rafmacalaba/data-use-mentions-extended huggingface dataset:
the fraction of jobs that can be done at home. Estimating the latter is challenging since choosing the tasks that determine whether a person can work from home is largely arbitrary, specifically if one does not have a model linking such tasks to the probability of WFH during a pandemic. Dingel & Neiman (2020b) and Saltiel (2020) consider that an occupation cannot be performed from home if at least one of several conditions holds. For instance, in the Dingel & Neiman (2020b) study, some of categories that are sufficient to consider that an occupation cannot be done at home include “Performing for or Working Directly with the Public is very important”, “Handling and Moving Objects is very important” or “Repairing and Maintaining Electronic Equipment is very important.” However, occupational requirements can change during exceptional conditions. For example, while for professionals in communications or in law it is very important to have contact with the public, they can still carry out some (but not all) of their tasks using ICT (Information and Communication Technologies); craft workers for whom handling and moving objects is crucial may still be able to sell their products through e-commerce; individuals repairing equipment can still work on portable objects at home, to name a few examples. More generally, occupations comprise a bundle of tasks, and while

> 1 An exception is Saltiel (2020), who uses STEP surveys.

2
|82.0|29.2**|29.9***|
|Mexico|75.3|4.9|3.4|
|Nicaragua|26.8|-5.9**|-1.9|
|Panama|45.2|-14.3**|-16.2***|
|Paraguay|28.9|-10.0|-8.2***|
|Peru|59.5|15.9***|16.3***|
|Venezuela|51.6|-14.5**|-15.8***|
|**Latin America and**|**61.1**|**4.3**|**10.9**|
|**Caribbean**||||

_Note_ : Regressions with Huber-White corrected standard errors. * Significant at the 10 percent level, ** at the 5 percent level, *** at the 1 percent level. Sample size is 144 countries. _Source_ : World Bank databases.

28
, as reflected in trust and membership of associations.

Temple and Johnson (1998) also examine the relationship between measures of trust and associational activity as measured in the World Values Survey and their social indicators. Although the overlap in countries from the two data sets was only 10, the results are suggestive. They find negative or insignificant relations between the two sets of social measures except when they divide up the associational activity measure into groups that are likely to be distributional coalitions (negative correlation) and groups that are Putnamesque (positive correlation).

La Porta and colleagues (1997) once again used the trust data from the World Values Survey to examine the thesis that trust or social capital determines the performance of society's institutions through cross-country regressions. Trust is the propensity to co-operate. They argue that trust is needed more to support co-operation between strangers or between those who 21
588***|0.588***|||0.512***|0.512***|
|wage in industry & services);|||||||
|{1} assigns sample mean to IT<br>_ITALY_ (does not have a|(0.052)<br>-2.693***|(0.052)|||(0.168)<br>4.580|(0.168)|
|minimum wage)|(0.715)||||(3.569)||
|log GDP per capita, PPP USD<br>l ld 1|4.823***|4.823***|8.350***|8.350***|-2.850|-2.850|
|(annua average, agge year)|(0.504)|(0.506)|(0.962)|(0.962)|(3.372)|(3.384)|
|Real GDP Growth, y-o-y,|0.109**|0.109**|0.039|0.039|-0.186***|-0.186***|
|<br>quarterly data lagged 1 quarter|(0.052)|(0.052)|(0.052)|(0.052)|(0.054)|(0.054)|
|Union Density,|-0.361**|-0.361**|-0.399***|-0.399***|0.062|0.062|
|% of employees|(0.035)|(0.035)|(0.058)|(0.058)
the control group mean. This indicates that the preferential treatment enrolled female students with lower Kankor scores. This favored treatment might raise concerns about applicants’ academic performance post-admission,

> 3I acknowledge the coarse nature of this classification. However, given the lack of granular metrics at the applicant and/or district-level in Afghanistan, the use of nightlight data provides a plausible indicator of economic development at the district-level. I am unable to differentiate between the socioeconomic statuses of individual applicants level and instead rely on district classifications.

