rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6: Effect of the Islamic Inheritance Rule on Fertility Preferences and Outcomes of Couples who Own and do not Own Housing**

||(1)<br>'|(2)<br>'|(3)<br>Dependent Vari<br>'|(4)<br>able:<br>'|(5)<br>|
|---|---|---|---|---|---|
||Womens<br>Ideal<br>Proportion<br>of Sons|Mens<br>Ideal<br>Proportion<br>of Sons|Womens<br>Ideal Total<br>Number of<br>Children|Mens<br>Ideal Total<br>Number of<br>Children|Actual<br>Number of<br>Children<br>Ever Born|
|_Panel A: Sample of couples who do not_|_own housing_|||||
|Muslim* At least one brother at|0.15|0.077|-0.65|-1.33|-0.64|
|childbearing|(0.084)|(0.14)|(0.83)|(1.04)|(1.33)|
|Muslim * At least one brother at|-0.093|-0.47*|3.81*|-4.06|-4.97|
|childbearing * Childbearing after 1994|(0.31)|(0.20)|(1.63)|(2.85)|(3.07)|
|Mean, dependent variable|0.42|0.33|3.19|3.52|3.77|
|R-squared|0.33|0.44|0.47|0.47|0.47|
|Observations|249|258|249|258|296|
|_Panel B: Sample of couples who own ho_|_using_|||||
|Muslim* At least one brother at|0.17**|0.13*|-0.030|0.77|0.038|
|childbearing|(0.063)|(0.061)|(0.45)|(0.52)|(0.62)|
|Muslim * At least one brother at|-0.73**|-0.92***|1.61|-5.23***|-3.38*|
|childbearing * Childbearing after 1994|(0.24)|(0.26)|(0.97)|(0.95)|(1.48)|
|Mean, dependent variable|0.39|0.36|3.05|3.20|3.79|
|R-squared|0.11|0.14|0.25|0.22|0.27|
|Observations|1358|1405|1358|1407|1626|
|Sample|Wife age|Wife age|Wife age|Wife age|Wife age|
||40 to 49|40 to 49|40 to 49|40 to 49|40 to 49|

* p<0.05,  ** p<0.01,  *** p<0.001.

Note: OLS estimates of interactions between indicators of Muslim religion, having at least one surviving brother at the time of first child birth, and starting childbearing in the post-compilation period. Parentheses contain standard errors clustered at the PSU-level. Regressions use survey weights. Baseline sample excludes mixed-religion and polygamous couples. Columns (1) to (3) report estimated coefficients for a subsample of couples in which women are 40 to 49 years old. All regressions include fixed effects for the men’s total number of siblings, women’s age, the year of first child birth, and area and region of residence. Other controls included are men and women’s educational attainment and indicator of household wealth. Data summary is provided in Appendix 1.

37
**Table A.2:** Effect of drought on welfare outcomes, Piecewise regression with 5 knots

||(1)<br>Total C|(2)<br>Non-Food C|(3)<br>Food C|(4)<br>Poor|(5)<br>Food Poor|
|---|---|---|---|---|---|
|NDVI Z-score 2018 (.,-2)|0.283|0.813<sup>_∗∗_</sup>|0.028|-0.141|-0.094|
||(0.197)|(0.370)|(0.176)|(0.172)|(0.185)|
|NDVI Z-score 2018 (-2,-1)|0.124<sup>_∗_</sup>|0.337<sup>_∗∗∗_</sup>|0.057|-0.029|0.03 7|
||(0.073)|(0.126)|(0.073)|(0.066)|(0.057)|
|NDVI Z-score 2018 (-1,0)|0.030|0.051|0.024|-0.061<sup>_∗∗_</sup>|-0.047|
||(0.035)|(0.053)|(0.041)|(0.030)|(0.029)|
|NDVI Z-score 2018 (0,1)|0.017|0.081|-0.042|-0.019|0.017|
||(0.038)|(0.060)|(0.040)|(0.032)|(0.032)|
|NDVI Z-score 2018 (1,2)|0.091|0.008|0.187<sup>_∗∗_</sup>|-0.097|-0.127<sup>_∗∗_</sup>|
||(0.074)|(0.107)|(0.073)|(0.063)|(0.063)|
|NDVI Z-score 2018 (2,.)|-0.065|-0.058|-0.074|0.053|0.162<sup>_∗_</sup>|
||(0.106)|(0.149)|(0.080)|(0.065)|(0.085)|
|Observations|14183|14183|14183|14183|14183|
|R-squared|0.340|0.463|0.240|0.146|0.149|
|Mean Dep var|7.751|6.648|7.229|0.414|0.455|
|Household Controls|✓|✓|✓|✓|✓>|
|Province FE|✓|✓|✓|✓|✓>|

