rafmacalaba/datause-encoder-data huggingface dataset:
Households in our sample report positive payments to teachers for about 37% of all schoolage children.<sup>17</sup> Payments to teachers typically vary from 5–40 USD per child per year, which is substantial given that public expenditure for teaching materials per pupil is about 30 USD per year, and wage bills per pupil are about 300 USD per year (cf. appendix S.1 and table S.2). In contrast, households only report tutoring expenses for approximately 10% of children (cf. figure S.1). Despite higher income, both per child informal payments to teachers and tutoring expenses are significantly lower in migrant households compared to non-migrant ones. For transportation expenditure there is no such difference. The differences in informal payments and tutoring are mostly driven by more migrant households reporting zero payments (not refusals or “don’t know” answers) rather than by smaller positive expenses. This is not only evident at the individual level but also results in a strong negative correlation at the community-level between the share of migrant households and the share of respondents reporting payments to teachers (table 2: panel A, column 1).<sup>18</sup> The slope of the regression line is approximately -0.4, a very high value that is statistically and economically significant. Note, though, that our data are designed to be representative at the national but not at the community level. The negative correlation also holds at the individual level (table 2: panel A, column 2-5).

# IV.  EMPIRICAL STRATEGY

To analyze whether this strong negative correlation between migration and petty corruption at the community-level as well as the individual level is indeed closely tied to migration, we estimate the stylized model:

> component analysis of the asset list. Dividing the divisible assets by the squared root of household size as an equivalent scaling rule does not change figure 1 in any qualitative way.

> 17. This figure is remarkably similar to the one reported in the 2013 Global Corruption Barometer: 37% of households in Moldova that came into contact with education authorities paid bribes in the 12 months before the survey (Transparency International <u>2013). We focus on the likelihood of paying informal fees rather than the values</u> paid since we assume the decision to participate in the informal fee scheme to be the most affected by a change in preferences. Note that we added one LCU to each private expenditure to ensure that the log exists.

> 18. See figure S.2 for an illustration.

9
state that they need electricity to run their businesses. In contrast, the HE-SE have little need for water while employers particularly need this service.

Electricity needs are generally met across firm types while water needs are not. Again turning to the IES survey, among those informal firms needing electricity, 76.9 percent are connected to the grid, with HE-SE having greater connectivity than the others (Figure 21). The conditional estimates confirm that those who are connected to the grid are more likely to be employers and less likely to be LE-SE. Water access is a different story. Among those needing water, only 24.5 percent have full access, again, with HE-SE having more access than the others. The conditional estimates find that LE-SE have particular water deficiencies (Table 4). This in contrast to water and electricity access in the homes of workers. If we consider all workers (rather than firms) surveyed in the GMD, regardless of whether they need water or electricity, we find high access to water and electricity in their homes, about 80 percent access for each (Figure 22), with few differences between formal and informal workers. Access to improved sanitation is markedly lower for informal workers than formal sector workers.

Informal workers and firms’ absolute and relative access to the internet is low, with significant variation across employment types. Only 40.6 percent of informal workers have access to the internet, with particularly low connectivity among the unpaid and LE-SE (Figure 22). The conditional correlates, controlling for poverty level, confirm that those workers with access to the internet are less likely to be unpaid or LE-SE as compared to all other job types (Table 5). The HE-SE have internet access on par with formal wage employees (58 percent) (Figure 22).

Cell phone access is much higher than internet access and relatively equal across employment types. It exceeds 80 percent for all employment types except LE-SE (76.4 percent), with the HE-SE having the most access (Figure 22). The IES reports that informal firms similarly report high usage (rather than simply access) of cell phones, with an average of 53.3 percent, with the LE-SE having lower usage rates than the other two categories of informal firms (Figure 21). The high rates of cellphone or smartphone usage could reflect ways of running the business, e.g., contact with customers and providers by phone or by social media, and also the possibility of using mobile money. In fact, the different rate of cellphone/smartphone use between firm types reflects the differential use of mobile money reported above.

Page **38** of **60**
however, that the mode of girls has shifted upwards over the period 1980–2010 compared to that of boys’. Finally, Yamaguchi (2018) uses using from the Panel Study of Income Dynamics (PSID) and the Dictionary of Occupational Titles (DOT) to study the determinants of the narrowing of the gender wage between 1979 and 1996. He reports a significant increase in women’s cognitive and general skills, which he argues contributed to the reduction in the gender wage gap.