> 4I use the average of luminosity across district as the source of operationalization. Low-socioeconomic status indicates districts with an average luminosity value less than the mean.

3
# **Figure 22. Access to urban infrastructure and services, by employment type**

<!-- Start of picture text --> Electricity access Improved water Improved sanitation<br>Total 80.3 Total 78.6 Total 41.3<br>Total Inf 75.5 Total Inf 77.2 Total Inf 35.8<br>Unpaid 68.8 Unpaid 71.8 Unpaid 34.1<br>Paid Info 81.4 Paid Info 80.3 Paid Info 36.5<br>LE-SE 74.2 LE-SE 77.0 LE-SE 35.9<br>HE-SE 87.4 HE-SE 79.3 HE-SE 44.1<br>Paid Form 91.9 Paid Form 83.3 Paid Form 57.2<br>0 20 40 60 80 100 0 20 40 60 80 0 20 40 60<br>Internet access Cellphone access<br>Total 45.2 Total 81.5<br>Total Inf 40.6 Total Inf 80.3<br>Unpaid 35.3 Unpaid 83.2<br>Paid Info 52.6 Paid Info 85.6<br>LE-SE 34.7 LE-SE 76.4<br>HE-SE 58.5 HE-SE 92.3<br>Paid Form 58.9 Paid Form 80.0<br>0 20 40 60 0 20 40 60 80 100<br><!-- End of picture text --> Source: Own elaboration based on GMD. Notes: Cross-country averages using population weights. Countries included in electricity, water, sanitation, and cellphone averages: Angola, Burundi, Benin, Burkina Faso, Chad, Cote d’Ivoire, Cameroon, Gabon, Ghana, Guinea, Guinea Bissau, Kenya, Liberia, Mali, Malawi, Niger, Nigeria, Senegal, Sierra Leone, Sao Tomé and Principe, Eswatini, Togo, Uganda, Zambia and Zimbabwe. Internet average includes Cameroon, Benin, Burkina Faso, Chad,
risk of rising unemployment because monetary policy is initially tighter (Franzese, 2001). To counteract this, governments may weaken labor market regulations. This contracts earnings, especially among the lower skilled and poorer workers. Third, CBI curtails the ability of governments to engage in welfare spending, which also increases disparities between the rich and the poor (Bodea and Higashijima, 2017).

In sum: a government that implements CBI finds itself nudged toward adopting policies that increase inequality. Note that in this model, inequality is a side e↵ect of CBI and not a goal in itself. Note also that we are not claiming that CBI is causing inequality. Rather, we are postulating that CBI modifies policymakers’ incentives to adopt compensating policies.

Empirically, we test these hypotheses using a panel dataset covering up to 121 countries from 1980 to 2013 (exact samples di↵er depending on data availability). We find evidence in support of our main conjecture – that CBI increases inequality. An increase of CBI by one standard deviation leads, ceteris paribus, to a decline in the share of income of all income groups up to the 6th decile. The share of income earned by the bottom decile declines by 0.3 percentage points. CBI, on the other hand, has a considerable positive e↵ect on the income share of the 80%-90% and the top decile. The latter saw its share of income increase by more than 1 percentage point. CBI, in other words, shifted income from the bottom half of the population to the top earners. In several robustness tests, we rule out competing interpretations of our results. In particular, we show that the e↵ect of CBI is not driven by government ideology.

Next, we explore the three mechanisms that link CBI to inequality: financial liberalization, labor market deregulation, and welfare state retrenchment. In terms of financial reforms, governments loosen financial regulations to fuel credit growth, despite a more conservative monetary policy stance. A one-standard deviation
’ calculations.

# **Albania (LSMS 2008)**

The data are taken from the Living Standards Measurement Survey (LSMS) 2008 administered by the Institute of Statistics (INSTAT) in the Republic of Albania. The fieldwork for the survey was carried out between June 2008 and August 2008.

