rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 1: Impact of formality on monthly profits and output per worker</u>**

||OLS|OLS|OLS|OLS|OLS|2SLS|2SLS|2SLS|2SLS|2SLS<sup>‡</sup>|
|---|---|---|---|---|---|---|---|---|---|---|
|**_Formality index (broad)_**|||||0.351***||0.453***|0.356*|0.342*|0.334**|
|**Formality index (narrow)**||||0.255***||0.332***|||||
|Has a tax identification number||0.195***|0.187**||||||||
|Requests receipts||0.111|0.099||||||||
|Workers affiliated with Social Security||0.421***|0.192*||||||||
|Issues written contracts to employees||0.089|0.043||||||||
|_Issues written contracts to employees_|||0.513***||||||||
|_Listed in the commercial registry_|||0.309**||||||||
|_Established via a public notary_|||0.104||||||||
|**Location characteristics**|||||||||||
|Number of firms in the parish|0.000|0.001|0.001|0.000|0.001|0.001|0.001|0.001|0.001|0.000|
|Average (parish-level) distance from tax office<sup>†</sup>|-0.263*|-0.278**|-0.257*|-0.284**|-0.254*|-0.291**|-0.251*|-0.254*||-0.155|
|Average (parish-level) distance from municipal office<sup>†</sup>|0.060|0.079|0.119|0.099|0.122|0.110|0.140|0.123||0.319**|
|Average (parish-level) incidence of inspections<sup>†</sup>|-0.320|-0.451*|-0.447*|-0.447*|-0.451**|-0.485**|-0.489**|-0.452*|-0.339|-0.104|
|**Firm/owner characteristics**|||||||||||
|Member of guild or union|0.262***|0.146*|0.097|0.171**|0.102|0.144|0.056|0.100|0.113|0.047|
|Parents had a business|0.009|0.024|0.021|0.016|0.023|0.018|0.026|0.023|0.018|0.005|
|Hours worked in this business/week (log)|0.204***|0.208***|0.215***|0.199***|0.205***|0.198***|0.206***|0.205***|0.204***|0.163***|
|Owns place of business|0.158**|0.135**|0.105|0.155**|0.115*|0.154**|0.102|0.114*|0.111*|-0.021|
|Age|-0.003|-0.005**|-0.004|-0.005*|-0.005*|-0.005**|-0.005**|-0.005*|-0.004*|-0.003|
|Gender (male = 1)|0.039|0.043|0.036|0.038|0.037|0.037|0.037|0.037|0.035|0.103*|
|Index of entrepreneurial ability|0.058|0.065*|0.066*|0.061*|0.061*|0.062*|0.062*|0.061*|0.065*|0.051*|
|**Owner education**|||||||||||
|Incomplete primary|-0.166|-0.136|-0.174|-0.153|-0.169|-0.148|-0.170|-0.169|-0.186|0.032|
|Complete secondary|0.172**|0.100|0.106|0.118|0.108|0.102|0.089|0.107|0.113|0.096|
|Incomplete tertiary|0.334***|0.220**|0.188**|0.259***|0.201**|0.236**|0.162|0.199*|0.200*|0.142|
|Complete tertiary|0.638***|0.472***|0.403***|0.526***|0.426***|0.493***|0.365**|0.423**|0.453***|0.414***|

14
ings, schooling, age, gender, etc. We use some these variables as controls or ancillary outcomes in our baseline regression.

To obtain measures of health, we use the Demographic and Health Survey (DHS) for 20072009.<sup>16</sup> This survey also includes geographical coordinates of each household’s residence. However, the location is randomly displaced by up to 3 kilometers. This introduces an additional measurement error. The DHS contains self-reported information on children’s health conditions in the last 2 weeks. Based on these data, we construct indicators of having an acute respiratory disease (i.e., cough accompanied by short breath), fever, diarrhea, or anemia.<sup>17</sup>

**Pollution data** We obtain daily measures of key pollutants such as PM2 _._ 5, PM10, nitrogen dioxide (NO2) and sulfur dioxide (SO2) for years 2007-2011. These measures provide average levels of pollutants for a 24 hour-period and were collected every 2-3 days each week.<sup>18</sup> The air pollution data come from five monitoring stations installed and operated by an office of the Ministry of Health (Direcci ́on General de Salud - DIGESA). The monitoring stations were located in the four cardinal points of the city plus one in the city center (see Figure 3).