We formalize the mean dynamics hypothesis in our framework as an increase in the mean scholastic performance of females relative to males over time. Under this hypothesis, as for the tail hypothesis, there exists a lower bound of scholastic performance ̄ _z_ such that individuals that attain a given level of schooling _s_ ̄. Making the normalizing assumption that _μmt_ = 0 for all _t_ , the share of individuals in the cohort that attain a level of schooling of at least ̄ _s_ at time _t_ is given by: A change from _E_ [ _zf,t_ ] _< E_ [ _zm_ ] to _E_ [ _zf,t_ ] _> E_ [ _zm_ ] is a necessary and sufficient condition for the gender gap reversal in education to occur, when the distributions of scholastic performance by gender are assumed to have the same variance. Under the mean dynamics hypothesis, the gender gap reversal therefore occurs if there is a reversal in the relative average scholastic performance of females relative to males. The gender ratio among individuals that achieve a minimum level of schooling ̄ _s_ under the mean hypothesis can be expressed as: and: 18
based in Kabul. We note that firms that register for corporate lines are unlikely to be representative of all firms in the country, particularly small, informal firms operating outside urban areas. Still, larger firms based in major urban areas compose a significant portion of formal employment, and thus may be of particular interest. The non-representativeness of the CDR is an important caveat in interpreting the results that follow.

For our analysis, we aggregate the CDR data to the district-month level.<sup>13</sup> We address potential measurement concerns about mobile network coverage being mechnically correlated with violent events by dropping any district-month that has less than one week of coverage, and we also confirm that our results are robust to dropping any district that ever has a month without coverage. The data aggregation process results in an unbalanced panel of 10,642 district-months, with an average (median) district-month including 81 (41) active firms and 391 (100) active subscribers. We merge this district-month panel with geocoded data from the Global Terrorism Database (GTD) on over 10,000 confirmed fatalities from terrorism in Afghanistan.<sup>14</sup> In Table 1, we show the mean (median) district-month records 1.4 (0) GTD killings, with a maximum value of 246 killings.<sup>15</sup> We also report that 12% of district-months are categorized as insecure in internal security tracking data from a national survey firm.<sup>16</sup> In Figure 2, we conduct a principal component analysis of the three main sources of variation in our district-month panel (log of active firms, active subscribers and calls) and plot the first principal component for April 2013 on a map of Afghanistan’s districts. As expected, major urban centers such as Kabul (center-north), Kandahar (south), Hirat (west), Mazar

> 13A brief description of the data processing required to complete this task is included in Appendix A1. 14Maintained by National Consortium for the Study of Terrorism and Responses to Terrorism (START) at the University of Maryland, the GTD database is constructed from keyword filtering of high-quality media sources and hand coded by teams of researchers, including providing geo-coordinates for the city or district an event takes place. Killings include confirmed fatalities of either victims or attackers. Thus, in order to be included in our dataset, a killing must be recorded by a credible media source and meet the GTD coding teams definition of terrorism: “the threatened or actual use of illegal force and violence by a nonstate actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation.” While this introduces the likelihood of under-measuring violent incidents, it increases our confidence that we are focused on meaningful events.

> 15The second highest value of 244 killings corresponds to the attack on Kunduz in September 2015 that we discuss in detail below.

> 16A coding of insecure indicates the survey firm was unwilling to send enumerators to that district in that month for survey data collection activities.

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difference, and there are two broad ways in which individuals’ answers can be read. On the one hand, we can assume that, when asked about their position in Cantril’s ladder, individuals’ answers have to be understood as such - where do they place themselves with respect to other members of society. Being in the fifth step of the ladder would imply having roughly half of the population "above" in terms of welfare and half of the population "below". In essence, the answer to the question is _distributional_ . On the other hand, that answer can be understood as their position with respect to some ideal benchmark - for instance, being "absolutely rich" or "absolutely poor". Being in the fifth step of the ladder would mean that individuals find themselves to be as far in terms of welfare from the richest and from the poorest of their country. In this sense, the position in Cantril’s ladder refers more to individuals’ perception of their _well-being_ , even though the question is not phrased in those terms.

The reasons behind the difference between perceived and objective welfare ranking will be very distinct depending on whether it is assumed that individuals’ self-reported positions in Cantril’s ladder are _distributional_ or that they refer to their _well-being_ . These represent two different analytical frameworks and in the next subsections we will address them separately.

# **3.1** **_Distributional_ explanations of the difference between objective and subjective ranking**

If we assume that individuals’ answers refer to their perceived position in the welfare distribution, then any explanation will ultimately have to address why responses are "normally" distributed, rather than uniformly distributed.

