement Imbert, Santhosh Mathew, and Rohini Pande** , “E-governance, Accountability, and Leakage in Public Programs: Experimental Evidence from a Financial Management Reform in India,” _American Economic Journal: Applied Economics_ , 2020, _12_ (4), 39–72.

- **Beck, Thorsten, Haki Pamuk, Ravindra Ramrattan, and Burak R. Uras** , “Payment instruments, finance and development,” _Journal of Development Economics_ , 2018, _133_ , 162 – 186.

- **Bellon, Mr Matthieu, Jillie Chang, Ms Era Dabla-Norris, Salma Khalid, Frederico Lima, Enrique Rojas, and Pilar Villena** , “Digitalization to improve tax compliance: evidence from VAT e-Invoicing in Peru,” IMF Working Paper, International Monetary Fund 2019.

- **B ́ergolo, Marcelo L, Martin Leites, Ricardo Perez-Truglia, and Matias Strehl** , “What Makes a Tax Evader?,” Working Paper 28235, National Bureau of Economic Research December 2020.

- **B ́ergolo, Marcelo, Rodrigo Ceni, Guillermo Cruces, Matias Giaccobasso, and Ricardo Perez-Truglia** , “Misperceptions about Tax Audits,” _AEA Papers and Proceedings_ , May 2018, _108_ , 83–87.

22
WPS3747 full liberalization excluding Indonesia (FLIBX); finally, in the third scenario, we analyze the combined impact of full liberalization including Indonesia (Full-Lib).

Unilateral liberalization, whereby Indonesia cuts all the duties facing imports from the rest of the world, has some impact on “nominal” GDP<sup>7</sup> and generates an increase in both exports and imports. At the sector level, there is some reallocation of factors out of the textile and wearing apparel sector and towards the paper printing, transport equipment and metal products industries. Although the removal of imports tariff hurts the sectors that benefited from higher relative protection levels, the total impact on household income is positive, as can be seen from Table 4. Per capita income increases by 0.6 percent at the national level, a number which is consistent with the increase in “nominal” GDP.

Results suggest that unilateral liberalization would generate an increase in the average per capita household income of 0.6 percent and that the impact on the distribution of income would be negative but small. As a result of the average per capita income increase, poverty decreases modestly. The poverty headcount decreases by 1.0 percent at the national level (from 18.3 to 18.1 percent) and the impact appears higher in the urban areas. Higher order poverty indicators vary by the same magnitude, a result that indicates that the poorest of the poor also benefit from the unilateral liberalization, despite with the slight worsening in the distribution of income. Overall, these changes translate into a total of 375,000 people escaping poverty.

As a result of full trade liberalization in the rest of the world, the Indonesian economy faces decreasing import and export prices but an improvement in overall terms of trade: while the export price index decreases by 0.6 percent, the import price index decreases by 2.3 percent.

> 7 “Nominal” GDP is computed with respect to the Consumer Price Index (CPI). Since the CPI is the appropriate _numéraire_ for the analysis conducted at the household level, nominal GDP is the macro aggregate which changes are consistent with the changes in per capita income at the household level. Also note that poverty and inequality indicators in the microsimulation module are based on total per capita earned income, with no deduction of direct taxes. The issue of tax replacement will be debated in subsequent simulations.

