rafmacalaba/datause-encoder-data huggingface dataset:
This paper is structured as follows. Section 2 describes the data and the empirical setting where the Rwandan genocide occurred. Section 3 explains the empirical identification strategy. Section 4 presents the main results as well as robustness tests. Section 5 concludes.

# **2. Data and Empirical Setting**

# _2.1 Political History and Impacts of the Rwandan Genocide_

Rwanda is a small, landlocked country in Central Africa with a current population of nine million and a high population density (337 people per square km). Since independence in 1962, political turmoil over the sharing of power and access to opportunities resulted in explosions of ethnic violence which have marked much of the recent history of the country.<sup>2</sup> A civil conflict pitting the Hutu-led government against the Rwanda Patriotic Front (RPF), a Tutsi-led rebel movement, culminated in genocide lasting from April to June 1994. Approximately 800,000 people (mainly Tutsis and moderate Hutus) were massacred by the army and the extremist Interahamwe militia. The RPF overthrew the regime in June 1994. More than two million refugees fled to Tanzania and the Democratic Republic of Congo. On July 19, 1994, a national unity government was established and a fragile process of recovery began to take hold. The country continued to experience large movements of people. Following unrest in the eastern region of the Democratic Republic of Congo, more than 600,000 refugees returned to Rwanda in November 1996. This massive wave of repatriation was followed at the end of December 1996 by the return of another 500,000 from Tanzania (World Bank, 2003).<sup>3</sup>

> 2 The first massacre of the minority Tutsis by the majority Hutus occurred in 1959, sending hundreds of thousands of Tutsis into exile in neighboring countries, and since then, Rwanda has experienced several episodes of ethnic fighting.

> 3 Note that this population displacement does not invalidate our identification strategy, which uses province level pre-war data to control for baseline schooling levels for a given age group (see Section 3 for details), since most displaced individuals returned to their same birth province. A 2001 Rwandan nationally representative survey, Enquête Intégrale sur les Conditions de Vie des Ménages, contains information about the province of birth as well as

4
**Same Question but Different Answer: Experimental Evidence on Questionnaire Design’s Impact on Poverty Measured by Proxies**

# **Talip Kilic and Thomas Sohnesen**<sup>**1**</sup>

**Keywords:** Poverty Measurement, Proxy-Based Poverty Measurement, Survey-to-Survey Imputation, Questionnaire Design, Household Survey Experiment, Malawi, Sub-Saharan Africa. **JEL Codes:** C83, D12, I32.

> 1 The affiliation of the authors: Talip Kilic: Senior Economist, Living Standards Measurement Study, Surveys and Methods Group, Development Research Group, The World Bank, 1818 H St. NW, Washington, DC, 20433 USA. Thomas Pave Sohnesen: Living Standards Measurement Study, Surveys and Methods Group, Development Research Group, The World Bank, 1818 H St. NW, Washington, DC, 20433 USA, and Visiting Professor with the Development Economics Research Group, University of Copenhagen, Denmark. The corresponding author: Talip Kilic; tkilic@worldbank.org; +1-202-458-5892. The authors would like to thank Heather G. Moylan for excellent research assistance; Phyllis Ronek for editorial assistance; Alberto Zezza, Astrid Mathiassen and the World Bank Poverty and Inequality Measurement and Analysis Practice Group members for their comments; and Astrid Mathiassen and Bjorn Wold for their inputs into the experiment design.
**Table 2: Modal Mix for Fuel Consumption in Transport Sector**

