rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Mali 2006, rural areas<br>Child aged 5-14 enrolled in school against own age and mother’s marital status<br>6s 2<br>S<br>£172) sie Baum Ria<br>no} os er bad “""_ mL<br>2 . @ua= ~<br>° ig vAoy? -- ~ a ~<br>oS 9<br>x‘<br>ite)I? x<br>Es N e ss<br>i) ¢a ‘s<br>S ¢ ‘<br>oe ¢<br>Ks} 5a Married, once<br>S | mae Married, prev. widow<br>5s oO ¢ ' mm Married, prev. div.<br>© m= Curr. widow<br>= == Curr. div.<br>7p)<br>4 6 8 10 12 14<br>Age of child<br><!-- End of picture text -->

<!-- Start of picture text --> Mali 2006, urban areas<br>_8 2 Child aged 5-14 enrolled in school against own age and mother’s marital status<br>=Do(‘=9 o*Foiledae | = ia =m=a4 = wfsa=m ep<br>fob) ~<br>= Aa 2<br>® As Ka<br>x J Ra<br>wb s+ o 7<br>mo} . FY ¢<br>oO ?<br>oOD o, a<br>¢<br><<br>27<br>= _ Married, once<br>S o,f m= Married, prev. wid.<br>Ss o ms Married, prev. div.<br>o ¢ m= Curr. widow<br>Bio of == Curr. div.<br>7p)<br>4 6 8 10 12 14<br>Age of child<br><!-- End of picture text -->
# **Figure 5 continued: Country-specific trends in adult mortality**

# **(F) Non Sub-Saharan Africa: Latin America and Caribbean**

# **(G) Non Sub-Saharan Africa: Asia, North Africa, and Middle East**

<!-- Start of picture text --> Haiti<br>Nepal<br>Guatemala Philippines<br>Brazil Indonesia<br>Dominican Republic<br>Bolivia Morocco Yemen<br>Peru Jordan<br>1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 . 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 .<br>Period Period<br>.04<br>.03<br>.02<br>.01<br>0<br>.03<br>.02<br>.01<br>0<br><!-- End of picture text -->

# **(H) Countries with episodes of civil or international conflict**

# **(I) Countries with episode of genocide**

<!-- Start of picture text --> Eritrea<br>Uganda<br>Congo Rep.<br>Cote d'Ivoire<br>DR Congo<br>Liberia<br>Ethiopia<br>Mozambique Sierra Leone<br>Rwanda<br>Cambodia<br>1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 . 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 .<br>Period Period<br>.06 .15<br>.04 .1<br>.02 .05<br>0 0<br><!-- End of picture text --> 38
Subsidies are in fact a major component of the overall expenditure program. The ratio of current subsidy to current expenditure for the provincial governments, over the past 12 years, has varied from 19 percent to 44 percent. The same ratio for the federal government has varied from 36 percent to 97 percent (Table 10). Similarly, development subsidies bome almost exclusively by the federal government have also been high, from 24-83 percent of development expenditure. Subsidies have fallen to some extent in recent years, both absolutely and relative to total expenditure (Tables 10 and I 1).

