rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>These messages were delivered via two outreach channels over a period of one week: i) individually delivered pamphlets by key individuals in the community, or “norm entrepreneurs (NEs)” (Treatment 1); or ii) mass communication campaign involving community events, posters at key locations around the village (entry, exit, schools, community halls and markets), as well as NE‐delivered pamphlets (Treatment 2). While T2 ensured maximum outreach, T1 utilized personalized delivery through individuals who are well connected or highly central to the village social network, and who could play an influential role in the creation of a new social norm around latrine use and act as the positive norm enforcers within their respective communities (see Appendix 1 for detailed theory of change).</mark> <mark>Treatment was randomly assigned at the village level. Two of the five baseline villages were assigned to T1 and the remaining three to T2—covering a total of 225 households with latrines across all five villages. In addition, 10% of the households (25) in the T1 villages were randomly selected to receive no intervention (and serve as a control group).</mark>

# **<mark>Results</mark>**

<mark>— The interventions targeted change in latrine use behavior through two pathways change in beliefs about the importance of using latrines via its association with cleanliness, and updating empirical expectations about the norms of latrine use in the community. A follow‐up survey was conducted in all baseline villages following the weeklong interventions to evaluate the impact of the interventions on latrine use behavior and related personal and normative beliefs. Of 204 households surveyed at baseline, 156 (76%) were included in the follow‐up survey. The attrition was due to the enumerators’ inability to resurvey the baseline respondents within the short follow‐up survey period. (Since beliefs and behaviors are subjective, baseline respondents could not be replaced with other members of the
draw on a series of data sources listed in Table 1. Importantly, we not only measure informality as the gap between overall economic activity and what is captured in the administrative data, but additionally rely on measures generated independently of the administrative records. This helps us to rule out the possibility that our measures of informality capture idiosyncratic patterns that are specific to the VAT system.

Throughout this paper, we draw on three types of measures for economic activity: employment figures, the number of firms, and value added (i.e. the difference between sales and purchases). Our preferred measure of informality uses formal sector employment as a share of total employment, drawing on a comprehensive labor force module in the 2019 population census (KNBS, 2019). Alternative measures of informality based on the number of firms rely on estimates of the universe of businesses in KNBS (2016),<sup>13</sup> while a value-added based measure utilizes estimates

> 12See Appendix A.2 for a detailed discussion of these margins and why administrative data may not fully capture economic activity.

> 13KNBS (2016) obtains information on the number of licensed businesses from county governments and estimates the number of unlicensed businesses based on household survey data.

8
Inclusion’s Costing Dashboard. Expanding this tool to more policies, in</u> particular the policies that affect adolescents, would be extremely beneficial for policy makers to determine the best policies for improving adolescent well-being. Once more costing data is available, we believe it would be highly valuable to categorize interventions targeted to adolescent girls based on their cost-effectiveness in a similar manner to the report from the Global Education Evidence Advisory Panel (2020): <mark>“Smart Buys: Cost-effective Approaches to Improve Global Learning Levels”.</mark>

# **5.2.Improving Targeting via Machine Learning Methods**

In addition to universal programs (e.g., universal free primary school, health care, etc.), governments want to offer policies that are targeted to certain groups of individuals (when budgets are limited, targeting programs can lead to substantial gains in social welfare - Hanna and Olken, 2018). Often, however, it is difficult for governments (especially governments in developing countries) to determine which individuals to target. Better targeting of programs can lead to much larger impacts on the outcomes of interest and is thus an important feature of program design. For example, many CCT programs only generate small (albeit statistically significant) effects on enrollment as many of the recipients are inframarginal. Conversely, Duflo et al. (2021), in the context of Ghana, find substantial gains in enrollment when targeting scholarships to students who gained admission to secondary school but did not immediately 19
The linear and quadratic regression show that larger displacement inflows are always correlated with larger NTL growth in settlement with higher initial NTL levels , at, 95% confidence interval level, see columns 3 and 4 of Table C.2.