||||1980|**Modal mi**|**x in terms o**|**f energy cons**|**umption**|2005|||
|---|---|---|---|---|---|---|---|---|---|---|
||Total|Air|Water|Rail|Road|Total|Air|Water|Rail|Road|
|Country|Mtoe|%|%|%|%|Mtoe|%|%|%|%|
|Bangladesh|0.3|n.a.|29.5|13.2|57.3|1.5|n.a.|16.6|12.0|71.4|
|China|24.4|1.2|5.2|43.6|50.0|107.5|7.0|8.6|13.9|70.5|
|India|16.9|n.a.|2.0|38.6|59.3|33.7|n.a.|1.9|8.3|89.8|
|Indonesia|6.0|5.9|3.0|0.2|90.8|25.0|2.4|6.9|0.0|90.7|
|Korea|4.8|4.6|22.3|7.4|65.7|29.1|4.3|3.0|1.7|91.1|
|Malaysia|2.2|n.a.|0.0|n.a.|100.0|13.3|n.a.|0.0|0.0|100.0|
|Mongolia|0.44|n.a.|0.0|19.4|80.6|0.39|n.a.|0.0|26.9|73.1|
|Pakistan|2.2|n.a.|0.0|5.9|94.1|8.1|n.a.|0.0|3.5|96.5|
|Philippines|1.8|4.2|12.4|0.0|83.4|8.0|3.2|10.3|5.2|81.4|
|Sri Lanka|0.7|18.7|0.6|5.1|75.5|1.8|9.5|1.2|1.4|87.9|
|Thailand|3.2|n.a.|0.1|n.a|99.9|18.6|n.a.|0.4|0.6|99.0|
|Vietnam|0.6|15.1|0.0|9.7|75.2|6.8|2.5|4.5|0.5|92.4|
|||||**Modal**|**mix in term**|**s of CO2 emi**|**ssions**||||
|Country|Mt|%|%|%|%|Mt|%|%|%|%|
|Bangladesh|1.0|n.a.|29.9|13.5|56.6|4.5|n.a.|16.8|12.2|71.0|
|China|83.6|1.0|4.7|51.5|42.7|338.6|6.7|8.6|18.2|66.5|
|India|58.1|n.a.|1.9|45.0|53.1|108.6|n.a.|1.9|14.2|83.9|
|Indonesia|17.9|5.9|3.2|0.3|90.6|74.6|2.4|7.2|0.0|90.4|
|Korea|14.8|4.4|23.0|8.4|64.2|86.8|4.3|3.2|2.3|90.3|
|Malaysia|6.5|n.a.|0.0|n.a.|100.0|39.6|n.a.|0.0|0.1|99.9|
|Mongolia|1.3|n.a.|0.0|18.3|81.7|1.2|n.a.|0.0|29.0|71.0|
|Pakistan|6.8|n.a.|0.0|6.1|93.9|24.7|n.a.|0.0|3.5|96.5|
|Philippines|5.2|4.3|13.1|0.0|82.6|24.3|3.1|10.7|5.4|80.8|
|Sri Lanka|2.1|18.4|0.6|5.2|75.8|5.5|9.3|1.2|1.5|87.9|
|Thailand|9.6|n.a.|0.1|n.a.|99.9|56.5|n.a.|0.4|0.6|99.0|
|Vietnam|2.0|14.8|0.0|12.6|72.6|19.2|2.5|4.8|0.8|91.9|

Note: Based on the available data and following normal practices in energy accounting systems, we considered four modes of transportation: road, rail, water and air. If data is available, road transportation, which is the primary mode for providing transportation services as well as energy consumption and associated emissions, can be disaggregated further into auto, bus, etc. Source: IEA (2007b, c 13
first birth, Kling-Liebman bounds remain positive and significant when estimated at 0.1 difference, and for the upper Lee bounds. However, coefficients lose significance in other specifications. Other outcomes are not robust to changes in assumptions about attrition: for each of them, ITT impacts are bounded within an interval of different signs, both of which are sometimes significant.

