rafmacalaba/datause-encoder-data huggingface dataset:
However, the sample for whom we had phone numbers is a non-random subset of the treatment group. Among those we found, those for whom we had a phone number are more likely to be a migrant. In addition, the assumption that we only did phone-based tracking throws away 55 percent of the data. To incorporate the sample that was found in the field, we apply the Behaghel at al intuition to construct another truncating instrument. Finding applicants is more difficult in highly populated unions. In our data, finding an increase in union population by 1 percent is associated with a fall in finding rate of 6 percentage points. Hence, we construct a truncating instrument which is defined as the number of phone call attempts for those for whom we had a phone numbers and the population decile of the union for those for whom we searched in the field. We qualitatively rank the phone attempts higher (low-effort) than population decile to reflect higher effort of finding someone in the field.

The BCGL bounds with this truncating instrument are presented in columns 4 and 5 of **Appendix Table 10** . With this approach, the treated group is truncated at the top decile of population if they were not found through phone-based tracking. This results in tighter bounds for most of our estimates with bounds significantly different from zero for our key results.

However, the BCGL bounds have some limitations in our context. Migration status, and treatment effect, differ by whether the respondent had a phone number. The BCGL, for example, constructs the bounds by comparing the control group with the treatment group that lives in lesser populated unions. This may introduce some bias or, at the least, change the interpretation of the impact.

Lastly, we resort to a pragmatic approach to characterize the likelihood of large bias due to differential finding rates. We assume that the higher finding rate of the treatment group is due to the artifact of winning the lottery and not some underlying characteristics that could directly affect the outcomes. With this assumption, we conduct 1 million monte-carlo simulations where we remove a random subset of the treatment group in order to match the finding rate and estimate the ITT on each of the samples. Column 6 of **Appendix Table 10** reports the proportion of the simulations in which we fail to reject the null of no effect. As seen in the table, for outcomes on migration, labor and income measures, per-capita consumption, and female-decision making, we do not fail to reject the null in any of the simulations. Even for other key outcomes, the proportion is fairly low. This shows that, for the key outcomes, any biases due to differential finding rates are extremely unlikely.

49
Estrades, Maliszewska, Osorio-Rodarte, and Seara E Pereira. (2021) Figure 23 Trade: Difference change of productivity kick scenario relative to business-as-usual (in million US dollars, 2035)

|Electrical equipment and machinery||
|---|---|
|Wearing apparel and leather||
|Food and beverages||
|Textiles||
|Chemical, rubber, plastics||
|Metals||
|Other manufactures||
|Crops||
|Wood and paper products||
|Motor vehicles and parts||
|Other transport equipment||
|Trade||
|Business services||
|Communications||
|Public administration and services||
|Air transport||
|Tourist services||
|Financial services and insurance||
|Petroleum, coal products||
|Road transport||
|Water transport||
|Non metallic minerals||
|Animal products||
|Basic pharmaceuticals||
|Meat products||
|Other extraction||
|Construction||
|Utilities||
|Fossil fuels||
|-5000|0<br>5000<br>10000<br>15000<br>20000<br>25000|
|Exports to RCEP members<br>Impo|rts from RCEP members<br>Exports to ROW<br>Imports from ROW|

Source: World Bank staff calculations using ENVISAGE model The RCEP induces faster structural change within Vietnam. While most of the sectors expand their production, some will contract compared to the baseline, as a consequence of a reallocation of resources away from less competitive sectors. In the productivity kick scenario (Figure 24), it is manufacturing (8.7 percent), namely wearing apparel (14.7 percent), electrical equipment (12 percent), and textiles (9 percent) that see the most gains. This increase in production is the result of a higher demand for exports from those sectors, where in the case of electrical equipment and textiles, the increase comes mostly within RCEP member countries. For wearing apparel, the increase is due to the rest of the world. Production contracts in basic pharmaceuticals (-1.6 percent) and wood products (-0.5 percent). In the scenario where only tariffs are reduced, the impact of the RCEP in total production is negligible, with sectors such as wearing apparel and textiles declining (-2.1 percent and -1.3 percent, respectively) relative to the baseline scenario, suggesting that small gains from improved market access in member markets outweigh potential losses from declining protection of select domestic industries.

