rafmacalaba/data-use-mentions-extended huggingface dataset:
For the benchmark years in between 1993 and 2008, there exists no unambiguous ranking of Lorenz curves. Hence the direction of change in inequality depends on the chosen inequality measure. For example, Figure 2 shows clearly that the Lorenz curves in 2003 and 2008 cross. This is also shown by the fact that the various GE measures move in opposite directions (with the exception of the change between 1998 and 2003).

Our results are unaffected by a number of robustness checks, such as using the original decile data, applying the 2005 PPP US$, and confining the analysis to the balanced sample (i.e. countries that are observed in both 1993 and 2008).<sup>30</sup> The results of these robustness checks are presented in the Appendix. First, using the original data set of decile groups without fitting a parametric distribution reduces the African Gini by about 0.5pp, while the time trend remains unchanged (Table A1, Panel ii). Of course, the fall in the African Gini is to be expected because every within-country distribution is now represented by ten instead of 100 points.<sup>31</sup> The changes in GE(0) over time and its within contribution are also robust to using the decile data. Second, the results from applying the 2005 PPP US$ remain comparable to the main results using the 2011 PPP US$, with the Gini index being 0.8pp lower on average, and increasing by about 9% over the 15-year period (Table A1, Panel iii). Third, we check whether the results are driven by changes in the sample of countries over time. The balanced sample raises the Gini increases by about 1.8pp in 1993 and 0.6pp in 2008 (Table A1, Panel iv). As a result, the Gini index increased by 6% between 1993 and 2008. This increase is slightly smaller than that observed in the main results but remains substantial.

> 30 These robustness checks are
## **Table 5. Summary of the robustness tests results: Regression estimates of the relationship between the availability of childcare and time allocation on primary activities**

||(1)|(2)|(3)|
|---|---|---|---|
||Market work<sup>d</sup>|Unpaid work<sup>d</sup>|Other<br>|
||||activities<sup>d</sup>|
|**1.**<br>**Limit the age of women to prime age: 25–49 years**<sup>a</sup>||||
|_Ref: A. No child or youngest child is 6 years and older_||||
|B. Youngest child is 0–5 years and childcare available|-2.197***|1.732***|0.465|
||(0.576)|(0.383)|(0.433)|
|C. Youngest child is 0–5 years and no childcare available|-3.238***|1.887***|1.351***|
||(0.520)|(0.347)|(0.428)|
|Observations|829|829|829|
|Individual controls|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|
|Regional controls|Yes|Yes|Yes|
||Yes|Yes|Yes|
|**2.**<br>**Limit the age of child to 0–12 years**<sup>b</sup>||||
|_Ref: A. Youngest child is 6–12 years_||||
|B. Youngest child is 0–5 years and
and are taken from Eurostat (downloaded 14-Oct-2022). ppcc(ii),tt xxii,tt<sup>25</sup> ppcc(ii),tt

**Data Cleaning.** Growth data are often highly volatile, and so extreme observations will receive a disproportionate importance in any least-squares algorithm. This is especially true if EU payments are

19 We exclude Ireland and Lithuania as changes in the definition of some NUTS2 regions mean that the NUTS2 regions in the payments data are different NUTS2 regions in the GDP data. The EU27 are the 27 member states of the European Union after Brexit, so it does not consider the UK. We also use a larger country-level sample in table A4.6 for comparison with Durand and Espinoza (2021), which includes EU27 + UK and has data that runs from 1994–2018. 20 EU payment data for Croatia only starts in 2007.

> 21 <u>https://ec.europa.eu/eurostat/databrowser/product/page/NAMA_10R_2GDP</u> GDP data at the NUTS2 level uses the 2021 NUTS2 definitions (see <u>https://ec.europa.eu/eurostat/web/nuts/history for the changes in NUTS2 regions), but</u> payments data use historical NUTS2 definitions. The two differ as historical NUTS2 regions sometimes had a change in their name or were split into smaller regions. We combined the new definitions of the GDP data to be consistent with the old definitions of the payments data. Some examples of changes include Croatia (HR04=HR02+HR05+HR06), Hungary (HU10=HU11+HU12) and Poland (PL12=PL91+PL92). Also, many names for NUTS2 regions have been changed across programming periods and we also had to adjust for that (in France and Poland). 22 Downloaded from https://cohesiondata.ec.europa.eu/Other/Historic-EU-payments-regionalised-and-modelled/tc55- <u>7ysv,  based
The output variable is only available for the year 2017. All input and control variables (except dummies) are averaged for the period 2000 to 2015. This seems to make sense because the output variable reflects the effects of past investments in the education sector. In addition, it may take some time for the input to impact on the output, so the input variable is delayed with respect to the output. Since resources necessary to improve access and quality require sustained investments over a relatively long period, considering the average expenditure over the last 15 years allows past investments into the education system to be taken into consideration. The data set used is then a cross‐section covering the period 2000‐ 2015 for the input and control variables and the year 2017 for the output variable. Data are collected from three main sources: The World Development Indicators, the UNESCO Institute of Statistics (UIS), and the OECD Creditor Reporting System (OECD‐CRS). Table 1 presents descriptive statistics of the data.

