rafmacalaba/datause-encoder-data huggingface dataset:
Theoretically, electricity access affects the ISSC in two steps: input-effect and production distributiondecision.

## **First step: input-effect**

As mentioned in the literature, since electricity is an essential input for the industrial production of manufacturing firms, it provides a productivity gain to companies, which increases their overall performance - output, employment, exports.<sup>7</sup> More precisely, these productivity gains, by making firms more competitive, would lead to an increase in the level of exports. However, this input-effect does not tell exactly how firms reorganize their sales between the domestic and export markets. Do firms export more than they sell on the domestic market when they have access to electricity? Understanding this reorganization of sales between markets is crucial to assess whether electricity access contributes to changing the structure of SSA’s economy. Analyzing the effect of electricity access on exports in level form or as a dummy - like in the existing literature - does not allow to understand such structural change dynamics.

## **Step two: production distribution-decision**

Once firms’ competitiveness has increased thanks to electricity access, companies decide to produce more. However, they may decide to sell much more on the export market at the expense of the domestic market, or they may do the opposite. But the decision to sell more on the export market than on the domestic market will depend both on internal and external factors to the firm. Regarding internal factors, a company's willingness to export instead of selling on the domestic market depends on firm's age, firm's size, ownership type, being a subsidiary of a parent company, the firm's development strategy, the marketing strategy and any other firm-specific factors that may explain the firm’s motivation to export. Concerning external factors, they are related to factors external to the company that highlight the economic opportunities for the firm if it starts exporting. These factors include: the existence of large economic margins in export markets; export facilitation measures in export markets; export incentives offered to firms by foreign authorities; the size of foreign market; the degree of openness of the foreign economy; the comparative advantage of foreign countries; and any other factor external to the company that offers economic opportunities for export activity.

These two effects can be summarized as follows: First step: input-effect Electricity access Productivity gain Greater production Internal factors Second step: production distribution-decision External factors ISSC=exports increase in sales However, the empirical study of these transmission channels is not the subject of this paper and can be the topic of another research paper.

3. Data, variables, and statistics

# 3.1. Data

Firm-level data are from WBES, which provides nationally representative data on companies in a set of countries, using stratified random sampling with replacement methodology. The population units are

> 7 See for example: Cole et al., 2018; Mensah, 2018; Allcott, 2016; Alam, 2013; Moyo, 2013.

5
shock over 1973-74. However, the current account worsened and the budget deficit grew, which signaled the structural problem of the Thai economy.

In 1973, an uprising engineered by students removed the military strongmen from the government. Even though a bloody coup in 1976 restored military to power the military involvement in government was limited to power-sharing with the civilians.

Even though the discovery of natural gas in the Gulf of Thailand helped to relieve the pressure of energy cost after the second oil shock over 1979-80. The second oil shock intensified the structural problem of the economy. At the same time, the world was in economic recession, and the world commodity price decreased. The import substitution strategy reached it limits.

# Slow **Structural Adjustment (1979-1984)**

Realizing the limitation of the import substitution strategy, the Thai government unilaterally started a World Bank style structural adjustment program (SAP) in 1980. In return, the World Bank agreed to a series of unconditional structural adjustment loans to Thailand two years later. The Fifth Five-Year Development Plan (1981/82-1985/86) embodied this program. However the reform was slow for the economy was still in a sound condition, with price distortion not that serious comparing to other developing countries and with capital inflows from other private and official sources being big enough to cover the growing foreign debt.

# **Recession and Export Promotion (1985-1995)**

The recession over 1984-85 accelerated the process of reforms. The government devalued the currency in November 1984 and started a far more vigorous export promotion policy. Tariffs were lowed and made more uniform, notably the rice and energy prices were brought into line with the market levels. The budget ran surplus after 1988 till 1996.

In 1988, a democratic government was elected through general election. Unfortunately, factionalism and corruption in the civilian government led to another military coup in February 1991. When the pro-military party tried to back the former army command-in-chief, Suchinda, as the premier, people took the street and the bloody clash happened. After the intervention from the King, Suchinda stepped down and a fresh election was held in September 1992.

