rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Improvement and worsening of business practices

||Baseline<br>Mean|Outcom|e<br>ITT|Churn<br>2-6|-month<br>ITT|||
|---|---|---|---|---|---|---|---|
|Dependent Variable|Control<br>(1)|2 months<br>(2)|6 months<br>(3)|Control mean churn<br>(4)|Improve<br>(5)|Worsen<br>(6)|Frac. catch up<br>(7)|
|Index of planning practices|0.164|0.122<br>[0.022]***|-0.002<br>[0.023]|0.574|-0.094<br>[0.029]***|0.117<br>[0.028]***|0.446|
|Has written budget|0.194|0.157|0.003|0.322|-0.064|0.090|0.418|
|||[0.030]***|[0.030]||[0.024]***|[0.022]***||
|Has set sales goals for next year|0.213|0.080|-0.033|0.322|-0.056|0.057|0.494|
|||[0.029]***|[0.030]||[0.023]**|[0.022]**||
|Has budget of approximate costs|0.085|0.127|0.023|0.358|-0.065|0.039|0.622|
|||[0.029]***|[0.030]||[0.024]***|[0.022]*||
|Index of accounting practices|0.273|0.069|0.021|0.759|-0.069|0.093|0.427|
|||[0.018]***|[0.019]||[0.031]**|[0.030]***||
|Keeps written records|0.499|0.090|0.036|0.208|-0.027|0.028|0.491|
|||[0.024]***|[0.024]||[0.019]|[0.019]||
|Records every purchase and sale|0.492|0.095|0.023|0.231|-0.027|0.046|0.366|
|||[0.026]***|[0.026]||[0.021]|[0.019]**||
|Records how much money business has|0.342|0.046|0.053|0.226|0.027|0.019|3.62|
|||[0.027]*|[0.027]**||[0.022]|[0.020]||
|Records sales trends|0.224|0.074|0.020|0.299|-0.011|0.043|0.204|
|||[0.029]**|[0.029]||[0.024]|[0.021]**||
|Calculates sales and expenses|0.345|0.073|0.026|0.322|-0.025|0.022|0.535|
|||[0.028]***|[0.028]||[0.023]|[0.022]||
|Knows most proftable products|0.380|0.039|0.038|0.246|0.002|0.002|-3.85|
|||[0.026]|[0.025]||[0.021]|[0.020]||
|Has records showing could pay off loan|0.104|0.053|-0.004|0.332|-0.023|0.034|0.404|
|||[0.030]*|[0.030]||[0.024]|[0.023]||
|Has documents of annual profts|0.045|0.045<br>[0.023]*|-0.009<br>[0.027]|0.261|-0.013<br>[0.024]|0.040<br>[0.018]**|0.249|
|Tracks cash income anually|0.030|0.051<br>[0.028]*|0.035<br>[0.030]|0.272|0.026<br>[0.024]|0.042<br>[0.020]**|-1.62|
|Separates household and personal fnances|0.268|0.110<br>[0.028]***|-0.019<br>[0.031]|0.315|-0.047<br>[0.020]**|0.082<br>[0.026]***|0.364|

**Notes:** Column 1 shows baseline means of the indices of planning practices and accounting practices, along with the individual practices that are included in these indices. Columns 2 and 3 show ITT treatment impacts from regressions which include randomization strata fixed effects and control variables selected via pdslasso. The 2 to 6 month churn in column 4 is the proportion of control firms that change the practice between the 2 and 6 month surveys. Column 5 shows the estimated treatment effect on improving (starting) the practice between 2 and 6 months, and Column 6 on worsening (dropping) the practice during this time frame. Column 7 shows the fraction of the change in treatment effect between 2 and 6 months which comes from the control group catching up (being more likely to improve). Robust standard errors in parentheses. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.
muted, but are apparent for all groups with urban and more educated women being the most “protected”. In Ethiopia there has been a big difference in male/female mortality over the entire period we are able to analyze—with mortality among adult men typically about a third higher than that of women. During the factional violence period of the mid- to late-1980s mortality increased for men, especially in urban areas and for those with more schooling—and only regained parity with their rural and less educated counterparts by the late 1990s. Urban and more educated women also experienced mortality “spikes” in the mid- to late-1980s, although these were somewhat smaller. Adult mortality among rural and among less education women did not appear to be affected in the same way. Last, the decade-long civil war in Sierra Leone shows up in adult mortality increases for all groups. The increase is especially acute for urban and more educated men in the 1990-94 and 1995-99 periods, and noticeable among women from all groups over the same period. Among rural and among less educated men there seems to be a longer run increasing mortality trend that starts in 1985-89 and peaks in 1995-99.

