rafmacalaba/datause-encoder-data huggingface dataset:
explanators _Kh_ :<sup>33</sup> As in previous analyses, _Efile_<sup>�</sup> is instrumented by assignment to group A. For the variables _Kh_ , we use variables that are significantly correlated with the risk scores and across which the effects of e-filing adoption could plausibly be heterogeneous. We use the following variables: _operating in the trade sector_ (this sector of activity may benefit more from e-filing adoption), _declaring no employees_ (one-(wo)man businesses may have simpler taxes and benefit less from e-filing), _rayon_ (interactions with officials may vary across different tax offices) and _monthly amount of time spent on tax related activities_ (firms with high tax compliance costs may benefit more from e-filing). All variables _Kh_ are demeaned so we can still interpret _γ_ and _α_ as the impact of e-filing on low-risk and high-risk firms respectively, measured at the mean of the other variables.

Results of these estimations are presented in the Appendix Table A8 and show that our main results are robust to the introduction of these additional controls. _γ_ and _α_ coefficients have similar magnitude and statistical significance as in Table 5.

# **8 Conclusion and Policy Implications**

This paper presents some of the earliest evidence on the impact of electronic tax filing. We find that firms’ baseline likelihood of tax evasion is an important determinant of their willingness to use e-filing, as well as of the impact e-filing has on them. For firms in a

> 33 We follow the same strategy as in De Mel et al. (2009).

34
68

- Thorbecke, Erik and David Berrian (1988). "Budgetary Rules to Minimize Societal Poverty." Mimeo.

- United Nations Development Programme (1990). _Hutman Development Report 1990._ The World Bank, Washington, D.C.

- Wahba, Sadek and John Whalley (1991). "Designing a Sustainable Macro-Analytic Capacity Building Program." Mimeo, The World Bank.

- Whalley, John (1984). "h;egression or Progression: The Taxing Question of Incidence Analysis." _Canadian Jou,r.-nal of Economics_ 4: 654-82.

- Whalley, John and Bernard Yeung (1984). "External Sector Closing Rules in Applied General Equilibrium Models." **_Journal /_** _nlluernational Economics_ Vol. ?, No. ?

- World Bank, The (1987). _The C6te d'lvoire in Transition: From Structural Adjustment to SelfSustained Growth,_ Vol.IV, Statistical annexes.

- World Bank, The (1990a). _Making Adjustment Work for the Poor. A Framevvork for Policy Reformn in Africa._ The World Bank, Washington, D.C.

- World Bank, The (1990b). _World Development Report 1990: Poverty._ The World Bank: Washington, D.C.

- World Bank, The (1990c). _Malawi, Growvth Through Poverty Reduction._ The World Bank, Washington, D.C.
capital is closely related to what some have called “civic virtue.” The difference is that “social capital” calls attention to the fact that civic virtue is more powerful when embedded in a dense network of reciprocal social relations. A society of many virtuous but isolated individuals is not necessarily rich in social capital.

Our networks data allow us to test if participation in the civic engagement activities mandated by the CDD program spilled over and encouraged participation in other areas of civic life. Combined with behavioral indicators of adherence to pro-social norms, these networks data allow us to test if these two components of social capital have improved as a result of the program we study.

Using these two measures we find no evidence that the program increased social capital. Subjects from treated communities did not act more pro-socially in the lab than did their counterparts from control communities. The estimated impacts of the program on these measures of social capital were precisely estimated zeros. Indeed in most cases the estimates of program effects were negative (and not significant) thus alleviating any concerns about low-powered tests. Furthermore members of our treated communities were no more involved in community networks than were members of the control communities; in fact they were significantly less involved on average.