The value of all non-purchased items was about 17 percent of total purchased expenditure, while non-purchased food accounted for about 83 percent of all non-purchased items. The former ratio tended to decline at the upper quintile levels. The latter was
_For labor or for divorce?_

# **1. Introduction**

One of the most persistent and salient features of labor markets in the Middle East and North Africa (MENA) region is that women participate significantly less than men despite substantial reduction of the gender education gap (Assaad et al., 2020) –the average female labor force participation rate in the region is a mere 18 percent (WDI, World Bank, 2021). For example, in Morocco only 23 percent of women participate in the labor market and this rate is even lower for countries like Jordan where it did not exceed 13 percent in 2021. Under the canonical collective household model, marriage markets and legislations governing divorce influence the labor supply of spouses and the intrahousehold decision process (Chiappori et al., 2002; Fernandez and Wong, 2014; Field et al., 2021). While a large body of literature has studied the consequences of the introduction of more liberal divorce laws on intrahousehold distribution of bargaining power and labor supply within the collective household labor supply framework (Chiappori, 1988; 1992; Apps and Rees, 1988), less is known about MENA region countries in this regard. Particularly in circumstances where traditional gender roles prevail as social norms and where women possess limited influence or bargaining power within the marriage, their choices regarding work may not be adequately acknowledged or facilitated (Alesina, 2013; Qian, 2008; Jensen, 2012; Heath and Mobarak, 2015; Jayachandran, 2015; 2021).

**Figure 1. Relationship between gender progressive laws and FLFP by Country**

<!-- Start of picture text --> 10 20 30 40 50<br>FLFP rate<br> MENA OECD East Asia Europe Central Asia<br> South Asia Sub-saharan Africa Latin America Fitted values<br>100<br>80<br>60<br>WBL_Index<br>40<br>20<br><!-- End of picture text --> Source
household is connected to and drawing from the water network, and to zero otherwise. This is determined by the main source of drinking water, as self-reported in household surveys. We then define , as the share of households that are connected to the network among those with potential access (i.e., located in a service area), which is a subset of all households _H._ HH|SSAA CC Hence, S<sup>xCC</sup> is the proportion of poor households that are connected, conditional on their having HH HH|SSAA<sup>is equal to the actual connection rate among all households. Analogously,</sup> potential access (or being located in a service area) or, equivalently, the number of poor PP|SSAA CC households that are connected divided by the number of poor households that have potential access to the network.

# **Quantity of water consumed among those connected (Q)**

We construct this variable by imputing water consumption volumes from self-reported household water expenditure, _EH|T_ , conditional on being connected to the network. The conversion is undertaken using the corresponding tariff structure from the IBNET data.<sup>10</sup>

In the first round of calculations, we impute quantities for households for which (i) the total bill for water consumption depends on the quantity consumed, (ii) the value of self-reported

> 9 The variable _service area_ ( _SA_ ) is equivalent to the variable _access_ ( _A_ ) in Komives et al. (2005) and Angel-Urdinola and Wodon (2007a). In this paper we prefer the phrase _service area_ because it better reflects the variable we are measuring.

> 10 Information on how this match is made for each country is provided in the appendix.

8
48 approximately constant across the quintile groups. All food items were deflated by an index provided in the survey data set.

The survey asked whether households used LPG and how much they paid per month on average. The survey asked what LPG and electricity were used for but did not have a question on the main cooking fuel. Monthly average retail prices of LPG from four main suppliers were available and the expenditure on LPG recorded in the previous month (May through July, 2008) was divided by the national average price for that month to estimate the quantity purchased by each household.

At least two thirds of households in each quintile group used LPG except the bottom rural quintile. The share of households using LPG peaked in the middle quintiles and then declined. Although not shown in the table, among households not using LPG for cooking, the proportion using electricity for cooking increased with quintile level: for the bottom rural quintile only 31 percent used electricity instead, while for the top urban quintile 89 percent used electricity for cooking. The quantity purchased per capita steadily increased with quintile level, and the quantity per household also generally increased, except for the top quintile in rural areas (Table A2.17).

