rafmacalaba/datause-encoder-data huggingface dataset:
20 while in the United States the equivalent figures are 16 percent and 32 percent.

_Non-marginal price increases._ Second and perhaps more important, prices often have to be increased substantially -- by a multiple of the controlled price rather than by only a small percentage. These factors alone would make raising prices politically challenging. But there are further complications. First, it is often not just the price of _one_ energy source or utility that needs adjusting, but _many_ public sector prices -- transport, rents, power, gas, water, heat. Jointly, these can add up to a significant proportion of household incomes, and there would be major income effects if the price increases were to take place simultaneously. Rent increases are likely to be especially burdensome, since housing is generally thought to be highly underpriced (Renaud). In addition, the still relatively large share of non-cash earnings in workers' total compensation package in some countries -- most notably enterprise-provided housing and social services'<sup>4</sup> -- means households have less truly disposable income than in market economies. While some countries have moved in the direction of charging for social services, the high payroll taxes used to fund them also reduce the discretionary spending envelope available for necessities, as compared to market economies, so that increases in energy/utility prices are keenly felt, since they reduce this small discretionary envelope even further. And, third, the socialist legacy has left people with a feeling of entitlement to free or subsidized goods.

A rapid adjustment of household energy and utility prices -- especially if combined with increases in other public sector prices, such as rent and transport -- would imply significant changes in the cost of living and in welfare (Table 7). Thus, changing public sector prices will likely result in large and unpopular distributional shifts if not properly managed. Such shifts could upset the fragile democracies that are emerging in the transition countries. As shown in Table 7, utilities in the energy sector absorbed almost 10 percent of total household expenditures in Poland in 1993. If transport and communication are included this total reaches about 16 percent of household 14/ There is some data in the Household Budget Survey on the non-cash component of compensation but it is not reliable because, inter alia, pricing non-cash items is difficult. What has been reported is in the range of 5% - 10% but it is probably an underestimate since many households did not respond on this item.
**Table 2: Imports in Select African Countries, by Product Category, 2011** _<u>% of all imports</u>_

|_Product category_|_Total_|_Kenya_|_Nigeria_|_South Africa_|_Uganda_|_Zambia_|
|---|---|---|---|---|---|---|
|_Chemicals and allied industries_|14|18|15|11|12|11|
|Foodstuffs|9|10|15|5|7|7|
|_Machinery and electrical_|27|25|32|30|30|18|
|Mineral products and metals|9|12|17|9|4|5|
|Miscellaneous|4|4|0|9|5|4|
|Plastics and rubbers|6|6|3|9|7|2|
|Service|2|4|0|2|1|0|
|Stone and glass|7|2|7|4|10|16|
|Textiles|5|4|0|16|4|2|
|_Transportation_|13|6|10|5|15|30|
|Wood and woodenproducts|4|8|2|0|5|5|

_Source:_ Authors based on firm surveys.

This might explain a certain selection bias in favor of equipment, but also a higher level of professionalism (in South Africa compared to the other four countries) and therefore might depict the situation as better than it is in reality. Moreover, the category of service products is small overall (two percent) and nonexistent in Nigeria and Zambia.

Average dwell time varies significantly across categories and countries (Table 3). For example, stone and glass products remain in port terminals for about 12 days. They are cleared more rapidly in South Africa (4.5 days) than in Zambia (14 days), which may explain differences in the degree of competition in Zambia and South Africa.

**Table 3: Cargo Dwell Time in Select African Countries, by Type of Product, 2011** _number of_ _<u>days</u>_

|_Product category_|_Total_|_Kenya_|_Nigeria_|_South_<br>_Africa_|_Uganda_|_Zambia_|
|---|---|---|---|---|---|---|
|_Chemicals and allied industries_|9.94|10.44|9.33|8.83|8.90|12.33|
|Foodstuffs|7.65|5.89|7.67|5.00|9.33|11.00|
|_Machinery and electrical_|9.03|7.68|9.32|8.35|10.56|8.80|
|Mineral products and metals|7.94|7.18|8.10|5.40|7.00|15.33|
|Miscellaneous|11.93|12.25|—|11.80|10.75|14.00|
|Plastics and rubber|8.89|6.60|9.50|3.60|14.83|10.00|
|Service|7.17|6.50|—|14.00|3.00|—|
|Stone and glass|12.28|10.00|9.75|4.50|13.75|14.33|
|Textiles|6.53|6.50|—|5.11|10.00|9.00|
|_Transportation_|9.21|13.20|8.67|3.67|11.83|7.35|
|Wood and woodenproducts|8.27|8.29|7.00|—|7.25|10.00|

_Source:_ Authors based on firm surveys. _Note:_ — = Not available.