In stark contrast, when answering retrospective survey questions, respondents from treated communities self-reported engaging in significantly more pro-social action than did members of the control communities and they characterized their communities as being much more socially cohesive than did members of control communities. This brings us to a third contribution of the paper: Our results corroborate suspicions voiced in the literature (Mansuri and Rao, 2013; Wong, 2012; Casey, Glennerster and Miguel, 2012; Fearon, Humphreys and Weinstein, 2009) about possible bias in self-reported levels of pro-sociality and social cohesion from retrospective surveys. Mansuri and Rao (2013) assert: Exposure to participatory messaging may [...] make members of program com4
macroeconomic stabilization” following a period of zero and negative GDP growth. These types of disbursements are clearly responding to current macroeconomic events and so cannot be thought of as plausibly exogenous to contemporaneous shocks. This is why I exclude disbursements in a given year on projects approved in the same year from my measure of disbursements on previously‐approved projects. However, it is worth noting that this approach is conservative because it also excludes approval‐year disbursements on many projects that may have been undertaken for non‐cyclical reasons.

A key ingredient in my identification strategy is that there are substantial lags between the approval of a project and the eventual disbursement of all of the funds approved for it. I document this in Figure 2, which reports the average across all projects of the fraction of total spending that is disbursed in year t of the project (t=0,...,10 with t=0 indicating the year in which the project was approved). For the average World Bank project, just over 12 percent of the original approved amount is disbursed in the year in which the project is approved, and the remaining 88 percent of the total is disbursed over subsequent years. Another way of seeing the importance of disbursement lags is to consider the fraction of total disbursements in a given country‐year that is associated with project approval decisions made in previous years. In my core regression sample (described in more detail below), the median across country‐years of this ratio is 99 percent. This means that for a typical country‐ year observation in my sample, 99 percent of total disbursements on World Bank loans are associated with project approval decisions made in previous years. Because of this, even changes in total disbursements are unlikely to be very strongly correlated with contemporaneous events.

In order to obtain meaningfully‐precise estimates of the multiplier, it is important that disbursements on World Bank loans are also large relative to borrowing‐country GDP. To ensure that this is the case, I focus on a set of aid‐dependent countries where (a) annual disbursements on World Bank loans are available for at least 20 of the 25 years between 1985 and 2009, and (b) total disbursements on World Bank loans as a share of GDP averaged over the sample period 1985‐2009 exceed one percent. This results in a set of 41 mostly low‐income countries where World Bank lending has been an important source of financing for public spending over the past 25 years.

My country sample is further limited by the availability of data on total public spending required to construct ∆�� . My primary source for this data is the IMF’s World Economic Outlook database, which provides information on total general government expenditures, typically beginning in the late 1980s. I supplement this with information taken from the World Bank’s African Development Indicators which also reports data on total general government expenditures, going back to the early 1980s for many 12
Dollar, D., and R. Gatti. 1999. "Gender, Inequality, Income, and Growth: Are Good Times Good for Women?" Background paper for Engendering Development. World Bank, Washington, D.C.

Drèze, J., and M. Murthi. 2001. “Fertility, Education, and Development: Evidence from India.” _Population and Development Review_ 27: 33–63.

Duflo, E. 2012. “Women Empowerment and Economic Development.” CEPR Discussion Paper No. DP8734.

Easterly, W. 1999. “Life During Growth.” _Journal of Economic Growth_ 4 (3): 239-75.

Fernandez, R. , A. Fogli, and C. Olivetti. 2004. “Preference Formation and the Rise of Women’s Labour Force Participation: Evidence from WWII.” NBER Working Paper 10589, National Bureau of Economic Research Inc.

Fernandez, R., and A. Fogli, 2005, “Fertility: the Role of Culture and Family Experience.” _Journal of the European Economic Association_ 4 (2-3): 552-61.

Fernandez, R. 2010. “Women’s Rights and Development.” Working Papers, Department of Economics, New York University.

Field, E., S. Jayachandran, and R. Pande. 2010. “Do Traditional Institutions Constrain Female Entrepreneurship? A Field Experiment on Business Training in India.” _American Economic Review_ 100 (2): 125-29.

Fortin, N.M. 2005. “Gender Role Attitudes and the Labour Market Outcomes of Women Across OECD Countries.” _Oxford Review of Economic Policy_ 21(3): 416-438.

