rafmacalaba/datause-encoder-data huggingface dataset:
to food, significantly more people state that they are experiencing bouts of food insecurity.

Table 3. Trends in experiential measures of food security

|Round 1 – 2010|Round 2 – 2013|
|---|---|
|_Panel A: Self-reported food insecurity, last 12 months_||
|_National_<br>0.510|0.658***|
|_(_0.018_)_|(0.017)|
|_Urban_<br>0.326|0.534***|
|(0.031)|(0.035)|
|_Rural_<br>0.545|0.682***|
|(0.020)|(0.018)|
|_Panel B: Number of months food insecure, last 12 months_||
|_National_<br>1.50|2.15***|
|(0.08)|(0.09)|
|_Urban_<br>0.71|1.45***|
|(0.07)|(0.11)|
|_Rural_<br>1.65|2.29***|
|(0.08)|(0.10)|

Change between two waves is statistically significant at *p<0.10, ** p<0.05, ***p<0.01.

Note: Standard errors in parentheses correct for clustering and stratification. The descriptive statistics have been estimated from the cross-sectional samples corresponding to our final balanced sample. The national sample size includes 11,410 individuals in each round; 2,967 and 2,882 individuals are included in the urban samples in rounds 1 and 2, respectively, and 8,443 and 8,528 individuals are included in the rural samples in rounds 1 and 2, respectively.

# **3. Methodology and Results**

# _3.1. Model – explaining the puzzle_

The descriptive statistics indicate that despite escalating food prices in late 2012-2013, Malawians appear to have improving levels of food security as measured by availability, access, and utilization over the study period. Yet, despite this, they report both worrying more about having sufficient food and state that they are experiencing food insecurity more frequently. The next section explores in more detail the association between food price surges and the increased worries and anxiety of households to meet their food needs. The hypothesis examined is that the dramatic increases in food prices led to increased anxiety about the stability of food security, despite no change in the actual levels of consumption.

11
Jones, Charles I., and John C. Williams. 1998. “Measuring the Social Return to R&D.” _The_ _<u>Quarterly Journal of Economics</u>_ 113: 1119-1135.

Jones, Charles I., and John C. Williams. 2000. “Too Much of a Good Thing? The Economics of Investment in R&D.” _Journal of Economic Growth_ 5(1): 65-85 Jones, Charles I.. 1995. “R&D –Based Models of Economic Growth.” _Journal of Political Economy_ 103(4):759-84.

Klette, Tor J. and Samuel Kortum. 2002. “Innovating Firms and Aggregate Innovation.” NBER Working Paper 8819.

Kortum, Samuel. 1997. “Research, Patenting, and Technological Change.” _Econometrica_ 65(6): 1389-1419.

Kortum, Samuel. 1993. “Equilibrium R&D and the Patent—R&D Ratio: U.S. Evidence” _American Economic Review_ 83(2):450-457.

Kortum, Samuel, and Josh Lerner. 2000. “Assessing the Contribution of Venture Capital to Innovation.” _RAND Journal of Economics_ 31(4):674-692.

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Lederman, Daniel, and Laura Saenz. 2003. “Innovation around the World: A CrossCountry Data Base of Innovation Indicators.” Mimeographed. Office of the Chief Economist for LCR, The World Bank, Washington, DC.

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Lucas, Robert E., Jr.. 1988. "On the Mechanics of Economic Development." _Journal of Monetary Economics_ 22:3-42.

Lundvall, Bengt-Ake, Bjorn Johnson, Esben S. Andersen, and Bent Dalum. 2002. “National Systems of Production, Innovation and Competence Building.” _Research Policy_ 31:213-31.

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23
**Table 1:** _Country Cases Considered under the HIPC Initiative_