29
Several additional covariates were also included to control for other sources of variation in dam counts. Table 3 reports summary statistics for the variables over the observations we are able to include in our analysis. It also divides observations by our principal explanatory variable, the presence of at least one foreign country downstream of the subbasin. The size of each subbasin-country area (in km<sup>2</sup> ) addresses the likelihood that larger areas will contain more dams. As Table 3 reports, areas upstream of an international border have more dams, and more dams of all the subtypes, than other areas. We also include the number of downstream subbasins, regardless of whether they are in the same country or a different one. The likelihood of finding a different downstream country is higher for areas that are further upstream. If dams are simply more likely to be located either upstream or downstream in a basin, such a tendency might otherwise confound the indicator we use for shared resources. As Table 3 reports, areas with a downstream country have on average twice as many downstream subbasins as other areas, but the two groups of observations do overlap substantially in this variable.

We also include several measures of the physical suitability and need for dams in the subbasincountry area. We control for slope, since areas with higher slope present better opportunities for dam construction. The Compound Topographic Index (CTI), a function of the slope and the upstream area contributing to flow, is a time-invariant wetness index that is highly correlated with soil moisture and might measure demand for irrigation water.<sup>13</sup> Both slope and the wetness index are available from HYDRO1K; the variables used in the analysis are the averages over the 6-digit subbasins in each of the 3- digit Pfafstetter subbasins.<sup>14</sup> As another measure of demand for dam services, we calculated 13 Specifically, CTI=ln (flow accumulation / tan(slope)). If the slope is equal to zero, the formula uses slope = .001. In our data, the index ranges from a minimum of about 3, to a maximum of about 11.

14 In previous versions of the equations, we also included local historical precipitation, calculated using gridded data on precipitation from 1950 through 2000 (Fekete _et al_ . 2002). The estimated coefficients on local historical 9
Policy Research Working Paper 8130

# **Abstract**

Power shortages present a significant challenge to manufacturers, who rely on power as a key input to production. In Pakistan, power shortages are commonplace, but empirical evidence on the impact of shortages is still lacking. Using a survey of 4,500 manufacturing firms for the year 2010–11, this paper estimates the impact of electricity shortages on firm productivity in Pakistan. The analysis finds that a 10 percent increase in the duration of outages on average leads to a 0.14 percent decrease in a firm’s total revenue and a 0.36 percent decrease in the value added, all else being equal. There is heterogeneity in the impacts of shortages across sectors: the industries that are most energy-intensive, such as manufacturers of metal, wood, and paper, are affected the most severely by shortages.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **E. Revenue and expenditure volatility**

# **F. Risk of debt distress**

<!-- Start of picture text --> Standard deviation Percent of countries<br>8 In debt distress High risk<br>60 Moderate risk Low risk<br>6<br>50<br>4 40<br>30<br>2<br>20<br>10<br>0<br>Small EMDEs Small EMDEs 0<br>states excl. small states excl. small 2015 2023 2015 2023<br>states states<br>Small states EMDEs excl. small<br>Expenditure Revenue states<br><!-- End of picture text --> _Sources:_ EM-DAT (database); International Monetary Fund; WDI (database); WEO (database); World Bank.

_Note:_ EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean.

A. GDP weighted average annual GDP per capita growth for groups of countries. Sample includes 34 small states, 111 other EMDEs and 37 advanced economies. Guyana is excluded.

B. Frequency is the average number of natural disasters per year, adjusted by land mass, for the period 2000-22. Unit of frequency is the number of natural disasters per year per one hundred thousand square kilometers. Average cost of natural disasters per year as percent of GDP for 24 small states and 93 other EMDEs. EAP small states include 8 economies, LAC small states include 11 economies, and other small states include 5 economies. Natural disasters include droughts, storms, floods, extreme temperatures, earthquakes, volcanic activity, and wildfires.