## _7.3.5. ToT Estimates_

Our paper contributes to the existing literature by providing experimental estimates of absolute longterm impacts of a large-scale governmental program, thus complementing the work of Baird, McIntosh and Ozler (2019), who also provide experimental estimates of absolute impacts, but of a smaller program run by an NGO. Thus, ITT are of primary interest. However, we also carry out ToT estimation by instrumenting household level participation in the 4P program with its barangay level assignment. We present tables with ToT estimates in the Appendix. Generally, our ToT estimates are similar to ITT estimates, and somewhat higher in magnitude. The impacts on age at marriage and age at first birth increase to 2.4 and 1 years, respectively. Similarly, the impacts on incidence and prevalence of economic IPV increase to 17 and 20 percentage points, respectively. Empowerment coefficients (increase in the fractions of decision domains where respondents feel heard and which respondents care about) lose their significance. However, we find small increase in the likelihood to be enrolled in non-formal school for women.

# 8. Deviations from the Pre-Analysis Plan

There are several deviations from the pre-analysis plan in the current analysis. First, we did not include two types of outcomes mentioned in the pre-analysis plan: empowerment within community and subjective well-being. When writing the paper, we felt a lack of strong theoretical basis for inclusion of these outcomes. However, we present them in the Appendix.

We structured the questionnaire to capture impact of the CCT after accounting for his/her partner’s exposure to CCTs during adolescence. By design, this estimation would only include married/partnered individuals. Unfortunately, we have very limited variation in the sample for women: partners of only 25 women were exposed to CCTs in adolescence: less than 10 percent of the sample of partnered women with non-missing information about partner’s exposure to CCTs.

We did not discuss explicitly in the PAP whether we are using only FTFDQ data as our primary specification, or pool FTFDQ and ACASI. We chose to pool outcomes yielded by the two methods, given lack of significant differences across methods, and in order to increase sample size.

19
to the ‘special category’ (Assam and HP) possess different fiscal arrangements with the central government and receive certain fiscal privileges such as 90% of expenditure covered by the central government on centrally sponsored schemes, preferential central funds, concessional excise duties, tax exemptions, a fund for infrastructure development. These fiscal supports are included in the corresponding variables, such as capital expenditure, investment, internal debt, and per capita income.

# **3.3. Data Sources, Model Specifications, and Econometric Methodology**

**Data Sources** : A balanced data set covering 18 major Indian states over the period 2005 to 2019 has been used for this study.<sup>7</sup> Data has been compiled from various official sources such as the Reserve Bank of India, Centre for Monitoring Indian Economy, different Central Government Ministries, and Census, 2011. A detailed list of variables, definitions, period of study, and sources are presented in Table 1.<sup>8</sup>

**Table 1: Variables Name, Definition, and Sources**

|**Index/Variable**|**Definition**|**Period of Study**|**Sources**|
|---|---|---|---|
|**_Physical Infrastructure_**||||
|Road density (sq. km (Road)|The ratio of the State’s total road<br>length to land area|2005 to 2019*|RBI|
|Rail density (sq. km (Rail)|The ratio of the State's<br>total rail network to the State’s land<br>area|2005 to 2019|RBI|
|Telecom Density (TEL)|_Telephone_(fixed-line plus mobile)<br>connections per 100 people|2005 to 2019|RBI|
|Per Capita Installed power<br>capacity (KW) (POWER)|Installed power generation capacity<br>per person|2005 to 2019|RBI|
|**_Social Infrastructure_**||||
|School Density (SCH)|School per 1000 people|2005 to 2019|CMIE|
|Teachers (TCH)|School teachers per 1000 people|2005 to 2019|CMIE|
|Hospital Beds (BED)|Hospital Beds per 1000 people|2005 to 2019|CMIE|
|Doctors (DOC)|Allopathic doctors registered with<br>the State Medical Councils/Medical<br>Council of India per 1000 people|2005 to 2019|Ministry of<br>Health & Family<br>Welfare|
|**_Financial Infrastructure_**||||
|Bank Accounts (AC)|Bank Accounts per 1000 people|2005 to 2019|RBI|
|Bank Branches (BB)|Bank Branches per million people|2005 to 2019|RBI|
|Post office (PO)|Post office per million people|2005 to 2019|Department of<br>Post|
|**_Control Variables_**||||

> 7 The selection of states and time period are based on consistent data availability.  The study is also fairly representative as it covers 18 major states that accounts for 88 percent of GDP and 93 percent of population in India.

