rafmacalaba/datause-encoder-data huggingface dataset:
20 decompose the impact of expenditure y on the calories q and on the cost p of calories as in Subramanian and Deaton (1996). In other words, specifying the food demand curve as p q = a y<sup>ε</sup> , we estimate: where ap aq = a and (εp + εq) = ε constitute the shifter and the elasticity of the food demand curve.

The regression results for three years 1997-2003-2009 of the Mozambican survey and for the Ghana survey 2013 are presented in Table 2. In avoidance of the potential bias due to underreporting in Mozambique and over-reporting in Ghana, we focus on the diets between 1000 and 2500 Kcal per capita per day. This subsample includes households with severe food constraints (less than half of requirements) up to a level where calories are no longer a main concern (20% above requirements).

Table 2: Calorie Engel curve and income elasticity of calorie cost , Mozambique 1997-2003-2009 and Ghana 2013

|||**MOZAMBIQUE**||**GHANA**|
|---|---|---|---|---|
||1997|2003|2009|2013|
|**National**|||||
|a|0.88 (99)|1.19 (79)|1.68 (65)|0.74 (157)|
|ε|0.79 (137)|0.71 (119)|0.66 (125)|0.53 (100)|
|εq|0.06 (12)|0.06 (11)|0.08 (18)|0.09 (18)|
|εp|0.73 (113)|0.65 (104)|0.57 (107)|0.44 (82)|
|R<sup>2</sup>|0.80|0.71|0.66|0.63|
|Sample size|3596|4059|5229|4466|
|**Rural**|||||
|a|0.85 (77)|0.97 (52)|1.05 (44)|0.72 (143)|
|ε|0.84 (102)|0.83 (85)|0.85 (108)|0.59 (77)|
|εq|0.09 (11)|0.14 (16)|0.19 (26)|0.13 (17)|
|εp|0.75 (80)|0.69 (66)|0.65 (77)|0.46 (56)|
|R<sup>2</sup>|0.77|0.69|0.75|0.64|
|Sample size|2332|2353|2793|2654|
|**Urban**|||||
|a|0.88 (47)|1.38 (49)|2.20 (43)|0.80 (70)|
|ε|0.77 (79)|0.63 (79)|0.54 (76)|0.48 (49)|
|εq|0.07 (9.6)|0.04 (6.4)|0.03 (5.6)|0.12 (13)|
|εp|0.70 (68)|0.58 (57)|0.51 (71)|0.36 (38)|
<!-- Start of picture text --> m Advanced economies<br>40<br>= EMDEs<br>3<br>6<br>4<br>2<br>0<br>eZ a é iS<br>a2 8685 «<br>(a) Oo €<br>£<br>o<br><!-- End of picture text --> <!-- Start of picture text --> = Non-commodity exporters<br>42<br>= Commodity exporters<br>10<br>8<br>6<br>4<br>2<br>@ ES iS cs<br>Ee 5 3 3 as<br>ag >e ow<br>x o6<br>® OO<br><!-- End of picture text -->
surveys. There is clear evidence that the rich households tend to decline responding to survey questions. Azerbaijan State Statistical Committee (SSC) informed us that most other causes of missing observations have been addressed by replacing originally selected households that could not be interviewed at the time of interview, through a random draw from the census list. A cursory examination of the proportion of the missing observations and income shows positive association. A two-way scatter plots of the share of missing households and mean consumption expenditures at the economic regions level show positive correlation between wealth and refusal to participate in the survey in 2004 (see Figure 2). There are other empirical evidences in the literature that high-income households are less likely to participate because of a high opportunity cost of their time or concerns about intrusion in their affairs (Atkinson, 1987; Korinek, et al. 2005). Groves and Couper (1998) found that higher socio-economic status tended to be associated with lower response rate in the US surveys.