**Table 1 Descriptive Statistics**

|Variable|Obs|Mean|Std. Dev.|Min|Max|Coef. of<br>Variation|
|---|---|---|---|---|---|---|
|**Output (y)**|||||||
|Learning‐Adjusted Years of Schooling (lays)|157|7.895|2.683|2.275|12.905|2.942|
|**Input (x)**|||||||
|Total public expenditure per school‐age individual PPP|||||||
|(expeduc)|175|2862.225|3852.216|29.981|22448.080|0.743|
|**Control variables (z)**|||||||
|Share of aid in public
interpretation for the decline in overall wage inequality. In section 6, we discuss the robustness of our results, and section 7 concludes the paper.

# **STYLIZED FACTS**

In this section, we describe the data that we use and present a few stylized facts on labor markets in Vietnam. Wage work represents a small but growing fraction of the total labor force. Among wage earners, public sector employees are the best paid, and this advantage has increased over time. Despite the increase in inequality between the public and private sectors, overall wage inequality has decreased because within-sector inequality has decreased.

# _Data_

Our analysis depends on the availability of reliable data with a panel dimension. We use two separate datasets, the Vietnam Living Standard Surveys (VLSS) for 1993 and 1998 and the Vietnam Health and Living Standard Surveys (VHLSS) for 2002, 2004, and 2006. These high-quality household surveys were implemented by the General Statistical Office (Hanoi) in collaboration with the World Bank. For the VLSS, the households surveyed in 1993 were interviewed for a second time in 1998. The VHLSS is a separate panel with a rotative design so that most households were interviewed twice, either in 2002 and 2004 or in 2004 and 2006. Very few households were surveyed three times. For the purposes of our analysis, we add the 2002–2004 and the 2004–2006 observations into a single dataset, which, by abuse of language, we will call the “2002–2006” panel and compare it to the 1993–1998 panel. This manipulation is necessary to ensure that we observe a sufficient number of transitions between employment sectors.

5
Prices. Prices are measured in two ways: (i) import unit values (import value divided by import weight); and (ii) national average prices of products collected by the International Comparison Program (ICP) (World Bank, 2011, 2017). Since import weight is not available for Chile, India, Mexico, and Vietnam, the results for unit values are based on a smaller sample.

Other variables. As measures of trade policy, applied tariff rates and NTMs for HS 6-digit products are sourced from TRAINS (UNCTAD, 2024). For tariff rates we use the simple average across origin countries of the applied tariff within an importing country-product-year. For NTMs, we focus on Chapters E “Non-automatic import licensing, quotas, prohibitions, quantity-control measures” and F “Price-control measures, including additional taxes and charges” as defined by UNCTAD (2019). We define an indicator that equals one if any of those NTMs are present within an importing country-product-year and zero otherwise. As controls, in some specifications we include log real GDP per capita at purchasing power parity and the most recent Gini coefficient from the World Development Indicators (World Bank, 2024a), and indexes of control of corruption and regulatory quality (Kaufmann, Kraay and Mastruzzi, 2010). Means and standard deviations of these variables are reported in Table S2.

# 3 Main result

Figure 1a showed a significant correlation between the commodity export share and import market concentration. We demonstrate that this relationship is robust to a battery of controls, using the following regression where αc,i is a country-product market fixed effect that captures unobserved market charac9
|**Totalpoverty line**<br>R$455<br>|R$1,061|
|s<sup>F</sup><br>0.235|0.243|
|_Source:_Own calculations using POF 2017/18 data.<br>_Note:_ These total poverty lines are based on the food poverty line of R$258, coming from our|main specification, which is based|

_Note:_ These total poverty lines are based on the food poverty line of R$258, coming from our main specification, which is based on the minimum calorie requirement of 2,100 calories per person per day and average cost per calorie of the bottom 40 percent of the welfare distribution, with calories having been assigned in a two-step procedure and deflated by household-specific foodprice Paasche indices, as described earlier in this section.

_Nonparametric approach._ To complement this result, we apply a nonparametric approach to estimating the food expenditure shares for the lower and upper poverty line. The approach taken here was to obtain the average food share of households with either total expenditures (for the lower poverty line) or food expenditures (for the upper poverty line) in the range of +/– 10 percent of the food poverty line. These estimated shares are used to obtain the lower and upper poverty lines following equations (8) and (10), respectively. Table 7 shows the results. The poverty lines remain generally robust to changes in the approach to the nonfood adjustment, with values being similar across the two approaches, especially for the lower poverty line.

