rafmacalaba/datause-encoder-data huggingface dataset:
each of the three treatments, respectively, on the outcomes. _Xi_ are control variables for whether the property owner owns more than one property, the value of the property, whether the property has a female owner or co-owner, whether any of the properties is used for commercial purposes, and the location (one of three blocks) of the property.<sup>19</sup> We report randomization inference _p_ -values obtained using the `ritest` command in Stata. To mirror the reassignment protocol described above, in each iteration, we rerandomize at the property level and use the property-level treatment to determine owner-level treatment, holding fixed the ownership structure in the data.

Since the delivery rate was balanced across the treatment and control groups (table A2), the subsequent analysis will focus on those property owners who received the notices. Table A3 shows that property/property owner characteristics are also balanced in this group.

# **4.3 Results and Discussion**

Table 2 shows the impact of the different treatments (figure 1 displays the results graphically). The identification and penalty notice leads to an 8.1 percentage point increase in the likelihood of responding to the notice, almost tripling the 4.4 percent response rate in the control group. Similarly, this combined intervention more than quadruples tax payment rates: it results in a 7.6 percentage point increase over the control mean of 2.2 percent. For the impact on the amount of tax paid, the coefficient (a US$66 increase) is 11 times the control mean of US$6. However, the standard errors are large given the low overall compliance rate, and we are unable to detect a statistically significant difference.

The other two treatments—the identification notice and the penalty notice—do not have a detectable statistically significant effect on any of the outcomes. For the identification notice, we can reject a 5.1 percentage point increase and a 4.3 percentage point decrease in the likelihood of making a payment. For the penalty notice, we can reject a 2.6 percentage point increase in the likelihood of making a payment and a 5.2 percentage point decrease. Importantly, the increase in payment from the identification and penalty notice is statistically different from the impact of the identification notice ( _p_ = 0 _._ 014) or the penalty notice ( _p_ =

> 19 For most property owners with multiple properties, the properties were colocated. When not colocated, the location of the highest-value property was used.

16
to each country’s data set in the RIGA database. Specifically, as in Clementi et al. (2020), we consider here the following household budget surveys: the Ghana Living Standards Survey of 2005; the Kenya Integrated Household Budget Survey of 2005; the Nigeria Living Standards Survey of 2004; and the Uganda National Household Survey of 2005.

For a second group of SSA countries—Burkina Faso, Ethiopia, Malawi, Niger, Rwanda, and Tanzania— we rely on data from the Rural Livelihoods Information System (RuLIS), a major database to access cross-country comparable data and information on household income and expenditures at the microlevel.<sup>5</sup> Started as a joint research project among FAO, the World Bank, and the International Fund for Agricultural Development, the RuLIS database builds on the methodology developed and adopted by the RIGA project, whose procedures were deeply revised, integrated, and extended to RuLIS. In general, while the RIGA project aimed at constructing comparable income measures from household surveys in order to provide annualized benchmark aggregates, which—despite differences in the quality of information in each survey—would be suitable for cross-country analyses (Carletto et al. 2007), RuLIS includes a more standardized database that is meant to be a tool for analysis and wider dissemination. The RuLIS surveys<sup>6</sup> considered in this study cover a diverse set of SSA countries,<sup>7</sup> some of which have more waves of data and give us the opportunity to gauge the trends.

The main consumption variable that is used in the paper is the household total annual expenditure on food and nonfood items. As for income, the household income aggregates and their components included in the RIGA/RuLIS database closely follow the definition given by the International Labour Organization (ILO 2003), which considers as income receipts those that (1) recur regularly, (2) contribute to current economic well-being, and (3) do not arise from a reduction in net worth (Carletto et al. 2007). These three criteria are embodied in each of the components of income; irregular payments such as lottery earnings or inheritances, investments, savings, and the value of durables are not included in the RIGA/RuLIS definition and measure of income. Furthermore, costs are also considered to ensure that

> 5 See FAO, “RuLIS—Rural Livelihoods Information System,” database. <u>http://www.fao.org/in-action/rural-livelihoodsdataset-rulis/en/.</u>

> 6 Burkina Faso, Enquête Multisectorille Continue (2014/15); Ethiopia, Ethiopia Socioeconomic Survey (2013/14, 2015/16); Malawi, Integrated Household Survey (2013, 2017); Niger, National Survey on Household Living Conditions and Agriculture (2011, 2014); Rwanda, Integrated Household Living Conditions Survey (2013/14); and United Republic of Tanzania, National Panel Survey (2009, 2012/13).