42
Figure 1. Conceptual Framework of the Extended Cost-Benefit Analysis

|**=**<br>**Net**<br>**Income**|**Change in**<br>**tobacco**|+<br>**Change in**<br>**medical**|**Change in years**<br>**of productive**<br>+|
|---|---|---|---|
|**Effect**|**expenditure**|**expenses**|**life lost**|
||(A)|(B)|(C)|

In addition to this more comprehensive understanding of the costs and benefits of reducing the medical burden of smoking, the ECBA methodology allows the various behavioral responses to the tax shock to be taken into account. The stylized finding in the literature suggests that households in lower-income countries and lower-income groups may be more responsive to tobacco price changes (see above). Decile-specific elasticities of demand for tobacco allow the heterogeneity in the sensitivity to price changes to be taken into account and the distributional impact of various price shock scenarios to be estimated. For further methodological details, see annex A or refer to Fuchs and Meneses (2017a).

# **4. Data Sources and Descriptive Statistics**

## **a. Household expenditures and tobacco consumption**

Data on household expenditures and tobacco consumption are taken from national household budget surveys. If available, surveys with nationally representative data for 2016 have been used. In case of data limitations, the most recent data sets have been used; a 2014 survey in South Africa is the oldest survey that has been used. See annex A on the specific sources, variable definitions, and data limitations in each country.

Table 1 summarizes the most relevant indicators for each country in the sample. Indonesia, followed by Ukraine and Russia, has the largest share of smokers in the sample. Indonesian households spend the largest share of their budgets on tobacco across all income groups. In terms of distribution, the richest households in Moldova and the middle class in Bangladesh, Indonesia, and Ukraine smoke the most among households that purchase cigarettes. In South Africa, the top decile exhibits the greatest probability of smoking, though these households allocate only a small share of their consumption expenditures to purchase tobacco relative to lower-income households. Poorer households allocate higher shares of their budgets to the purchase of cigarettes. This negative relationship between income level and the share of tobacco in household budgets is monotonic only in Chile and Ukraine. Except for Moldova, the richest 10 percent of the population consistently allocates the smallest share of their consumption expenditures to tobacco.

Page 8
**Table 3: Antitrust and Profits.** The regression left-hand side variable is the estimated change in the operating profit margin after a decision by the Commission to either impose a sanction or close the case. Operating profit margin is value added (sales minus the value of intermediate inputs) minus the wage bill, divided by sales. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

|Change in (the percentage point)|(1)<br>operating<br>proft<br>margin|(2)<br>operating<br>proft<br>margin|(3)<br>operating<br>proft<br>margin|(4)<br>operating<br>proft<br>margin|(5)<br>operating<br>proft<br>margin|
|---|---|---|---|---|---|
|Financial sanction (=1)|-0.030|-0.539|-3.749***|-5.069***|-0.677|
||(0.618)|(0.628)|(0.652)|(0.531)|(2.028)|
|Constant|1.429***|1.604***|8.127***|1.700***|1.630|
||(0.474)|(0.333)|(0.394)|(0.326)|(1.313)|
|_γ_<sup>2</sup>|||||56.16|
|Number of cases|130|90|90|90|90|
|Meta analysis framework|FE|FE|FE|FE|RE|
|Outcome change estimator|FD|SDID|DID|SC|SDID|
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

# Structure of nonresidential water tariffs

The average industrial price for the first block is $4.5/m<sup>3</sup> ; the commercial price is about $3.7/m<sup>3</sup> ; and the government and institutional price is about $3.9/m<sup>3</sup> , with a median commercial tariff of around $0.75/m<sup>3</sup> . These prices

**Table 2. Structure of unmetered tariffs**

|Utility|Country|Type of tariff|Fixed charge<br>($)|Connection fee<br>($)|
|---|---|---|---|---|
|GWCL|Ghana|Fixed charge|3.11|235.29|
|FCT WB|Nigeria|Fixed charge|3.14|15.68|
|Kaduna WB|Nigeria|Fixed charge|10.59|55.41|
|Katsina WB|Nigeria|Fixed charge|5.45|n.a.|
|SWSC|Zambia|Fixed charge|12.16|50.00|
|LWSC|Zambia|Fixed charge|7.84|n.a.|
|NWSC|Zambia|Fixed charge|6.53|235.29|