**Table 7. Total poverty lines – Nonparametric approach, in 2018 urban Southeast prices**

||**Lower**|**Upper**|
|---|---|---|
|**Totalpoverty line**|R$452|R$973|
|**Average food share**|0.2457
br>a (0.5|
|||||.|Czech<br>Rep<br>(0.01|Lybi|a<br>(70.4.|tsk)|
||||||tsk)<br>France<br>(0.01|Bots|wana|(43.9|
|LG<br>aver-<br>||0.01<br>|70.4<br>||tsk) Lebanon (0.01<br><br><br>|tsk) <br>|Somalia <br>|(35.4<br>|
|agearea|177|tok|tek||tsk)<br>India<br>(0.01<br><br>|tsk) <br>|Namibia <br>|(13.7<br>|
|||||Sree|tsk)<br>Phillipines|tsk)|Congo|DR|
||||||(0.01tsk)|(11.6|tsk)||

Source: Authors’ calculations based upon data sources reported in Annex Table Al. Note Units of measurement: th - thousand people, min - million people, fsk - thousand square kilometers. Distribution of 6G avernge population is only for countries with this indicator lower than 200 thousand people (87% of the sample). Distribution of LG averge aree is only for countries with this indicator lower than 4 thousand sq. kilometers (92% of the sample}
<!-- Start of picture text --> Frequency in which Foreign Investors<br>Have shut down an affiliate in a Developing Country Foreign Investors Who Have Closed an Affiliate in a Developing Country by Sector<br>Share of respondents (percent) (percentage of respondents)<br>38%<br>@ Manufacturing<br>55% @ Primary<br>m Services<br>S<br>mYes @No Don't Know<br>‘Source: Global Investment Competitiveness (GIC) Survey 2017 Source: GIC 2017<br>i<br><!-- End of picture text --> Services tend to be more frequently affected by irregular government conduct than any other sector Types of Government Conduct Inducing Investors to Cancel Investment Expansion Plans: Manufacturing vs. Services (percentages of total respondents affected) Suddennegativechange impactin laws onand yourregulations companywith a eT Expropriation orbytaking the government of your property or assets == Breach of contract by government | eee Restrictions in your ability to transfer and convert Fa m Services Lack currency m Manufacturing of transparency & predictability in dealing with public agencies aa Delays& approvals toin obtaining necessary start or operate government a businesspermits ees Oo 10 20 30 40 50 60 70 80 Source: GIC 2017 31
6 This is due to the fact that only a small fraction of the population lives in Dar.<sup>4</sup> Consequently, even the large drop in poverty in Dar made only a small dent in the national poverty rate, while the small drop in the rural poverty rate had a relatively large effect on the national poverty rate.

The decomposition also shows that just 11.6 percent of the decline in the headcount at the national level is attributable to the shift of the population from poorer rural areas to wealthier urban areas, chiefly Dar es Salaam. Most of this shift is probably due to migration. The small size of this “population-shift effect” demonstrates that rural-urban migration cannot substantially reduce poverty in Tanzania in the short run. Because the population is overwhelmingly rural, even the rapid shift of population to wealthier Dar, which saw its population grow at a 4.4 percent annual rate versus 2.9 percent for the country as a whole, did not substantially reduce the national poverty rate.

**Table 4: Sectoral Decomposition of the Change in Poverty**

|||**Contributi**<br>**in Nationa**<br>**R**|**on to Change**<br>**l Headcount**<br>**ate**|
|---|---|---|---|
||**Population**<br>**Share in**<br>**1991/92**|**Absolute**<br>**Change**|**Percentage**<br>**of Total**<br>**Change**|
|**Dar es Salaam**|5.35|-0.56|17.08|
|**Other Urban Areas**|12.6|-0.34|10.49
These policies could be implemented by furloughing workers, who then receive unemployment insurance benefits, or transferring resources to firms so that they pay the wages of employees who are “unemployed” within the firm. This sort of policies were adopted by New Zealand and the United States, among other countries. For example, the Small Business Pulse Survey conducted by the US Census Bureau reports that over 70% of small businesses received assistance from the PPP and other programs. During the shutdown, their cash-on-hand increased and the arrears in servicing loans and other costs decreased.

The cross-country differences in the speed of recovery could be the result of weaker balance sheet support policies, longer lockdowns, or reallocation shocks. In our interpretation, the slow recovery of employment and output in the United States relative to New Zealand is the consequence of a more prolonged lockdown, which could also have induced a reallocation shock that would not have materialized under a shorter lockdown. Concerning the latter, there is mounting evidence in the US that this is indeed the case. Barrero et al. (2020) report evidence on reallocation from an online survey of businesses, showing that excess job and sales reallocation across firms rose sharply since the pandemic struck. The Business Formation Statistics (BFS) reported in Figure 3 and Dinlersoz et al. (2021) also suggest a reallocation shock, manifested by a surge in new business applications, following a temporary contraction in the first weeks of the pandemic.<sup>8</sup> We assess the implications of a reallocation shock in our model in Section 5.