Thailand continued its reform in the area of financial deregulation. In 1993, the Bangkok International Banking Facility (BIBF) was established for easy access to funds. This and many other factors led to accumulation of huge debt, concentrated in the financial firms and banks, and finally triggered off the 1997 economic crisis. The crisis now spread across Southeast Asia and causes uncertainty for the world economy. See World Bank 1998 for financial crisis after 1997.

# Reference

1. World Bank. _CountryBrief: Thailand._ March, 1996. September, 1997.

2. Economic Intelligence Unit. _Country Profile: Thailand_ London, United Kingdom. 1991/92, 1992/93, 1993/94, 1995/96, & 1996/97, 1997/1998.

3. World Bank 1998. East Asia: The Road to Recovery. Washington DC.

67
<!-- Start of picture text --> Agency and household income contribution<br>100<br>=<br>= 90<br>a)<br>S 1975-1979 1970-1974 _ sR ERRE,, 1955-1959<br>a 1oggSaeee* 1975-1979<br>° oes Os0-1984 1955-1958 oo<br>£ 970-1974<br>= 1980-1964<br>A7a<br>S<br>3<br>o60<br>oO<br>=<br>7)<br>50<br>0 10 20 30 40<br>Share agreeing that both spouses contribute to income (%)<br>— BGD — IND — PAK<br><!-- End of picture text --> <!-- Start of picture text --> First birth age and having children<br>22<br>21 z eee:ghee!<br>6 20 L 1984 1ASAgtaa9a79<br>=<br>B 1 451-1944<br>we 19-4<br>ic)<br>S 18 aah<br>< 1970-1974 1868-181<br>a5-1949 1955-1959<br>17) 1950-1954<br>16- t 1<br>75 30 85 90 95 100<br>Share who agree that child fulfils a woman (3)<br>— BGD — IND — PAK<br><!-- End of picture text -->
# **Figure 1C: Distribution of Biased Weights**

**All individuals in federal and regional governments**

<!-- Start of picture text --> Individual population weighting errors for 50th percentile woreda<br>5<br>4<br>3<br>2<br>1<br>Respondent weighting<br>0 Equity per capita<br>0 100 200 300 400<br>Rank of weighting<br>Managers in federal and regional governments<br>Managers' population weighting errors for 50th percentile woreda<br>5<br>4<br>3<br>2<br>1<br>Respondent weighting<br>0 Equity per capita<br>0 20 40 60 80 100<br>Rank of weighting<br>Organization-level average population weighting error, federal and regional governments<br>Organisation average population weighting errors for 50th percentile woreda<br>5<br>4<br>3<br>2<br>1<br>Respondent weighting<br>0 Equity per capita<br>0 10 20 30 40<br>Rank of weighting<br>Federal and regional respndent effective weighting<br>Federal and regional respndent effective weighting<br>Federal and regional respndent effective weighting<br><!-- End of picture text -->

**Notes:** The figure shows the organization-average estimated population weights for the median district in the region in terms of population.  The top panel shows the observations for all employees and managers in the sample; the middle panel shows the observations for all managers in the sample; the bottom panel shows the organization-level averages of all employees and managers in the sample.  The weights are calcuated based on respondent estimates of the population across the 25th, 50th, and 75th percentile district in the region.  The black line represents equity per capita -- the correct weights if the actual data were used; the black circles represent the weight for the median disrict based on the region and federal civil servant respondents' estimates of the populations of the 25th, 50th, and 75th percentile districts. The organization average weight is then used for the purpose of this figure.  The sample for the top panel is all federal and regional employees and managers that responded to the information module in the Ethiopian Civil Servants Survey; the sample for the middle panel is all federal and regional managers that responded to the information module in the Ethiopian Civil Servants Survey; the sample for the bottom panel is all federal and regional employees and managers that responded to the information module in the Ethiopian Civil Servants Survey.
<!-- Start of picture text --> Adjusted predictions with 95% Cls<br>-1.2<br>go I<br>5 14<br>xo)8 —-&“<br>z -1.6 — —e~— No Maternal Depression or Parenting Stress<br>5 —e— No Maternal Depression; Median Parenting Stress<br>Ke) —e— Median Maternal Depression; No Parenting Stress<br>2ce . 1.8 Median Maternal Depression and Parenting Stress<br>3)<br>2<br>2<br>ao 6-2<br>o<br>Cc<br>5<br>-2.2<br>0 4<br>Maternal Anxiety<br><!-- End of picture text --> <!-- Start of picture text --> Adjusted predictions with 95% Cls<br>Re)<br><<br>®<br>£<br>o<br>o 4<br>><br>x)a |<br>5 $ joo—e— No Maternal Depre ssion;ssion orMedia Pare n tingParenS t ressing Stress<br>2 ——*®— Median Maternal Depression; No Parenting Stress<br>2 Median Maternal Depression and Parenting Stress<br>is)<br>2<br>2<br>a 2<br>w<br>®<br>¢<br>£<br>1<br>0 4<br>Maternal Anxiety<br><!-- End of picture text -->
African Republic to Kisangani in Congo DR is expected to increase the volume of trade by 7.93 percent.