**Figure 2** . Rate of refusal is higher in the richer regions

<!-- Start of picture text --> Baku<br>Absheron-Guba<br>Sheki-Zagatala<br>Shirvan<br>Ganja-Gazakh<br>Qarabagh-MilNakhchivan<br>Mughan-Salyan Lankaran-Astara<br>190000 200000 210000 220000 230000 240000<br>Mean per capita consumption<br>Source: Azerbaijan HIES, 2004.<br>.06<br>.05<br>.04<br>.03<br>Proportion of missing households<br>.02<br><!-- End of picture text --> With this backdrop, for the purpose of hypothesis 2, we assume that all missing households came from the upper wealth echelon due to their unwillingness to participate in the survey. The assumption that all missing households are due to either lack of participation of the rich is quite plausible for reasons stated in the last paragraph. But we also make an opposite assumption that all missing households came from the bottom wealth echelon for reasons stated under hypothesis 3. Moreover, we embrace the possibility that the missing households came from both upper and lower end of the wealth ladder. This scenario is explored for testing hypothesis 4.

11
Policy Research Working Paper 10967

# **Abstract**

This study uses a cluster randomized controlled trial to evaluate the impact of a nationwide malaria prevention advertising campaign delivered through social media in India. Ads were randomly assigned at the district level, and the study relies on data from two independently recruited samples (8,257 individuals) and administrative records. Among users residing in solid (concrete) dwellings, where malaria risk is lower, the campaign led to an 11 percent increase in mosquito net usage and a 13 percent increase in timely treatment seeking. Self-reported malaria incidence decreased by 44 percent. Consistently, recorded health facility data indicate a reduction in urban monthly incidence of 6.2 cases per million people, corresponding to 30 percent of the overall monthly incidence rate of malaria. Conversely, the study finds no impact on households living in non-solid dwellings, which face higher malaria risk, nor among rural settlements where such dwellings are more prevalent. To disentangle if this lack of impact stems from ineffective content or insufficient reach, an individual-level trial was conducted (1,542 individuals), ensuring campaign exposure for both household types. The findings indicate an increase in bed net usage and timely treatment seeking for both groups, underscoring the need for improved targeting in social media campaigns to fulfill public health goals..

This paper is a product of the Development Impact Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dd3137@gsb.columbia.edu, amunozboudet@worldbank.org, vorozco@worldbank.org or nandan@vlab.digital. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
public firms are only a small portion of economically important firms in many developing countries. As a result, there is likely to be selection bias, particularly in the 1990s. To some extent, these problems with data have recently been mitigated by large scale multi-country firm surveys conducted by the World Bank. In particular, the World Bank‟s Enterprise Surveys program takes a stratified random sample of firm-level data from over 120,000 establishments in 125 countries. The surveys are quite exhaustive, providing information about firm financials, the obstacles they face, the level of competition and their interactions with the government and regulatory authorities. They have been used in many applications in finance, accounting and management research.

A second issue pertains to the measurement of the variables used in development finance. Often, the questions of interest hinge on how differences in institutions‟ across countries affect financing. Countries differ across many institutional dimensions, and it is often a priori unclear how to measure the relevant characteristics. Thus, for example, characteristics like “economic freedom,” “security from arbitrary government seizures of property,” and “the impartiality of the court system” are likely to have an important bearing of firm financing and often cannot be omitted from analysis. However, the conceptual and practical difficulties of measuring such characteristics often result in the use of indices created from survey data or drawn from appointed panels of experts. Some of these variables are likely to be subject to measurement errors. A somewhat less recognized problem is that these indices are then entered into regression equations in a linear manner, usually with little justification of why the dependent variable in question should vary linearly with an arbitrary index over its whole range. While this approach can be justified as reducing the risk of over-fitting in situation where theory does not offer much guidance on the functional form, it may also lead to the incorrect conclusion that no relation exists when in fact it does.<sup>12</sup>

# **4. Institutions and Access to Finance in Developing Countries**

Firms‟ access to finance varies across countries, determined not only by differences in firmspecific characteristics, but also by the constraints posed by countries‟ varying financial

> 12For example,  Ayyagari, Demirguc-Kunt, and Maksimovic (2008a) show that an index of democracy is associated with property rights protections over its higher ranges, but not over its lower ranges, suggesting that the usual linear specifications might be mis-specified.