The linear and quadratic regressions analysis on the settlements starting with lower initial NTL levels return mixed results. Table C.2. column (1) show that equation (4) is positive, i.e., the NTL growth increases with the Total Inflow, as the interaction term is positive. The linear regression suggests that a marginal increase in net inflows of persons would increase the NTL in places with medium-high levels of human activities, i.e., with NTL above 0.30. The quadratic regression displays interesting patterns:

- In outbound settlements, a reduction in the net outflows of displaced persons raises the NTL level in outbound areas with important human activity. A sufficient condition for a marginal decrease in outflows (i.e., an increase in Total Inflow) to be associated with an increase in NTL levels is that past NTL be larger than 0.9.<sup>10</sup>

- In inbound areas, a marginal increase in net inflows of persons would increase the NTL in places with small levels of human activities, i.e., with NTL between 0 and 0.29.

_Table C.2. Regression of NTL on net inflows of persons in settlement on two different samples (higher vs lower initial NTL levels)_

|||Y = NTL l<br>|evels<br>||
|---|---|---|---|---|
||Sample: Sm<br>|all initial NTL<br>|Sample: Lar<br>|ge initial NTL<br>|
|VARIABLES<br>|(1)<br>Linear|(2)<br>Quadratic|(3)<
power in Sweden (Molinder et al, 2021), and drives structural change through the decline of employment share in agriculture in the United States (Gaggl et al, 2021) and Norway (Leknes and Modalsli, 2020). Lewis and Severnini (2020) show that the rural U.S. counties with early access to electricity experienced increased economic growth compared to their counterparts. This impact persisted decades after the county was fully electrified. Electrification also has long-lasting effects on the productivity of firms. Using cityindustry level data from the United States for the years 1890 – 1940, Fiszbein et al (2020) show a rapid increase in the labor productivity rates in the manufacturing sector.

As in developed economies, electricity is a key input to producing goods and services and improving the quality of life in developing countries. Therefore, achieving universal access to electricity is an important policy goal for developing countries. Despite large investments, relatively little is known about the causal effects and channels of electrification on economic outcomes, such as on household welfare, firm productivity, or economic growth. This section 14
well as more micro-level evidence confirm the poverty-reducing and growth-enhancing impact of a better distribution of productive assets.

This apparent potential notwithstanding, actual experience with land reform has in many instances fallen short of expectations. Despite-or because-of this, land reform remains a hotly debated issue in a number of countries (e.g. Zimbabwe, Malawi, South Africa, Guatemala, El Salvador, Brazil, Colombia) some of which are spending considerable amounts of resources for this purpose. A mechanism to provide an efficiency- and equity-enhancing redistribution of assets that would increase overall investment at a cost that is comparable to other types of government interventions would be very desirable.

This paper describes a new type of _negotiated_ land reform that relies on voluntary land transfers based on negotiation between buyers and sellers, where the government's role is restricted to establishing the necessary framework and making available a land purchase grant to eligible beneficiaries. Section two discusses the land reform experience in general, thus providing the historical background and conceptual basis for the subsequent argument. Section three is devoted to a more detailed description of negotiated land reform in Colombia, covering the reasons for choosing a negotiated approach, its principles, and its implementation in a number of pilot municipios. Section four compares the mechanisms utilized to those adopted in Brazil and South Africa and briefly highlights some of the implications for monitoring the new approach. Section five concludes.

# **2 LAND** REFORM: POTENTIAL **AND** HISTORICAL EXPERIENCE

While land reform has long been the subject of policy debate, the motivation for addressing land issues has shifted considerably over time. Initial discussions were largely motivated by political considerations, supported by the notion of the inverse farm-size productivity relationship. This notion had received empirical support from cross-sectional studies across states (Kutcher and Scandizzo, 1979), countries (Berry and Cline 1979), or individual farms (Barraclough, 1970). 1 More recent contributions have emphasized the importance of asset ownership in situations characterized by incomplete contracting (Bardhan et al. 1998),
. Control variables included but not reported. Full regression tables are available upon request. The description and source of variables are provided in Table A3 in the Appendix.