Table **10**

**Povinial/Federal** Expenditure and Subsidy **(in** constant Rupes -Million)

|**Year**|**Provincial**<br>Expend<br>**(Fotal)**|Provincial<br>Subsidy|Subsidy/<br>Expend|**Federal**<br>Expend<br>(Total)|Fcdcral<br>Subsidy|Subsidy/<br>Expend|
|---|---|---|---|---|---|---|
|**CURRENT**|||||||
|82-83|2155|**na**|na|na|na|na|
|**83-84**|2462|na|na|na|na|na|
|**84-85**|3792|1149|0.30|2917|2744|0.94|
|**_85-86_**|4409|1498|0.40|1498|**1318**|0.88|
|86-87|5025|1836|0.37|**313**|112|0.36|
|**87-88**|5129|2278|0.44|535|358|0.67|
|**88-89**|4173|1209|0.30|4919|4755|0.97|
|89-90|2894|na|na|3319|3150|0.94|
|90-91|3950|975|0.25|2393|2229|0.93|
|91-92|4236|821|0.19|2085|1914|0.92|
|92-93|4114|877|0.21|1196|978|0.82|
|**93-9410**|3474|963|0.28|540|492|0.91|
|DEVELOPMENT|||||||
|82-83|1458|2|.00|4604|1625|0.35|
|83-84|1259|13|.01|4130|1233|0.30|
|84-85|1286|13|.01|3665|1151|0.31|
|85-86|1365|12|.01|5240|1778|0.34|
|86-87|1633|13|.01|3665|801|0.22|
|87-88|1638|6|.00|3621|1396|0.38|
|88-89|1187|_5_|**.00**|3150|1433|0.45|
|89-90|1422|4|.00|2941|652|0.22|
|90-91|1837|0|0|2422|678|0.28|
|91-92|1897|0|0|2791|1224|0.44|
|92-93|1802|**0**|**0**|1732|**333**|0.19|
|93-94|1306|0|0|1139|309|0.27|

Source: Tables 8 **and** 9.

**'° The** 1993-94 figures **in** Tables 4.4 and 4.5 are Budget figures, and thus subjec **t** o majo **r** evision.

24
used.

# **3 Experimental design**

In the second phase of PVP, we worked with the State Government of Tamil Nadu to evaluate the effectiveness of providing GP presidents with public incentives based on their evaluation grade. The experiment was designed to use the evaluation scheme that was already in place during the first phase of PVP and test the added effectiveness of financial and nonfinancial incentives on the performance of elected GP presidents.

## **3.1 Sample and randomization**

In the second phase, the project was rolled out in 10 new districts (and 4 blocks in each district), which had not previously received the project in Phase 1. From those 10 districts we selected one block in each district, and randomly selected 25 GPs from each block.<sup>8</sup> For blocks that had less than the target number of GPs, all GPs in the block were selected.<sup>9</sup> This gave us a total sample of 198 GPs across 10 blocks in 10 districts.<sup>10</sup> In each block, we randomly divided the GPs into three groups (two treatment and one control). The randomization was done by the authors privately using Stata, and this resulted in 67 GPs each in the two treatment groups, and 64 GPs in the control group. The GP presidents in the treatment group received one of two incentives—a or one.

> 8In each district, the number of blocks covered by PVP was determined by the available budget, and the PVP- blocks were selected based on their backwardness score. This score comprised of SC/ST share of the block population, share of below poverty line households, and other measures that included poor infrastructure, poor public services, and industrial backwardness. All blocks within each district were ranked according to this score, and 4 blocks starting from the highest score (most backward) were chosen first for PVP. The PVP-blocks chosen for this study comprise of the last block chosen to receive the project in each district. Appendix Table A1 compares the means of block-level demographic and infrastructure characteristics between PVP-blocks in our sample and PVP-blocks not in our sample. Across all outcomes, we do not find any statistically significant pair-wise difference in the means between sample and non-sample PVP blocks. Additionally, we cannot reject that all means are not jointly different between the two groups at conventional level of statistical significance.

> 9Six out of 10 study-sample blocks had all their GPs included in the study sample.

> 10The total sample size was therefore determined by a combination of operational feasibility and budget constraints.

9
health care use models specified in Section 6. Both standard probit and IV probit specifications are estimated for the fertility and health care choice models.

# **Effects of the school construction program on education**

The identification strategy outlined above can be generalized to an interaction term analysis. Based on in Duflo (2000; 2001), I begin with the following specification: where, _Sijk_ = years of schooling for individual _i_ , born in region _j_ , in birth cohort _k_ α 1 _j_ and β 1 _k_ are region of birth and cohort of birth fixed effects respectively _dil_ is a dummy that indicates whether individual _i_ was age _l_ in 1974 (a year of birth dummy) _Pj_ = program intensity (number of schools built per 1000 children) in region _j_ _C j_ is a vector of region specific control variables Each coefficient γ 1 _l_ may be interpreted as the estimate of the impact of the program for a given cohort. The cohort aged 24 in 1974 forms the control group and is omitted from the regression.