22
this adjustment but differences are larger for longer time periods and in comparing different countries.<sup>13</sup>

# **III. Trends in the rates of orphanhood**

This section provides a brief discussion of the trends in orphan rates across time and countries. This updates and extends the statistics reported in Bicego, Rutstein and Johnson (2003) and in Monasch and Boerma (2004), and adjusts for changes in age and gender composition (by standardizing to a 7-year-old male child, as described above). From the results, a typology of countries is proposed based on initial levels and changes in orphan rates. This allows us to subsequently investigate how changes in living arrangements might differ across countries depending on these conditions.

# _Overall orphan rates and the prevalence of HIV_

Table 1 presents the percentage of children ages 0-14 who were single or double orphans in the earliest and latest years for which data are available. Orphan rates from the most recent round of survey data range from a low of about 5 percent of all children under age 15 in Mali having lost one or both parents to as many as 20 percent in Zimbabwe. In nine of the 21 countries, 10 percent or more children are single or double orphans. To be sure, these national averages mask within-country variation. For example, while the overall orphan rate in Kenya is 12 percent, in the rural Kenyan community studied by Nyambedha, Wandibba and AagaardHansen (2003), one out of three children (below 18 years) is a single or double orphan.

As reflected in levels and changes in orphan rates, there are four main types of countries.<sup>14</sup> In the first group of countries (Group A in Table 1), the orphan rate was relatively low in the earliest period (defined here as less than 10 percent) and did not change much between

> 13 For example, the largest discrepancy in the change in the orphan rate between the raw and adjusted methods is in Kenya. The raw rate increased from 6.9 to 11 percent between the earliest and latest rounds (4.1 percentage points), while the adjusted rates increased from 7.4 to 12.3 percent (4.9 percentage points); for a difference of 0.8 percentage points. Excluding Rwanda (which, as discussed in the paper is a special case due to ethnic conflict in that country) the largest gap across countries in the change in the raw orphan rate is between Madagascar where there was a 2.4 percentage point decline in the orphan rate and Zimbabwe where there was a 12.5 percentage point increase; after adjusting, these countries still lie at the extremes but the rates are a decline of 2.9 percentage points and an increase of 13.1 percentage points respectively.

> 14 Due to the big differences in patterns across these four groups of counties averages across all countries in this study are not presented.

9
Table 7: Actual vs Intended Participation <u>(Marginal</u> Effects from Ordered Probit Models)

|**Panel A: Health Insurance**||Equa|tion 2||Equat|ion 3|
|---|---|---|---|---|---|---|
||One Inte|raction|Two Inter|actions|||
||Intention|Actual|Intention|Actual|Intention|Actual|
|Treated|-0.001|-0.017|||0.005**|0.064**|
||(0.002)|(0.018)|||(0.002)|(0.026)|
|Treated*Price|||||-0.069**|-0.820**|
||||||(0.030)|(0.343)|
|Treated*No-contract|0.003*|0.038*|0.002*|0.021*|||
||(0.002)|(0.022)|(0.001)|(0.012)|||
|Treated*Contract|||-0.001|-0.017|||
||||(0.002)|(0.018)|||
|No. of Observations|4,587|4,587|4,587|4,587|4,587|4,587|
|**Panel B: Pension**||Equa|tion 2||Equat|ion 3|
||One Inte|raction|Two Inter|actions|||
||Intention|Actual|Intention|Actual|Intention|Actual|
|Treated|-0.003*|-0.038*|||-0.002|-0.030|
||(0.001)|(0.022)|||(0.002)|(0.040)|
|Treated*Price|||||0.013|0.193|
||||||(0.013)|(0.234)|
|Treated*No-contract|0.004***|0.062**|0.002|0.024|||
||(0.002)|(0.027)|(0.001)|(0.015)|||
|Treated*Contract|||-0.003|-0.038*|||
||||(0.002)|(0.022)|||
|No. of Observations|2,925|2,925|2,925|2,925|2,925|2,925|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: All regressions are using the Model 3 specification (see note to Table 2). Robust standard errors clustered at workplace of household head are in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 8: Are their Differences in Effects on Employed and Self-Employed Workers?