_Source:_ AICD WSS Survey Database, 2007. n.a. = not <u>applicable.</u> are driven by utilities that charge exorbitant tariffs, such as those in Mozambique, Oshakati, and Dodoma. In Mozambique, for instance, minimum consumption of up to 25 m<sup>3</sup> is enforced, at a price of about $16. The effective cost is reduced to about $4/m<sup>3</sup> only when nonresidential consumers use about 100 m<sup>3</sup> , an average consumption level for industrial and commercial customers. About 17 utilities enforce a linear commercial tariff, and the rest have adopted an IBT structure. About 21 utilities levy the same charge on industrial, commercial, and government consumers (annex E).

# Structure of wastewater tariffs

The majority of Africans use on-site sanitation to meet their basic needs, as sewerage facilities are very limited. The responsibility of providing sewerage services may rest with water utilities or the government. Only in Senegal does a separate sanitation utility (ONAS) provide these facilities to the population. Of the 45 utilities in the sample, 18 do not have any wastewater responsibility. The structure of payment for sanitation varies; it can either be part of the water bill, calculated as a percentage, or it can be a block or fixed-tariff structure. In the case of eight utilities, the sanitation charge is levied as part of the water bill, which can range from 30 percent in Zambia to 85 percent in Lesotho, with an average of 53 percent. Oshakati enforces a fixed charge, and Walvis Bay levies both a fixed charge and a block tariff structure (figure 3 and annex E).

**Figure 3. Wastewater tariff structures**

<!-- Start of picture text --> 8<br>8<br>7<br>6 5<br>5<br>4<br>3<br>2 1 1<br>1<br>0<br>Number of Utilities<br><!-- End of picture text --> <!-- Start of picture text --> Wastewater tariff part of water bill<br>Fixed fee<br>Block tariff<br>Fixed fee + Block tariff<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007.

7
2014; Lewis and Severnini, 2014). Access to electricity can also spur technology adoption among local (cottage) industries thereby boosting productivity and possible spillover effects on employment and wages (Fried and Lagakos, 2021).

Further, electrification like most technology shocks offer opportunities for the creation of new businesses and induce structural change (Fried and Lagakos, 2021). Access to electricity can foster the creation of new jobs as it spurs prospective entrepreneurs to take advantage of the enabling conditions the infrastructure provision offers. For instance in many developing economies where the informal sector dominates, access to electricity can enable households to set up small firms that produce intermediate or final goods (and services) for the market. Additionally, appliance use and modernization of agriculture and local industries through the use of electricity can shift employment into skilled non-agricultural employment, thereby reducing the share of labor employed in agriculture. A booming non-agricultural sector increases demand for labor and thus reduces out-migration in electrified communities, while attracting labor from neighboring localities to participate in the booming economy (Fried and Lagakos, 2021). Implicit in the above is the assumption that electricity services are stable and reliable. Meanwhile, electricity outages are pervasive in many developing countries thereby constraining the realization of the full impact of electrification. In this section, I outline three channels through which unreliable electricity provision affect job creation.

First, the quality of electricity supply can affect firm entry and exit. Persistent outages signal high production cost and uncertainties in the business climate thereby reducing the incentive(s) of potential entrepreneurs (investors) in establishing (investing in) businesses. For instance, persistent outages may reduce foreign direct investment in nonenergy intensive sectors such as manufacturing due to the associated effects of unreliable power supply on cost of doing business.<sup>8</sup> A negative effect of outages on investments into

> 8Investment in the energy sector may however increase as the outages may be associated with factors such as capacity constraints or underdevelopment of the power sector, hence the potential for higher returns on investment(s) in the sector.