# **3 Modeling the Great Shutdown**

Building on Buera et al. (2015), we model an economy with entrepreneurial production subject to financial and labor market frictions. There is a continuum of individuals, who are heterogeneous in their entrepreneurial productivity, employment opportunity, and wealth. Furthermore, an unanticipated shock can shut down a fraction of them temporarily, but since it is completely unanticipated, it has no impact on peoples’ behavior ex
, Carvalho, L., Lleras-Muney, A., 2010. Child Gender and Parental Investment in India: Are Boys and Girls Treated Differently, mimeo, Rand and UCLA.

- Bardhan, P. K., 1974. On Life and Death Questions. Economic and Political Weekly (Special Issue).

- Becker, G. S., 1973. A Theory of Marriage: Part I. Journal of Political Economy 81 (4), 813–846. Behrman, J., 1990. Intrahousehold Allocation of Nutrients and Gender Effects: A Survey of

27
income tax revenues. Household data simulations by Jorratt (2010) suggest that these five income tax exemptions raise the Gini coefficient by about 0.7 percentage point.

23. **Tax retentions at the source of income penalize independent workers, who are not entitled to reimbursements through income tax declarations.** Colombia‟s tax system facilitates the retention of income at the source (e.g. wage income, commissions, interest income), which is good practice. Unfortunately, the system of retention is not fully integrated with the annual tax declaration. In effect, not all persons have to present an annual income declaration, which implies that the original tax retention remains definitive. This is the case for independent workers who are subject to a retention of 6.1 percent of their income, on average, but do not have to present a declaration if their income is lower than US$43,000 per year. This legislation, undoubtedly, does not encourage formal job generation.<sup>16</sup> The resulting „overtaxation‟ is equivalent to 0.5 percent of GDP.

24. **The introduction of a voluntary income tax declaration combined with a reduction of the income tax could reduce the Gini coefficient by 1.9 points at a fiscal cost (in terms of lost revenues) of 0.4 percent of GDP.** One difficulty in changing current legislation related to retention of income taxation at the source would be the associated loss in tax revenue. To compensate for this effect, the income tax threshold could be reduced to 75 percent of its current value (which would still make it the highest in the region), yielding a saving of 0.1 percent of GDP. Under the assumption that all independent workers would now voluntary declare annual income, most income tax would be paid by the richest quintile, while the poorest three quintiles would not contribute.

# **3.2 Indirect Taxation**

25. **The Value Added Tax
|
||(1.698)|(2.143)|(1.687)|(2.084)|(2.054)|(2.358)|
|Country characteristics|NO|YES|NO|YES|NO|YES|
|Observations|300|296|300|296|300|296|

Note: OLS coefficients from a cross-country regression using the employment rate as the dependent variable.

# **7. Conclusions**

This paper contributes to a growing body of literature investigating the skill content of jobs. While most articles impute US-based measures of the task content of occupations to other countries, we use harmonized data on the task content of jobs for 11 developing countries. We find that indexes based on the US and on developing countries lead to similar conclusions regarding the stock, changes and drivers of the non-routine cognitive and routine manual content of jobs. However, the 30
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bound” on the number of jobs that could feasibly be performed entirely from home, as it “neglects many characteristics that would make working from home difficult.”<sup>5</sup> For many jobs, one of the principal constraints on performing them from home is internet access. Even when a job is in principle amenable to working from home (teleworking), that option may not be available in practice if the worker does not have internet access at home. To properly measure this constraint and account for the importance of ICT, we first need to split the telework jobs identified by the DN2020 index into two categories - jobs that require internet and those that do not require it. Then, for those telework jobs that require internet, we must identify which workers actually have access to internet and for which workers the lack of access to internet constitutes a constraint. Our final objective is to classify jobs as amenable to being performed from home only if they do not require internet or if they require internet and are held by workers who have internet access.

Our first step is identifying telecommutable jobs that require internet access using detailed information on occupation characteristics from the O*NET surveys. We use two specific questions on the importance and frequency of computer and email use in the performance of the tasks. The answers to these questions are scored on a 5-point scale with higher numbers indicating greater dependence on computers and email use. We consider an occupation as requiring internet access if the combined average score exceeds 8 (of a total of 10). This leads to 55% of all SOC 8-digit occupations in O*NET as being classified as requiring internet. By combining this measure with the DN2020 index, we can now distinguish four different types of occupations: (a) those that can be performed from home and require internet; (b) those that can be performed from home without the use of internet; (c) those that cannot be performed from home and do not require internet; and (