Children aged 13 and older in 1974 were never exposed to the program. Therefore, a testable restriction is that γ 1 _l_ is small and insignificant for _l_ >12 and starts increasing for _l_ smaller than some threshold (the oldest age in 1974 when an individual could still benefit 30
# **1 Introduction**

The idea that the digitization of transactions through electronic payment technology can help increase tax compliance has been prominent in academic circles (e.g. Rogoff 2016), in the policy advice provided by international organizations (OECD 2018, Gupta et al., eds 2017, World Bank 2016), and it is reflected in actual policy implementation, most prominently in India’s 2016 demonetization campaign (Das et al. 2022). Unlike cash transactions, electronic transactions are processed by a third-party, distinct from the two transacting partners, creating a paper trail which governments can access for tax compliance purposes. The existence of such a third-party paper trail, combined with a tax audit function which leverages the information, can deter taxpayers from under-reporting taxable transactions (Kleven et al. 2011, Pomeranz 2015, Naritomi 2019). This would increase reported taxable sales and tax liabilities. Following this logic, governments in numerous countries have attempted to accelerate the pace of digitization through fiscal incentives for transactions conducted with electronic payment methods (see Table A.1 for an overview).

Yet, whether such policies have the intended effect on tax compliance depends on endogenous technology adoption decisions by firms and consumers and on the share of transactions ultimately covered by electronic records. If only firms which are already tax compliant respond to the incentives, or if electronic records cover a smaller share of transactions than the share which firms already report for tax purposes, an increase in electronic transactions might not affect tax compliance. In addition, electronic records can help deter evasion only if tax administrations actually use them to detect misreporting, and if taxpayers are aware of this.

We study the effect of VAT rebates on the adoption of electronic payment technology and on tax compliance, exploiting policy variation from Uruguay in regression discontinuity and difference-in-difference estimations. The rebate program was introduced in August 2014, at a time when Uruguay lagged behind peer countries in key financial inclusion measures (Figure A.1). There was significant scope to increase the use of electronic payment technology, and the reform program provided large and salient incentives: the rebates reduced the VAT payable on debit card transactions by up to 40 perent. The rebates were immediately granted to customers paying by card, without the need for refund claims or other hassle costs. We evaluate this program using transaction-level data on all electronic transactions and monthly firm-level VAT declarations for 2006-2015.

We document three main results. First, we use the high frequency of our data and a regressions discontinuity design in time to show that the introduction of the rebates lead to an 2
# **References:**

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- Chiputwa, Brian, David J. Spielman, and Matin Qaim. 2015. “Food standards, certification, and poverty among coffee farmers in Uganda.” _World Development_ 66: 400–412.

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- Forman, Chris, Avi Goldfarb, and Shane Greenstein. 2005. “How Did Location Affect Adoption of the Commercial Internet? Global Village vs. Urban Leadership,” _Journal of Urban Economics_ 58(3): 389–420

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25
social capital. While there is some evidence that CDD programs have been able to achieve local public service delivery, several well-executed recent studies have been unable to find much of a causal impact of CDD programs on villages’ capacities for local collective action and public good provision. What has not been clear from these previous studies is whether the disconnect between CDD and improved capacity to provide public goods was occurring at the level of local governing institutions (local leaders were thwarting the greater pro-sociality of citizens), pro-social preferences (local leaders were open to change and greater participation from villagers but villagers free rode and shunned opportunities for more involvement) or both. We study villagers’ behavior in a controlled laboratory setting allowing us to isolate the effects that the program had on villagers’ pro-social behavior, stripping away any causal impact that local governing institutions could have on results. We also study the effect of the program on forging new social relationships among community members—a key feature of social capital.

