rafmacalaba/datause-encoder-data huggingface dataset:
annual exit rates of 16 percent for privately-owned Ethiopian manufacturing firms in the census. These censuses offer the advantage of providing population rather than sample data on the dynamics of larger firms. However, they are unsuited for examining the death of small firms for several reasons. First, many of the censuses are restricted to firms with 10 or more workers, and/or those which are formally registered, and they are extremely unlikely to capture nonvisible businesses operating within households. Second, Shiferaw (2009) notes that it is not possible to distinguish firms that have closed from those that have switched sector out of manufacturing, or which have fallen below the cut-off point for inclusion. Third, they do not include firms in the retail and service sectors, which comprise the majority of small businesses.

A third approach is to use multi-purpose household living standards panel surveys such as the World Bank’s Living Standards Measurement Study (LSMS). This approach was used in Vietnam by Vijverberg and Haughton (2004) who report 61 percent of firms dying over a fiveyear period; and McCaig and Pavcnik (2016) who find 30 to 35 percent death rates over two year periods. Kraftt (2016) uses the Egypt Labor Market Panel Survey to find 51 percent of firms died over an 8-year period, and 61 percent over a subsequent 6-year period. The advantages of these datasets are that the samples can be large and representative, and they capture micro and small businesses operating within households. However, these authors note several challenges to the use of these household surveys to track enterprises. Since the surveys do not identify specific enterprises by either name or code, authors need to match firms over time based on characteristics such as the age of the firm, identity of which household member runs the firm, and its sector. This can result in measurement errors if firms switch their sector of business or have trouble recalling the age of the firm. Further downsides are that the surveys are often conducted at irregular intervals, limiting the periods over which death rates can be measured, and often contain relatively little information on firm characteristics to enable exploration of which firms die and why.

The final approach is then to use dedicated firm panel surveys to track a sample of firms over time. The main challenge for this approach has been the dearth of firm panel surveys in developing countries, small sample sizes, and the difficulty of tracking firms with relatively low attrition. This approach was first tried by Mead and Liedholm (1998), who report an annual closure rate for micro and small enterprises in the Dominican Republic of 29 percent in 1992 and 22 percent in 1993, based on a sample of unspecified size with an unspecified attrition rate. They note that they also tried the approach in Zimbabwe, but were unable to relocate 42 percent 5
Table A2: Summary Statistics

||No. of obs.|Average|Median|St. Dev.|
|---|---|---|---|---|
|Summary statistics based on country avera|ges in the|1990s, 200|0s and in|the 2010s|
|EORA backward GVC participation share|290|23.39%|20.85%|13.21%|
|EORA forward GVC participation share|290|19.16%|18.35%|6.85%|
|EORA backward GVC participation (log)|290|7.54|7.30|2.42|
|EORA forward GVC participation (log)|290|7.44|7.32|2.35|
|Total exports (log)|290|16.12|16.06|2.22|
|Med/High-skilled labor / GDP (log)|290|-16.64|-16.84|1.33|
|Low-skilled labor / GDP (log)|290|-18.62|-18.62|1.50|
|Rents from resources / GDP|290|6.60|2.34|9.77|
|Land / GDP (log)|290|-12.83|-12.64|2.02|
|Capital / GDP (log)|290|-12.11|-12.13|0.46|
|Avg. tari¤ rate (%)|290|7.64|5.25|6.93|
|FDI in‡ows (log)|290|6.92|7.02|2.08|
|Distance to GVC hubs (log)|290|10.02|10.07|0.30|
|Political stability index|290|0.01|0.02|0.87|
|Domestic industrial capacity (log)|290|22.51|22.39|2.26|
|Exch. rate appreciation|290|-13.55|0.00|220.77|
|Exch. rate misalignment|290|-7.59|0.59|66.49|
|NAFTA|289|0.02|0.00|0.15|
|EU|289|0.11|0.00|0.30|
|ASEAN|289|0.05|0.00|0.22|
|MERCOSUR|289|0.04|0.00|0.20|
|Nb. of PTA partners (log)|266|2.64|2.71|0.93|
|Depth of PTAs (log)|266|4.95|5.00|1.50|
|Time to clear imports (log)|191|2.97|2.98|0.67|
|Female labor force partic. (%)|290|40.67|43.69|8.85|
|Instruments|||||
|Import elasticity|290|1.36|1.23|0.46|
|Import elasticity X Population (log)|290|22.44|19.75|9.30|
|Population (log)|290|16.20|16.10|1.69|
|Corporate tax rate (%)|290|28.72|30.00|8.92|
|Transition economy status|290|19.31%|0.00%|39.54%|