C. Average ratio of trade (exports plus imports of goods and services) to GDP, 2000-22, for 27 small states and 114 other EMDEs. Whiskers indicate interquartile range.

D. Standard deviation of annual real GDP growth in 34 small states and 115 other EMDEs for the period 2000-23.

E. Standard deviation of annual change in revenue and expenditure to GDP ratios in 35 small states and up to 117 other EMDEs for the period 2000-23.

F. Shares of small states and other EMDEs in overall debt distress or at risk of debt distress, based on the published Joint World Bank-International Monetary Fund Debt Sustainability Framework for LowIncome Countries as of March 30, 2024. Sample includes up to 19 small states and 48 other EMDEs.

28
9

**Galal** & Nauriyal

Whatever the interpretation, the bulk of privatization occurred in the telecommunications (60 percent) and power (27 percent) sectors (table 1). In contrast, privatization was limited in the railroads, roads, ports and water sectors. We speculate that this phenomenon is due in part to labor intensity, for example, in railroads and ports, where labor opposition may have prevented privatization from taking hold. We also speculate that the limited privatization in water is due in part to the fact that tht provision oL water typically involves a high subsidy, which governments find difficult to give to the private sector on political grounds.

Within the telecommunications sector, we identified 29 developing countries which shifted from public to private ownership of basic and/or value added telecom services between 1989 and 1993. More countries may have followed suit since. From this universe, we selected all the seven countries with private sector participation in basic telecom services. Although the sample is small and not random, table 2 illustrates that these seven countries are diverse in their level of economic development as measured by their real per capita GNP, rate of economic growth, initial (1981) level of development of the telecom sector, the timing of the regulatory reform and the extent of divestiture.

**Table 2. Sample countries with private sector participation in telecom**

|Country|Year of<br>Regulatory<br>Reform'|Share of<br>Private<br>Sector<br>_(%,_1993)|GNP<br>Per<br>Capita<br>_($,_1981)|GDP<br>growth<br>rate<br>(1981-92)|Years of<br>Waiting Time<br>For Phoneb<br>|Teledensity<br>(lines per 100<br>people, 1981)|
|---|---|---|---|---|---|---|
|Argentina|1990|100|3442|1.4|4.1|7.7|
|Chile|1987|100|1995|4.5|5.7|3.4|
|Jamaica|1988|100|1242|1.9|9.0|2.6|
|Malaysia|1987|25|2096|6.3|1.6|3.6|
|Mexico|1990|100|2510|1.4|4.9|4.4|
|Philippines|1986|100|669|1.2|14.7|0.9|
|Venezuela|1991|40|3647|2.5|2.5|5.6|

a. Prior reforms were undertaken in Chile (1978, 1982) and Jamaica (1982); additional reforms were undertaken in Malaysia in 1990. With the exception of Philippines, where the telecom sector has been privately owned for decades, and Malaysia, this is also the year of privatization.

b. As of 1987 for Argentina and 1986 for Jamaica. Calculated as a ratio of the number of applicants on waiting list to the average number of main lines added over the last three years.

Sources: World Development Report 1994, International Telecommunications Union, and Author's search.
Table 19. Between-group differences in entry salary: **1990-97** cohort, with and without U.S. men

||1|990-97 with U.S. M|en|199|0-97 without U.S.|Men|
|---|---|---|---|---|---|---|
||Total<br>Difference|Characteristics|Structure|Total<br>Difference|Characteristics|Stiucture|
|**MI-M2**|8.75|3.13|5.62|8.93|4.63|4.30|
|**Ml-Fl**|10.29|6.27|4.02|10.46|6.67|3.80|
|M1-F2|15.34|9.02|6.32|_15.51_|10.10|5.41|
|M2-Fl|1.54|3.14|-1.60|1.54|2.04|_-0.50_|
|M2-F2|6.59|5.89|0.70|6.59|5.48|1.12|
|F1-F2|_5.05_|2.75|2.29|_5.05_|3.43|1.61|