Ganguli, I., R. Hausmann, and M. Viarengo. 2010. “Schooling Can’t Buy Me Love”: Marriage, Work and the Gender Education Gap in Latin America.” CID Working Paper 197, Centre for International Development, Harvard University.

Goldstein, M., and C. Udry, 2008. "The Profits of Power: Land Rights and Agricultural Investment in Ghana." _Journal of Political Economy_ 116 (6): 981-1022.

20
5 finds a positive and robust relationship between both income and growth and child mortality and nutritional status.

A few other key variables are typically found to be important determinants of child health and nutrition. Thus Filmer and Pritchett (1999) find that 95 percent of cross country variations in infant or child mortality can be explained by just 5 variables -- GDP per capita, income distribution, female illiteracy rates, ethnolinguistic fractionalization and the predominant religion of a country. They argue that cross country studies on the topic can be summarized as follows: a) socio-economic characteristics explain nearly all the variation in mortality rates across countries; and b) public expenditure on health has much less impact on average health status than expected or hoped for. The latter point is explored in Filmer, Hammer and Pritchett (1997) who emphasize the importance of understanding the health seeking behavior of individuals, as well as the incentives facing health care providers in order to design health programs that are efficient and effective.

Two recent studies based on aggregated household data from demographic and health surveys (DHS) provide additional cross country evidence. Rutstein (2000) finds the strongest determinants of child mortality to be, in decreasing order of importance, the percentage of births that are the mother’s fourth or more, the percentage of children born to mothers younger than 18, the percentage of children born to underweight mothers and the percentage of households with drinking water from a surface source. The regressions do not include income variables however. Wang (2002) who combines DHS and World Development Indicators data for a sample of 41 developing countries finds that the child mortality rate decreases with higher GDP per capita, higher rural share of the population, vaccination, and greater access to electricity. For infant mortality rates, public expenditure on health and access to improved sanitation were also found to be significant. The work of Wang suffers from several limitations however, including the small sample size and the absence of income or inequality variables.

There has also been increased interest in recent years on the impact of environmental health factors on child and maternal health. Thus, according to the WHO’s 2002 World Health Report, unsafe water, poor sanitation and hygiene are the cause of 4 to 8 percent of the overall burden of diseases in developing countries and nine-tenths of diarrheal diseases. Given the importance of infrastructure – particularly water and
supply and the demand should be undertaken together. That is, each one carried out in isolation will not work because they are complementary. For instance, in times of supply shortages drug prices may increase and purity levels decline, and it is more likely that, first, chronic users will seek treatment, and, second, potential new users will have less opportunity to obtain drugs. Also, as the demand for cocaine goes down as a result of programs to reduce consumption, there are fewer addicts and the criminal networks in charge of selling drugs might be weakened, which in turn makes it more costly for drug traffickers to smuggle illicit drugs and to make them available to consumers. Drug substitution therapies and personalized therapeutic programs decrease the cost to drug addicts of seeking treatment and decrease the number of users under the influence of criminal organizations, which, in turn has implications for the cost to criminal organizations of supplying drugs.<sup>50</sup>

# **CONCLUDING REMARKS**

“ Many tens of billions” is probably the right figure for cocaine expenditures per year in consumer countries.<sup>51</sup> Close to 14 million people are cocaine consumers in the world. Two-thirds of these are in the Americas. Only three countries in the world produce cocaine: Bolivia, Colombia and Peru. Potential cocaine production in 2004 was estimated to be about 650 metric tons. A pure gram of cocaine is worth, at the retail level in consumer countries, as much as ten times its weight in gold. In producer countries, the same gram is worth, on average, slightly more than onetenth of its weight in gold. While these figures might be striking enough by themselves to generate interest in the topic, they hide huge complexities.

> 50 See INCB, 2004.

> 51 $35 billion is the low-end estimate and $115 billion the high-end estimate (see Reuter and Greenfield, 2001)

42
rest of South Asia. For Pakistan, reductions in corporate tax rates have been a large positive factor in enhancing FDI/GDP growth compared to other developing countries and the rest of South Asia, while control of corruption and improvements in investment policy growth have been relatively large detractors.