30
|**Table 6: Logit regression of treatment using ho**|**usehold and v**|**illage ex**|**planatory**|**variab**|**les**||
|---|---|---|---|---|---|---|
|**Variables**|**Ne**<br>**partici**|**w**<br>**pants**|**Conve**<br>**partici**|**rted**<br>**pants**|**No**<br>**partici**|**n-**<br>**pants**|
|**Household characteristics**|||||||
|Household lived in hamlet|0.78|***|1.18|***|0.21||
|Scheduled tribe/caste|0.14||-0.03||0.03||
|Other backward caste|0.50||0.50|*|0.23||
|Household size|0.27|***|0.13|***|0.10|***|
|Household female headed|0.29||0.39||0.05||
|Some member can write|0.12||0.55|**|0.35|**|
|**Initial household conditions**|||||||
|Female social capital index|-0.33|***|-0.33|***|-0.32|***|
|Female econ. empowerment index|-0.07||-0.06||-0.20|***|
|Female political participation|0.13||0.58|***|0.22||
|House had mud floor|0.11||0.30|*|-0.16||
|House had only unprotected water|0.89|***|0.89|***|0.88|***|
|House owned any land|0.68|***|0.84|***|0.55|***|
|Value of consumer durables p.c. (1000Rs)|0.16|*|0.18|**|0.08|**|
|**Village Demographics**|||||||
|Number of households (1000s)|-1.04|***|-1.50|***|-0.04||
|Scheduled tribe/caste (%)|1.35|**|1.93|***|0.43||
|Other backward caste (%)|2.99|***|3.14|***|2.90|***|
|Economically active females (%)|-0.07||-0.23|**|0.01||
|Mean education by head (years)|14.75|***|12.57|***|16.53|***|
|**Initial village characteristics**|||||||
|Availability of public bus|-1.16|***|-1.34|***|-1.17|***|
|Availability of telephone|-0.49|**|0.07||-0.37|***|
|If child marriage existed|-1.09|***|-0.82|***|-0.93|***|
|If untouchability existed|-0.45|*|-0.56|***|0.23|*|
|<br>If temple prostitution system existed|-2.06|**|-0.47||0.77|***|
|Ifcaste panchayats existed|1.80|***|1.35|***|1.24|***|
|**Pseudo R-squared**|||||||
|**Number of observations**|1239||1651||2804||

Significance levels of coefficients based on robust standard errors indicated by stars as follows: * significant at 10%; ** significant at 5%; *** significant at 1% 22
suggests an upward trend in earnings between 2014 and 2016, which seems to be reversed after 2016.

Similarly, Figure 4 (bottom) plots the estimates corresponding to the log hourly wage. The figure shows parallel trends up to 2016 for the groups of cantons defined as having low (solid blue line) and high concentration of Venezuelans (dashed green line) _cantons_ . Though less dramatic than in the case of monthly earnings, we again observe a divergence of the two series after year 2016. As before, the estimates for the cantons with medium concentration of Venezuelans are more erratic and less informative, but also consistent with a reversal in the upward wage trend after 2016.

Taken together, these two figures provide evidence in support of the parallel trends assumption, in the years prior to the arrival of Venezuelans, in _cantons_ that would eventually receive the highest and the lowest arrivals of Venezuelans relative to the size of their populations. Thus, a causal interpretation is reasonably plausible.

# **6.4 Robustness**

As noted earlier, our main estimation sample includes all individuals (age 15 or above) living in Ecuador, regardless of country of birth. It is thus possible that the worsening conditions for low-educated workers in the cantons with a high concentration of Venezuelans might be partly due to a compositional effect. That is, the recently arrived Venezuelan migrants are probably employed in low-quality employment with high rates of informality, as one would expect on the basis of the studies of the labor market effects of Syrian refugees in Turkey (Ceritoglu et al. (2017), Tumen (2016), DelCarpio and Wagner (2015)), which would adversely affect the composition of employment in terms of informality and earnings.