Clearly, episodes of conflict and violence are country-specific, and perhaps less amenable to the types of broader generalizations we make above. But these four cases do suggest that while mortality events precipitated by civil conflicts tend to affect all groups—men and women, urban and rural, more and less educated—they often appear to affect men, and in particular urban and more educated men to a greater extent than the other groups.

# **4) Conclusions**

In this paper we combine data from 84 DHS datasets from 46 countries (59 and 33 of which are from Sub-Saharan Africa) to analyze trends and socioeconomic differences in adult mortality. We calculate mortality based on the sibling mortality reports collected from about 850,000 female respondents aged 15-49. In total, the estimates are based on mortality histories of almost 5 million individuals. In the analysis, we use age and gender of each individual, and proxy for urban residence and education attainment with that of the respondent.

The analysis yields four main findings. First, adult mortality is different from child mortality. This is perhaps obvious to most readers, but bears stating since under-5 mortality is often used—perhaps implicitly—as a measure of “population health”. Several key features of 22
**<u>Table 7: Exploring heterogeneity in labor markets</u>**

||**Pre-harvest**<br>**labor**<br>**(1)**|**Total labor**<br>**(2)**|**Pre-harvest**<br>**labor**<br>**(3)**|**Total**<br>**labor**<br>**(4)**|
|---|---|---|---|---|
|Log of land|0.359***|0.338***|0.360***|0.340***|
||(16.998)|(17.329)|(16.863)|(17.246)|
|Log of agricultural wage|-0.228***|-0.238***|-0.232***|-0.242***|
||(-4.005)|(-4.514)|(-4.060)|(-4.582)|
|Log of nonagricultural wage|-0.088*|-0.197***|-0.082*|-0.192***|
||(-1.786)|(-4.348)|(-1.666)|(-4.230)|
|Price of fertilizer|<br>0.155***|<br>0.152***|<br>0.153***|<br>0.150***|
||(7.656)|(8.138)|(7.573)|(8.056)|
|Village price of hiring bullock|<br>-0.083**|<br>-0.085**|<br>-0.086**|<br>-0.085**|
||(-2.124)|(-2.333)|(-2.186)|(-2.336)|
|Log of household size * Low (α1)|0.135***|0.120**|0.143**|0.121**|
||(2.653)|(2.545)|(2.518)|(2.291)|
|Log of household size * Medium (β1)|0.065|0.035|0.037|0.009|
||(1.369)|(0.809)|(0.671)|(0.166)|
|Log of household size * High (γ1)|-0.039|-0.044|-0.053|-0.076|
||(-0.735)|(-0.916)|(-0.883)|(-1.360)|
|Share of prime male adult * Low (α2)|||<br>-0.071|<br>-0.138|
||||(-0.458)|(-0.963)|
|Share of prime male adult * Medium (β2)|||-0.136|-0.126|
||||(-0.843)|(-0.842)|
|Share of prime male adult * High (γ2)|||0.004|-0.040|
||||(0.028)|(-0.283)|
|Share of prime female adult * Low (α3)|||<br>-0.033|<br>0.067|
||||(-0.166)|(0.367)|
|Share of prime female adult * Medium (β3)|||<br>-0.023|<br>-0.036|
||||(-0.123)|(-0.208)|
|Share of prime female adult * High (γ3)|||<br>0.087|<br>0.028|
||||(0.465)|(0.162)|
|Share of elderly male adult * Low (α4)|||0.632**|0.516*|
||||(2.074)|(1.834)|
|Sh of elderly male adult * Medium (β4)|||-0.134|-0.029|
||||(-0.471)|(-0.110)|
|Sh of elderly male adult * High (γ4)|||-0.213|-0.322|
||||(-0.738)|(-1.211)|
|Sh of elderly female adult * Low (α5)|||-0.118|-0.080|
||||(-0.362)|(-0.266)|
|Sh of elderly female adult * Medium (β5)|||-0.295<br>|-0.358<br>|
||||(-1.095)|(-1.438)|
|Sh of elderly female adult * High(γ5)|||0.033|-0.030|
||||(0.116)|(-0.115)|
|No. of observations|7,400|7,400|7,400|7,400|
|R-squared|0.217|0.251|0.218|0.251|
|Joint F-test for Low level|||2.493|2.443|
|P-value1|||0.0292|0.0322|
|Joint F-test for Medium level|||0.814|0.640|
|P-value2|||0.540|0.669|
|Joint F-test for high level|||0.394|0.676|
|P-value3|||0.853|0.641|