5
to a pure control group (Tusiime et al. 2013; Lehmann and Masterson 2014; Kurdi et al. 2019; Tranchant et al. 2019).

For cash transfer interventions, Lehmann and Masterson (2014) find that unconditional cash transfers (UCTs) to Syrian refugees totaling US$575<sup>12</sup> over five months increased food and water expenditure by $25 per month relative to the control group.<sup>13</sup> Similarly, Kurdi et al. (2019) find that CCTs of $30/month in Yemen increased spending on non-staple food items. However, they find no impact on monthly spending.

In terms of food assistance interventions, Tranchant et al. (2019) examined the impact of a food assistance program in the form of general food distribution (GFD) and school feeding (SF) on food expenditure outcomes during conflict in northern Mali. They find that both GFD and SF increased monthly food expenditures by approximately 20 percent. In contrast, Tusiime et al. (2013) find that the GFD of a World Food Programme (WFP) intervention in northern Uganda, which provided at least 40 percent of the recommended dietary allowance, _decreased_ food expenditure by 35 percent.

Overall, these findings suggest that in settings characterized by chronic food insecurity and among conflict-affected populations, UCTs and food transfers can positively impact the food expenditure of vulnerable populations.<sup>14</sup> It also suggests that food expenditure alone is not the best measure of food security; the impact on food expenditure can be positive or negative depending on the intervention type. For example, food expenditure may decrease when direct food transfers are provided to recipients as there is less need to purchase food with their own money. At the same time, expenditure may increase if they spend more on nutritious foods rather than calorie-based staple foods. When the type of aid is cash, it is most likely to increase food expenditure if they were previously food insecure.

# **Non-food expenditure**

> 12 All dollar amounts are US dollars unless indicated.

> 13 In the winterization cash assistance program for Syrian refugees in Lebanon, both the treatment and control group also received a restricted food voucher of approximately $30 per person per month. In this review, we focus on the winterization cash transfer of $575 that was only received by the treatment group.

> 14 These effects on food expenditure are similar to those in the general literature on social protection. For example, a recent meta-analysis of social assistance programs, including 48 studies of 39 social protection programs, found that transfers increased monthly food expenditure by 17 percent on average (Hidrobo et al. 2018).

15
13 There is also no visual evidence of differential pretreatment trends across these region groupings in the raw data. See figure S2 of the supplemental appendix for plots of the mean test score and number of test takers by time to treatment.

> 14 We also looked for district-level heterogeneity in the effect of the program on test taking by interacting the program with dummies for low enrollment, rural, and wealthy districts in separate specifications. None of the interaction terms are statistically significant for girls or boys, indicating that increased access for girls was widely shared across districts. Results appear in supplemental appendix table S2.

15 We have also used administrative data from the Ministry of Education to explore the enrollment effect of the policy by grade. Not surprisingly, the effects are largest in the early secondary grades and diminish as the students’ progress through school. Results appear in figure S3 of the supplementary appendix.

16 Another potential explanation for the results is that additional students retook the exam in response to the scholarship. However, the scholarship covered exam fees only for enrolled students, which would mute this response.

> 17 In columns (9)–(11), we also find no change in the number of private schools in response to the program, either overall or differentially by the characteristics considered. This suggests that private schools experiencing enrollment gains grew in size as a result of the program.

