rafmacalaba/datause-encoder-data huggingface dataset:
As the data for medical costs only consists of public expenditure, medical costs in the analysis cannot be understood as a direct household expenditure change. Instead, it offers an estimated reflection of the overall change in medical costs attributable to taxation. This encompasses the impact on patients in both the public and private sectors, with the public sector's cost data serving as the foundational reference point.

_Table 4_ - Medical expenditure related to dietary risks

|Diseases related to dietary risks|2018|2019|2020|2021|2022|Average<br>2018-2022|
|---|---|---|---|---|---|---|
|Other heart diseases|173.06|169.51|129.86|122.58|149.26|148.85|
|Intracranial hemorrhage|121.89|124.07|121.75|116.90|123.61|121.64|
|Other peripheral vascular diseases|25.63|27.00|27.11|28.20|32.89|28.17|
|Malignant neoplasm of trachea, bronchus, and lung|37.74|40.03|36.77|34.09|38.27|37.38|
|Other hypertensive diseases|16.10|14.12|12.97|11.92|15.85|14.19|
|Other ischemic heart diseases|710.05|711.00|559.94|500.76|553.90|607.13|
|Conduction disorders and cardiac arrhythmias|279.89|291.49|249.04|238.26|225.23|256.78|
|Diabetes mellitus|100.56|104.30|99.55|95.91|104.32|100.93|
|Malignant neoplasm of the breast|148.18|152.16|137.79|136.29|146.14|144.11|
|Malignant neoplasm of the colon|109.23|113.33|111.49|110.72|114.07|111.77|
|Other liver diseases|199.15|202.69|183.55|166.64|165.39|183.48|
|Cerebrovascular disease, ischemic transient and syndromes|22.16|21.45|18.49|17.70|21.72|20.30|
|Chronic rheumatic heart disease|77.79|84.59|62.92|62.13|82.64|74.02|
|Neoplasms (tumors)|1,754.21|1,777.43|1,585.28|1,554.07|1,645.61|1,663.32|
|Malignant neoplasm of the esophagus|29.37|29.05|26.22|24.39|23.04|26.41|
|Other diseases of arteries, arterioles, and capillaries|141.59|150.32|125.75|123.28|152.97|138.78|
|Malignant neoplasm of the stomach|69.28|71.64|65.44|64.14|61.68|66.43|
|Other circulatory system diseases|10.51|10.15|8.91|8.81|10.46|9.77|
|Other cerebrovascular diseases|74.97|74.62|60.19|51.27|58.09|63.83|
|Total|4,101.36|4,168.95|3,623.03|3,468.04|3,725.13|3,817.30|

Source: SIH/SUS (Sistema de Informações Hospitalares do Sistema Único de Saúde). Values in millions of Brazilian reais, deflated using INPC to 2018 average values.

13
Although this effect is statistically significant in model 5, and almost significant in model 3, the parameter estimates are substantively small. When district fixed effects are included (2 and 6), the program effect becomes negligible and statistically insignificant. The result is similar when province-level fixed effects models are estimated (4 and 7). Thus, we find no evidence that expenditures on the VL program have a direct positive impact on the nutritional outcomes of young children-the group to whom the program is directed-using either the preferred approach (controlling for the initial conditions in communities with district fixed effects), or other models.

In both sets of IV models, the identifying instrument (FONCODES index of unmet needs) is significantly correlated with per capita VL district expenditures. Although the firststage parameter estimate for the FONCODES index is negative for 1996 in the basic IV models (3 and 5), it is positive and strongly significant in the fixed effects models. While this confirms that the instrument is valid in terms of its correlation with VL expenditures, it also implies that marginal program targeting is propoor (Lanjouw and Ravallion 1999). In other words, the positive parameter estimates for the instruments suggests that the incidence of inframarginal VL spending benefits those districts with higher FONCODES indices (i.e., the poor benefit more from marginal increases in program spending that may not be distributed homogeneously across all districts).