46
# **2.5 Parental Support at School**

The publication of the bestselling book [Chua, 2011] “Battle Hymn of the Tiger Mother” in 2011 ignited a firestorm of controversy. The book gave prominence in popular culture to a vast academic literature regarding parenting styles and the perceived higher performance of children from Asian immigrant families in the US and other Western countries. One of the ways that parents influence their children’s educational outcome is through the interaction that parents have with their child’s teachers and others at school. The PISA data includes a question that tries to examine parental expectations towards schools. The question SC24 includes a statement “There is _constant pressure_ from many parents, who expect our school to set very high academic standards and to have our students achieve them.”<sup>6</sup> Table 5 indicates a higher level of PARPRESSURE (an index derived from SC24) for Vietnam, compared to Dev7. Another question (SC25) asks school principals about the proportion of parents that take part in a set of 12 activities. While the question does not specify which parent (or both) may be involved, the variables, that may contain more than one of these activities, have been named after the mother for ease of exposition.

**Table 5: Parental Support at School**

|||Dev7 co|untries|Vietn|am|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|PARPRESSURE|Parental achievement<br>pressure|0.2665<br>(0.4421)|40372|0.3837<br>(0.4863)|4866|
|TIGERMOM|Parent initiates -<br>progress discussion|52.4472<br>(38.097)|41394|62.4183<br>(41.3743)|4882|
|DUTYMOM|Teacher initiates -<br>progress discussion|66.9737<br>(36.727)|41394|68.5543<br>(37.4796)|4882|
|VOLUMOM|Parent Participation -<br>Volunteering|35.2134<br>(38.8428)|41394|38.3623<br>(39.9773)|4882|
|TEACHMOM|Parent Participation -|12.1764|41394|38.2821|4882|
||Teaching Assistance|(23.4241)||(41.5357)||
|FUNDMOM|Parent Participation -<br>Fundraising|23.0784<br>(35.2134)|41394|59.6022<br>(44.0376)|4882|
|COUNCILMOM|Parent Participation -<br>School government|36.4546<br>(37.2252)|41394|23.1174<br>(36.4406)|4882|

Notes: The variables relate to the questionnaires administered to schools. For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically different at the 95% significance level. Figures in parenthesis represent standard deviations.

TIGERMOM refers to the reported proportion of parents who discussed their child’s behavior or the child’s progress “on their own initiative”, to differentiate from cases where parents might have done so following the initiative of the teacher, termed as DUTYMOM.

> 6[Hsin and Xie, 2014] investigate in great detail data from a set of longitudinal surveys that cover thousands of children over a long period of time starting from their early childhood through high school. As part of the explanation of the superior performance of Asian immigrant children, the authors report that “Asian students report greater parental expectations of academic success.”

12
53

# **Table 3a**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Information and Communications Technologies (ICT) Pillar Indicators**