11
|**Table 2. US**|**Non-Agricu**|**ltural Im**|**ports: GS**<br>**Av. Tariff**|**P Coverage**<sup>**1**</sup><br>|**, Utilizati**|**on**<sup>**2**</sup>**and A**|**verage Ta**<br>**Av. Tariff**|**riff Preference**<sup>**3**</sup>|**, 2003**||**Av. Tariff**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Coverage**<br>**(%)**|**Utilization**<br>**(%)**|<br>**Preference**<br>**(%)**|<br>|**Coverage**<br>**(%)**|**Utilization**<br>**(%)**|<br>**Preference**<br>**(%)**||**Coverage**<br>**(%)**|**Utilization**<br>**(%)**|<br>**Preference**<br>**(%)**|
|**_Albania_**|51|69|4.2|**_Latvia_**|4|87|4.1|**_Tunisia_**|20|50|4.1|
|_Anguilla_|94|92|2.8|**_Lebanon_**|85|98|3.8|**_Turkey_**|31|86|3.9|
|**_Argentina_**|26|78|3.8|**_Lithuania_**|3|80|4.1|**_Turks_**|100|1|2.9|
|**_Armenia_**|83|100|4.5|**_Macedonia_**|9|85|4.6|**_Uruguay_**|70|93|3.7|
|**_Bahrain_**|34|99|3.8|**_Moldova_**|1|75|2.2|**_Uzbekistan_**|6|68|4.4|
|**_Bosnia-Herc._**|74|86|5.1|**_Mongolia_**|1|12|3.1|**_Venezuela_**|7|96|3.8|
|**_Br Indian_**|39|0|1.7|**_Morocco_**|13|52|4.3|**_West Bank_**|84|74|6.1|
|**_Brazil_**|46|64|3.9|**_Niue_**|14|0|4.0|**_Western Sahara_**|100|0|2.7|
|**_Bulgaria_**|12|85|3.8|**_Norfolk_**|8|0|1.9|**_Zimbabwe_**|88|98|4.7|
|**_Chile_**|92|32|3.8|**_Oman_**|16|99|4.7|||||
|**_Christmas_**|68|0|3.1|**_Pakistan_**|5|80|4.3|**_GSP-LDC_**||||
|**_Cocos_**|64|0|4.7|**_Papua_**|(.)|53|3.1|**_Afghanistan_**|27|0|2.7|
|**_Cook_**|76|8|5.2|**_Paraguay_**|51|97|4.4|**_Angola_**|100|94|2.0|
|**_Croatia_**|76|94|3.7|**_Philippines_**|39|65|4.0|**_Bangladesh_**|2|89|6.2|
|**_Czech_**|79|52|3.9|**_Pitcairn_**|97|0|2.3|**_Bhutan_**|22|62|8.0|
|**_Egypt_**|3|82|3.9|**_Poland_**|70|62|3.8|**_Burkina_**|93|61|4.3|
|**_Estonia_**|23|92|4.2|**_Romania_**|30|79|4.0|**_Burundi_**|(.)|||
|**_Fiji_**|2|92|3.6|**_Russia_**|14|87|4.0|**_Cambodia_**|1|72|5.7|
|**_Georgia_**|33|96|2.9|**_Slovak Rep._**|13|74|3.7|**_Eq Guinea_**|100|93|1.5|
|**_Gibraltar_**|84|0|3.2|**_Solomon_**|96|0|4.3|**_Kiribati_**|100|0|2.9|
|**_Hungary_**|71|52|3.7|**_Sri Lanka_**|7|90|4.6|**_Nepal_**|5|76|4.0|
|**_India_**|50|87|4.0|**_St Helena_**|99|0|4.6|**_Samoa_**|8|2|4.1|
|**_Indonesia_**|39|65|4.1|**_Suriname_**|100|55|2.7|**_Somalia_**|95|0|1.3|
|**_Jordan_**|15|63|4.0|**_Thailand_**|56|76|4.0|**_Togo_**|25|100|4.6|
|**_Kazakhstan_**|62|100|4.3|**_Tokelau_**|22|65|4.0|**_Vanuatu_**|100|100|3.2|
|**_Kyrgystan_**|7|100|5.1|**_Tonga _**|95|17|3.1|**_Yemen_**|100|86|1.7|

> 1 Ratio of eligible imports to total dutiable imports.   2 Ratio of imports entering under preference to total eligible imports. 3 Difference between nominal _ad valorem_ tariff equivalent and nominal preferential tariff.  Covers all HS 8-digit lines with eligible US imports in 2003. (.) indicates less than one percent.

30
2 then leaves the sample. By matching individual survey responses in successive quarters, flows between labor force states can be roughly estimated. This allows us to analyze certain aspects of unemployment --such as unemployment duration, persistence and turnover-- which could not be analyzed with purely cross-sectional surveys. The ENEU's main drawback, however, is its limited coverage: it provides no information on the populations from the rural areas or from smaller urban centers.