The remaining characteristics of the model are similar across estimation methods. With regard to characteristics of the child, we find first that the gender variable is negative, implying that boys have worse nutrition, holding household characteristics constant. 11 Second, as expected, children who are from multiple births show significant reduced linear growth. This effect of being a twin (or triplet) results in stunted growth that is approximately 0.45 standard deviations below the average. Third, the increasingly negative and significant effects associated with the birth order dummy variables indicate that as the birth order increases, children have lower height-for-age z-scores. This may be due to parity effects, however, these dummies may be picking up intrahousehold effects, whereby there is less investment in younger siblings. Fourth, we find a pattern, as illustrated by the dummy variables for the age of the child, where stunting worsens as the child gets older up to the age of the left-out category. This is attributable to the cumulative effect of periods of nutritional and health stress leading to a continued deterioration in growth relative to age and gender standardized norms, though there are some signs of catch-up or leveling off after the weaning age (24 months).<sup>12</sup>

**11** We note that this differs from Ruggeri Laderchi's (2001) finding of no statistical effect using the 1997 ENNIV data.

**12** Sahn and Stifel (2002) find that when errors in the measurement techique are not accounted for, the post weaning-period catch up tends to be underestimated.

_28_
Zambia’s public sector and about 14% of Zambia’s annual flow of nursing graduates. The following graph shows Zambia’s position relative to other countries in the supply of nurses to the UK..

<!-- Start of picture text --> Nurses recruited in the UK in 2001 :<br>per million population of source country<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Phillipines Botswana Mauritius South Africa Zimbabwe Zambia Singapore Ghana Jordan Malawi Kenya Nigeria Pakistan Malaysia India<br><!-- End of picture text --> Zambia is clearly amongst the main suppliers of nurses to the U.K. and there is a clear upward trend since the late 1990s. The total number of nurses in the UK from Zambia increased from 15 in 1998/99 to 135 by 2002/03 implying that Zambia’s share in total foreign nurses in the U.K. increased from 0.3% to 1.13%. Clearly, some corrective steps should be taken to ensure that the trend does not translate into a health crisis in Zambia. Anecdotal evidence on shortages of health professionals is also worrying. For example, a recent study by IOM (2005) noted that international migration of the health workers, mainly to Britain and the United States, has exacerbated staff shortages at major hospitals such as the Ndola Central Hospital. The hospital requires 567 nurses but there are only 133 staff left to provide nursing services. The Ndola School of Nursing, which should be staffed with 27 tutors, is left with only three causing a decline in nursing graduation rates. (Later in the section we take a closer look at the graduation rates of healthcare professionals in Zambia relative to other countries.) We next provide evidence on Zambia’s performance in producing skilled professionals. We believe that this is important not just in itself but also for a better understanding of the likely impact of skilled migration. Table 3 shows Zambia’s endowment of human capital.

14
Before considering the program impact, we describe the child labor patterns in the control communities.<sup>11</sup> Child labor is measured in number of hours worked per week in economic activities, chores and domestic activities. Economic activities include labor in agricultural and livestock activities, as well as labor in nonagricultural activities.<sup>12</sup> Child labor in agricultural and livestock mostly consists of help with the crops or livestock selfemployment activities of the household, but also includes wage labor in agriculture. Labor in nonagricultural activities consists of help by children in the commercial or manufacturing self-employment activities of the household, or possibly wageemployment outside of agriculture. Chores consist of wood or water gathering, and domestic activities include cooking, cleaning, washing and caring for younger siblings. Table 2 shows that including chores and domestic work is important as they constitute a large part of child labor.

Table 2 further shows very clear gender patterns in the allocation of child labor within the household.<sup>13</sup> Boys work more hours in economic activities, in particular agriculture, and also spend more time carrying water and wood. On the other hand, girls work more hours in domestic activities. As a result, there are no significant differences between girls and boys in total number of hours worked. The table shows that boys work on average almost 2.5 hours per week more than their sisters in economic activities. The difference between boys and girls falls to less than an hour, and is not significant, when chores and domestic activities are included. Overall, these patterns suggest withinhousehold specialization along gender lines.<sup>14</sup> Not surprisingly, the data also indicate that older children work more hours than younger children. Given that the age of the child and possibly gender might be correlated with other variables of interest (such as school attendance and grade achievement), we

> 11 We use the control, instead of the baseline, for the descriptive statistics, as information on child labor in chores and domestic work was not collected in the baseline survey. For the other variables, descriptive patterns in the baseline are similar to those described for the control communities at follow-up.

> 12 There is a small number of observations for which the total hours in economic activity is known, but it is unclear whether the hours were dedicated to nonagricultural or agricultural work. As a result, the coefficients of hours in nonagricultural activities, agriculture, and livestock in the tables do not add up exactly to the coefficient of hours in total economic activity.