**Overall levels of FDI to GDP are relatively small in Bhutan and Nepal, suggesting a large potential for future growth in foreign investment, which are complemented by high natural resource endowments due to the unexploited hydropower potential.** Nonetheless, the deterioration in investment policies has been a relatively large deterrent to foreign investment growth in Nepal.  For the Maldives, the current large stock of FDI to GDP suggests that potential for future growth in foreign investment is modest.

**Control of corruption is a significant detractor to the growth in FDI/GDP for most countries in the SAR region, with the exception of Bhutan, where improvements have been a positive contributor to foreign investment.** However, Bhutan has raised its level of trade protection, somewhat offsetting the positive impact of the gains through controlling corruption.

**The estimated model suggests potential for improving FDI inflows to South Asia via policy enhancements.** Table 8 shows actual and predicted FDIs from 2005 to 2015.  The actual annual growth of FDI/GDP fell for most countries in South Asia in the second half of the 2000s, on average decreasing from 6.5 percent to 1.5 percent with the exception of India and Pakistan. India’s FDI/GDP growth increased from 6.7 percent during 2000-05 to 14.9 percent over 2005-10, while Pakistan’s rate increased from -0.6 percent to 3.3 during the same period.

**Using fitted regression model with actual data from 2005-2010, we can predict what the average annual rate of growth will be up to 2015.** Overall, the annual average growth of FDI/GDP is expected to increase from 1.5 percent in the second half of the 2000s to 3.9 percent up to 2015 (column 3, Table 8). This reflects a large jump in FDI/GDP growth Bangladesh, and more moderate increases in Bhutan, Nepal, and Pakistan, but slower growth in the other South Asian countries.

**An alternative forecast is provided in column 4, which makes a hypothetical forecast that assumes that South Asian countries are to make progress on policies that attract FDI/GDP (up to the developing country average) and maintain their other policies that are already above average.** Under this scenario, annual FDI/GDP growth would increase to 6.6 percent through 2015, which is 2.7 percentage points higher annually than if policies were maintained. Overall, the results suggest South Asia has the potential, through policy changes, to take important steps to becoming a much greater magnet for foreign investment.

31
The elasticity of country c is then given by _γ1 + γ2c,_ where _γ2c_ is the coefficient of the corresponding country interaction.

The resulting elasticities of access are in the expected range and strongly significant throughout. For example, the Latin American average water access elasticity is 0.08, and country-specific values vary between 0.02 for Argentina and Costa Rica, and 0.18 for Peru. The values for toilet ownership and sanitation access are higher on average, at 0.16 and 0.17 respectively, and range between 0.07 and 0.25.

Similarly, the average Latin American electricity access elasticity is 0.06, and it ranges from 0.01 for Argentina, 0.02 for Costa Rica and Ecuador, to 0.16 for Peru. Interestingly, elasticities for appliances ownership are in general quite larger, with 0.15 averages for refrigerators and washing machines, and ranges between 0.08 and 0.24, indicating faster progress on the intensive margin of consumption.

In the ICT cluster, elasticities are high across all indicators. Notably, these are higher for landline phones than cell phones.

Finally, the elasticity of vehicle ownership is strong for cars, with a Latin American average of 0.16, ranging between 0.09 in Bolivia and 0.20 in Mexico, but very small for motorcycles, with an average of 0.01 only.

Gertler et al. (2016), looking at refrigerators, suggested that the elasticities of asset ownership vary along the income distribution. The Latin American data display a rich pattern, with heterogeneity across assets. Figure 4 shows the rate increase for each decile between 2002 and 2012 for refrigerators, washing machines and cars, as well as the related income elasticity computed separately at each decile. While the lowest income groups have experienced the largest increase for refrigerators, a clear inverted U-shaped pattern emerges for washing machine, with the largest increase in the middle of the income distribution. Finally, car ownership displays the opposite pattern, with access increasing mostly for the richest households.