While entirely possible, these compositional effects are unlikely to be quantitatively important in the case of Ecuador. The reason is that new evidence from a new survey on the Venezuelan population in Ecuador indicates that the average education level of Venezuelan workers is substantially higher than for their Ecuadoran counterparts.<sup>24</sup> At any rate, here we address this concern by removing all foreign-born individuals from the ENEMDU micro data prior to creating our canton-level labor market indicators. Table 5 presents our difference-in-difference estimates based on the only-natives sample. As it turns out, the estimates that we obtain here are practically identical to the main

> 24EPEC is a survey conducted in the summer of 2019 that will soon become available. According to a preliminary version of these data, 35% of Venezuelans in Ecuador have tertiary education, compared to 15% among Ecuadoran nationals.

18
**Figure 3: Trends in Investment Intentions and Manufacturing Output**

<!-- Start of picture text --> 7000 16<br>14<br>6000<br>12<br>5000<br>10 Investment Intentions<br>4000 8 (number filed)<br>3000 6 Real Grow th in<br>4 Manufacturing Output<br>2000<br>2<br>1000<br>0<br>0 -2<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> _Source: Handbook of Industrial Policy and Statistics_ As part of the liberalization process, quantitative trade restrictions were eliminated, and tariff levels and their dispersion came down. The number of private companies operating in India, also increased significantly, and nearly doubled in the 5 years post-1991, from 249,181 in 1992 to 449, 730 in 1997. As highlighted by Topalova (2004b) in her analysis of the Indian corporate sector, promising trends in liquidity, profitability and leverage of the sector also emerged in the early 1990s (Figure 4).

<!-- Start of picture text --> Figure 4:<br>Aggregate and Median Operating Profit to Net Sales Ratio<br>0.08<br>0.07<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Aggregate Median<br><!-- End of picture text --> _Source: Topalova (2004b)_ The investment boom in the early 1990s appears to have been facilitated by the established business relationships between an oligopolistic real sector and a few large public financial sector institutions (Mohan 2004; and Mor, Chandrasekhar and Wahi 2005). Prior to 1991, a few large business houses relied on a few large development finance institutions, DFIs, to provide long12
schooling reduces math skills by 0.30 SDs (a finding significant at the 1% level). Moreover, there is no statistically significant evidence that attending VET improves computing skills (an increase of 0.12 SDs, p=0.16). The magnitude of the point estimate, even if it were statistically significant, is not large given the much greater number of class hours spent on learning computing in vocational schools compared to academic schools.

The results of the CEM analysis also tell the same story (table 4). Attending vocational high school increases dropout rates by 3 percentage points (over academic high school students— row 1, column 1). This finding is significant at the 1% level. Attending vocational high school has a negligible effect on computing skills. Although vocational high school students appear to do slightly worse than their academic high school peers on the computer skills exams (by 0.05 SDs), the estimated coefficient is not statistically significant (row 1, column 2). The CEM analysis—which matches similar students from vocational high school and academic high schools—demonstrates that attending vocational high school decreases math skills by 0.42 SDs (significant at the 1% level—row 1, column 3).

Taken together, our findings demonstrate that attending vocational high school actually hurts students relative to attending academic high school. First, vocational high school encourages drop out (or at least does not encourage students to stay in school). Second, vocational high schools are failing to equip students with computing skills relative to academic high schools (which spend little class time teaching computing). Third, attending vocational versus academic high school results in a loss of math skills.
# 3.1. Business costs of infrastructure disruptions: Self-reported losses due to power outages

This section uses self-reported Enterprise Surveys data to compute the value of sales lost due to electricity disruptions in our sample. It then constructs a regression model to estimate the impact of power outage durations on these sales losses. Aggregating firm data from 138 mostly small- and middle-income countries accounting for 32% of global GDP, it finds sales losses worth 81.6 billion 2018 real USD due to power outages. As a considerable fraction of firms (about 15%) did not respond to the question registering their sales, this number can be interpreted as a lower bound. The total annual blackout duration reported by firms is estimated to increase sales losses by 0.29 percentage points for each 100 hours of blackout.

On average, countries with higher power outage durations experience larger sales losses. A review of the literature described in Section 2 shows that the relationship between number of outages and sales losses is relatively flat for countries where firms, on average, face power outages of up to 10 hours (see Figure 3). For countries with longer outages, however, the sales losses appear to increase in an almost exponential pattern. Investigating this phenomenon at scale, our analysis confirms the non-linear and positive relationship between monthly hours without power and sales lost due to outages. Looking at the spatial distribution of relative losses, Figure 4 shows that Africa and South Asia are most affected.