> 18 A potential explanation for the increases in test takers and private school share is migration in response to the program. Although Gambian household surveys lack data on residential migration that would allow us to test directly, we think that such a response is unlikely, because the monetary and psychic costs of migration should exceed the scholarship’s value of less than US$50 annually. Switching schools without changing residence is also unlikely given the sparse geographic distribution of schools, particularly in regions 3–6, as shown in supplemental appendix figure S1. If such switching occurs, it would most likely be at schools close to the border between an eligible and ineligible region. To test this possibility, we analyzed the number of test takers and share in private schools by district as in tables 3 and 4 but included an interaction effect between program receipt and whether the district was located on the border of a regional grouping with different program rollout dates (i.e., the borders between Regions 1/2, Regions 2/4, or Regions 3–4/5). If students switched schools to benefit from the program, then these border districts should see a differential change in enrollment. None of the interaction terms are significant, suggesting that such switching was not a common response. See supplemental appendix table S3 for results.

19 Scores for boys are similar; see figure S4 of the supplemental appendix. Regressing test scores on a full set of age, school, and year dummies in pre-treatment periods yields a similar pattern, although the age coefficients are noisy.

26
# **Box 3  Power Quality Indices**

# **Total harmonic distortion (THD)**

THD is the ration of the RMS value of the sum of the individual harmonic amplitudes to the RMS value of the fundamental frequency.

# **Unbalance**

The unbalance is a condition in a poly-phase system in which the rms values of the line voltages (fundamental component), and/or the phase angles between consecutive line voltages, are not equal.

# **Voltage dips**

Voltage dips, also called sags, are brief reductions in AC main voltage and are characterized by a pair of data: retained voltage or depth and duration--reduction of the supply voltage to a value between 90% and 1% of the declared voltage, followed by a voltage recovery after a short period of time whose duration is 10 ms-1 min.

_Source_ : Cigre (2004).<sup>43</sup>

**Figure 12  Dip performance vs NRS-048 limits**

(Type X dips for network voltages between 44kV and 132 kV) <!-- Start of picture text --> 140 250<br>120<br>95% CPF statistic 200<br>100<br>NRS-048<br>150<br>80<br>no. instruments<br>60<br>100<br>40 average<br> 50<br>20<br>0 0<br>1998 1999 2000<br>Number of Dips<br>Number of Instruments<br><!-- End of picture text --> _Source_ : Koch et al (2003).

Voltage magnitude performance improved substantially through the use of PQ instruments providing data directly to the national control center. When NRS-048 was implemented in 1996, there was a large measured gap between actual performance and the limits set in the standard. These gaps led to customer unhappiness and complaints about the quality of power supply. One quality metric that was particularly problematic Source: Voltage Sag Indices – Draft 2 working document for IEEE P1564, November 2001 (http://grouper.ieee.org/groups/sag/IEEEP1564_02_01.doc).

> 43 Joint Working Group Cigre C4.07/Cired. 2004. _Power Quality Indices and Objectives—Final WG Report_ .

27
11 Under the non-linear restrictions across equations implied by (12) and (13), consistent estimation now requires a non-linear method; an iterative maximum likelihood procedure is used here.<sup>15</sup> The realism of only allowing these two prices to vary spatially is, of course, questionable. We attempted to include other food-staples in the model besides rice (notably cassava and corn), but found that the relatively high proportion of zero consumptions in the data for these goods created considerable difficulties, both in the attribution of prices and the demand estimation. Household specifhic prices for other goods (notably non-food-staples) cannot be determined from the SUSENAS, and only piecemeal price data for some cities (and virtually no rural areas) are available from other sources.

In defense of our two-price (three-good) model we note that these two goods do account for a sizeable proportion of total expenditure - a mean of 25 percent in urban areas and 27 percent in rural areas (though the composition is very different; an average of 10 percent of total expenditure is on housing in urban areas, as opposed to 4.7 percent in rural areas). The omitted food-staples account for a small share of expenditures (2.4 percent).

Furthermore, it is far from clear that any omitted price variability has led us to under-estimate the relative cost-of-living in urban areas; while the omitted food items are undoubtedly cheaper in rural areas, many of the omitted non-food goods are likely to be dearer, notably those that are manufactured in, or transported via, urban areas.

The demand models are estimated on a random sample of 4187 households, being all households in the primary data set living in the 20 districts used for constructing the hedonic price index for housing, after
survey was fielded in spring 2006 while the follow-up survey was fielded in fall 2008 at the beginning of the school year.