8
**Table 10:  Robustness to endogeneity of quality of institutions, 2SLS IV regressions**

||Tota|l Trade|Inter|mediate|Cons|umption|
|---|---|---|---|---|---|---|
|qxQ (institutions)|First stage|Second Stage<br>0.247***<br>(9.993)|First stage|Second Stage<br>0.204***<br>(5.684)|First stage|Second Stage<br>0.358***<br>6.976|
|txT (infrastructure)|0.143***|0.167***|0.227***|0.205***|0.085***|-0.002|
||(23.481)|(11.514)|(25.084)|(8.943)|(7.288)|-0.063|
|kxK (capital)|-1.347***|0.514***|-1.382***|0.437***|-1.255***|0.515***|
||(-62.42)|(8.12)|(-44.20)|(4.738)|(-30.87)|4.097|
|hxH (human capital)|0.045***|0.044***|0.006|0.085***|0.054***|-0.026|
||(8.796)|(3.764)|(0.744)|(4.694)|(5.52)|-1.113|
|qxIV (quality of<br>institutions by legal origin)|0.662***<br>(90.41)||0.638***<br>(61.84)||0.660***<br>(46.33)||
|R-squared|0.951|0.753|0.95|0.745|0.951|0.716|
|Number of observations|27153|27153|13749|13749|7322|7322|
|Nunmber of countries||95||95||95|
|Number of industries||342||241||135|
|Widstat||8174.639||3823.676||2146.721|

Notes: The dependent variable is the natural log of exports in industry k by country i to the World. qxQ is instrumented using qxIV where the instrument (IV) is a variable that assumes the value of the average quality of institution across all countries with the same legal origin. Reported are the beta standardized coefficients of the second stage IV regression with t-values shown in brackets. * Significant at 10%; ** at 5%; *** at 1%.

# **V.  The Distance Puzzle Revisited**

Our finding that timeliness is particularly important as a determinant of trade in intermediate goods supports Harrigan and Venables (2004)'s view that fragmentation of production is a force of agglomeration and suggests that intermediate goods should be more sensitive to distance than final goods. To support our findings, in this section we also assess whether these expectations hold true using an alternative methodological approach: the standard gravity model of bilateral trade flows. In doing so, we also relate our results to the recent literature on the so-called "distance puzzle", that is the typical finding of gravity models that, contrary to expectations, the elasticity of trade flows to distance has been rising over time (Brun et al., 2005; Coe et al., 2007; Melitz, 2007; Disdier and Head, 2008). A number of explanations have been proposed as a solution to the puzzle. Melitz (2007), for example, investigates the role that the composition of trade flows in terms of intra- and inter-industry trade may have played. Our finding that trade in intermediate goods are more time sensitive than trade in final goods points to an additional effect of the composition of trade on the distance coefficient. That is, can the growing importance of vertical specialization for trade help to explain the distance puzzle?

Using a gravity model on bilateral trade flow in the period 1980-2010, we study the evolution of the distance coefficient for trade in intermediate goods and total trade. Figure 1 shows the results obtained on the basis of a standard gravity model equation estimated using exporter and importer time-varying fixed effects, distance 20
The dependent variable represents a fiscal variable of interest – including the revenues from own sources, the different components of realized expenditure and budget surplus/deficit – for district _m_ at time _t_ (2001 to 2006). By construction, the binary variable takes the value 1 if district _m_ is in the treatment group (i.e. direct elections were held in the district between June and December 2005) and equal to 0 otherwise. The binary variable takes the value 1 for post _Pilkada_ years (2006) and 0 otherwise (2001-2005), while denotes the district fixed-effect summarizing the role of all observable and unobservable variables at the district level that do not vary over time. The disturbance term summarizes the influence of all other unobserved variables that vary across districts and over time, assumed to be uncorrelated with the variables in the regression, though it is allowed to be correlated over time. In this framework, the parameter then identifies the effect of any systemic difference between the treatment and control groups of districts on the dependent variable in the pre- _Pilkada_ reference year (i.e. 2001). The parameters identify the ―year effect‖ on the dependent variable, namely the effect of any systemic changes that affected all districts in the pre- _Pilkada_ years. The parameter is the parameter of our interest since this identifies the _difference-in-differences_ (or DID) of the impact of direct election on the dependent variable.<sup>12</sup> Specifically, the DID estimate of the impact of _Pilkada_ provides an estimate of the average change in the outcome variable in the treated group from pre- _Pilkada_ (denoted by the subscript _preE_ ) to post- _Pilkada_ years (denoted by the subscript _postE_ ) relative to changes in outcome variable in the control group over the same period of time, i.e.,

# _Data_

To conduct the analysis, we assemble a large dataset, compiled from multiple sources and linked at the district level. These are regional electoral information from the government (Ministry of Home Affairs, MoHA), and regional budget data from SIKD for the period 2001 to 2006.