Source: Authors’ computation from 1982, 1999, and 2008 ARIS-REDS surveys.

Note: Constant, time dummy, gender, education, and age of head and its square are included but not reported. Low, medium, and high refer to terciles of village-level demand for nonagricultural labor. t-statistics are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

22
the database. Commercial loans include loans to agriculture, animal husbandry, industry, trade, and services. Consumer loans are categorized as such, and mortgage loans are taken from the line item for housing. There are no data for the maturity of total gross loans, and so, Paraguay only has data for the sectors of total gross loans.

# **G. Trinidad and Tobago**

The loan information for Trinidad and Tobago is available from the Central Bank of Trinidad and Tobago.<sup>36</sup> The distribution of loans by purpose is reported for commercial banks. Commercial loans include loans to the following sectors: manufacturing; miscellaneous manufacturing; distribution; hotels and guest houses; services - personal service; agriculture; food, drink, and tobacco; printing and publishing; wood and related products; chemicals and nonmetallic minerals; assembly-type and related industries; and electricity and water loans. Consumer loans include the following: bridging finance, land and real estate, home improvement/renovation, motor vehicle, insurance and repairs to motor vehicles, domestic appliances and furnishings, purchase of other financial assets, education, medical, travel, insurance (life and non-life) and professional services (legal, funeral, and so on), refinancing, consolidation of debt, miscellaneous and personal services, and other purpose. Mortgage loans include real estate mortgage. This loan purpose distribution is available from 2000 onward. There is no maturity information of bank loans for Trinidad and Tobago.

> 36 - - Central Bank of Trinidad and Tobago banking sector aggregates. <u>https://www.central bank.org.tt/statistics/selected aggregates.</u>

42
in targeting training to those it will benefit more.

Even when training is offered for free, it is common for many business owners to not show up, and to not attend all sessions. McKenzie and Woodruff (2014) found that, on average, only 65 percent of those invited to training attend when it is free among 14 different randomized trials. In our BDM sample, which already consists of firms that had at least expressed enough interest in training to attend a demonstration session, only 76 percent of those offered the training for free attended at least one session, and the mean firm attended only 5.4 out of 10 sessions. We find that entrepreneurs who have a higher willingness-to-pay for training are more likely to attend training when selected, so that charging for training helps screen out those individuals who are less likely to show up. Moreover, there is some evidence of sunk-cost effects. Conditional on willingness to pay, those who are randomly assigned higher prices for the course and pay for training are more likely to attend, and attend more sessions. This effect is stronger in our BDM sample where we have larger variation in prices and a larger sample size.

Taken together, our results suggest the optimal price for business training is unlikely to be either free or full cost. Charging for training does screen out those firm owners who are less likely to attend, selects those who expect to benefit more from it, and induces more effort in attending training. But a lot of these effects occur from charging only one-quarter of the full cost, and charging close to or at full recovery cost ends up screening out many business owners and increasing recruiting costs.

This paper contributes to two main literatures. The first is a literature on business training programs in developing countries. Business training is one of the most common forms of firm support offered by governments, and a growing number of impact evaluations have offered various training programs to firms for free in order to measure impacts on business practices and firm performance. McKenzie (2020) provides a recent review and meta-analysis, finding that the typical training program increases profits and sales by an average of 5 to 10 percent. Training programs that go beyond teaching business practices to teach personal initiative, a psychological-based mindset intervention, have had larger impacts on firm growth in Uganda (Glaub et al., 2014) and Togo (Campos et al., 2017). However, a recent experiment in Jamaica finds only short-term average effects of a soft skills training program provided for free, with the impacts only observed for men (Ubfal et al., 2019). Two key challenges in the literature are how to target these programs to those who will benefit more, and how to scale them to serve many more firms in a financially sustainable way. A market-based solution, which charges firms to participate in training, has been proposed as a possible solution to both challenges. Our paper shows some of the 4
Somali regions interviews were conducted during Wave 2 after an incident and close to it (Table 7 in the Appendix).