As an additional assessment of the impact of school feeding on enrollment of neverschooled children, we also estimated a single-difference model of current enrollment, restricting the sample only to those children who were of school age at baseline (2006) but were not currently enrolled. This model is estimated only for 2008. Results (Table 5, model 5A) show 9 percent higher enrollment in takeup villages in Phongsaly but no significant difference for Ngoi or Nhot Ou.

Another possible effect of school feeding on enrollment is that children enter school at an earlier age. School feeding programs could encourage timely school entry by changing parental perceptions about the costs and benefits of schooling for young children around the school entry age. Previous research has shown that starting school at the recommended school entry age is associated with future school success. In our sample, the most common reason offered by parents to explain non-enrollment of children was that the child was “too young” or “too small.” The availability of school meals may shift parent preferences toward sending a child perceived as too young or small to school. If so, we should observe a drop in the age at school entry. On the other hand, if older children who have never attended school also enroll for the first time, the age at school entry may go up. We investigate this in another single-difference estimate of the impact of school feeding on age at school entry (Table 5, model 5B). Four of six specifications show earlier school entry for children in take-up villages, with results most compelling for Phongsaly. . Stratified models (not shown) also show a large and significant negative coefficient (younger age at entry) in Phongsaly for younger children in particular.

18
# Table 6.11 (Con't.)

||Base<br>in|d on Value-A<br>Domestic Price|dded<br>s|Based<br>in|on Value-Ad<br>World Prices|ded|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|In (Upper Managenent)||0571<br>(0340)|||0550<br>(0341)||
|In (Wage Bill of<br>Temporary Workers)|_025S1-3_<br>(0.201-3)|_02SE-3_<br>(0221-3)|_1.31B4_<br>(1.31-3)|02SE-3<br>(02DE-3)|026E-3<br>(0.2-3)|1.OE-4<br>(1.3B-4)|
|InK<br>Tune|0.153<br>(0346)<br>-0.034<br>(0.039)|4.134<br>(0319)<br>4.068e<br>(0.037)|0238<br>(0.149)<br>0.054<br>(0.032)|0.149<br>(0347)<br>.0034<br>(0.039)|4.167<br>(0319)<br>.0065+<br>(0.037)|0296"<br>(0.150)<br>-.057'<br>(0.032)|
|R2|0.963|0.944|0.923|0.9.74|0.960|0940|
|N|S0|S0|61|S0|S0|61|

- Sgnificant at the S % statsical test leveL

-Sgnificant at the 10 % statistical test leveL BDecause of a relatively serious multiolinearilys problem, columns 3 and 6 present estimates based on a specifcation without ownership variables. Our sample in these industries consists of 11 rms.
Focusing on the model specification that includes all the control variables (Columns 7–9), Table 2 shows that having children 0–5 years is associated with 17.4 percentage points lower likelihood of being an income earner than those with no children or with children 6 years or older. The disadvantage is larger for women who do not have access to childcare. Women who have young children 0–5 years but no access to childcare (Group C) are 30.5 percentage points less likely to be earners, compared to women with no children or children aged 6 or older (Group A). The same findings hold for the other two labor market outcomes as well. Compared to Group A, women in Group B are 15.7 and 15.0 percentage points less likely to participate in the labor market or to have worked in the last 30 days, respectively, whereas women in Group C are 27.3 and 30.9 percentage points less likely to participate in the labor market or to have worked in the last 30 days, respectively. Once again, mothers with young children but no access to childcare support face a penalty nearly twice as large as the one experienced by mothers who have childcare support. This suggests that the motherhood penalty can be partly offset by making childcare services more widely available to the mothers of young children. The Wald tests confirm that the differences in the marginal effects between Groups B and C are statistically significant for all three labor market indicators (see Appendix B, Table B1).