> 8Although the roles of good institutions and favorable business climate play a critical role in promoting infrastructure development, we could not include them in this study due to unavailability of reliable and consistent data over the study period at the state level in India.

13
Under this strand of literature, determining whether yield growth has encountered deceleration or stagnation has been a topic of substantial debate with most studies focusing on grain and oilseed crops. At the global level, Alston et al. (2009) conducted a comprehensive study documenting a discernible slowdown in the growth of grain yields. Their findings underscored the potentially far-reaching implications for food price trends should this deceleration persist. Cassman (1999) contributed to this discourse by highlighting that the rate of yield increase for cereal crops potentially fell significantly short of the anticipated rise in food demand. Examining the trends in yields for the Big-4 commodities (maize, soybeans, wheat, and rice), Ray et al. (2012) concluded that, while yields continue to ascend in many regions, a noteworthy proportion—ranging from 24% to 39% of the Big-4 growing areas—exhibits a concerning pattern of either negligible improvement, stagnation, or outright collapse. Subsequent research by Ray et al. (2013) revealed that the growth rates of yields in these commodities fall considerably short of the levels required to meet the projected demand for food commodities by 2050.

At the regional level, Van Ittersum et al. (2016) asserted that the stagnation of yields in Sub-Saharan Africa presents a formidable food security risk, particularly as the population of the region is anticipated to reach 2.1 billion by 2050 (from 1.4 billion in 2020). Using grid-level data, Iizumi et al. (2014) found increased yield instability for the Big-4 commodities across a broad region of the Southern Hemisphere, corroborating previous findings of yield stagnation and collapses in these regions. Lin and Huybers (2012) examined wheat yield data across 47 regions, finding that approximately half of the production within their sample continued with a linear growth trend, while the remainder exhibited yield stagnation. Michel and Makowski (2013) estimated that wheat yield growth exceeded 0.06 ton per hectare per year in 1961-2010 in various countries across Europe, Asia, Africa, and the Americas, but it experienced stagnation in many other countries. Focusing on 24 African countries from 1960 to 2012, Saito et al. (2015) found that 15 countries experienced accelerating yield growth, while the rest saw stagnation or decline.

Numerous studies examined country-level crop yield growth as well. In a focused analysis of China’s yield performance spanning 1980-2010, Li et al. (2016) observed yield stagnation in 50% of rice-producing areas, 54% of maize-producing areas, and nearly 16% of wheat-producing areas. In the context of analyzing food security in China, Wei et al. (2015) determined that rice yields face pronounced stagnation in 53.9% of the regions examined, followed by 42.4% in maize and 41.9% in wheat. Finger (2010) found that while the yield growth of maize, barley, and rye remained at a linear trend in Switzerland from 1961 to 2006, other commodities, including wheat, experienced yield growth deceleration. Examining yields in 29 Indian states for the 1967-2017 period, Madhukar et al. (2020) _— 4 —_
# Table 5: Mixed regime test statistics

|Vuong test score|
|---|
|Mixed model versus standard Poisson<br>2.59<sup>a</sup>|
|Mixed model versus standard negative binomial<br>3.53<sup>a</sup>|

a denotes statistical significance at the 1 percent level.