|**Years : 1960 - 2000**|**Reg 3.1**|**Reg 3.2**|**Reg 3.3**|**Reg 3.4**|
|---|---|---|---|---|
|**Growth of Capital**<br>**per worker**|**0.2293*****<br>**(0.0507)**|**0.2298*****<br>**(0.0419)**|**0.2360*****<br>**(0.0444)**|**0.2119*****<br>**(0.0424)**|
|**Initial GDP (1960)**|**-3.5813*****<br>**(0.8771)**|**-2.9514*****<br>**(0.8905)**|**-2.9244*****<br>**(0.8668)**|**-3.7103*****<br>**(0.8717)**|
|**HC15**|**3.9910*****<br>**(1.4484)**|**5.2399****<br>**(2.0078)**|**5.7432*****<br>**(2.0243)**|**4.4661****<br>**(1.9854)**|
|**Sq of HC15**|**-1.0697*****<br>**(0.3554)**|**-1.2780****<br>**(0.4985)**|**-1.3677*****<br>**(0.4947)**|**-1.0965****<br>**(0.4726)**|
|**Log (Educational**<br>**Quality)**|**0.5363**<br>**(0.5569)**|**0.4788**<br>**(0.0633)**|**0.6516**<br>**(0.5336)**|**0.3719**<br>**(0.5412)**|
|**Log (Patents)**|**0.2474*****<br>**(0.0640)**|**0.2586*****<br>**(0.0633)**|**0.2636*****<br>**(0.0649)**|**0.1956*****<br>**(0.0701)**|
|**Computers**<br>**Internet Users**|**0.0086*****<br>**(0.0024)**|**0.0084*****<br>**(0.0024)**|||
|**Hosts**|||**0.0017*****<br>**(0.0006)**||
|**Phones**||||**0.0040*****<br>**(0.0011)**|
|**Constant**|**-4.4327****<br>**(1.8692)**|**-5.7535*****<br>**(2.0604)**|**-6.8771*****<br>**(2.0484)**|**-4.5304****<br>**(2.2424)**|
|**R squared**<br>|**0.6165**<br>|**0.5779**<br>|**0.5687**<br>|**0.5912**<br>|
|**Number of Countries**<br>*, **, *** denotes significance at th<br>Robust standard errors obtained u|**81**<br>e 90, 95 and 99 percen<br>singWhite's correction|**85**<br>t confidence level respec<br>for heteroscedasticity.|**85**<br>tively|**85**|
fixed effects, likely due to its nature of being a stock variable.

Taking the estimates from the last column as the preferred results, the estimates suggest that (1) a subsidy of ¥10,000 increases EV sales by nearly 16% ; (2) an increase of 1,000 charging ports increases EV sales by 20%; (3) the exempt from driving restriction does not seem to impact EV sales; and (4) the green plate policy increases EV sales by nearly 37%. The coefficient estimate on the price variable implies the own-price elasticity of -2.5 for a vehicle at the average price of ¥158k. The implied price elasticity is in line with the estimates from recent studies on EVs in other countries. For the US EV market, Li et al. (2017) estimates a price elasticity of -1.3 based on the initial stage of EV sales from 2011-2013 using a similar empirical framework. Xing et al. (2019) provides an elasticity estimate of -2.7 using a rich random coefficient discrete choice model with the second-choice data from US consumer surveys. Based on California data, Muehlegger and Rapson (2019) reports a price elasticity estimate from -3.1 to -3.9 among households with an annual income less than $100k. Springel (2019) estimates the price elasticities for EVs to be between -1 to -1.5 in the Norwegian EV market. In the policy analysis section below, we further discuss the implications of our parameter estimates and compare our findings with those in the literature.

# **4.2 Alternative Specifications**

We estimate several alternative model specifications to examine the robustness of our findings. First, we replace consumer price in Equation (1) with its logarithm, leading to a log-linear specification. The semi-log specification estimated above implies the price elasticity being larger in magnitude for more expensive vehicles while the log-linear specification implies a constant price elasticity across products. The results are presented in Table 5 with the eight columns corresponding to those in Table 4. The coefficient estimate on log(consumer price) gives the price elasticity and it is -2.36 in column (8), very close to the average price elasticity of -2.5 from the semi-log specification. The parameter estimates on the number of charging ports and policy variables are very similar to those in Table 4.

In both Tables 4 and 5, the dependent variable is log(sales) as specified in Equation (1). There are 13% of observations with zero sales. The observations concentrate among less popular models and in small cities. We added 0.5 to the sales for these observations in order to keep them in the sample instead of dropping them as these observations could contain useful information for consumer demand. For example, if zero sales are more likely to occur in cities with less generous policy incentives, it would help us identify the effects of the policies. To examine the 22