|**Panel A: With Contract Interaction**|Health|Insurance|Pension|(restricted sample)|
|---|---|---|---|---|
||employed|self-employed|employed|self-employed|
|Treated|-0.026|0.024|-0.031|0.039|
||(0.019)|(0.021)|(0.021)|(0.028)|
|Treated*No-Contract|0.067**||0.070*||
||(0.032)||(0.040)||
|No. of Observations|2,638|1,949|1,848|1,077|
|R-squared|0.546|0.284|0.530|0.349|
|No-contract treatment efect|0.041*||0.039||
|Prob _>_F|[0.098]||[0.224]||
|**Panel B: With Price Interaction**|Health|Insurance|Pension|(restricted sample)|
||employed|self-employed|employed|self-employed|
|Treated|0.101***|0.058|-0.013|0.006|
||(0.035)|(0.073)|(0.055)|(0.075)|
|Treated*Price|-1.514***|-0.448|0.067|0.194|
||(0.469)|(0.958)|(0.321)|(0.420)|
|No. of Observations|2,638|1,949|1,848|1,077|
|R-squared|0.546|0.284|0.529|0.349|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: All regressions are using the Model 3 specification (see note to Table 2). Robust standard errors clustered at workplace of household head are in parentheses. P-values are in brackets. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

35
ah ( <u>~to-/(N</u> ~~>)~~ (hh) j-1 i2 ; y: éh = dna(0) + Sscay Ko) + eka) ik == 52)On(2) ( Sncay(naa) J) F ~~y1~~ i=s UD) F Seco)(2) ( (6s(1) K(0) T ~~y,1~~ iIK=k ix(1)) + Cijk(2) fieent1) — & ebikmy| S NE

jO = ~~—~~ Wy ( ~ ~~~~~ ik=k ) A 1-1 k(0) = ~~—~~ ijk=k ~~s~~ y m € [0,1] z=0
The growth of 1-person establishments is specifically concentrated in tradable industries, as the next columns show. Apparel definitely stands out in this area, but it is not alone. Panel A of Figure 4 shows that apparel accounts for about half of the net growth in tradables from 1989 to 2010. The final column of Table 3 provides 1-person average establishment shares across the two decades. Interestingly, there is only a minimal correlation of 0.02 between these shares and the growth rates from 1989 to 2010. This correlation increases to 0.36 if “Radio, television, and communication equipment and apparatus” industries (NIC 32) is removed. For growth from 2000 to 2010, the correlation to average levels is negative at -0.05.

Before proceeding, it is worth pausing to comment further on the potential hypothesis that these trends are due to coding changes in the NIC system. While it is true that the definitions of some industries adjust during the sample period, we have not found evidence that this could produce these trends. Perhaps most important in this regard is that the very concentrated rise among the smallest establishment sizes, which does not depend upon such definitions or industry reclassifications. To this end, Panels C and D of Figure 4 also show smooth trends with respect to new entry and the transitions between non-tradables and tradables.<sup>4</sup> These trends do not display a dramatic flip that would be evidence for a reclassification issue.

# _State-level trends_

Table 4 lists states and their growth properties for informal sector employment. We start with comparable statistics to Table 3 about aggregate growth rates and 1-person establishments, and then we document each state’s growth in informal tradable industries specifically. Reflective of its manufacturing prowess, Gujarat shows perhaps the most robust growth across the various dimensions. Some states are not surveyed in 1989, and they are excluded from the long-term growth calculations.