|Country|Decision Point|Completion Point|
|---|---|---|
|Afghanistan|July 2007|January 2010|
|Benin|July 2000|March 2003|
|Bolivia*|February 2000|June 2001|
|Burkina Faso*|July 2000|April 2002|
|Burundi|August 2005|January 2009|
|Cameroon|<br>October 2000|<br>April 2006|
|Central African Republic|September 2007|June 2009|
|Chad|May 2001|April 2015|
|Comoros|June 2010|December 2012|
|Democratic Republic of Congo|July 2003|July 2010|
|<br>Republic of Congo|<br>March 2006|<br>January 2010|
|Cˆote d’Ivoire|March 2009|July 2012|
|Eritrea||Pre-Decision Point|
|Ethiopia|November 2001|April 2004|
|The Gambia|December 2000|December 2007|
|Ghana|February 2002|July 2004|
|Guinea|December 2000|September 2012|
|Guinea-Bissau|December 2000|December 2010|
|Guyana*|November 2000|December 2003|
|Haiti|November 2006|June 2009|
|Honduras|July 2000|March 2005|
|Liberia|March 2008|June 2010|
|Madagascar|December 2000|October 2004|
|Malawi|December 2000|August 2006|
|Mali*|September 2000|March 2003|
|Mauritania|February 2000|June 2002|
|Mozambique*|<br>April 2000|September 2001|
|Nicaragua|December 2000|January 2004|
|Niger|December 2000|<br>April 2004|
|Rwanda|December 2000|April 2005|
|S ̃ao Tom ́e and Pr ́ıncipe|December 2000|<br>March 2007|
|Senegal|June 2000|April 2004|
|Sierra Leone|March 2002|<br>December 2006|
|Somalia||Pre-Decision Point|
|Sudan||Pre-Decision Point|
|Tanzania|April 2000|November 2001|
|Togo|<br>November 2008|December 2010|
|Uganda*|February 2000|May 2000|
|Zambia|<br>December 2000|<br>April 2005|

*Part of the original HIPC Initiative. Original Decision Point and Completion Point dates: Bolivia September 1997 and September 1998, Burkina Faso September 1997 and July 2000, Guyana December 1997 and May 1999, Mali September 1998 and September 2000, Mozambique April 1998 and June 1999, Uganda April 1997 and April 1998 Source: IMF (2014, 2015a)

25
methods, thus, each have their advantages and disadvantages. This paper attempts to demonstrate, therefore, that a careful combination of the two can supplement the limitations of one with the benefits of the other (Rao and Woolcock 2003, White 2011).

The intervention we study here is a citizen training and facilitation program that attempted to transfer the lauded “People’s Campaign” from the Indian state of Kerala to the feudal, highly unequal, and far less literate region of Northern Karnataka. Its goal, as in Kerala, was to make the system of democratic village government more responsive, transparent and efficient by improving the quality and quantity of citizen participation. The intervention was randomly assigned to 50 Gram Panchayats in Northern Karnataka, with another 50 chosen as controls. The RCT which analysed data from two rounds of data collected in 2007 and 2009 found substantial changes over time in both control and treatment and GPs but almost no discernable changes between the treatment and control groups. The qualitative investigation was based on a 10 percent subsample of the quantitative sample. Resident field investigators filed monthly reports about these 10 Gram Panchayats over a four period from 2007 to 2011. These monthly reports, and supplemental interviews conducted with higher government officials and Resource Persons (RPs) responsible for implementing the program, constitute our qualitative data.

Both the qualitative and quantitative data reveal considerable improvements over time in the quality of public services, governance and participation. A very sharp increase in village government funds led to increased political competition, better public infrastructure and increased citizen participation particularly in village meetings known as gram sabhas, and in the use of requests filed under the right to information act. However, the attempt to replicate the Kerala campaign in Karnataka “failed” in terms of not showing significant differences across treatment and control villages. The ethnography we conduct of these villages finds several reasons for the lack of observed outcomes:

- 1) Challenging context:  High levels of inequality, conflict, elite domination and low literacy made working in this environment particularly difficult. Moreover, a number of government programs raised GP budgets by 700 percent between 2005 and 2007. This provoked a series of changes – increased political participation and competition over funds and high levels of corruption, increased awareness of GP functions and processes, and increases in citizen participation.  Within this tsunami of change, the intervention was unable to add significant value.

- 2) The lack of a “sandwich”:  The RPs essentially operated on their own rather than as agents of government with the backing of local bureaucrats.   The bureaucrats were not integrated into the change process.  Consequently higher-level bureaucrats did not

25
# **5.3 Ethnicity and Gender**

Does ethnicity-based discrimination vary by gender? If so, what is the extent? To understand the interaction between gender and ethnicity, we use specification 3 that adds to specification 1 a gender dummy and its interaction with the ethnicity variable: We would like to know if ethnic discrimination is enhanced or muted by gender. A statistically significant coefficient on either of the interaction terms _γj_ would suggest that gender heterogeneity matters for ethnic discrimination. Table 8 present the results of specification 3. The interaction terms of ethnicity and female are zero for all the hiring stages we observe. Gender does not seem to be relevant enhancing or attenuating ethnic discrimination against Indian and Malay sounding names.