> 7 Notice that both the RIGA and the RuLIS projects cover more SSA countries and provide datasets that are sometimes more recent than those used in the present analysis. However, limited coverage of the population and issues of accuracy caused us to focus only on the countries (and years) mentioned in the text.

6
|Can help: Parents have too many personal/financial problems|0.43|0.40|0.03|
|---|---|---|---|
|SE|||0.05|
|Should receive additional bonuses if students perform well on official exams|0.86|0.81|0.05|
|SE|||0.04|
|Able to help even the lowest performing students learn|0.93|0.85|0.08|
|SE|||0.03**|
|Confident: Motivate students to learn regardless of their financial status|0.94|0.97|0.04|
|SE|||0.02*|
|Confident: Compensate for students'poor prior preparation|0.75|0.78|0.03|
|SE|||0.04|
|Confident: Overcome the influences of a student's home environment|0.84|0.79|0.04|
|SE|||0.04|
|Promotion or transfer should partly be dependent on student's performance|0.32|0.43|0.11|
|SE|||0.05**|
|Acceptable absence: Complete assigned curriculum|0.15|0.18|0.03|
|SE|||0.04|
|Acceptable absence: Leave students with work to do in absence|0.43|0.45|0.01|
|SE|||0.05|
|Acceptable absence: Doing something useful for the community|0.52|0.69|0.17|
|SE|||0.05***|

36
Middle East and North Africa Economic Update. Washington D.C., Washington D.C., USA. Retrieved 02/29/2024, from https://elibrary.worldbank.org/doi/abs/10.1596/978-1-4648- <u>1974-2</u> Gavin, W., & Mandal, R. (2002). Predicting inflation: food for thought. The Regional Economist,, Jan, 4-9.

Green, R., Cornelsen, L., Dangour, A., Turner, R., Shankar, B., Mazzocchi, M., & Smith, R. (2013). The effect of rising food prices on food consumption: systematic review with metaregression. BMJ, 346(f3703). doi:10.1136/bmj.f3703 Headey, D., & Ruel, M. (2023). Food Inflation and Child Undernutrition in low and middle income countries. Nature Communications, 14(5761). doi:10.1038/s41467-023-41543-9 Hirvonen, K., Minten, B., Mohammed, B., & Seneshaw, T. (2021). Food prices and marketing margins during the COVID-19 pandemic: Evidence from vegetable value chains in Ethiopia. Agric Economics, 52(3), 407-421. doi:10.1111%2Fagec.12626 Joutz, F. (1997). Forecasting CPI Food Prices: An Assessment. American Journal of Agricultural Economics, 79(5), 1681-1685.

Kalkuhl, M., von Braun, J., & M., T. (2016). Volatile and Extreme Food Prices, Food Security, and Policy: An Overview. In M. Kalkuhl, J. von Braun, & M. Torero (Eds.), Food price volatility and its implications for food security and policy. Cham, Switzerland: Springer. doi:10.1007/978-3-319-28201-5 Kisan, G. (2015). Review of global food price databases: Overlaps, gaps and opportunities to improve harmonization. Food Security Information Network. Retrieved 01 10, 2024, from <u>https://reliefweb.int/report/world/review-global-food-price-databases-overlaps-gaps-and-</u> - <u>opportunities improve</u> Lentz, E., & Maxwell, D. (2022). How do information problems constrain anticipating, mitigating, and responding to crises? International Journal of Disaster Risk Reduction, 81(103242). doi:10.1016/j.ijdrr.2022.103242 Lu, Y., Shen, M., Wang, H., & Wang, X. (2021). Machine Learning for Synthetic Data Generation:. Journal of Latex Class Files, 14(8). Retrieved from <u>https://arxiv.org/pdf/2302.04062.pdf</u> Manners, R., Adewopo, J., Niyibituronsa, M., Remans, R., Ghosh, A., Schut, M., et al. (2022). Leveraging Digital Tools and Crowdsourcing Approaches to Generate High-Frequency Data for Diet Quality Monitoring at Population Scale in Rwanda. Front. Sustain. Food Syst., 5. doi:10.3389/fsufs.2021.804821 Minet, J., Curnel, Y., Gobin, A., Goffart, J., Melard, F., Tychon, B., et al. (2017, November). Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach. Computers and Electronics in Agriculture, 142(Part A), 126-138. doi:10.1016/j.compag.2017.08.026 Nguyen, T., Nguyen, H., Lee, J., Wang, Y., & Tsai, C. (2023). The consumer price index prediction using machine learning approaches: Evidence from the United States. Heliyon, 9(10), e20730. doi:10.1016/j.heliyon.2023.e20730 18
firms appear constrained, but only 10 percent of the large firms. About two-thirds of the large firms choose not to participate in the credit market, compared to only a third of the micro firms. The notion that the smallest firms are credit constrained is supported by regression results indicating that, controlling for other important factors such as expected profitability and indebtedness, the likelihood of a successful loan application varies with firm size. While this suggests banks are biased against small firms, the authors note that this result may reflect transaction costs on the part of banks. In any case, the size effect is substantial: for a micro firm to have an equal chance of getting a loan as a large firm, the micro firm needs to have an average return on fixed capital more than 200 percentage points higher than the large firm.