4 New firms in the data are defined as firms born in the last 3 years. We have NSS/ASI data for 1994/1995, and then again for 2000/01, a six-year jump in between cross-sections. Hence, any firm births from 1994-1997 would not be captured by the new firm variable. There was net growth of 1-person firms in the informal sector from 1994-2000 of roughly 3 million, and we see about 1.5 new informal sector firms in 2000. Our favored explanation for the disconnect observed between net 1-person firm employment growth and new firm growth from 1994-2000 is that a similarly large number of new firms were born from 1994-1997, which we are not able to pick up in our data.

10
At the time of this writing (in 2012), the available data indicate that 1.2 billion people in the world live in poverty, as judged by the frugal standards used for defining poverty in the world’s poorest countries—giving an international poverty line of $1.25 a day (at 2005 purchasing power parity). This paper draws on recent research on global poverty to assess how long it might take to lift one billion people out of such extreme poverty. The paper identifies both “pessimistic” and “optimistic” paths to that goal.

International development institutions and almost all developing countries track poverty measures, and the results are keenly watched. The development literature has devoted less analytic attention to the important task of benchmarking performance.<sup>2</sup> When can we say that performance against poverty is “good” or “bad”?

Setting poverty-reduction goals can help motivate their achievement. The Millennium Development Goals (MDGs) were clearly conceived with that intention. As Hume (2009, p.4) states, the MDGs aimed to “stretch ambitions and mobilize political commitment and public support.”<sup>3</sup> The first MDG was to halve the developing world’s 1990 “$1 a day” poverty rate by 2015. Using the $1.25-a-day poverty line in 2005 prices, the first MDG was attained in 2010, a full five years ahead of the goal (Chen and Ravallion 2012). Even so, that important achievement leaves over one billion people living in extreme poverty.

To motivate extra effort, the goals must not be either too easy or too hard. Past experience can provide useful data for assessing proposed benchmarks for future performance. At the time that the first MDG was set, there were very few time-series observations to draw on. Fortunately, the situation has changed dramatically. Although the data are still far from ideal, we now have a firmer empirical basis for considering alternative goals that are within the range of experience.

The benchmarks proposed here rest on explicit scenarios for future economic growth and distributional change, informed by knowledge of recent past performance in reducing poverty and current expectations about economic growth prospects across the developing world. Unavoidably, there is a measure of conjecture in the establishment of any targets. But it is hoped

> 2 The only careful attempt to benchmark performance at the country level appears to be Newman et al. (2010), who draw on time-series evidence across developing countries. They use the empirical distribution of absolute changes in the poverty rate as data to inform the establishment of performance benchmarks for countries in Latin America and the Caribbean.

> 3 The MDGs were not the first such effort at goal setting; Hume identifies a number of antecedents, including the UN Declaration of Human Rights.

2
Policy Research Working Paper 6434

# **Abstract**

Despite trade liberalization efforts made by Eurasian countries, the export structure of the region shows significant levels of concentration across export destinations. To shed light on this observation, this research analyzes trade policy barriers in Eurasia, East Asia and the Pacific, and the European Union. Using the most recent data from sources including the World Trade Organization, the United Nations, and the World Bank—including the Overall Trade Restrictiveness Indices, the Services Trade Restrictions Database, and the Temporary Trade Barriers Database—the role of tariffs, non-tariff measures, temporary trade barriers, trade agreements, and trade barriers in services are explored to explain the lack of diversification by destination. Several conclusions can be drawn from the analysis.

First, China, Korea, and Japan, as well as the European Union, impose high levels of protection on products of animal origin, which may explain the lack of Eurasian export diversification toward the East Asia and the Pacific and the European Union regions. It also highlights the potential benefits of diversifying the structure of production in Eurasia toward more sophisticated and technologically intensive goods. Second, the East Asia and the Pacific region (especially China) appears to be more protectionist than the European Union, suggesting a greater challenge for Eurasian countries in diversifying exports to the destination. And third, few or no regional trade agreements exist between Eurasian countries and countries in the European Union or East Asia and the Pacific.

This paper is a product of the International Trade Department,, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at acusolito@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team