# **Figure 5 Costs Associated with Completing Export Procedures (U.S. dollars)**

<!-- Start of picture text --> (1900,5000]<br>(1400,1900]<br>(1000,1400]<br>[500,1000]<br>No data<br><!-- End of picture text --> _Source_ : Graph constructed with data from Doing Business (2008).

Landlocked countries in Africa are particularly at a disadvantage. To access overseas markets, landlocked countries rely on the physical infrastructure and logistic capacity of transit countries. They are also subject to costs related to the administrative practices and political stability in transit countries. As for African landlocked countries, dependence on a transit country implies higher transaction costs. Figure 5 shows the costs associated with completing export procedures as reported by Doing Business in 2008 for several African countries. The fees include costs for documents, administrative charges for customs clearance and technical control, terminal handling charges, and inland transport costs. Not surprisingly, export costs are ranked among the highest for most landlocked countries.

Limao and Venables (2000) estimate that the median landlocked country’s transport costs are 46 percent higher than the equivalent costs in a median coastal economy. They also find that distance explains only 10 percent of the change in the transport costs. Poor road infrastructure represents 40 percent of the transport costs predicted for coastal countries and 60 percent for landlocked countries, which is especially relevant for African 15
closures and fears around the COVID-19 pandemic further reduced both the ability and desire to migrate (Bah et al, 2021).

The number of recorded Gambian migrants fell from 8,522 in 2017 to only 394 migrants in 2019 and 285 in 2020. The extent to which this dropped was unanticipated by us, and not well-known in the country, even ex-post. In June 2021, we held a dissemination event at the University of The Gambia, and showed 30 local researchers, aid officials, and journalists the Frontex numbers for each year from 2014-2017, and asked them what they thought rates had been in 2018, 2019, and 2020. The median belief was for the migration numbers to have fallen from 8,522 in 2017 to 7,532 in 2018, and down to 6,000 in 2020. That is, most locals believed there had been a much more gradual decline than recorded. Part of this may reflect underreporting in the Frontex data, since anecdotally we heard accounts of Gambians claiming to be from other countries such as Sierra Leone to increase their chances of asylum status. But the decreasing trends seem to be accurate and in line with the results from our own survey.

# **2.2 Sample and randomization**

Since international migration is a rare event, we aimed to focus on regions of higher migration prevalence, and then within these regions, focus on the demographic group most likely to migrate. Our sample was selected as follows.

# _Selection of settlements_

The Gambia is divided into five administrative regions and the capital city area of Banjul. We selected the two Eastern-most regions of the Upper River Region (URR) with a population of 240,000; and the Central River Region (CRR) with a population of 226,000. These regions are remote, largely rural, and are at a driving distance of 300 km or more from the capital city, and 450 km or more from Dakar, Senegal. These regions were chosen due to their high propensities of irregular migration and poor access to conventional sources of information about migration. According to estimates from the 2018 Gambia Labor Force Survey, URR has the highest share of irregular migrants to working population of all regions (more than 5%), while in CRR about 3% of the population are irregular migrants. Most people work in agriculture, with limited alternative opportunities, making migration to Europe appear particularly attractive. Using estimated population sizes projected from the 2013 Gambian census, we identified settlements that were predicted to have at least 35 males aged between 18 and 30, and that had total population sizes 11
8 The quantities are measured in pieces and the prices are in US dollars per piece. The sources of these data and details on how they were created are available from the authors on reque;.t.