Note that these monitoring stations were installed as part of the first project to systematically measure air quality in Peru.<sup>19</sup> We complement this dataset with data on monthly average temperature and humidity collected by SENAMHI.

**Matching labor, health and pollution data** To construct the measure of pollution for a given household, we first define the reference period for labor outcomes. This corresponds to the seven days prior to the interview date. We call this reference period week _t_ . Then we obtain the average level of a pollutant from monitoring stations in week _t −_ 1.<sup>20</sup> Thus, we use exposure to pollutants in the week before labor outcomes are realized. This responds to previous evidence

> 16We cannot use other years due to lack of geographical information. 17The ENAHO survey also contains self-reported information on health conditions, such as illnesses or accidents in the last 4 weeks. However, we do not use this information due to its longer time horizon, which difficult to match with environmental conditions, and because it does not distinguish between respiratory and non-respiratory diseases.

> 18The collection dates in a given week were randomly chosen. The collection process was done by trained personnel of the Ministry of Health using active air sampling procedures. The detailed data collection protocol is available at `http://www.digesa.sld.pe/norma_consulta/protocolo_calidad_de_aire.pdf` .

> 19As mentioned in Section 2.2, the meteorological agency (SENAMHI) started regular, hourly, collection of air quality indicators only since late 2011. We cannot use these new air quality data due to lack of georeferenced labor data for this period.

> 20We also examine the results using other lags and leads. We find that the only significant effect is obtained when using one-week lag. These results are available upon request.

8
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# III. Trends in income growth and poverty reduction

Information on real income per capita for households in Uganda across time is presented in Figure . The data represent weighted averages of income from crop farming, livestock production, wage employment (in agriculture and non-agriculture sectors) and non-farm self-employment. All values are in 2011 prices. Figure 4 provides the same information, but for households that were in the bottom 40 percent of the consumption distribution during at least one of the survey rounds. Figure 3 and Figure 4 show that agricultural income is the main source of income for households, particularly for those in the bottom 40 percent. Together, crop and livestock income comprised 57.5 percent of the income of Ugandan households in 2012 and 70 .2 percent of the income of the bottom 40 percent. Within agricultural income, crop income dominates livestock income.

Income from non-farm self-employment is the second most important source of income when considering all households and it is followed by non-agricultural wage income. Finding a measure of non-agricultural self-employment income that compares well to the measures of gross agricultural income used in this analysis is not straightforward. Much self-employment income comes from trade and taking only gross sales does not give an idea of how much was earned. We use net self-employment income in the analysis, which is gross self-employment income net of raw materials, operating expenses and wages paid to others. Raw materials account for 81 percent of these expenditures. Operating expenses and wages paid to others account for 12-13 percent of gross income, suggesting that self-employment income would be a marginally more important source of income were these expenses not netted out. Non-farm self-employment income is also the second most important income source for poor households, but only just. For poor households non-agricultural wage income is also a very important source of income.

Figure 3 and Figure 4 indicate substantial growth in real per capita agricultural incomes from 2006 to 2012 based on household survey data. On average, real per capita crop income grew by 9 percent per year, and by 8 percent for the poorest 40 percent. This reflects an increase in crop income between 2011 and 2012, but even when the period prior to 2011 is considered, growth in per capita crop income was robust: 4 percent on average across all households and for the bottom 40 percent. Growth in real per capita livestock income was quite constant at 5 percent annual growth ( 10
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26
- 27 -

**Figure 7. Credit to GDP ratio, 1987-96**

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**_Rising Leverage Ratios._** The rapid growth of credit was reflected in a rising credit to GDP ratio, which increased from 64 percent in 1987 to 142 percent in 1996 (see Figure 7). The expansion of credit relative to GDP implied two problematic domestic trends. First, the gearing of the economy increased by an average of 20 percent per year over 1990-97. Secondly, an increasing share of loans from the financial sector was fcr the accumulation of nonproductive assets, which is reflected in decreasing returns on capital investment. One measure of return on capital investment is the incremental capital output ratio (ICOR), which compares the increases in investment relative to the increases in GDP. A rising ratio implies that investment is becoming less productive, or of lower quality. The ICOR rose steadily in Thailand from 2.8 in 1988, to almost 5 in 1991, to reach 6.2 in 1996.37 Also, The rising domestic debt level created an environment of declining liquidity, as interest expenses consumed greater amounts of income (see T'ables 6 & 7 below). Between 1988 and 1996, average interest expenses of the business sector increased 2.5 times as a proportion of GDP, reaching 19 percent (See Section IV.2.a.).