A few individual and household characteristics are statistically significantly associated with the three labor market outcomes examined. These point to the gendered nature of decisions pertaining to women’s economic role and could partly explain the labor market outcomes discussed above. Table 2 shows that the marginal effects on age, sex of household head, household living standard index, and the number of working-age men in the households are statistically significant. The presence of one additional working-age man in the household decreases the likelihood of being earners, participating in the labor market, and the probability of working for women by 13.1, 12.6, and 12.3 percentage points, respectively. This suggests reinforcement of gender roles within households, where women are primarily considered homemakers and men breadwinners. In addition, wearing a _burkha_ , a proxy measure for conservative norms practiced by the household, is statistically significantly associated with a 9.7 percentage point decline in the likelihood of participating in the labor market in the last 30 days. This finding is consistent with previous literature on the relationship between conservative norms and women’s economic outcomes in Bangladesh (Ahmed and Sen 2018).

A series of robustness checks, presented in Table 3, tested the sensitivity of these results. First, we limit the sample of women to the prime working age of 25–49 years to remove from the sample women who might not be working because they are still attending school (we call this Subsample 1). Our conclusions remain unchanged. Table 3 shows that compared to the reference category, women in Group 16
<!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0% .. .. . ....<br>2co C 0Co<br>2 a, 5- >between<br>Figure 4: MPTC/MTL - Utilized Exchange Capacity.<br>Source: Intemational Telecommunication Union (2001)<br><!-- End of picture text --> Low access to telecommunications services was aggravated by the disparity between investment in exchange equipment and investment in the transmission network. In the late 80s, about 65 percent of the exchange capacity was utilized. However, this level fell to 50 percent by 1998, suggesting a large asymmetry investment in the local loop and investment in exchange equipment (See Figure 4). Rampant cable theft, which forced President Bakili Muluzi to warn vandals of severe penalties, further increased the gap between exchange equipment and transmission networks.

In addition to the low-level of coverage, there was also a huge imbalance between urban and rural areas. More than 80% of the main lines were in the largest cities, with 75% of lines in Lilongwe (the capital) and Blantyre (the largest city). Consequently, as in many countries in Sub-Saharan Africa, there were very few telephones in the rural areas where more than 78 percent of the population lived in 1998. At this time, the ratio of teledensity in the largest cities to teledensity in the rest of the country was around 124:1 in Malawi, compared to an average ratio of 5:1 in low-income countries (International Telecommunication Union, 2001).8

# **11.3 Tariffs**

Before the 1998 reform, tariffs were specified in the Posts and Telecommunications Act of 1994 and the Posts and Telegraphs (Charges) (Amendment) Regulations of 1996. Even when the tariffs were initially set in 1996, they were low by regional standards. The charge for a three-
et al theorize that ...rms exporting to high income destinations will hire a higher proportion of high skilled workers and will pay them higher wages than ...rms that either export to low or middle income countries or sell their products domestically. They test their theory with 1998-2000 panel data from Argentine manufacturing ...rms. Their ...ndings show that Argentine ...rms exporting to high-income countries are associated with higher skilled workers and higher average wages than ...rms that either do not export or export to middle income countries. However, they ...nd no signi...cant di¤erence in ...rms’use of skilled labor between those ...rms selling their product domestically and those exporting to low and middle income countries. The authors reason that these results are due to the similarities in the domestic and export markets among low and middle income countries. Their theory and results are in line with Verhoogen’s (2008) ...ndings on the quality upgrades of exporting ...rms and Bustos’ (2007, 2009) work on technology and skill upgrading behavior of exporting ...rms. The results further suggest that non-homotheticities in demand are important for income distribution (as suggested in theory, though not with the same mechanism, by Fajgelbaum, Grossman and Helpman (2009)).

To sum up, the emerging work on trade and inequality with ...rm-level data appears to be con...rming a central role for between-...rm e¤ects in governing the relationship between trade and inequality, and the available results seem to support the thrust of theoretical models in Section 2.2 that predict a rise in inequality with more openness.

# 3.3 New Empirical Work on Trade in Tasks.

Much new empirical work focuses on the fragmentation of the production process, or o¤shoring. Recall from Section 2.3.1 that one of the pioneering theory models in this area was provided by Feenstra and Hanson (1996); not surprisingly, they also pioneered the empirical work (Feenstra and Hanson (1996, 1997, and 1999)). Recall that in that model, o¤shoring increases the relative demand for skilled labor in both countries involved because the o¤shored 34