There are several possible explanations: on the one hand there are data issues: (i) the survey data miss rich and poor people, thus more people are actually in the middle because the sample does not include the full population. On the other hand, there are misperceptions or misinterpretations of the questions: (ii) people simply do not know, and in absence of any additional information they bunch in the middle; (iii) people answer the question referring to their permanent income, rather than their current situation, and the dispersion of permanent income is lower than that of the current income. Lastly, it could be possible that individuals’ responses are based on their objective position not within the overall population within a smaller reference group - this reference group being specific for individuals who share some kind of characteristic, like their education level, their gender or their geographical location. This is (iv) the reference group explanation.

**Missing data.** First, the survey might miss persons that would rank themselves in the lowest and highest steps of the ladder. Household surveys like LiTS suffer from a known

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**Table 3. Ex-ante evaluation of the impact of minimum wages: Baseline simulation results (an increase to 250 euros)**

||Nopolicy|Short-run|Long-run|
|---|---|---|---|
|Employment rate, %|28.80|28.65|28.58|
|||[28.61; 28.68]|[28.54; 28.60]|
|Unemployment rate, %|29.59|29.97|30.15|
|||[29.90; 30.05]|[30.07; 30.24]|
|Youth unemployment rate, %|55.41|56.13|56.48|
|||[55.89; 56.40]|[56.15; 56.78]|
|Share of youth NEETs, %|30.11|30.28|30.34|
|||[30.24; 30.34]|[30.28; 30.41]|
|Change in real earnings, %||0.24|0.03|
|||[0.15;0.33]|[-0.08;0.12]|

_Note: Kosovo LFS 2018 is used. Wages are imputed as described in Section 2. Short-run (long-run) labor demand elasticity values are set at -0.5 (-0.8) for skilled labor and -0.7 (-1.0) for unskilled labor. Only employees with wages between the old and the proposed minimum wage are assumed to be affected. Aged 15-64. 95 percent confidence intervals are in brackets._

**Figure 13. Simulated reduction in earning inequality as a result of minimum wage increase**

<!-- Start of picture text --> 2.47 2.56<br>2.15<br>1.75 1.72<br>1.53<br>5/1 decile 9/1 decile<br>Actual<br>Simulated, long-run. With 0 earnings those who loose a job<br>Simulated, long-run. Only who remain employed<br><!-- End of picture text --> _Source: Own estimates using 2018 Kosovo LFS._

**Although overall disemployment effects are small, unemployment among the young (aged 15-24) is simulated to increase by 1.1 percentage points in the long run; unskilled workers are especially affected; and there are more women than men among job losers.** Figure 14 Panel a depicts the effect of the policy on unemployment by demographic characteristics. The youth unemployment (aged 15-24) is simulated to increase by 0.7 percentage point in the short run, and 1.1 percentage points in the long run.  The unemployment rate among those with primary school or less is expected to increase up to 0.8 percentage point. There are potentially more women than men among job losers.

**When looking at the impact on earnings, the minimum wage increase is effective in increasing earnings among young workers (aged 15-24) in the short run but has little effect on those aged 25-64. The net increase in earnings is slightly higher for women than for men, decreasing the gender gap in**

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adults, we have no information on the households in which they lived during their childhood, or on the migration status of their parents. We must therefore restrict our sample to children of school age. However, while we have information on the migration status of the households in which these children are living, many of them are still attending school and so we do not observe their completed level of schooling.

The data are thus right-censored for children who are attending school. OLS and 2SLS estimation ignores this censoring, treating the educational attainment of children still in school as identical to those who have finished schooling. This results in biased estimates of the impact of migration on final schooling attainment. Censoring is likely to be especially important for schooling outcomes of 12 to 15 year-olds, since many in this age group will not have finished schooling.

A second issue is that OLS and 2SLS assume a continuous distribution for the dependent variable, years of schooling attained. However, as seen in Figure 2, the observed schooling distribution is characterized by large spikes at 6 years and 9 years, representing the completion of primary and lower secondary school. As noted in the education literature (see e.g., Glick and Sahn, 2000), grade attainment is the outcome of a series of ordered discrete choices. The choice to continue onto junior secondary school or onto high school for one year is thus likely to be different from the choice to continue for an extra year once one has started junior high or high school.