> 13 In order to look within the household, we regressed each of the child labor outcomes on a gender dummy and a household fixed effect.

> 14 The intra-household patterns are consistent and somewhat stronger than the inter-household differences between boys and girls.

9
**Table 1: Selected Indicators, Definitions, and Summary Statistics**

|**Variable**|**Definition**|**Mean**|**SD**|**Min**|**Max**|**Obs**|**Years**|**Source**|
|---|---|---|---|---|---|---|---|---|
|**Macroeconomy**|||||||||
|Trade openness|Sum of exports and imports of goods and<br>services to GDP ratio.|96.2|61.8|0.2|860.8|1,894|2005-<br>2014|World Development<br>Indicators|
|Financial depth|Domestic credit to private sector refers to<br>financial resources provided to the private sector<br>by financial corporations as a percent of GDP.|52.1|45.3|0.4|312.1|1,753|2005-<br>2014|World Development<br>Indicators|
|Fiscal balance|General government net lending/borrowing as a<br>percent of GDP. It is calculated as revenue<br>minus total expenditure.|0.1|6.8|-40.3|123.5|1,820|2005-<br>2014|World<br>Economic<br>Outlook|
|**Human Capital**|||||||||
|Measles<br>immunization|Child immunization, measles, measures the<br>percentage of children ages 12-23 months who<br>received the measles vaccination before 12<br>months or at any time before the survey.|87.0|13.9|22.0|99.0|1,903|2005-<br>2014|Health, Nutrition<br>and Population|
|Sanitation facilities|Access to improved sanitation facilities refers to<br>the percentage of the population using improved<br>sanitation facilities.|64.2|29.1|5.5|100.0|808|2005-<br>2014|World Development<br>Indicators|
|Life expectancy|Life expectancy at birth indicates the number of<br>years a newborn infant would live if prevailing<br>patterns of mortality at the time of its birth were<br>to stay the same throughout its life.|70.2|8.8|43.6|85.4|1,992|2005-<br>2014|World Development<br>Indicators|
|Educational quality|PISA’s reading performance score measures the<br>capacity to understand, use and reflect on written<br>texts.|462.9|51.5|284.7|569.6|634|2005-<br>2014|OECD|
|**Social Inclusion**|||||||||
|Access to finance|The percentage of respondents, as a percentage<br>of total population 15 years or older, who report<br>having an account (by themselves or together<br>with someone else) at a bank or another type of<br>financial institution or report personally using a<br>mobile money service in the past 12 months.|49.6|32.2|0.4|100.0|284|2011 &<br>2014|Global Findex|
|Old-age pension|Active contributors to pension schemes as a<br>percentage of working-age population.|27.5|32.7|0.0|481.6|313|2005-<br>2013|International Labour<br>Organization|
|Income inequality|Measures the extent to which the distribution of<br>income (or, in some cases, consumption<br>expenditure) among individuals or households<br>within an economy deviates from a perfectly<br>equal distribution.|36.8|8.6|16.6|64.8|736|2005-<br>2014|World Development<br>Indicators|

19
businesses” would be considered small enterprises in other economies. This explains the apparently low number of SMEs in the data presented in this section.<sup>**12**</sup>

**Table 2.1  Chilean classification by firm size.**

|**FIRM SIZE**|**Value of Annual Sales in**<br>**_Unidades de Fomento_**(UF)<br>(see note 15)|**Value of Annual Sales**<br>**(US$ equivalent)**|
|---|---|---|
|Micro|Less than 2,400|Less than $52,800|
|Small|2,401 thru 25,000|$52,800 to $550,000|
|Medium|25,001 thru 100,000|$550,001 to $2,200,000|
|Large|More than 100,001|More than $2,200,000|

# **SECTORAL DISTRIBUTION**

The tables below show the distribution of firms by size and sector and the contribution of SMEs to employment and sales. The crucial importance of broadly defined SMEs to the Chilean economy is illustrated by their share in the number of the country’s firms (99 percent) and of national employment (77 percent). Although they predominate in certain sectors, broadly defined SMEs are found in a large number of economic activities. Around 58 percent concentrate in commerce and services; 11 percent are in agriculture; 11.5 percent are in transportation; and only 6.5 percent are in industrial activities.<sup>13</sup>