Table 6 offers a summary comparison of the demand elasticity of infrastructure assets and access using different approaches: Latin American average, countries average, country-level elasticities computed at the 10<sup>th</sup> , 25<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> percentiles respectively, as well as crosscountry averages from Fay and Yepes (2003) and some more recent estimations for Latin America from Perrotti and Sanchez (2011). The first immediate observation is that elasticities 14
27 substantial relative to household expenditures, more than doubling the poverty gap in 17 out of 22 countries including, predictably, every country where the lifeline block is smaller than 50 kWh a month. Going from 50 kWh to 100 kWh and from 100 kWh to 250 kWh a month more than double the poverty gap in 16 countries each. A rapid rise in the poverty gap between 30 and 50 kWh raises concerns. At the opposite end of the spectrum, there is no compelling reason to make 250 kWh of monthly consumption so affordable in Angola— which unfortunately could not be covered in the analysis of quasi‐fiscal deficits for lack of data.

# Subsidy to make the subsistence level of electricity affordable to all

Given limited resources, and in line with the affordability criterion for the multi‐tier access framework, enabling households to consume 30 kWh a month would arguably be an appropriate goal for the foreseeable future in Africa. Table 9 lists the grid‐electricity poverty gap and headcount for 30 kWh, the access rate (according to definition 1, or definition 2 in the absence of data for grid connection), the percentage of the total household expenditures needed to pay for 30 kWh of grid electricity a month, and the subsidies needed to make up the difference between the amount in excess of 5 percent of household expenditures and the monthly electricity bill, expressed as percentage of the quasi‐fiscal deficit, total revenue collected by the utility, and GDP. These subsidy calculations assume a perfect world where precise targeting of individual households based on income is possible. The countries are listed in order of decreasing grid‐electricity poverty gap. The calculation procedures and detailed results for quasi‐fiscal deficits are available in Trimble et al. (2016).

Table 9: Electricity poverty, access, and subsidy statistics for monthly consumption of 30 kWh

|Country|%<br>Gap|Poverty<br>Headcount|Access<br>%|30 kWh as % of<br>HH expenditures|QFD|Subsidies as % of<br>Cash collected|<br>GDP|
|---|---|---|---|---|---|---|---|
|Madagascar|30|71|11|9.6|2.6|6.4|0.06|
|Rwanda|27|63|11|8.9|7.3|6.3|0.08|
|Burkina Faso|24|60|11|8.8|7.2|3.5|0.07|
|Togo|15|39|34|6.9|2.6|0.9|0.04|
|Sierra Leone|9|33|13|5.1|2.7|3.4|0.02|
|Zambia|7|24|22|4.1|0.3|0.2|0.00|
|Uganda|7|22|9|4.4|‐2.1|0.1|0.02|
|Botswana|5|12|43|3.7|0.0|0.0|0.00|
|Senegal|2|8|53|2.9|0.1|0.1|0.00|
|Niger|2|10|10|3.6|1.8|0.7|0.01|
|Swaziland|2|3.2|38|1.9|0.0|0.0|0.00|
|Mozambique|1.4|4.6|15|2.3|0.1|0.1|0.00|
|Côte d’Ivoire|0.9|2.5|57|1.8|0.0|0.0|0.00|
|Malawi|0.6|2.9|9|2.2|0.1|0.1|0.00|
|Tanzania|0.6|2.5|16|1.9|0.2|0.0|0.00|
|Ghana|0.3|1.1|66|1.4|0.1|0.0|0.00|
|South Africa|0.2|0.9|87|1.2|0.0|0.0|0.00|
|Ethiopia|0.2|0.8|19|1.3|0.0|0.0|0.00|
|São Tomé and Príncipe|0.2|0.9|56|1.7|0.0|0.0|0.00|
|Mali|0.1|0.4|22|1.4|0.0|0.0|0.00|
|Nigeria|0.0|0.0|56|0.3|0.0|0.0|0.00|
|Angola|0.0|0.0|41|0.1|—|—|—|

_Source_ : World Bank staff calculations based on utility information and household survey data. _Note_ : For South Africa, see the note for Figure 5. Quasi‐fiscal deficits are current deficits. HH = household; QFD = quasi‐ fiscal deficit; — = not available.