variable bias if not accounted for.<sup>32</sup> First, we consider the number of documents required to import taken from the Doing Business database. Since it covers the documents required for clearance by the entities involved in the import process, including government ministries, Customs authorities, port and container terminal authorities, health and technical control agencies as well as banks, it captures the extent to which importing can be difficult due to cumbersome administrative formalities. Since any misstep in furnishing required documents by the importer or its delegate Customs broker may cause delays that are not necessarily related to the efficiency of the border control process but rather to the characteristics of the importer itself, or the managerial quality and competencies of its broker, we expect this variable to influence import uncertainty, beyond its direct implications for export performance already documented in the literature.<sup>33</sup> In addition, failing to directly take into account broader indicators of trade costs might bias the results if they happen to be correlated with each other. Table 9 (Columns 1-5) evidences a negative association of the number of documents required to import with the entry rate and the thirdyear survival rate of entrants, but its introduction does not change our initial results. Second, following the same rationale, we include an indicator derived from the Logistics Performance Index (LPI) and measuring the timeliness of shipments in reaching their destination within the scheduled or expected delivery time. Again, our baseline estimates remain unchanged, while the LPI index influences neither the entry nor the survival rate of exporting firms (Columns 6-10). This result is meaningful for two reasons. First, the LPI index covers the timeliness of the whole import process, including transportation from the port of origin to the port of destination as well as road transportation to deliver the imported

> 32Full regressions are available upon
of COVID-19 in March 2020, we observe significant spikes and dips in demand for some services. Demand for services that require face-to-face interaction, such as transport, hotel and restaurant, tourism, and retail, has substantially contracted, with hotel and restaurant services worst hit. On the contrary, demand for services that require less face-to-face interaction or provide solutions to reduced personal interaction, such as ICT and delivery, has increased. For instance, countries that have recorded 10 or more confirmed cases saw a 63-79 percent decline in demand for hotel and restaurant services, and a 46-77 percent increase in the demand for ICT services.

> 2 We have dropped 13 small island nations and territories from our analysis sample owing to high volatility in their internet search data, which makes identifying search trends problematic.

4
# **6.4 No rise in consumption inequality since 2011, but indications of a rise in 2019**

**The unreleased NSS-2017 shows a moderation in inequality but the magnitude of the reduction is comparatively large.** Based on leaked NSS-2017 results, Subramanian (2019) estimates rural and urban consumption inequality to have reduced by 0.0291 and 0.0387 Gini points since 2011 (based on modified mixed reference period in both NSS rounds). The direction of changes to inequality between NSS-2011 and NSS-2017 agrees with our findings. Our results differ, however, on the magnitude of the inequality reduction. Based on our estimates, average inequality reduction since 2015 in rural- and urban-India are 0.0007 and 0.007 Gini points (using the uniform recall periods of NSS-2011).

In Figure 26, we put the inequality estimates in a global context. Data on inequality is obtained from World Development Indicators. Countries that report at least one estimate of the Gini coefficient between 2009-2013 (two years before and after NSS2011) and 2015-2019 (two years before and after NSS-2017) are included. We average the Gini coefficients for each of the two time-periods and evaluate the difference in mean values to observe how much inequality has changed between the two points in time across countries. The MMRP-2011 level of Gini and the change in inequality based on NSS-2017 data is highlighted in blue; whereas the URP-2011 level of Gini and the change in inequality from our analysis is highlighted in red. It follows that there are only a handful of countries that report inequality reductions that are comparable to what is reported between the NSS-2011 and NSS-2017. By comparison, the rate of reduction based on URP-2011 and our analysis is found to sit well with global trends.