Using our most trusted measures, our findings are consistent with earlier studies that have found little or no effect of these programs on social capital. Our estimates of the impact of the program on pro-social preferences were very close to zero using behavioral measures from the lab and the mean estimated effect of the program on the density of social networks was actually negative, suggesting the possibility of some crowding out of naturally occurring social relationships. We measured pro-social behavior in a laboratory setting where the effects of the communities’ governing institutions and any possible informal enforcement of social norms have been carefully excluded. We have thereby isolated at least one broken link in the causal pathway between CDD programming and better local governance—the program is not making citizens preferences more pro-social. While our research design does not allow us to comment on whether the CDD program we study had an impact on improving local governing institutions, it is quite clear from our results that it did not have the desired impact on the villages’ stocks of social capital as measured by our laboratory activities or the network survey.

34
and less likely to receive key health services (p. 137). The study also notes disconnect between health outcomes that were pro-rich and interventions that tended to be propoor. Another study by Victora et al. (2012) analyzed time trends in inequalities of selected health indicators from the MDGs.<sup>4</sup> In general, they note prevalent pro-rich inequalities, but services tend to be pro-poor when there are rapid changes in overall coverage.

There are a number of other health inequality studies on low-income countries. Some of these studies looked at one or more health status indicators (e.g., McKinnon et al. 2014 and Quentin et al. 2014), while others focused on health intervention indicators (e.g., Kruk et al. 2008). McKinnon et al. (2014) investigated wealth-related and educational inequalities in neonatal mortality (NMR) for 24 low- and middle-income countries with substantial heterogeneity in both the size and direction of NMR inequalities between countries. They find that inequalities declined in most of the studied countries. They also find pro-rich inequalities in NMR increased in a few countries, including Ethiopia.

While the evidence in multi-country studies offers some useful insight, an indepth analysis of country-level inequalities would provide a more detailed description and a focused message based on the factors that are particularly relevant to the country’s context. There are a few relevant studies on health inequalities in Ethiopia. Most of them analyzed one or two indicators at the national level (e.g., Onarheim et al.,

> 4 The indicators include skilled birth attendants, measles vaccination, and a composite coverage index, and examined coverage of a newly introduced intervention, use of insecticide-treated bednets by children. They use DHS and MICS data from 35 countries.

4
# Introduction

This section of the paper shows the international context of internet use in Colombia.

**Figure 1 OECD Fixed broadband subscriptions per 100 inhabitants, by technology, June 2015**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>05<br>00<br>Chile Mexico OECD Colombia<br>DSL Cable Fibre Satellite Fixed wireless Other<br><!-- End of picture text --> Source: OECD, Broadband Portal, http://www.oecd.org/sti/broadband/oecdbroadbandportal.htm, June 2015.

Figure 1 shows that as of June 2015, Colombia had a total number of internet subscriptions per 100 inhabitants similar to Mexico (11.2), but lower than Chile (14.6). The latter’s is half of the OECD average of 28.8 subscriptions per 100 inhabitants. DSL and cable are the dominant technologies in Colombia and both have the same number of subscriptions (5.2) per 100 inhabitants, despite an ambitious plan to expand the optical fiber network to almost every municipality ((Ministerio de Tecnologías de la Información y las Comunicaciones, 2017).

**Figure 2 OECD fixed broadband penetration (per 100 inhabitants), percentage increase, June 2014‐June 2015, by**

**country**

<!-- Start of picture text --> 12.00<br>10.00<br>8.00<br>6.00<br>4.00<br>2.00<br>0.00<br>Chile Mexico OECD Colombia<br><!-- End of picture text --> Source: OECD, Broadband Portal, http://www.oecd.org/sti/broadband/oecdbroadbandportal.htm, June 2016.