Table 1: Number of households by group and Wave for each sample alternative

||Alternative|Group|Wave 1|Wave 2|
|---|---|---|---|---|
|(1)|Moadishu|Exposed|21|113|
||g|Control|664|775|
|(2)|Mogadishu with overlapping exposed|Exposed|21|78|
||households in Wave 1 and 2|Control|664|775|
|(3)|<sup>Mogadishu with overlapping districts</sup>|Exposed|21|113|
||in Wave 1 and 2|Control|519|775|
|(4)|All urban areas|Exposed|21|135|
|||Control|2,712|3,876|
|(5)|Urban areas with exposed and control|Exposed|21|135|
||households in Wave 1 and 2|Control|664|1,468|

Source: Authors’ calculations based on data from the SHFS and ACLED.

The main sample considered in the econometric analysis corresponds to Mogadishu. The capital of Somalia is one of the most fragile cities in the world (Pape and Karamba 2019). It concentrates 16 percent of Somali households and poverty is higher in Mogadishu than in other urban areas of Somalia. A few additional samples are used to provide further robustness to the results from the econometric analysis (Table 1). We consider a few variations within Mogadishu; one restricting the group of exposed households to overlapping Wave 1 and 2 areas (Figure 6 in the Appendix), and another option restricting the sample to overlapping Wave 1 and 2 districts. Then, we consider all urban households across Somali regions. This alternative includes exposed households from Mogadishu and South West urban, as well as control households from all urban areas of Somalia. Finally, the other sample considered in the econometric analysis refers to only urban areas with exposed households in Wave 2; that is, exposed and control households only from Mogadishu and South West urban.

All these different alternatives have a relatively small sample size for the group of exposed households in Waves 1 and 2. This is determined by the location and timing of interviews in relation to attacks. Further, the sampling strategy clustered households into PSUs, with a target of 12 interviews per PSU. Households within each of these 10
both interviewed over the phone, differences in reporting are not driven by modality. We use the reports of those in the phone weekly group as the benchmark, since the 7 day reference period remains the norm. We again confine attention to those who reported working at baseline.

To assess the impact of the reference period on labor market reporting the following equation is estimated: where i is a labor market outcome of interest of individual _i_ in week _w_ as defined above and T o W is a dummy variable that takes the value 1 if respondent _i_ was interviewed YY over the phone three times a week and 0 otherwise; the omitted category is thus the weekly phone ii PPho oo T WW interviews; τ is a vector of calendar week dummies; and i is a random error term. Triweekly labor market reports are aggregated to the weekly level in order to compare them with weekly ii εε labor market reports obtained in the weekly arms (see the discussion in section 4.2 and Appendix C). Standard errors are again clustered at the treatment level, i.e. by household, and all observations are again attrition weighted, with weights corresponding to the inverse of the number of complete weeks for which an individual was observed.

Analogous to reference period effects we analyze heterogeneity by interacting the dummy for the triweekly phone treatment with an indicator for having been self-employed at baseline. We also examine heterogeneity among the self-employed.

# **6.1 Results**

Respondents in the triweekly phone arm (who were asked to respond to two 24-hour recall questions) were 7 percentage points more likely to report having done any work in a given week than respondents in the weekly phone group with a one-week reference period, as is shown in Table 6 which presents the main reference period results. There was no change in the average reported number of days of work or hours worked overall. However, conditional on reporting having done any work that week, the number of days fell significantly, by 0.6. The number of hours worked conditional on working dropped by 4.7. Shortening the reference period from one week to two days thus yielded reporting of more employment spells but not a significantly different 15
_Connecting Lagging and Leading Regions: The role of labor mobility_ education pay a smaller percentage of their wage for amenities. But their absolute payment for amenities is higher than people with lower education. And for some amenities, better educated people are not marginal and therefore do not really benefit from an improvement at the margin (e.g., connecting more people up to a sewer line does not likely help a rich person, as he was likely hooked up to the sewer line before -- the less educated person is more likely to benefit from that improvement).