**Quintile consumption growth estimates in IHDS-3 show higher consumption growth in the bottom parts of the distribution** . Figure 27 compares quintile consumption growth rates from the IHDS-3 to our estimates. Average consumption growth in the bottom quintile of the distribution is higher than the growth rates
resulted in a total of 10,449 observations. The two stage sampling procedure adopted ensures that statistically reliable estimates of key demographic and health variables are available for all regions of Ethiopia.<sup>3</sup> The Ethiopia DHS used three questionnaires for the survey. The household questionnaire collects information on basic socioeconomic characteristics of all household members, such as the age, sex, marital status, education, and so on. It also includes information on the dwelling, such as the floor, ceiling, wall materials, and the number of rooms; the source of drinking water and the type of toilet facilities used by the household; as well as ownership of a variety of durable assets. The women’s questionnaire collects information on reproductive history; contraceptive use and knowledge; fertility preferences; antenatal, delivery, and postnatal care; infant feeding practices, child immunization and health; among other things. All eligible women (15-49 years of age) and children born after September 1994 were weighed and measured. Finally, the males’ questionnaire interviews eligible males (15-59 years of age) in every fifth household to collect information on the respondent’s background; contraceptive knowledge and use; marriage; fertility preferences; attitudes about family planning; and knowledge of HID/AIDS and STDs. Interviews were completed for 99 percent of occupied households, with no difference in the overall response rate for urban and rural eligible women. Rural men were slightly more likely than urban men to have completed an interview.

# **3. Malnutrition in Ethiopia**

The Ethiopia DHS data add to the vast and growing international database on demographic and health variables collected as a part of the MEASURE DHS+ project. These surveys have been carried out in 32 countries in Sub-Saharan Africa, facilitating the comparison of malnutrition indicators among low income countries in the region. In a wellnourished population, the distribution of children’s height and weight at a given age approximates a normal distribution. For comparison purposes, children’s height and
that have calorie intake assigned and account for 98.6 percent of total expenditures on food consumed at home.

Table 2 shows the top 10 food groups in POF in terms of average calorie intake per 100g. It shows that 'Meat’ is the most expensive food group, followed by ‘Breads, Cakes and Pies’. On the other hand, ‘Kitchen Oil’ is displayed as the most caloric food group, as expected. It is followed by ‘Flour Derivatives’ (which includes pasta). The cheapest food group is ‘Beans and Legumes’, while the least caloric is ‘Fruits’. Therefore, the data – including our calorie mapping – show some expected patterns of caloric and price distribution across food groups.

**Table 2. Top 10 Calorie intake food groups average kcal and price per 100 g, in 2018 urban Southeast prices**

|**Food Group**|**Kcalper 100g**|**Average priceper 100g**|
|---|---|---|
|Cereals|485.6|2.7|
|Kitchen Oil|1173.8|7.6|
|Meat|254.5|13.1|
|Sweets|548.7|5.0|
|Breads, cakes and pies|497.2|10.8|
|Flour Derivatives|508.0|4.5|
|Dairies|151.0|5.5|
|Potatoes and tubers|450.6|3.3|
|Beans and Legumes|430.7|3.7|
|Fruits|89.7|3.8|

_Source:_ Own calculations using POF 2017/18 data and TBCA, based on food items categorizations by IBGE.

Next, we calculate the household-specific cost per calorie of each food item consumed by dividing the expenditures reported on each food item by the product of the associated quantity purchased in kilogram and the calorie intake per kilogram
research.<sup>2</sup> Modules on stepping out of one’s comfort zone and exposing women to success stories aim to build soft skills. Critically, the program does not include access to credit or any transfer of cash or capital. Women self-select into participation after attending a detailed orientation session. Self-selection mitigates concerns about ineffective program targeting, allowing us to focus on the importance of skills for economic inclusion among women motivated to learn new skills.<sup>3</sup> We collect three types of data to build a holistic understanding of the role skills play in economic inclusion among the women in our sample. First, we collect detailed in-person survey data on households and businesses at baseline before the intervention begins, midline shortly after women graduate from the program, and endline 12–18 months after graduation.<sup>4</sup> Our three rounds of in-person data collection elicit information about all businesses a woman runs, allowing us to observe how women’s business portfolios change over time. Second, we collect high-frequency SMS data on revenues over the entire study period to understand whether the program enables women to better cope with negative shocks. Finally, we collect data on the social and business networks of women in our sample. Doing so allows us to quantify network-based spillovers.

The program strengthens hard and soft skills. We observe improved business tracking, price management, and more hours spent working in the main business. Effects on select business practices persist in the medium-run (18-24 months). Entrepreneurial soft skills also improve, with treated women being 38% more likely to set goals than women in the control group and exhibiting improvements in grit at endline.