# 3. **Are** Price Differences Wholly **ue** to Quality Differences?

Krishna, Erzan, and Tan (1991) established that there was a gap between the US price of apparel and the license-inclusive Hong Kong price. We further showed that this price differential did not seem to be the inadvertent result of data aggregation.

Another obvious explanation for the price difference 'etween US and Hong Kong produced apparel is that the quality of the two products is not the same. For example, the price of US-made skirts may be higher than the price of skirts imported from Hong Kong not because of rent sharing but because domestically produced skirts are of a higher quality. Unfortunately, we did not have measures of the quality of US apparel and the quality of Hong Kong apparel so we could not include the quality difference as an independent variable. However, we made the simplifying assumption that the US-Hong Kong quality differential was fixed over time, although it could vary across apparel group. Then we incorporated these fixed quality effects by introducing them as apparel group specific dummy variables.

Our estimation procedure is related to the earlier regression in Krishna, Erzan, and Tan (199i). In that regression, we ran tha adjusted Hong Kong price on a constant, nine apparel group dummies, the US price, the quota levei, the quota utilization ratio, and the numbers equivalent of the Herfindahl index, which measures concentration in the license holdings and proxies for market power in the license market: _, =a+ a,D1_ + _... + + iP us .9D._ + _yH1 c_ + _WUit + TV1c_
education, and then those with only primary education. It could be that local residents with college degrees are treated well and have fewer reasons to initiate a grievance. Employees with skills and education also may quickly find new employment and may not bother with the time and expense required to pursue a dispute to its final resolution.

# **4. Implications for China’s workers**

**_Employment_** . One of the main concerns about the Labor Contract Law was that it would raise labor costs, which would reduce employment. The 2009 firm survey asked managers direct questions about whether the new Law had increased labor costs, and whether the Law had affected hiring and firing decisions. Only 20.6% said there was no increase in labor costs, while 68.2% said there had been some increase and 11.2% said that there had been a significant increase. Table 10 summarizes the responses to the questions about actual hiring and firing decisions. Just over one third (34.5%) of managers reported that the new Labor Contract Law had made it more difficult for their firms to hire and fire workers. Interestingly, more managers reported that the Law had reduced firing (30.8%) than hiring (15.8%). Table 10 also shows that share of firms reporting that the new Law had an impact on hiring and firing decisions that varied considerably among different ownership types and across different regions. The impacts were greatest for foreign-invested enterprises (38.3%), followed by joint ownership firms (35.4%), private firms (31.6%), and state-owned or collective firms (28.1%). With respect to provinces, the impacts were greatest in Zhejiang (46.5%) and Guangdong (45.5%) and least for Shandong (21.5%), Hubei (21.4%), and Sichuan (20.4%).

Analyzing the same firm data, an econometric study of the impact of the new Labor Law on employment by manufacturing firms finds that cities with lax prior enforcement of labor regulations experienced a greater increase in enforcement after 2008 and slower 22
Three patterns are evident in Figure 4. First, for the afternoon period observed by Sentinel-5P (2 PM - 3 PM) the NO2 cloud produced by vehicles centered in the core area of Dar (Figure 3) is consistently displaced to neighboring areas. Second, the displaced cloud seems to follow an annual cycle, from northwest of the city in August-September 2018, to due west in October 2018, then shifting progressively southwestward through March 2019, and finally shifting to the area north of the city during April - May 2019. The third evident pattern is variation in the intensity of the pollution cloud. Particularly notable are the light NO2 concentrations in January, April and May 2019.