Figure 3: Sample firms’ average sales losses and power outages at the country-level <!-- Start of picture text --> 35<br>Enterprise Survey estimates<br>30 From literature<br>25<br>20<br>15<br>10<br>5<br>0<br>0.1 1 10 100<br>Monthly hours without power (logarithmic scale)<br>% of sales lost due to power outages<br><!-- End of picture text --> While all countries with high losses have long outage durations, countries exhibit varying capacities to deal with electricity disruptions. Figure 5 displays the 15 countries for which firms, on average, report the highest shares of sales lost due to outages. While all these countries suffer significant power outages, at this high level of outages the losses are no longer clearly related to electricity downtime. This indicates that the relationship is mediated by a number of other factors that determine firms’ vulnerability to electricity network disruptions. Such factors include the sector a firm operates in, the competition it is exposed to, and the technology it employs for production. One key determinant of firms’ vulnerability is possession of a power generator which will be discussed in the next section.

12
<!-- Start of picture text --> Percent<br>2.0 —95% confidence interval —Mean<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>123 4 5 67 8 9 10 11 12<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>0.8. —95% confidence interval —Mean<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>12 3 4 5 6 7 8 9 10 11 12<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>2.0 —95% confidence intervals Mean<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>123 4 5 67 8 9 10 11 12<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>20 —95% confidence interval —Mean<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>123 4 5 67 8 9 10 11 12<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>08 —95% confidence interval —Mean<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>12 3 4 5 6 7 8 9 10 11 12<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>2.0 —95% confidence interval —Mean<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>12 3 4 5 6 7 8 9 10 11 12<br><!-- End of picture text -->
23 _Perspective, 1955–2007_ , London: Palgrave Macmillan and Washington DC: World Bank.

Anderson, K. and J.L. Croser (2009), _National and Global Agricultural Trade and Welfare Reduction Indexes, 1955 to 2007,_ database at <u>www.worldbank.org/agdistortions.</u>

- Anderson, K., M. Kurzweil, W. Martin, D. Sandri and E. Valenzuela (2008), “Measuring Distortions to Agricultural Incentives, Revisited”, _World Trade Review_ 7(4): 1-30, October.

- Anderson, K., W. Martin and E. Valenzuela (2006), ‘The Relative Importance of Global Agricultural Subsidies and Market Access’, _World Trade Review_ 5(3): 357–76.

Anderson, K. and E. Valenzuela (2008), _Estimates of Global Distortions to Agricultural_ Incentives _, 1955 to 2007_ , database at <u>www.worldbank.org/agdistortions.</u> Diao, X,, A. Somwaru and T. Roe (2001). ‘A Global Analysis of Agricultural Policy Reform in WTO Member Countries’, in M. Burfisher et al. (eds.), _Agricultural Policy Reform in the WTO: The Road Ahead_ , Agricultural Economics Report No. 802, Economic Research Services, U.S. Department of Agriculture, Washington DC.

- Feenstra, R.C. (1995) “Estimating the Effects of Trade Policy”, in G.N. Grossman and K. Rogoff (eds.), _Handbook of International Economics, Vol. 3_ , Amsterdam: Elsevier.

Harberger, A.C. (1959), “Using the Resources at Hand More Effectively”, _American Economic Review_ 49(2): 134-46, May.

Hertel, T.W. and R. Keeney (2006), ‘What’s at Stake: The Relative Importance of Import Barriers, Export Subsidies and Domestic Support’, Ch. 2 in K. Anderson and W. Martin (eds.), _Agricultural Trade Reform and the Doha Development Agenda_ , London: Palgrave Macmillan and Washington DC: World Bank.
**Table 6: Count of enterprises in presumed comparative advantage industries by employment size group, UBR 2001 and UBR 2010**