As a result of these features of education grade data, King and Lillard (1987) and subsequent studies in the economics of education literature (e.g. Glick and Sahn (2000), Holmes (2003), Maitra (2003)) have adopted a censored ordered probit framework when examining the impact of household characteristics on schooling. In this framework, an individual’s desired latent propensity for schooling, _yi_ is determined by a linear relationship analogous to equation (1): 14
<!-- Start of picture text --> 100%<br>50%<br>60%<br>70%<br>60%<br>50%<br>40%<br><!-- End of picture text --> <!-- Start of picture text --> 30%<br>20%<br>10%<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015.<br>—— Full degree of competition (Retail electricity market)<br>—=—Full degree of restructuring (Vertical and horizontal unbundling)<br>——Full degree of private sector participation (PSP in generation and distribution)<br>== Establishment of regulator<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br><!-- End of picture text --> <!-- Start of picture text --> 30%<br>20%<br>10%<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>=——‘Some degree of competition (At least Independent Power Producers)<br>——‘Some degree of restructuring (At least partially unbundled)<br>=——Some degree of private sector participation (PSP of at least generation or distribution)<br>—— Establishment of regulator<br><!-- End of picture text -->
which asylum seekers are randomly allocated. They find that inclusive labor market access regulations substantially increase the employment chances of asylum seekers, in particular if the language distance is short. Zetter and Ruaudel (2018) and Aiyar et al. (2016) argue that, for refugees, the right to work and access to labour markets are key for becoming self-reliant and maximizing their net contribution to the public finances in the longer term.

The literature on the link between policies and public preferences is relatively scarce. Bansak et al. (2016) find that public preferences over asylum seekers are shaped by sociotropic evaluations of their potential economic contributions as well as humanitarian concerns about the deservingness of their claims. Zhou (2018) finds that citizens who live near refugees in their country are substantially more likely to support restrictions on citizenship access compared to fellow citizens farther away. She finds that the effect is stronger for more recent arrivals but does not address whether the effect varies by government and humanitarian policies. Blair et al. (2021a) construct an original dataset of _de jure_ asylum and refugee policies covering more than 90 developing countries that are presently excluded from existing indices of migration policy. They find that unlike in the Global North, forced displacement policies in the Global South have become more liberal over time. Betts et al. (2021) explores the role of inter-group interaction in shaping social cohesion between refugees and host communities in East Africa. The authors find mixed results: host community attitudes towards refugees (and vice versa) are likely to be shaped by a combination of intra-group attitude formation at the neighbourhood level, and inter-group interaction, with different mechanisms of interaction likely to be more salient for attitude formation in particular contexts (e.g. urban versus camp-based).

Our data and empirical setting provide some unique advantages that allow us to provide new evidence in several dimensions. First, we use sub-national data on refugee populations covering almost all low- and middle-income countries, which enables us to provide large-scale, cross-country evidence on forced migration and attitudes towards immigrants but at a more local level. Second, we study the role of national-level policies in shaping attitudes towards refugees which is facilitated 8
Table A2: Mincer Regressions for Wages

||Log monthl|y real wages|
|---|---|---|
||(1)|(2)|
|Male|0.2730***||
||(0.001)||
|(Age - 40)<sup>2</sup>|-0.0004***|-0.0008***|
||(0.000)|(0.000)|
|(Age - 40)<sup>4</sup>|0.0000***<br>(0.000)|0.0000***<br>(0.000)|
|Higher education or more|0.9081***<br>(0.001)||
|Tenure frm (log)|0.1316***<br>(0.000)|0.0752***<br>(0.000)|
|Observations|4,823,417|4,873,093|
|R-squared|0.619|0.895|
|Worker FE|No|Yes|
|4-digit industry FE|Yes|Yes|
|Year FE|Yes|Yes|

_Note:_ Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. The dependent variable is the workers’ log monthly real wage in each year. FE stands for fixed effects.

Table A3: Additional Variable Definitions

|Variable name|Variable defnition|
|---|---|
|_Worker covariates_<br>Previous formal sector attachment|Share of months worked between 2004 and 2007 (number of months worked between 2004 and 2007 / 48).|
|_Firm performance_||
|Net revenue|Logarithm of net revenue defned as total revenues minus taxes.|
|Materials [per worker]|Logarithm of intermediate input expenses [divided by number of workers].|
|Capital [per worker]|Logarithm of capital computed using the perpetual inventory method [divided by number of workers].|
|Labor productivity|Log of value added per worker with value added defned as the diference between gross value from production and intermediate<br>input expenses.|
|TFP|Log of TFP estimated using a value added production function with labor and capital as inputs following either Levinsohn & Petrin (2003),<br>Ackerberg et al. (2015) or Wooldridge (2009) estimation. For each methodology a diferent production function is estimated for each 2-digit sector.|
|Proft rate|Profts defned as revenues minus cost of inputs minus labor costs divided by total revenues.|

We define two additional worker outcomes following Yagan (2019). First we define a measure of relative months worked: where _monthsis_ is the number of months that worker _i_ is formally employed in year _s_ . Second, we define a measure of relative wages: 41