Table A2.17: Number of households purchasing LPG and estimated average quantity purchased per month by purchasing households in Albania

|**Quintile**|**Number of p**<br>**Rural**|**urchasing househo**<br>**Urban**|**lds (percent)**<br>**Total**|**Kg purchase**<br>**Rural**|**d per household**<br>**Urban**|**(per capita)**<br>**Total**|
|---|---|---|---|---|---|---|
|
respondents with a wide range of current incomes, we framed the monetary variable relative to current monthly income, rather than a fixed wage.<sup>xiv</sup> The full survey instrument, including the choice experiment module, is provided as Appendix S2.

# **Experimental Design**

Given the numbers of attributes and levels, it was not possible to include all combinations of them in the choice experiment. Therefore, we used an efficient experimental design in order to create a reasonable number of choice sets. We developed an efficient design that would allow identification of all main effects, using Ngene software by ChoiceMetrics. The attributes were combined to create 48 choice sets in 8 blocks, each with 6 choice situations. To mitigate fatigue and cognitive burden, each respondent was presented with one block of 6 choice sets. The blocks were randomly assigned to respondents, and the choice sets were randomly ordered within each block. Further details regarding the design of the choice experiment are provided in Appendix S1.

# **Model Estimation**

To estimate WTP using the choice experiment data, we follow standard practice by starting with a random utility model (McFadden 1973). Suppose that the utility an individual receives from a particular alternative _j_ depends on the attributes _xj_ associated with that alternative: where ε _j_ reflects individual characteristics that are unobserved by the researcher. Each individual chooses the alternative that provides the maximum utility, and the probability that the individual selects alternative _i_ from choice set _S_ is given by: 13
location: household diaries significantly underreport consumption in urban areas (but not rural areas) suggesting the relative prevalence of personal consumption opportunities in urban areas; (3) the educational attainment of the household head: education had little relation to module performance except in the usual month approach, wherein inaccuracy was greater among less well educated households; and (4) household wealth as captured by a household asset index: the underreporting in recall modules is greatest among the poorest households and the deviation significantly declines with wealth. It is currently an open question whether these household characteristics, shown to be important mediators for consumption reporting accuracy, are affected to differing degrees by the various types of reporting error. This possibility is investigated in the error decomposition framework introduced in the next section.<sup>9</sup>

# **IV. Reporting error decomposition**

Earlier analyses of consumption reporting errors has focused on a net measure of total misreporting. This masks two aspects of consumption reporting: whether any consumption occurred and, if it did occur, the value of the consumption. Our main analytic approach in this paper is to examine these two aspects of misreporting in comparison with the benchmark module by modeling total food consumption as a product of two vectors whereby each ordered element of the two vectors corresponds to an individual food good _f_ . The first vector records, through an indicator function, whether the household reports any positive consumption of _f_ . The second vector records the stated consumption value of each element. More formally, total consumption _C_ recorded for household _h_ by survey module _m_ can be written as the following: where the first vector in the product is the consumption incidence vector, and the second vector is the consumption value.<sup>10</sup> This decomposition enables a separate analysis of survey design effects on consumption incidence and consumption value (or quantity). Different survey designs may differentially affect these two sources of error, and simple summary cross-module comparisons of total consumption can obscure these error patterns and, consequently
The results reported for the EPZ surveys are simple unweighted averages due to the non‐randomness of the sample and the different coverage strategies described above. Furthermore, the small sample size of respondents and some item non‐response issues in some countries prevent the use of more sophisticated statistical analysis with the data. Finally, it should be noted that the small sample size of EPZ firms in some countries (and enclaves) makes our averages sensitive to the presence of outliers and firm and item non‐response. In the most extreme case – Tanzania, with only 17 operational EPZ firms – a relatively small number of outliers (or refusals to respond) can significantly bias our estimates.