**Table 2. Credit Constraints by Firm Size**

||Micro|Small|Medium|Large|All|
|---|---|---|---|---|---|
|No credit demand|33|50|67|66|55|
|Demand, but rejected*|64|42|21|10|33|
|Received loan|3|8|12|23|12|

*Includes firms that suggested that a loan application would be rejected by banks _Source_ : Bigsten et al. (2003)

Another novel approach for analyzing the links between formal credit and company investment is adopted by Habyarimana (2004) who uses matched bank-firm data from Uganda, collected as part of the 2002 RPED, to estimate the effect of losing a banking relationship on firm performance. The background to this study is that four Ugandan banks were closed between September 1998 and 1999 because of imprudent banking practices. As a result, 30 percent of the firms in the sample lost one or several banking relationships. Habyarimana estimates that over the three years following the banking crisis, the average annual growth rate of employment among firms that lost a banking relationship was 2.3 to 4 percent lower than the average growth rate of unaffected firms. These estimates are obtained whilst controlling for firm fixed effects and time effects that are allowed to differ across sectors. Further, firms affected by the banking crises are more likely to report being credit constrained, suggesting that losing a banking relationship hampers investment.

11
and Wang, 2005, Kapteyn and van Herwaarden, 1980; Kapteyn et al., 1997; McBride, 2001). Alpizar et al (2005) use a group of students from Costa Rica and a specifically designed questionnaire to test the relative income hypothesis using the ratio approach and the differential approach with both income and consumption measures. They find a negative and significant sign for relative income in all equations.

Two studies that use the German SOEP data (D’Ambrosio and Frick, 2007 and 2012) find that the role of relative deprivation (measured with the Yitzhaki, 1979 income measure) in explaining subjective well-being carries a negative sign and is more important than the role of absolute income. Interestingly, these studies also find that both absolute and relative income in the past contribute to shape the perception of well-being in the present, what we referred to as the “ego” comparisons in section four.

For China, there is overwhelming evidence that relative income and deprivation is very important in both urban and rural areas. Knight and Song (2006) using a sample of Chinese households find that people who perceived their own income to be above average were much happier than those who perceived their own income to be below average after controlling for absolute income. Appleton and Song (2008) and Knight and Gunatilaka (2008) find that relative income is very important in rural China to explain happiness and Wang and Vander Weele (2011) find that relative deprivation is very relevant in explaining happiness also for urban residents.

Despite the evidence reported for countries like China and Costa Rica, several studies that have focused on poorer countries found that relative income is positively correlated with happiness or satisfaction. For example, Ravallion and Lokshin, (2010) find a positive sign for relative income measure using a survey from Malawi and the same sign is found by Senik (2004) for Russia and by Kingdon and Knight (2007) for South Africa.