2
# 1. INTMODUCTION

The corporate income tax holiday is a tax incentive frequently used by less developed countries (LDCs) to promote capital investment. The usual form of the holiday is to allow a 'pioneer" firm operating in a designated industry to be fully or partly exeompt from corporate taxation during its formative years with full taxation applying after the holiday period. Of the 54 LDC tax systems described in a Price Waterhouse survey (19861, 27 of these include tax holidays of one form or another. Although tax holidays are prevalent in LDC's, it is not difficult to find examples of holidays used in developed countries such as France and Belgium.

Much of the current literature on capital formation and effective tax ratesl has concentrated on investmen **t** ax credits, accelerate **d** epreciation, and statutory tax rate abatements as tax incentives (see, for example, King and Fullerton (1984] and Boadway, bruce and Mintz [1984]). These tax incentives are not particularly difficult to analyze since it can be assumed that the firm anticipates the tax system to be unchanging overtime. With additional assumptions, time invariant effective tax rates are derived that are useful for describing the long run impact of the tax system on capital. For example, the usual assumptions include the following: (i) real capital good prices increase at a constant rate over time, (ii) capital depreciates 1/ The notions of the effective tax rate and the cost of capital are fairly well known now in the literature so they are only briefly defined here. The user cost of capital is depreciation and financing costs, adjusted for taxes, that are incurred by the firm when holding capital. Effective tax rates are conventionally defined as the difference between the marginal gross-of-tax rate of return (the user cost of capital net of depreciation costs) and the net-of-taA rate of return that savers earn when investing in the firm's capital. This difference may be divided by the gross-of-tax or net-of-tax marginal rates of return.
probability of being multi-dimensionally poor by 2.6 percent, and decreases the probability of being employed in the agriculture sector by 4 percent among male household members and 5.3 percent among females. Hence, reducing transportation costs plays an important role in income diversification and structural change.

Nevertheless, the effects of road interventions are neither systematic nor uniform. Drawing on the 2001 National Household Survey for Cameroon, Gachassin et al (2015) demonstrate that better road access increases the number of economic activities within reach of households living in the most isolated locations, based on a “pull” factor, which draws them into new and higherearning opportunities. However, the impacts of isolation on household well-being via labor market opportunities are heterogeneous and depend on local characteristics.

An important channel through which rural roads generate economic benefits is by improving the productivity of the agriculture sector. Dorosh et al (2012) examine the relationship between transport infrastructure, population location, and agricultural production in Sub-Saharan Africa. Both population and agricultural production are spatially concentrated near large cities. A 10 percent decrease in travel time from a local crop production location to a nearby city of more than 25,000 people increases local crop production by 23 percent in the long run. A 1 percent reduction in travel time to the nearest city with 100,000 people or more increases low-input (high-input) crop production by 2.9 (1.6) percent. In a similar vein, Kiprona and Maksumoto (2018) show that rural road infrastructure improvements in Southwestern Kenya increase agricultural productivity and market participation by rural smallholder farmers.

Developing countries, especially in Sub-Saharan Africa, have lagged in agricultural technology adoption, despite widespread experimentation with input subsidies. To understand the reasons for this, Aggarwal et al (2018) quantify market access in the 1,183 villages in rural Tanzania and examine the extent to which it constrains agricultural productivity. They find that less favorable input and output prices in remote areas resulted in lower profitability leading to lower agricultural technology adoption in these locations. For instance, a village in the 90<sup>th</sup> percentile of the travel-cost adjusted price distribution faces input and output prices 40 to 55 percent less favorable than a village at the 10<sup>th</sup> percentile. The rates and magnitudes of fertilizer use and maize sales also display a large and significant distance gradient. An additional standard deviation of travel time is associated with 20 to 25 percent lower input adoption and output sales for remote villages.

These findings are in line with other studies on this topic. Suri (2011) shows that many Kenyan farmers do not adopt hybrid seeds with high returns due to high fixed costs of obtaining seeds, possibly related to travel costs. Minten et al (2013) document significant farmer-to-retailer transaction costs to reach price-controlled input cooperatives in a rugged region in northwestern 26