Table 8: Ethnicity and Gender

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Rejected|Profle<br>viewed|N of profle<br>views|Contacted|Interview<br>requested|
|Malay|0.132***<br>(0.028)|-0.268***<br>(0.029)|-0.847***<br>(0.096)|-0.113***<br>(0.019)|-0.081***<br>(0.016)|
|Indian|0.156***|-0.308***|-0.928***|-0.134***|-0.094***|
||(0.029)|(0.029)|(0.095)|(0.018)|(0.015)|
|Female|0.029|-0.024|-0.016|0.033|-0.004|
||(0.027)|(0.032)|(0.120)|(0.024)|(0.020)|
|Female _×_ Malay|-0.067*|0.054|0.091|-0.027|0.012|
||(0.041)|(0.042)|(0.139)|(0.028)|(0.023)|
|Female _×_ Indian|-0.040|0.035|0.029|-0.031|0.010|
||(0.041)|(0.041)|(0.134)|(0.026)|(0.021)|
|Constant|0.234***|0.516***|1.283***|0.159***|0.110***|
||(0.019)|(0.022)|(0.085)|(0.016)|(0.014)|
|_R_<sup>2</sup>|0.016|0.074|0.081|0.058|0.030|
|N|2,995|2,995|2,995|2,995|2,995|
|_Note:_ This table pr<br>Rejected takes the v<br>Column 2 Profle vie<br>least once. Column 3<br>takes the value of 1 i<br>5 Interview requested<br>an interview. Robust|esents the res<br>alue of 1 if t<br>wed takes the<br> is the numbe<br>f the Company<br>takes the valu<br> Standard err|ults of specif<br>he company d<br> value of 1 if <br>r of visits to <br> contacted th<br>e of 1 if the C<br>ors. * p_<_0.1,|cation 3.<br>The o<br>isregarded the <br> the profle was <br> the website pro<br>e candidate thro<br>ompany contact<br> ** p_<_0.05, ***|utcomes are: <br>application as <br> visited by the <br>fle. In column <br>ugh email. Fin<br>ed the candidat<br>p_<_0.01|In column 1<br>not-suitable.<br> company at<br> 4 Contacted<br>ally, column<br>e requesting|

23
Assuming a labor income share of 70%, pension<sup>fund assets would</sup> amount to 350% of GNP. A smaller labor<sup>share, say 40%, would</sup> still imply total pension fund assets of 200%<sup>of GNP.</sup> Such an increase in capital funds would<sup>imply an increase in</sup> the rate of saving, at least until the funds<sup>reach maturity.</sup> Historically, the adoption of funded pension<sup>schemes has not been</sup> associated with an increase in the rate<sup>of household and national</sup> saving, probably because of offsetting changes<sup>in household</sup> behavior. But the absence of a clear effect<sup>on the rate of</sup> saving may also be explained by the lack<sup>of universal coverage,</sup> the interrupted careers of many workers,<sup>the absence of full</sup> funding, the relative youth of most funded<sup>pension schemes, the</sup> low level of pension benefits (relatively<sup>few workers are covered</sup> by pension funds that provide a pension<sup>rate equal to 50% and</sup> indexed to wages), the low level of real<sup>returns (it is unclear</sup> whether a 5% real rate of return would<sup>prevail over a 60-year</sup> period in a country with universal coverage)<sup>and the pyramidal</sup> demographic structure (instead of the pillar<sup>structure assumed</sup> here). A forced saving scheme that does not<sup>suffer from these</sup> shortcomings may more than offset the reduction<sup>in voluntary</sup> saving.

These factors may also explain why the total<sup>assets of</sup> pension funds in different countries are<sup>nowhere near the very</sup> high levels quoted above. The highest level<sup>of assets in</sup> relation to GNP of pension funds and life insurance<sup>companies</sup> combined is found in Switzerland, the Netherlands<sup>and the United</sup> Kingdom where they range between 120% and<sup>150% of GNP (Vittas and</sup> Skully 1991) 14. In Singapore, where the CPF has been<sup>in</sup> existence sir.ve 1955 and where contribution<sup>rates are quite high,</sup> the total assets of the CPF correspond to around<sup>70% of GNP. In</sup> some countries, total financial assets, including<sup>assets of banks</sup> and other deposit institutions, exceed 300%,<sup>and even 400% of</sup> GNP, but these totals include a significant amount<sup>of double</sup> counting.