There is also evidence that _relative income predicted_ with an income equation is a significant factor that explains satisfaction. In these types of models, predicted income is interpreted as the income that people expect given their own characteristics and relatively to the characteristics of the reference group, where the reference group can be the whole population or selected sub-sets of the population. Hamermesh (1977) uses the residual from a wage equation as reference income and finds this residual to have a positive and significant effect on job satisfaction. Clark and Oswald (1996) using a similar approach with a sample of British employees find both income and reference income to be significant and with opposite signs in a job satisfaction regression. Sloane 21
11 regression of poverty on city size is negative in all cases, and significant at the 10% level in five (four) out of eight cases for FGT(0) (FGT(1)). See Table 3.<sup>11</sup> The inverse relation between poverty and city size can also be discerned in Figure 1, which presents the distribution of poverty incidence _within_ each size category, by means of box-plots. The box-plots indicate that there is much greater variance in poverty rates among smaller towns, as one might expect from their sheer number. But the _median_ poverty rate falls markedly and consistently with city size in Brazil and Kazakhstan. It also falls in Albania, although less markedly. In Mexico, the gradient is clear across all city-size categories except for the metropolitan areas for which the median poverty rate is slightly higher than for the large city-size category (but below all other categories). In Morocco, the negative correlation detected in Table 3 is driven by much lower poverty in metropolitan areas, with no clear pattern among the other size categories. In Sri Lanka and Kenya, the relationship owes to greater poverty incidence in small and very small towns, with no clear pattern among medium and larger towns. Similarly in Thailand it is noteworthy that the overwhelming majority of urban centers belong to the extra small category, with metropolitan Bangkok representing the one very large exception. (Chiang Mai, Thailand’s second largest city, had a population of only just over 280,000 in 2008.) The median poverty rate in Thailand’s smallest towns is markedly higher than in all other city-size categories.

These patterns are also clearly visible in Figure 2, which presents the nonparametric regressions of FGT(0) on the logarithm of city size for each country. Here again, it is least visible in Kenya and Sri Lanka. In Morocco, as we have seen, the negative relationship is driven by markedly lower poverty in Casablanca. With the exception of Kenya and Mexico, metropolitan poverty incidence is less than half of the average urban poverty in every country in our sample that has at least one metropolitan area.

To investigate the robustness of the inverse poverty-city size relationship with respect to variations in the poverty line, we plotted the cumulative distribution function

> 11 These regression coefficients are presented as illustrative of correlations only. City size is clearly endogenous, and there are evidently many omitted variables, so no inference of causality is possible. Some countries do not display the full set of regressions for lack of data (the Kenyan census for instance being very short, no information is available on infrastructure access).
and researchers have promoted standardized methods to determine comparable cross-country poverty estimates by harmonizing a spatially-deflated welfare aggregate and by estimating a unique poverty line. At the regional level, $2.5 and $4 per person per day at 2005 PPP values have become the poverty lines widely used in LAC. Using these lines, 12 percent of the population in the region qualified as extremely poor in 2013, while 24 percent qualified as moderately poor during the same year.

This paper provides inputs to guide the update of regional extreme and moderate poverty lines for measuring poverty in LAC as a whole, and it is the first attempt to explicitly propose and document these inputs in the region. The recently released 2011 PPPs represent an excellent opportunity to estimate regional extreme and moderate poverty lines in LAC. In order to achieve this objective, we collected the most comprehensive and up-to-date available information on extreme and moderate official poverty lines combined with the standardized microdata available under the SEDLAC project. Unlike previous global estimates, we do not find a strong relationship between per capita income and the value of the poverty line. Therefore, we did not exclude any countries from regional estimates in LAC.

This paper sets regional extreme and moderate poverty lines ranging from $2.5 to $3.2 and from $5 to $6.8 per person per day, respectively. Depending on the regional poverty line selected, we find that approximately 8.0 percent to 12.0 percent of LAC’s population qualified as extremely poor in 2013, while approximately 25.0 percent to 33.0 percent of the population qualified as moderately poor.

We believe that the approach described in this paper, together with the results and underlying data, will serve as valuable inputs for guiding the regional debate on poverty measurement in LAC. Additional research going forward includes updating the middle-income line currently set at $10 per person per day at 2005 PPPs, which reflects a level of income at which the probability of falling back into poverty (based on $4 per person per day) is less than 10 percent (Ferreira et al. 2012).