# Table 6: Results from Poisson-selection model.

|**Determinants**|**Coefficient**|**Std. Error**|
|---|---|---|
|AIJ investment equation|||
|Investment climate|0.002<sup>c</sup>|0.028|
|AIJ subsidy|0.405|0.331|
|Host commitment|0.822<sup>b</sup>|0.344|
|Investor commitment|1.314<sup>a</sup>|0.313|
|Government effectiveness|0.083|0.297|
|Political stability|0.724<sup>a</sup>|0.275|
|Constant|‐6.338<sup>a</sup>|0.580|
|Bilateral aid equation (selection)|||
|Income|‐0.117<sup>a</sup>|0.044|
|Population|0.225<sup>a</sup>|0.028|
|Proximity|0.581<sup>a</sup>|0.136|
|Trade agreement|0.445<sup>a</sup>|0.143|
|Government effectiveness|0.125|0.134|
|Political stability|‐0.134<sup>c</sup>|0.071|
|Voice and accountability|0.140<sup>b</sup>|0.058|
|Corruption|‐0.192|0.137|
|Constant|‐1.505<sup>a</sup>|0.576|
|**Additional parameters**|||
|Standard deviation of Poisson latent<br>heterogeneity term|2.182<sup>a</sup>|0.162|
|Correlation of heterogeneity and<br>selection errors|‐0.495<sup>a</sup>|0.172|

Note:<sup>a, b</sup> and<sup>c</sup> denote statistical significance at the 1, 5 and 10 percent level, respectively.

28
|**HS**||||Year|||
|---|---|---|---|---|---|---|
|<br>**code**<br>|**_Type of products_**|2001|2002|2003|2004|2005|
|**2 digits**|||||||
|37||11.8|11.8|11.8|11.8|11.8|
|||_(4.5)_|_(4.5)_|_(4.5)_|_(4.5)_|_(4.5)_|
|||136|136|136|136|136|
|38||6.3|6.3|6.3|6.3|6.3|
||_MISC CHEMICAL PRODS_|_(3.3)_|_(3.3)_|_(3.3)_|_(3.3)_|_(3.3)_|
|||279|281|277|281|287|
|39|_PLASTICS_|11.3|11.5|11.6|11.3|11.2|
|||_(3.7)_|_(5.7)_|_(6.4)_|_(4.3)_|_(4.6)_|
|||113|113|113|113|117|
|40|_RUBBER_|9.0|10.7|10.2|9.7|9.5|
|||_(5.8)_|_(16.2)_|_(12.0)_|_(9.3)_|_(9.2)_|
|||79|79|79|79|79|
|41|_RAW HIDES AND SKINS_|5.0|5.0|5.0|5.0|5.0|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|42|_ARTICLES OF LEATHER_|39<br>36.4|39<br>34.6|39<br>36.4|39<br>32.6|39<br>28.0|
|||_(80.3)_|_(42.4)_|_(27.7)_|_(19.8)_|_(15.1)_|
|||111|111|111|111|111|
|43|_FURSKINS_|9.6|9.6|9.6|9.6|9.6|
|||_(4.2)_|_(4.2)_|_(4.2)_|_(4.2)_|_(4.2)_|
|||173|173|170|170|170|
|44|_WOOD_|15.0|15.0|14.9|14.9|14.9|
|||_(2.3)_|_(2.3)_|_(2.2)_|_(2.2)_|_(2.2)_|
|||13|13|13|13|13|
|45|_CORK_|5.0|5.0|5.0|5.0|5.0|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|46||12<br>200|12<br>200|12<br>200|12<br>200|12<br>200|
||_PLAITING MATERIALS_|.|.|.|.|.|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||23<br>|23<br>|23<br>|23<br>|25<br>|
|47|_PULP OF WOOD_|15.0|15.0|15.0|15.0|14.6|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(2.0)_|
|||212<br>|214<br>|213<br>|213<br>|213<br>|
|48||13.8|13.8|14.1|13.7|13.6|
||_PAPER & PAPERBOARD_|_(4.0)_|_(4.3)_|_(5.4)_|_(3.9)_|_(3.6)_|
|||27|27|27|27|27|
|49|_PAPER & PAPERBOARD_|6.1|6.1|6.1|6.1|6.1|
|||_(7.5)_|_(7.5)_|_(7.5)_|_(7.5)_|_(7.5)_|
||_SILK_|26|26|26|26|26|

28
Figure 1 shows preschool enrollment over time as reported by primary caregivers for children ages 3 to 9 in 2010, in the treatment and control groups. We observe that, prior to 2007, preschool enrollment was virtually non-existent for children in both groups. There is a slight increase in preschool enrollment in treatment communities in 2007, though still under 4%.<sup>21</sup> Starting in 2008 when the program was fully operational, we observe a sharp increase in enrollment among children in treatment communities, with 29% of children enrolled by January 2010. We also observe a slight positive trend in preschool attendance in control communities in the period between 2008 and 2010, though total enrollment rates never surpass 6%. We attribute this to the construction of 6 government and church preschools in control communities over the same period, but we cannot rule out some contamination from program preschools.