The district-level electoral information from the government has been compiled by the Jakarta World Bank team with the collaboration of the Ministry of Home Affairs and a number of

> 12 The specification of (1) follows closely what is suggested by the literature on the use of DID method for impact evaluation. See, for example, Imbens and Wooldridge (Imbens and Wooldridge, 2009) (2009), pp. 67-70.

14
– – September) and 633 kWh per month more than double in the winter (November to February). On average, households have 8 electricity-using durables<sup>40</sup> and many households (39 percent) report heating with electricity.<sup>41</sup> A small proportion have an electric hot water heater (14 percent), and almost none (2 percent) have air conditioners.

Households largely indicate that they both frequently worry about saving electricity (95 percent) and take measures to save electricity (86 percent). More than half the households report knowing about energy efficient lightbulbs (56 percent). However, CFLs use was low. Only a few households had them, and in small numbers (resulting in 0.17 CFLs per household, on average). The majority of households did not know or believe that CFLs consume less electricity (70 percent), did not expect savings in their electricity bill from replace incandescent bulbs with CFLs (72 percent), and did not believe the electricity savings would pay back the upfront costs of the CFLs (69 percent).<sup>42</sup>

# **4.3 Randomization balance**

Baseline characteristics were compared both at the transformer and household levels to understand the outcome of randomization. Comparisons use both household survey data and data on transformer characteristics, provided by the electricity utility.

Transformer-level balance test results are shown in Appendix Table 1. There are no statistically significant differences between the transformers treated with a low saturation and the control transformers, nor are there any statistically significant differences between the high saturation transformers and the control transformers. The high and low saturation transformers do have one significant difference from each other. They differ in the number of households within the transformers. Following Bruhn and McKenzie (2008), we chose not to re-randomize and instead control for this characteristic in related regressions and perform additional robustness checks.

Results from the household-level balance tests are shown in Appendix Table 2. The two

> 40Almost all homes have a television and refrigerator. Approximately three-quarters of households have electric stoves, an iron, and a clothes washing machine.

> 41Households conserve on heating. Most households report heating their houses at least sometimes with coal (80%), and on average they heat 3/4 of all their rooms during winter.

> 42Households were also unaware of differences in quality (88%) and in potential electricity savings (80%) between different types and brands of CFL.

15
Figure 4: Validation with SICONFI data - verification <!-- Start of picture text --> CE MG<br>4000<br>1000<br>3000<br>2000<br>500<br>1000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PB PE<br>1000 600<br>750<br>400<br>500<br>200<br>250<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PR RS<br>800 1500<br>600<br>1000<br>400<br>200 500<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>SP % difference from SICONFI data<br>6000<br>4000<br>2000<br>0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage deviation in total amount of budget verifications, at the municipality-year level, between our dataset and SICONFI, the public finance dataset of the Brazilian Treasury. Values are positive whenever the total amount in our dataset, aggregated from individual verifications, is larger than that of SICONFI. See Table 1 for more details on our data coverage. We truncate observations at -25% to the left and at 25% to the right.

24
<!-- Start of picture text --> 1970 1980 1990 2000 2010 2020<br>Year<br>1<br>0<br>China Price<br>−1<br>−2<br><!-- End of picture text --> Figure 2: Prices in China over Time Note: The figure plots the median of the filtered price of raw ivory tusks in China over time.

This pattern is in line with the theoretical predictions of a model of speculative storage of ivory (Kremer and Morcom, 2000) with a positive-storage condition that is almost never binding. The observed constant average growth rate is consistent with the Hotelling noarbitrage condition holding for ivory prices, while the sudden drop in 1993 reflects market anticipations of lower future demand.

# **4.2 Poaching data**

The Monitoring the Illegal Killing of Elephants (MIKE) Programme put in place a standardized monitoring system that relies on data from conservation area rangers in 60 sites across 30 countries dating back to 2002. “MIKE data consists of data collected by anti-poaching patrols and other sources. This patrol data includes records of elephant carcass(es) encountered – cause of death (...), and the estimated age of the carcass(es)” (Burn et al., 2011).

A commonly-used variable in the proportion of illegally killed elephants (PIKE), defined as the ratio of the number of carcasses classified as illegally killed to the total number of carcasses encountered at each site. The rationale behind using a ratio instead of say the 13