# **4.  Conclusions**

In this paper, we examine the determinants of internal migration paying particular attention to the role of amenities such as access to health and education services and urban infrastructure in the migration decisions of working age individuals. We use Brazilian census data for the analysis and find that the poor from the country’s lagging regions not only migrate in search of better economic outcomes, but are often pushed from their hometowns where they are deprived of access to basic public services such as health care, water supply and electricity.

These findings have important implications for territorial development and urbanization policies in Brazil. First, economic activities in industry and services are concentrated in the country’s leading areas, benefiting from internal scale economies as well as positive externalities from agglomeration. In Brazil and elsewhere, fiscal incentives and infrastructure programs that have tried to develop industrial clusters in lagging regions have been largely unsuccessful (World Bank 2008, Lall et al 2005, Carvalho et al 2007, World Bank 2005). However, our results show that people have been leaving lagging regions in search of better economic opportunities – particularly in the leading Southeast. Policies should encourage this mobility of labor, and the best way is to help in improving human capital accumulation in lagging regions. Considerable evidence shows that education is the best instrument for overcoming the barriers of distance. One of the biggest success stories comes from the United States, where rise in the schooling of 16
||**2000**|<br>**2004**|
|---|---|---|
||**RMB/capita**|**percent RMB/capita Percent**|
|**County-to-Township Transfers**|**58**|**84**|
|**Disposable Fiscal Resources**|**119**|**119**|
|Total Extra-budgetaryIncome|55|53|
|Total Self-raised Income(incl._tiliu_)|37|10|
|_Tongchou_ (townshiplevy)|20|0|
|_Tiliu_(village accumulation)|14|0|
|Other|4|10|
|**Total Fiscal Revenues**|**211**|**182**|

Source: fieldwork survey.

# **3.3.  The Village**

Villages figure prominently in the NSC as the basic unit of the xiaokang society in the countryside. In 2005, China had a total of 640,139 administrative villages (China Statistical Yearbook 2007).<sup>30</sup> A typical “administrative village”<sup>31</sup> ( _cun_ ) generally has a population of 1,000 to several thousand people, but can be as small as only a few hundred or as large as 10,000 people. The average village in our sample survey of 100 had a population of about 1,400 people, a labor force of about 700, an average net rural income of RMB 2,600 (2004), and a total cultivated area of about 2,270 mu (150 ha).<sup>32</sup> Most administrative villages consist of a number of “village groups” that are generally established at the level of a natural village. Usually an administrative village comprises only a few village groups, but some have up to ten or even more in the more mountainous areas. These natural villages are the most basic unit of the community; the administrative village is formed to have sufficient scale for local administration and the provision of basic public services.

Village administrations are “self-governing,” and are led by the Party. By law, the village assembly is the highest authority in village self-governance. Day-to-day activities of self-governance are carried out by the village committees. The village head (village committee director) and village committee members are elected by the villagers. However, the village

> 30 The number of village committees has declined by about 11.5 percent in the five-year period from 1999 to 2004.

> 31 The traditional term “administrative village” is used here, since the formal term “village committee” has a second meaning referring to the self-governing organization of the village.

> 32 This sample is described in detail in World Bank (2007 - RPF), Annex 5.

28
i This paper draws heavily on: Mansuri, Ghazala and Vijayendra Rao, op cit. page 25. We gratefully acknowledge support from the Knowledge for Change Prorgram TF094551.

ii Mansuri and Rao, op cit. page 25. In the book we review over 500 studies, focusing at greater length on large scale participatory projects that have been evaluated using representative samples of target populations with good counterfactuals. While much of the literature on participation tends to use a case study approach, which is undoubtedly quite invaluable for understanding the process of change, only representative sample studies of large-scale projects with good counterfactuals can generate enough data to make statistically valid judgments on large-scale policy interventions. In other words, we focus on studies that have a valid control group for the communities that have been targeted (or “treated”) by the intervention. Without an adequate counterfactual, there is no effective way of attributing the observed changes to the intervention rather than to changes that might have occurred outside the intervention. Generally speaking, this means that our findings derive from econometric analysis. However, we do draw on several case-studies to develop ideas and to illustrate our conceptual framework. Ideally, this econometric work should be complemented by good qualitative work, which can help to illuminate the processes that resulted in the observed impact. There is an unfortunate dearth of such work. We refer the reader to the book for a detailed synthesis of the evidence and the bibliography.

25