37 See Armstrong, Garber, Spencer (1998).
Against this premises, wealth is calculated as the net present value of the return to land (rents from selling the above-listed livestock products) using a discount rate of 4 percent over a 25 year time horizon. Future rents are projected based on annual growth rates of 2 percent<sup>74</sup> .

Rents are calculated as:

# Rents = (total) Rent * Production * Unit Price

Where (total) Rent is represented by a constant of 45 percent of revenues, assumed to calculate returns to pastureland.

||**Average production**|**Average price (2001-2012,**|
|---|---|---|
|**.**|**(thous. tons, 2007-2012)**|**USD per ton)**|
|Camel milk|~144|206|
|Cow milk|~61|462|
|Goat milk|~50|215|
|Ovine milk|~30|188|
|Eggs (number)|1,750,000|0.25 (price of each egg)|
|Chicken meat|~4|4,677|
|Sheep meat|~41|4,115|
|Goat meat|~23|4,177|
|Beef meat|~22|3,367|
|Camel meat|~25|3,733|
|**Natural Wealth Per Capital Estimate ($) in 2013**|**2**|**’017**|

# **Methodological clarification with respect to externalities**

The rent computations estimated by this paper are based on market prices, production data and other pieces of information or assumptions, but have not included any externalities resulting from mining activity; either the positive or the negative ones, such as CO2 emissions, ecosystem’s degradation, water contamination, etc. Moreover, several impacts on employment, capacity-building, development of local infrastructure, improved public service delivery, etc may be strictly correlated to resource rents. These are issues of great interest and relevance, at the same time, the methodology followed by this paper is not so sophisticate to factor also these (or other similar) externalities or impacts; in addition, even if that was possible, it would not be expected to have any (improving) bearing on the level of confidence of the whole analysis, since the overall frame remains anchored to high level aggregates and assumptions.

74 Average estimate assumed based on: Global Food Projections to 2020,Emerging Trends and Alternative Futures, Rosegrant, Paisner, Meijer and Witcover, International Food Policy Research Institute, August 2001 36
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on this data to generalize to the entire target population and to study how the same program might have different outcomes in different geographic locations depending on their initial conditions. Second, it is created as a natural by-product of program implementation, making it a readily-available, low-cost source of data. Finally, it is collected since program inception and therefore has a time dimension that would be worth exploring when assessing the effectiveness of a program over time.

In this paper we argue that monitoring data can be fruitfully analyzed in the context of assessing program impact to address a different set of questions, questions that are highly relevant for programs where the treatment impact crucially depends on duration of exposure to the program. Specifically, we examine the relationship between the duration of program exposure and nutrition outcomes. The returns to exposure is important from a policy perspective in programs where gains are cumulative over time and where (as in most cases) the program impacts are expected to be heterogeneous across socio-economic groups or areas in across the country. If the returns to exposure are positive and the program is gradually phased in within a country, it might pay to target sequentially groups/areas with the highest marginal return to exposure first, in order to maximize the total gains from the investment in the program.

When estimating the differential returns to program duration on child nutritional outcomes, we use a community-based nutrition intervention in Madagascar. The program (SEECALINE)<sup>3</sup> was initiated under World Bank support in 1999 and has been gradually scaled-up to cover about one-third of the total targeted population. Its gradual expansion provides substantial variation in program exposure. Its large scale feature allows us to exploit the richness of the monitoring data that was collected over time during its implementation.

We quantify the marginal effect of increasing duration to the program by comparing at any given point in time the nutritional outcomes of the “comparable” communities that experience different durations of program exposure. We call this parameter of interest _differential treatment effect (DTE)._ Figure 1, which plots the outcome trends for sites joining the program at different points in time, illustrates this scenario. The vertical distance between these curves is the observed difference in outcomes between the longer and the shorter exposure sites. These vertical

> 3 Seecaline stands for " _<u>Surveillance et Education des Ecoles et des Communautés en matière d’ALImentation et de Nutrition Elargie</u>_ " (Surveillance and Education of Schools and Communities on Food and General Nutrition) and is among the few examples of African countries to embark in a nationwide, large-scale community-based nutrition program.

3
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<!-- Start of picture text --> Percent<br>mRange Mean -EMDEs (World Bank)<br>o omg<br>-105 A | * | ia Gg<br>“18<br>-20<br>1year 5year|1year 5Syear 1year 5 year<br>Disasters Wars Financial crises<br><!-- End of picture text -->
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