19
Table 2: Summary of Model Variables

||Own|Formal C|Formal NC|Informal|Both|
|---|---|---|---|---|---|
|_Loan size_||||||
|Mean|—|125|37|35|126|
|Std. Dev.|—|297|57|98|257|
|_Wealth_||||||
|Mean|1,715|1,952|1,207|982|1,182|
|Std. Dev.|6,116|6,014|4,495|4,223|5,087|
|_Expected Income_||||||
|Mean|1,946|2,311|1,440|1,198|1,459|
|Std. Dev.|6,319|6,417|4,745|4,386|5,423|
|_Working to Total Capital ratio_<br>mean|0.649|0.513|0.706|0.747|0.684|
|Std. Dev.|0.331|0.318|0.320|0.298|0.329|
|_Credit Constraints_<br>mean|0.380|0.563|0.668|0.517|0.652|
|Std. Dev.|0.486|0.497|0.471|0.500|0.477|
|_Mean Household Characteristics_||||||
|Years of education|3.877|4.217|4.377|3.797|4.440|
|Head of Household is Male|0.694|0.791|0.852|0.775|0.831|
|Past client of formal inst.|0.457|0.802|0.787|0.523|0.712|
|Past client of informal inst.|0.155|0.203|0.226|0.229|0.215|
|Formal inst. present in village|0.278|0.783|0.759|0.443|0.762|
|Number of formal inst. in village|0.540|1.534|1.516|0.888|1.583|
|Savings in formal institution|0.510|0.814|0.834|0.329|0.800|
|Member of a village committee|0.056|0.121|0.140|0.074|0.119|
|Observations|880|446|494|447|345|

Note: The category “Own” includes households who do not have outstanding loans. “Formal C” includes institutions that require collateral: Commercial Banks, Finance and Insurance Companies and BAAC individual loans. “Formal NC” includes BAAC group loans, loans from Agricultural Cooperatives and loans from village-level institutions. “Informal P” and “Informal R” are merged into Informal. “Both” include households who actively borrowed from both formal and informal sources. Capital requirements, wealth and income figures are in 1,000 Baht.

Source: Townsend-Thai Data.

wealthier than informal borrowers but the high standard deviation suggests that there is more dispersion. The model can also rationalize these facts. Holding the ratio _η_ and ability _z_ constant, wealthier households will rely on formal institutions for additional 17
tions of such programs are very few (Bhalotra et al., 2017). A few recent studies have examined the long-run impacts of access to midwives or trained nurses (Bhalotra et al., 2017; Lazuka, 2018; Hjort et al., 2017). However, much of the evidence is from developed countries, or is based on an historical context in such countries which may not be applicable in developing countries.<sup>5</sup> Moreover, most studies focus on a limited set of long-run outcomes. This paper provides some of the earliest evidence of long-run effects of midwife services on a large set of outcomes capturing effects on human capital formation in a less developed country.<sup>6</sup> This paper also contributes to the small literature on early-life origins of cognition in developing regions.<sup>7</sup> A large number of animal and human studies in the field of developmental psychobiology show that under-nutrition, micro-nutrient deficiencies, environmental toxins, and poor stimulation early in life can lead to permanent changes in brain structure and function and thus have long-lasting cognitive and behavioral effects (Liu et al., 2000; Pollitt et al., 1993; Cas, 2012). In particular, poor nutrition and micro-nutrient deficiencies _in utero_ and during the first two years of life are associated with cognitive deficits among humans (Pollitt et al., 1993; Cas, 2012). Thus, the paper adds to the literature by providing causal evidence for the role of primary public health care in improving adult cognition.

> 5Especially quality, access, and demand for primary health care delivery are very different in the developing world as compared to say, the US (Macfarlane et al., 2000).

> 6This paper also adds to the scant literature of long-term effects of positive, policy-based treatments, especially in developing countries. In this way, this paper differs from studies that analyze the impacts of interventions early in life in developing countries such as Ito and Tanaka (2018) and Field et al. (2009) in that we focus on a host of adult outcomes rather than the child or adolescent outcomes.

> The study by Cas (2012) comes closest to the present study, which examines the impact of the same program as ours on education and cognition outcomes for children of age 11-17 years, using the data from the first four waves of IFLS. The analysis of this paper expands on this by focusing on adult cognition (as opposed to early and middle adolescents) by considering the latest fifth wave of IFLS and considering a wider variety of outcomes such as health and earnings of these adults. Please refer to the subsection S1.1 “Additional Literature Review” in the Appendix available at _The World Bank Economic Review website_ for more details.

7The authors are not aware of any large-scale intervention study other than Venkataramani (2012) and Cas (2012) to study later life cognition in developing countries for an early-life health intervention.

4