Our second source of data is the National Employment Survey (ENE), which expands beyond the ENEU sample to cover in addition all other main urban areas and a sample of the rural population. The ENE is carried out every 2-3 years. The first ENE was fielded in 1988 and a second in 1991. The next ENE is planned for 1993. In its urban coverage, the ENE is very similar to the ENEU: the sampling schemes are alike and the survey questionaires identical. This allows us to work with both surveys, drawing on the larger ENE sample principally for the analysis of the characteristics and determinants of unemploymen. and on the ENEU, and its panel structure, for the analysis of the dynamics of unemployment.

# 11. **STRUCTURE AND TR** ENDS OF UNEMPLOYMENT

# A. General Trends .n Unemployment

Official unemployment figures for Mexico are reported quarterly by the National Strtistics Institute (INEGI) on the basis of the ENEU. These figures reflect the official definition of unemployment, which consider s an individual as unemployed if he/she participates in the labor force and fulfills the following conditions:

- worked for less than one hour during the week preceding the survey

- was not sick, on paid vacation or waiting to return to work wvithin the following month

- was actively searching for a job during the month before the survey

Table I reports these official unemployment rates for the 1980-91 period, separately for men and women.3' The trends should be interpreted with some care since the coverage of urban areas in the survey has increased over the period, from 12 in 1980 to sixteen from 1985 to date. The table shows that the aggregate urban unemployment rate in Mexico is quite low. In 1990, the unemployment rate stood at 2.8%, and even in the worst years of the ac justment process, the average rate did not rise beyond 6. 1 **%.** The table also reveals that the unempioyment rate has consistently been higher among women than among men. In 1983, the unemployment rate for men peaked at **_5.3%,_** while that for women stood a full two percentage points higher at 7.6%.

# B. Characteristics and Structure of Unemplovment

Table 2 presents unemployment figures by age and education categories for men and women. These figures were obtained using individual response data from the 1988 National Employment Survey (ENE). The numbers show that unemployment rates are highest for the young, particularly

- 3/ Figures for 1980-82 are from the Continuous Survey on Occupation, the predecessor of the ENEU.
- <u>Okun, A. (1962). “Potential GNP: Its Measurement and Significance.” Reprinted as</u> _Cowles Foundation Paper_ 190.

- <u>Pissarides, C. (2001). “Employment Protection,”</u> _Labour Economics_ , 8: 131–159.

- <u>Ravallion, M. (2019). “Guaranteed employment or guaranteed income?”</u> _World Development_ , 115: 209-221.

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- <u>Shavell, S. and L. Weiss (1979). “The Optimal Payment of Unemployment Insurance Benefits</u> over Time.” _Journal of Political Economy_ 87: 1347–1362.

- <u>Schwellnus, C., Koelle, M. and B. Stadler (2020). “Flattening the unemployment curve? Policies</u> to support workers’ income and promote a speedy labour market recovery.” _Policy note_ , OECD Economics Department.

- <u>Stock, J. and M. Yogo (2005). “Testing for Weak Instruments in Linear IV Regression,” Chapter</u> 5 in Andrews DWK _Identification and Inference for Econometric Models_ . New York: Cambridge University Press: 80-108.

- <u>World Bank (2020a).</u> _Global Economic Prospects, January 2020_ . Washington, DC: World Bank. doi: 10.1596/978-1-4648-1468-6

- <u>World Bank (2020b).</u> _Global Economic Prospects, June 2020_ . Washington, DC: World Bank. doi: 10.1596/978-1-4648-1553-9

- <u>World Bank (2021a).</u> _ASPIRE: The Atlas of Social Protection Indicators of Resilience and Equity_ . Washington, DC: World Bank. Data and methodology available at <u>https://www.worldbank.org/en/data/datatopics/aspire</u>

- <u>World Bank (2021b).</u> _Global Economic Prospects, June 2021_ . Washington, DC: World Bank. doi:10.1596/978-1-4648-1665-9.

- <u>World Bank (2022).</u> _Global Economic Prospects, January 2022_ . Washington, DC: World Bank. doi: 10.1596/978-1-4648-1758-8

- <u>World Bank (2022b).</u> _World Development Indicators_ . Washington, DC: World Bank.

25