It should be noted that even for the ten countries included in the study, the data are not comprehensive. In some zone programs (e.g. Nigeria) we were unable to get access to any reliable time series data on the free zone program. In other countries – e.g. Senegal – data are available on the small “free zone” program, but not always from the other parallel programs (the now‐defunct “points francs” and the large “EFE” regime). Moreover, the operation of single‐factory programs, especially in Ghana and Senegal but also on a smaller scale in Kenya (and a much smaller scale in Tanzania) makes direct comparisons (and in some cases, conclusions) difficult. Where possible, we have separated data on the zones programs delineated within specific industrial parks or enclaves, and dispersed through “single factory” licensing.

# **6. Assessing performance in African zones**

This section provides a summary and discussion of outcomes across the ten zone programs included in the study, with the specific focus on the six African zones.

## <u>Investment</u>

The first proximate measure of success of an EPZ program is the investment it attracts. Without investment there will be no employment or exports and no possibility of realizing structural economic benefits
with official demobilization registers should capture respondents who feared reporting that there is an ex-combatant in their family. Second, cross-checking self-reported demobilization status with official demobilization registers should exclude households who wrongly 17We undertook this matching exercise using generalized Levenshtein edit distance, which is the total number of insertions, deletions and substitutions required to transform one string into another. We matched the names, age, sex and the code of the colline of return of the ex-combatants listed in the official demobilization registers with the household information available in our panel dataset. We use a maximum number of transformation of 2 for string variables (first name, last name, both), a range of [-10,+10] from the age reported, and the match had to be perfect for the colline’s code and gender. We did this exercise twice, once matching each entry in the registers to the panel data; and once the other way around. As names are very similar in Burundi, and many people can have the same last name (all twins have the same last names; siblings’ last names are different and are chosen according to various contextual factors), we made sure to only consider as matches cases where names contain typos. We found eight matches, which supports the fact that we were very restrictive.

> 18One should note that the number of registered ex-combatants in our sample is representative of the true excombatants’ density and that an over-sampling of ex-combatants was not compatible with having a panel.

14
19 Columns 3 and 4 of table 3 show the same expenditure regressions at the household level. Using the household level data we get very similar results on social capital, a coefficient of .56 or .34 depending on the instrument set compared to .49 using the cluster level. Not surprisingly, the estimates on the control variables, such as education, are more reasonable using the household data.

The standard test for the exogeneity of the instrument set (which is essentially a test that trust does not itself cause higher incomes except through its effect on social capital) does not reject the instruments in the cluster data, but the test does reject the instrument set in the household data. The variable 'trust in strangers' causes the instrument set rejection, which is puzzling as it is the most plausibly exogenous a _priori_ as we thought trust in strangers would be the least likely to be affected by income or associational activity. Estimation using the household data without "trust in strangers' as an instrument provides a quantitatively similar (.34) but less precisely estimated (t-stat 1.27) coefficient on social capital and the exogeneity test is not rejected.

_Is "social capital"social?_ The second question is whether participation in associational life raises incomes only of those who directly participate or whether social capital produces spillovers to other individuals. We can address this question in two compelling ways.

cluster level average social capital will contain a substantial component of measurement error in measuring the true cluster social capital. If this were a univariate regression, the ratio of the OLS to IV estimates is an estimate of the ratio of the true signal to the total variance. The estimates suggest the noise is very large. The correlation in repeated measurements is also a measure of the noise to signal ratio. While we do not have repeated measurements in the same villages for social capital, the correlation of the two village level estimates of expenditures per person is .45, so if the magnitude of measurement error between the two variables is
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- Forbes, D. J. (2013). Multi-scale analysis of the relationship between economic statistics and DMSPOLS night light images. GIScience & remote sensing, 50(5), 483-499.