**Table 2.2    Number of Firms by Sector and Size (2000)**

|Sector|**Micro**|**Small**|**Medium**|**Large**|**Total**|**Micro, Small and**<br>**Medium as % of**<br>**Total**|
|---|---|---|---|---|---|---|
|**Mining**|1,219|451|85|87|1,842|95.28|
|**Agriculture**|60,635|9,303|806|243|70,987|99.66|
|**Industry**|30,388|8,761|1,745|1,137|42,031|97.29|
|**E.G.W.***|863|222|43|88|1,216|92.76|
|**Construction**|23,113|6,386|1,047|484|31,030|98.44|
|**Commerce**|216,014|31,016|4,607|204|251,841|99.92|
|**Restaurants**|26,466|3,591|363|98|30,518|99.68|
|**Transport**|63,251|9,763|975|416|74,405|99.44|
|**Services**|92,139|23,601|3,428|1,443|120,611|98.80|
|**UC.* **|19,391|748|60|29|20,228|99.86|
|**Total **|533,479|93,842|13,159|4,229|644,709|99.34|

Source: Caracterización de las Micro y Pequeñas Empresas (2002). *E.G.W.: Electricity, Gas and Water; UC: Unclassified The sectoral distribution of SMEs follows the regional patterns _._ SMEs have the greatest market share in services (38 percent), agriculture (35 percent), construction (31 percent) and trade (24 percent). SMEs concentrate sales in four sectors: trade (35 percent), finances, technical and professionals services (16 percent), industry (12 percent) and agriculture (9 percent). These sectors represent 76 percent of SME sales. SMEs are found in sectors with low barriers of entry, low capital requirements,

> 12 The unit's purchasing power is defined daily by the previous month’s variation in Consumer Price Index. The approximate exchange rate on March 22, 2004 was US$27.86/UF (UF value of Peso 16,820.82, dollar set at Peso 607.64).

> 13 _Caracterización de las Micro y Pequeñas Empresas_ (2002).

9
relatively short-lived impact on country-level economic wealth.<sup>9</sup> Realistically, however, due to heterogeneities in ex-ante and ex-post resilience, country-specific effects may be widely dispersed around this 'mean' impact, and thus may not provide a clear indication of what is to be expected of individual countries. In this paper, we explicitly focus on examining what the economic wealth consequences of tropical storms are for the Dominican Republic.

In considering how to estimate to what extent tropical storms might impact economic activity, it must importantly be realized that damages arising from such a storm are inherently local in nature and often display considerable spatial heterogeneity. Not taking account of this local heterogeneity can induce considerable measurement error in trying to estimate the aggregate economic impact of tropical storm damage.<sup>10</sup> One of the main obstacles in incorporating such local differences in damages due to these storms in economic assessments has traditionally been the lack of comprehensive economic data over space and time. However, the recent availability of satellite derived nightlight intensity measures at a highly spatially disaggregated level has provided researchers with a potential proxy of local economic wealth and activity.<sup>11</sup> Unsurprisingly, these data have now found also use in the context of tropical storm impacts. More specifically, Bertinelli and Strobl (2013) and Elliott et al (2015) have examined the impact of these weather events on nightlight intensity for the Caribbean and China, respectively. Importantly both studies have shown that ‘localizing’ the nature of the investigation can provide important insights into the question as to what economic impact tropical cyclones may have.

9 See du Pont and Noy (2016) for a review.

10 See Strobl (2011).

> 11 See Chen and Nordhaus (2011).

4
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# **Evaluating SME Support Programs in Chile Using Panel Firm Data**<sup>**1**</sup>

## Hong Tan

_Keywords:_ small and medium enterprises, program impact evaluation, Chile _JEL codes:_ L25, L53, O25, O54 _Contact information:_ <u>htan@worldbank.org</u> OR <u>hongwtan@comcast.net</u>