The accumulation of large pension fund<sup>balances would also</sup> imply a domination of the financial system by<sup>pension funds.</sup> This would have far-reaching implications for the<sup>management,</sup> regulation and supervision of their assets<sup>and for the role of</sup> pension fund managers in corporate governance. Failure to properly regulate and supervise pension funds and<sup>ensure that</sup> they are invested safely and profitably would<sup>undermine their</sup> ability to pay the targeted pensions.

14 Davis (1992) has data showing the rapid growth<sup>of total</sup> pension fund and life insurance assets over<sup>the past two decades</sup> or so.

29
developed by Fernandez et al. (2001). The data set contains 41 explanatory variables that are potentially important for growth in 72 countries. Here, we describe the variables that do not assess financial development. Financial indicators, which we add to this data set, are described in the main text.

We update the data set by incorporating economic growth from new PWT, extending the time period considered from the former 1960–1992 to 1960–2011. Our dependent variable is the average growth of real output-based GDP per capita. The mean value of the growth rate across the data set is 2.27% with a standard deviation of 1.45%. The regressors in the data set comprise various measures of economic, political, geographic, demographic, social, and cultural factors. As many of the variables are endogenous with respect to growth, the data typically come from 1960 or before.

The economic variables primarily capture established factors from neoclassical growth theories. The initial level of GDP is included to capture conditional convergence, such that lower starting levels imply higher growth rates (Barro and Sala-i-Martin 1992). Additionally, physical capital investment is considered, distinguishing between equipment investment (machinery) and nonequipment investment (other). This follows Long and Summers (1991), who find that the impact of the former is a stronger driver of long-term economic growth. Human capital enters through primary school enrollment, higher education enrollment and public education share from Barro (1996). Life expectancy is often assumed to capture human capital other than education; therefore, it is also included among the regressors. Exchange rate fluctuations, the black market premium, and the volatility of the black market premium account for the degree of economic uncertainty. Exchange rates can affect a country’s foreign direct investments and net exports, subsequently influencing economic growth. The black market premium then reflects the surplus on the exchange rate over the official 51
13 the timing of deregulation reflects the interactions of these technological innovations with preexisting conditions. Thus, the driving forces behind deregulation and its timing were largely independent of state-level changes in income distribution. Consequently, we exploit cross-state, cross-year variation in income distribution and deregulation to assess the impact of a single policy change on different state economies.

BLL use the differences-in-differences estimation technique to assess the relationship between branch deregulation and income distribution. Specifically, Ys,t = α s + β t + γ Ds,t + δ Xs,t + ε s,t, s=1, ..., 50; t=1976, ..., 2005. (2) where Ys,t is a measure of income distribution in state s during year t, α and β are vectors of state and year fixed-effects, Xs,t is a set of time-varying state-level variables and ε s,t is the error term. The variable of interest is D, a dummy variable that takes on the value one after a state deregulates. The year-dummy variables control for economy-wide shocks that might drive income distribution over time, such as business cycles, long-term trends in income distribution, and changes in female labor force participation. The state-dummy variables control for unobserved, time-invariant state characteristics that shape income distribution across states. The coefficient, γ, therefore indicates the impact of branch deregulation on income distribution. A positive and significant γ suggests that deregulation exerts a positive effect on the degree of income inequality, while a negative and significant γ indicates the deregulation pushed income inequality lower.