||**_A. All comparat_**|**_ive advantag _**|**_e industries_**||**_E. Commercial_**|**_farming_**|% share i|n sector|
|---|---|---|---|---|---|---|---|---|
|**Size Group**|Number of en|terprises|% share in se|ctor total|Number of e|nterprises|<br>total||
|**(number of**|||||||||
|**workers)**|2001|2010|2001|2010|2001|2010|2001|2010|
|less than 5|5,934|24,400|78.7|85.2|93|3,198|20.0|65.2|
|5‐20 workers|1,309|3,668|17.4|12.8|286|1,602|61.5|32.6|
|21‐100 workers|178|483|2.4|1.7|43|87|9.2|1.8|
|More than 100|122|95|1.6|0.3|43|20|9.2|0.4|
|Total|7,543|28,646|100.0|100.0|465|4,907|100.0|100.0|
||**_B. Comparative_**<br>**_industries_**|**_advantage p_**|**_rimary_**||**_F. Food and b_**|**_everages indu_**|**_stry_**<br>% share in|sector|
|**Size Group**|Number of en|terprises|% share in se|ctor total|Number of e|nterprises|total||
|**(number of**|||||||||
|**workers)**|2001|2010|2001|2010|2001|2010|2001|2010|
|less than 5|215|5,745|30.2|70.9|1,946|4,742|70.5|79.9|
|5‐20 workers|401|2,174|56.3|26.8|685|927|24.8|15.6|
|21‐100 workers|50|160|7.0|2.0|79|212|2.9|3.6|
|More than 100|46|23|6.5|0.3|52|51|1.9|0.9|
|Total|712|8,102|100.0|100.0|2,762|5,932|100.0|100.0|
||**_C. Comparative_**|**_advantage m_**|**_anufacturing in_**|**_dustries_**|**G. Thegarmen**|**ts industry**|||
|**Size Group**|Number of en|terprises|% share in s|ector total|Number of e|nterprises|% share in<br>total|sector|
|**(number of**|||||||||
|**workers)**|2001|2010|2001|2010|2001|2010|2001|2010|
|less than 5|5,360|18,178|84.2|91.6|3,298|13,062|96.3|97.4|
|5‐20 workers|835|1,338|13.1|6.7|120|324|3.5|2.4|
|21‐100 workers|103|263|1.6|1.3|6|17|0.2|0.1|
|More than 100|67|67|1.1|0.3||2||0.0|
|Total|6,365|19,846|100.0|100.0|3,424|13,405|100.0|100.0|
||**_D. Comparative_**|**_advantage se_**|**_rvices_**||**H. Land trans**|**port services**|% h i||
|**Size Group**|Number of en|terprises|% share in se|ctor total|Number of e|nterprises|sare n<br>total|sector|
|**(number of**|||||||||
|**workers)**|2001|2010|2001|2010|2001|2010|2001|2010|
|less than 5|359|477|78.6|68.3|324|260|77.88|60.75|
|5‐20 workers|73|156|16.0|22.3|62|118|14.9|27.6|
|21‐100 workers|25|60|5.5|8.6|23|46|5.5|10.7|
|More than 100||5||0.7|7|4|1.7|0.9|
|Total|457|698|100.0|100.0|416|428|100.0|100.0|

40
according to the margination index produced by CONAPO (Consejo Nacional de Poblacion) and then turn to the sample of sixth graders enrolled in indigenous schools in 2007-2008. Note that we follow sixth graders to whatever school they attend after sixth grade; the secondary and high schools that students attend thus are not restricted to be in marginalized areas.

Tables 6a to 6c presents the effects of PROSPERA on enrollment in seventh grade, ninth grade and twelfth grade. As described earlier, we measure enrollment using attendance the day of the ENLACE test application. One advantage of this measure is that it may have less biases than selfreported data on children’s enrollment reported by parents in household surveys, which has been the usual source of data for previous evaluation studies. Tables 6a and 6b show state fixed effects models and school fixed effects models. The results indicate that the impact of PROSPERA on enrollment in secondary school is about 8 percentage points for seventh grade, 9 to10 percentage points for ninth grade and decreases to about 5 percentage points for twelfth grade. The results based on propensity score matching (Table 6c) are similar but about 1 percentage point smaller for enrollment in seventh and ninth grade.

Table 6a: Impacts of PROSPERA on School Enrollment All 6th Graders in Medium, High and Very Highly Marginalized Communities 2008 State Fixed Effects

||7th grade|9th grade|12th grade|
|---|---|---|---|
|Variables|(1)|(2)|(3)|
|PROSPERA beneficiary<br>2008|0.081***|0.101***|0.045***|
||(0.006)|(0.006)|(0.006)|
|Female beneficiary|0.005|0|0.002|
||(0.007)|(0.007)|(0.008)|
|Female|-0.014**|-0.022***|-0.013**|
||(0.006)|(0.006)|(0.006)|
|Constant|1.039***|1.207***|0.806***|
||(0.036)|(0.037)|(0.037)|
|Observations|62,870|62,870|62,870|
|R-squared|0.223|0.207|0.124|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

19