> 1 This country study was funded by a research grant RF- P105213-RESE-BB from the World Bank’s Research Committee for the project “Evaluating small and medium enterprise support programs in Latin America” managed by LCSPP and directed by Gladys Lopez-Acevedo and Hong Tan.  We acknowledge José Guilherme Reis for providing us with access to the Chile Investment Climate Survey and ENIA panel, and Michael Goldberg whose study provided invaluable background information on SME programs in Chile.  We thank David McKenzie in particular for his expert guidance on key methodological issues that arose over the course of the study.
|Sri Lanka|1985|||||2001q1|7|2001q1|7|
|---|---|---|---|---|---|---|---|---|---|
|Thailand|1985|1997q2|6|1997q2|15|1997q2|6|1997q2|15|
|||2008q3|3|2008q3|4|2008q3|3|2008q3|4|
|Turkey|1985|1994q1|3|1994q1|5|1994q1|3|1994q1|5|
|||2000q4|3|2000q4|8|2000q4|3|2000q4|8|
|||2008q4|3|2008q4|6|2008q4|3|2008q4|6|
|Ukraine|1994|||||2008q4|5|2008q4|5|
|||2014q1|4|2014q1|4|2014q1|4|2014q1|4|
|Venezuela, RB|1994|2006q1|2|2006q1|3|2006q1|2|2006q1|3|
|Vietnam|2005|||||||||

SS1 denote sudden stop dates identified using the filters laid out in the text: a sudden stop episode starts when portfolio and other flows by nonresidents decline below the average of the previous 20 quarters by more than one standard deviation, and for more than one quarter; and in at least in one quarter of this period, flows are two standard deviations or more below the average. Sudden stops end when capital flows recover to a level above mean minus one standard. In SS2 a sudden stop ends when the flows have recovered to the average of the past 20 quarters. In SS1 modified and SS2 modified we make some judgment calls by looking at the trends in the data and include sudden stops even if the respective criteria are missed by a whisker. By design SS2 lasts longer than SS1.

30
international trade at the region wide level which discriminate against the rest of the world will lead to repercussions in and perhaps retaliation from the rest of the world. Without being able to predict those reactions, it is difficult to estimate the costs and benefits of forming a region wide bloc.

The paramount objective of East Asia's regional trade policy has to be to ensure an open world trading system. Despite some redirection of trade towards itself in recent years, East Asia ships two thirds of its exports to the rest of the world. There is little doubt that the phenomenal growth of East Asia during the past three decades has been facilitated greatly by relativelv open world markets. Almost without exception, studies of the NIEs draw a direct connection between growth i3'. exports and that in the GDP. More recently, Indonesia, Malaysia, Thailand and China have been repeating the experience of the NIEs.

This suggests that the case for an East Asian bloc should be evaluated primarily, not on the basis of static gains including those arising from an improvement in the terms of trade, but in terms of its impact on the world trading system. If a regional approach is to be pursued, it should help keep the world markets open. There are two arguments in favor of a discriminatory regional bloc which deserve a close scrutiny.

First, an East Asian bloc may serve as a deterrent to the formation of closed trading blocs around the world. According to this argument, the world is already dividing into blocs. To ensure that the blocs do not become overly protective of their own markets and limit East Asia's access to them, East Asia should be united and be in a position to retaliate. Unilateral actions such as those taken by the United States under its Super 301 provisions will also be harder to take if East Asia is united.

31
Here are the empirical findings:

# **Table 4: Ordered probit estimation of perceived consequences of rising health litigation**

||(1)|(2)|(3)|
|---|---|---|---|
||_Litigation effects_|_Litigation effects_|_Litigation effects_|
|_Perception of lobby influence_|-0.458**|-0.455**|-0.207|
||(-2.24)|(-2.22)|(-0.83)|
|_Organized lobbies (producers)_||-0.342||
|||(-0.62)||
|_Unorganized interests (consumers)_|||1.436*|
||||(1.94)|
|_cut1|-1.589**|-1.683**|-0.474|
||(-2.19)|(-2.26)|(-0.50)|
|_cut2|-1.086|-1.173|0.0906|
||(-1.56)|(-1.64)|(0.10)|
|_N_|_24_|_24_|_24_|
|t statistics inparentheses||||
|* p<0.10; ** p<0.05; *** p<0.01||||

The results are quite nuanced. On one hand, the conditional effect captured by the model (actors’ awareness of litigation causing higher lobby influence) is significant and substantive. The more stakeholders are aware (or perceive) that judicialization makes the public sector agency more permeable to organized lobby interests, the less positive they evaluate the overall consequences of rising litigation.

We do find some level of confirmation for our unorganized group hypothesis: on average, consumer and civil society groups are more likely to see judicialization as positive even after controlling for awareness of lobby influence. With respect to _organized producers’ groups,_ the results are quite 34