BLL’s major finding is that deregulation of branching restrictions reduced income inequality. After a state deregulates restrictions on bank branching, the Gini coefficient of income inequality drops relative to its long run trend. The drop becomes statistically significant three years after deregulation. The negative impact of bank branch deregulation on income inequality is a level effect that fully materializes over the six years following deregulation. The negative relationship
a total sample of 2,000 households. In our analysis we re-weight the data with community level population sample weights equal to the inverse probability of selection, though results are generally robust to the exclusion of weights as would be expected from block randomization based on community population size.<sup>19</sup> In each sampled household, we collected a detailed baseline survey including child development tests and anthropometric measurements for one preschool aged child per household, identified as the “target child.” In households with more than one preschool aged child, the youngest child was selected. We additionally interviewed the target child’s primary caregiver and the head of household to collect demographic and socio-economic information about parents, caregivers, and other household members. In addition, in each community we conducted a community leader survey and a primary school survey, interviewing school principals and first grade teachers to collect school performance indicators on a sample of 1<sup>st</sup> graders. Baseline characteristics are balanced between treatment and control communities, with no significant differences for community infrastructure, child characteristics (sex, age, language, orphan, health status or anthropometrics), child development tests (described in detail in section 4) or caregiver and household characteristics (Table 2).<sup>20</sup> In 2010, approximately two years after the start of the program, we conducted a follow-up survey on the panel of target children and their households as well as the current community leader and a cross-section of 1<sup>st</sup> graders in the same primary schools interviewed at baseline. We also visited the preschools in treatment communities to collect information on the status of the program’s operation. An intensive tracking effort was made to locate the target child and minimize sample attrition. If the child had moved from his or her original place of residence we attempted to interview the child (and their current household) so long as he or she maintained residence in Gaza Province (including outside the three intervention districts) or had moved to the capital city, Maputo. Overall, we successfully located 94.9% of the baseline sample, for an average attrition of approximately 2.5% per year and with no differential attrition between treatment and control (94.8% re-contact in treatment, 94.9% re-contact in control).

> 19 The modified Breusch-Pagan test suggested by Solon et al (2013) indicates the presence of heteroskedasticity associated with cluster sizes. In this case weighting can improve the precision of the estimates. Unweighted estimates are presented in Supplementary Materials Table 28.

> 20 See Supplementary Materials Table 6 for baseline balance of community level variables.

10
days of a child’s life are the most critical for early childhood development and for determining whether a child will grow up stunted. It has also been shown that height at age three strongly predicts adult height (Maccini & Yang, 2009). Lower height‐for‐age scores can lead to severe consequences for cognitive development, overall health, and even socio‐economic conditions that carry into adulthood.

We assign each woman a fractional measure of the share of years exposed to high levels of nitrate‐nitrogen between the year of birth and age 3 in the district where she was born. Since districts have split over time, we use parent districts to allow comparability across time. We then compare later‐life health outcomes among cohorts with more and less pollution exposure after accounting for a rich set of controls.

Even though direct measures of drinking water quality are unavailable, in‐situ monitoring data serve as a reasonable proxy for proximate levels of nitrates in drinking water. This is because nitrates are notoriously expensive and difficult to clean out of water, and cannot be sufficiently treated using conventional methods.<sup>5</sup> Evidence from a slew of countries around the world, including Morocco, Niger, Nigeria, Senegal, India, Pakistan, Japan, Lebanon, Philippines, the Gaza Strip and Turkey, show that nitrates in drinking water often cross conventional safety thresholds (Ward et al., 2018).<sup>6</sup> 2.3 Additional Controls We control for both average rainfall in millimeters and average temperature in degrees Celsius as these have been shown to impact adult outcomes (Maccini and Yang 2009, Fishman et al. 2019, Hyland and Russ 2019) and are known to also interact with nitrate loadings in waterways (Zheng et al. 2016). These are obtained from the Indian Meteorological Department. In some specifications, we also control for fecal coliforms from the CWC data set. They are an oft‐used measure of domestic pollution, and are a major focus of water supply, sanitation, and hygiene (WASH) operations. It is measured as the “most probable number” of coliform organisms per 100 mL of water (MPN/100 ml, reported in thousands).

> 5 Indeed, even in the United States, the percentage of public water systems that have violated safety limits for nitrates in drinking water has increased in the 15 year period between 1994 and 2009, due to the difficulty of coping with the rising nitrate pollution and the concomitant rise in the costs of water treatment (Ward et al. 2018). 6 In Senegal, studies have recorded nitrate-nitrogen levels going beyond 40 mg/l, more than 4 times the safety limit for NO3-N. Extremely high levels of nitrate have also been reported in the Gaza Strip, where nitrate reached concentrations of 500 mg/L NO3 in some areas (10 times the safety limit for NO3), and more than 50% of publicsupply wells had nitrate concentrations above 45 mg/L NO3. Other site-specific studies in India have found nitrates in drinking water supplies to be particularly high in rural areas, where average levels are reported to be between 46 mg/L NO3 and 66.6 mg/L NO3 with maximum levels exceeding 100 mg/L NO3 in several regions.

6