To confirm program effects on preschool participation we disaggregated enrollment by age.<sup>22</sup> We found significant differences in enrollment rates for children in the cohort of 3 to 7 year old children who were eligible for program participation in the period between 2008 and 2010, but no differences for children 8 to 11 who were at least 6 years old in 2008 and thus too old to enroll in preschool. For the sample of target children (3 to 5 years old at baseline), enrollment in treatment communities was 41% compared to 8% in control, resulting in a treatment effect of 33 percentage points in preschool enrollment. Among children enrolled in preschool, on average children attended 4.9 days a week, for a total of 3.7 hours per day. Average travel time from home to the preschool was 20 minutes and average reported fees were 5 meticals (US$0.16) in the treatment group and 23 meticals (US$0.76) per month in the control group.

We also asked caregivers of children that did not enroll in preschool about access to a preschool in their area. Approximately 77% of households in treatment communities reported having access to preschool compared to 27% in control communities. This result suggests that about a quarter of households in the treatment communities were either unaware of the preschools in their community or viewed them as being too far or otherwise inaccessible. When analyzing the primary reason given for not enrolling their preschool-aged child in preschool, the three most common

> 21 The baseline survey was timed prior to the construction of any preschool classrooms, however some communities had already started the community mobilization process and had recently begun operating preschools in outdoor or temporary spaces at the time of the baseline survey. Some of the reported preschool participation in the pre-program period may also be attributed to recall bias. However, it is likely that some children in treatment communities had already been enrolled when the baseline survey took place. Given the very short exposure to treatment on this group of children, we do not expect this would significantly alter longer term measures of child development collected at baseline.

> 22 See Supplementary Materials Table 8.

11
We do not find that the firms that pay bribes obtain greater benefits in obtaining government services than firms that do not pay bribes.

We also find that there is a significant association between bribes and tax evasion. Firms that pay bribes underreport their revenue on average by 6.13% more than firms that do not pay bribes. This is consistent with theories that suggest that government corruption breaks an implicit contract between citizens and the state, causing firms to retaliate by evading taxes. In instrumental variable regressions, using time spent dealing with government officials (other than the tax inspectorate) as an instrument for bribes, we find a significant causal association between bribe payments and tax evasion.

When we examine the net burden of corruption on innovators, we find that while some innovators do retaliate by evading taxes, overall innovators are more likely to be victims, who pay bribes and not evade taxes, than perpetrators, who do not pay bribes but evade taxes.

Finally, firms that use bank finance to finance their new investments and working capital are more likely to pay bribes and not evade taxes, whereas firms that use informal financing and financing from family and friends and other sources are more likely to evade taxes and not have to pay bribes.

We obtain similar results after several robustness checks, including estimating on a subsample of countries in Europe and Central Asia (BEEPS Sample) that has alternate measures of tax evasion (wage-bill and labor) and bribes and also allows us to better control for profitability.

Our paper contributes to our understanding of the relations between corruption, innovation and formal financing in several ways. First, most cross-country corruption studies treat countries as monoliths without attention to corruption in particular firms or industries. By contrast, we focus on firms and industries, in particular innovative firms. Second, existing research takes the approach that firms in countries where corruption is rife, are victims of illegal activity by government officials, and thus most studies focus only on bribe payments and firm performance (e.g. Kaufmann and Wei, 1998; Svensson, 2001; Fisman and Svensson, 2007). We take a broader approach in viewing firms as both victims and perpetrators and analyzing the relation of corrupt behavior with innovation and financing decisions.

5