Our traffic volume data from Google Traffic are limited to the period from late March to late May, so we cannot test whether longer-term fluctuation in traffic volume is a significant contributing factor in this context. However, we have complete daily weather data for the study period.<sup>2</sup> Table 2 reports results from a linear regression fitted to Sentinel-5P data for 288 daily observations from August 2018 to May 2019. The dependent variable is mean daily NO2. We have excluded rainfall from the estimation because prior experimentation revealed that is insignificant when included with the other factors. We find that temperature, wind speed and humidity all have negative impacts on NO2 concentration with high levels of statistical significance. We have also included cloud cover, on the expectation that Sentinel-5P NO2 measurement would be reduced on cloudier days. Our result for cloud cover is highly significant and consistent with this expectation.

Figure 5 aggregates daily NO2 predictions from the regression model into monthly means. The figure illustrates the striking seasonal effects of the weather variables: Weather-predicted NO2 drops sharply from December 2018 to January 2019, increases through March 2019, and then drops even more sharply in April and May 2019. Inspection of Figure 4 reveals the strength of this seasonal pattern at regional scale, with a notably smaller, weaker NO2 cloud in January 2019 and virtual disappearance of the cloud in April and May 2019.

We also consider the effect of prevailing winds on spatial NO2 dynamics. First, we calculate the mean wind direction angle for each month. Then, for each grid cell, we construct monthly onekilometer-wide elongated rectangular strips aligned with the wind direction that extend upwind to cover all cells where traffic volume has been measured. We calculate mean traffic volumes for the strips, as well as weighted mean distances of strip cells to the downwind cell where pollution is measured by Sentinel-5P. We use distance weights that are proportional to traffic volumes.

> 2 We obtained daily weather data from World Weather Online (https://www.worldweatheronline.com).

10
This is because all the poorest receive transfers with universal targeting, while some of them do not with CBT (although those who do receive higher amounts). Finally, PMT is only slightly above universal targeting in terms of poverty-severity reduction – by 0.23 to 0.73 points.<sup>27</sup>

# _Performance in reaching the most food-insecure households_

When considering food insecurity as a well-being metric using measures which are not distributionsensitive, the median PMT and CBT targeting schemes perform similarly to a random allocation of benefits (Table 3). The median CBT and PMT programs provide, respectively, 5% and 2% more resources to the most food insecure households than would a random allocation. There are however notable exceptions in two databases, where both PMT and CBT targeting schemes make a significant difference. These are the two data sets with the lowest CBT selection rates. In Burkina Faso 2, the median CBT and PMT program provide 83% and 38% more resources to the food insecure, respectively. The opposite result is found in Senegal 1, where the CBT and PMT programs provide 13% and 27% more resources to the food insecure, respectively. This opposite result may be a result of several factors, including the scope of geographical targeting and the inequality levels.

Turning to distribution-sensitive measures based on the FCS, the difference between CBT and PMT appears negligeable (Table 5). PMT targeting schemes outperform CBT schemes in all cases but one, according to the simulated reduction in the gap and the severity of food insecurity, but magnitudes are very small. Moreover, in a majority of cases (Chad, Niger 1, Niger 2, Senegal 2), a universal allocation of benefits performs better than alternative targeting schemes based on both the simulated reduction of the gap and the severity of food insecurity. For example, in Niger 2, where the difference is the largest, the FCS severity decrease for PMT is 1.08 versus 1.50 for a universal allocation of benefits.

# _PMT performance and program coverage_

In this sub-section, we assess the performance of PMT targeting schemes based on the same selection thresholds (or program coverage) across databases with two objectives in mind. The first objective is to compare the performance across PMT schemes, net of threshold effects. The second objective is to systematically explore the role of thresholds in the performance of targeting. We achieve these objectives by comparing the performance of targeting methods based on a range of harmonized selection thresholds.

> 27 In the Addendum, available upon request, we also compare PMT selection with geographic selection, i.e. selecting all households in villages with the lowest, average PMT scores (Figure A2). Geographic selection performs relatively well in some data sets and for some selection rates, and achieves error rates that range generally between PMT and random selection error rates. However, given that the programs studied are generally implemented in homogenous areas _after_ some geographical targeting has occurred, the potential of geographical targeting tested here is relatively low.

17