Improvements in skills lead to gains in women’s economic inclusion. At

> 2These program components are similar in spirit to the ILO’s Start and Improve Your Business program as in deMel, McKenzie, and Woodruff (2014) and the personal initiative training in Campos et al. (2017).

> 3We
lack any treatment by the IHC program, the post-treatment RMSPE for the placebo firms is a good indicator of how well SCM forecasts the outcome variable. The results are reported in Table 13. Dube and Zipperer suggest that the preferred set of conditioning variables should be those that produce the smallest post-treatment RMSPE. By this metric, the benchmark set of conditioning variables is the set that performs best for each of the five outcome variables.

**<u>Table 13.  Forecast Accuracy among Placebo Firms</u>**

||RMSPE for p<br>|lacebo firms<br>||
|---|---|---|---|
||Pre-treatment|Post-treatment|95% CI for population|
|**(**<br>|**log) Median Clear**<br>|**ance Time**<br>|medianof forecast error<br>|
|Benchmark|0.356|0.629|(-0.008, 0.062)|
|Matchallpre-treatment outcomes|0.271|0.631|(0.002, 0.080)|
|Match average pre-treatment<br>outcomes|0.420|0.651|(-0.028, 0.017)|
|**(log)**<br>|**Clearance time - 7**<br>|**5**<sup>**th**</sup>**percentile**<br>||
|Benchmark|0.405|0.740|(-0.049, 0.068)|
|Matchallpre-treatment outcomes|0.291|0.772|(-0.040, 0.073)|
|Match average<br>pre-treatment outcomes|0.485|0.793|(-0.137,-0.026)|
|**
652*|-3.746|
|men Y:1 N:0|||(2.459)|(3.127)|
|Exports (%, cell average)*GDP per capita||0.065||0.096|
|(logs)||(0.078)||(0.072)|
|Exports (%, cell average)*WBL Index||-0.112||-0.165|
|||(0.392)||(0.370)|
|No. of Workers (logs)*Women can work the<br>same night hours as men Y:1 N:0||0.445<br>(0.726)|||
|No. of Workers (logs)*Women can work in<br>the same industries as men Y:1 N:0||||-0.202<br>(0.529)|
|No. of Workers (logs)*WBL Index||-3.368*<br>(1.732)||-2.942*<br>(1.712)|
|No. of Workers (logs)|0.902***<br>(0.291)|2.887**<br>(1.271)|0.916***<br>(0.290)|3.066**<br>(1.225)|
|GDP per capita (logs)|5.876|4.791|7.974|6.291|
||(5.710)|(5.74)|(5.494)|(5.524)|
|Herfindahl-Hirschman Index|-5.197|-5.022|-7.021**|-6.775
rates, however, are low, and the situation is expected to worsen since the labor force is forecasted increase significantly by 2050 (Bloom et al. 2010). In addition, the region is struggling with post conflict and conflict situations. Employment opportunities are central to reduce tensions there, since they offer potential combatants alternatives outside the conflict.

## **Rural regions have missed out on employment generation, and there is a persistently high share of**

**informal and unorganized employment.** There is evidence for a strong divide between urban growth poles and rural regions, which are lagging behind in both economic growth and social indicators (The World Bank, 2011). For instance, small and medium sized firms in Bangladesh and Sri Lanka failed to grow, increasing the “missing middle” problem. Similarly, larger firms in India have created most jobs in urban areas. At the same time, the informal sector of India has grown from approximately 47% to an estimated 62 percent of non-household manufacturing employment in the period of 1989 to 2005 (Dutz et al, 2011). Hence, this paper will discuss the following topics:

- The missing middle indicates that established companies are performing better than new firms. Hence, we will analyze if there is a link between size and age, and firm growth patterns.

- Census evidence shows that urban areas have been growing faster than rural areas (The World Bank, 2011). This suggests a disadvantage of being rural. In particular, we explore if rural and small (large) firms doing relatively worse (better) than their urban counterparts?

- South Asia is a conflict ridden region, however, to varying degrees. We seek to establish a link between conflict and employment growth at the establishment level.

4