rafmacalaba/datause-encoder-data huggingface dataset:
# **Introduction**

Conflict can affect a country’s development through various channels, including the loss of physical and human capital, the disruption of production processes, and the undermining of markets (Blattman and Miguel 2010). Not surprisingly, the empirical evidence available shows a negative correlation between economic activity and conflict intensity. But the available evidence is diverse in nature. Of the 42 studies reviewed for this paper, 20 assess impacts on aggregate economic activity and 12 focus exclusively on the formal sector of the economy. Only seven of the 42 studies deal with the impact of conflict on illicit activities, while just three focus on the informal sector.

Estimates of the impact of conflict on aggregate economic activity differ in their magnitude, but they are all negative and sizeable. De Groot et al. (2016) claim that global gross domestic product (GDP) would have been 15.7 percent higher in 2007, had there been no conflict since 1960. According to Collier (1999), civil wars result in a 2.2 percent decline in GDP per capita annually, primarily because of slow production but also due to the loss of capital. Similarly, Acemoglu et. al (2011) find that cities that suffered the Holocaust most intensely have grown less since then, and that administrative districts where the Holocaust had the largest impact have lower GDP per capita and lower average wages today. And Singh (2013) reports that Punjab districts experiencing a major terrorist incident saw their long-term fixed investment decline by around 17 percent.

A more recent literature has relied on detailed microeconomic data, rather than on aggregate GDP, average wages or fixed investment. And again, the estimated impact is unambiguously negative. For example, Besley and Mueller (2012) show that house prices in Northern Ireland declined with conflictrelated killings. Berman et al. (2013) find that construction permits decreased in number in the Philippines when rebel groups triggered an escalation of conflict. And Blumenstock et al. (2018) report that mobile phone traffic by formal firms in Afghanistan fell when conflict-related casualties increased.

However, the data used in these analyses – both aggregate and disaggregate – are mostly from the formal sector of the economy. This is despite informality being prevalent in developing countries affected by conflict. Institutions are most often weak in these countries, and enforcement is ineffective. Households usually engage in subsistence agriculture or self-employment, whereas firms are small and have low capital intensity. Illicit activities, including drug production and commercialization, are common.

Several studies suggest that the response of informal activities to conflict is more muted than that of the formal sector. For example, Looney (2006) claims that the only part of the Iraqi economy to have survived 2
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# **1. Introduction**

Indiscriminate use and improper handling of synthetic pesticides in agriculture have caused serious problems for human health in many developing countries during the past three decades. However, the true extent of the problem is hard to determine for a variety of reasons. Farmers with mild pesticide poisoning often do not report it because treatment services are costly or inaccessible, or because they fear losing their jobs. Health care professionals in rural areas often fail to correctly diagnose poisoning, as many of the related symptoms are quite general in nature (e.g., headaches, dizziness, vomiting) (FAO, 2001). Fragmentary international evidence also indicates that pesticide poisoning is underreported in clinical data. The World Health Organization (WHO) and the United Nations Environment Program (UNEP) estimate that pesticide poisoning injures between one and five million agricultural workers per year (World Resources Institute, 1998). At least 20,000 workers die from exposure every year, the majority in developing countries (WHO, 1990; Kishi _et al_ ., 1995; Pimental _et al_ ., 1992; Rosenstock _et al_ ., 1991).

Although the health hazards of pesticides are serious, support from policy makers for remedial measures has been lukewarm in developing countries. There is widespread concern about diverting resources to alternative pest control methods when poverty, illiteracy and infant mortality are still major problems. In part, the resistance of policy makers is due to uncertainty about the severity of the problem, its sources, and suitable interventions.

Their uncertainty is understandable, since systematic studies of the health effects of pesticides are scarce. Most existing studies rely on farmers’ self-reported symptoms, as appropriate pathological tests are costly and relatively difficult for many developingcountry institutions to administer. In contrast, this study of rice farmers in Vietnam uses the results of clinical exams by doctors from the Vietnam Association of Occupational Health (VINOH), along with blood tests that detect pesticide poisoning (cholinesterase inhibition due to contamination by organophosphates and carbamates). Drawing on an extensive survey of farmer characteristics, we econometrically estimate a model of 3
The mean value of nr1_3 was the highest compared with the other definition of net revenue. Furthermore, minimum value was negative because some households had higher relative costs and it was the lowest as additional costs were deducted. Therefore, to avoid having several negative data values for the dependent variable we decided to use nr1_3 as the dependent variable for the estimation of our empirical models.

# _4.1.3 Explanatory variables_

# _Climate data_

The climate set that gave the best results came from ARTES. These data were for temperature and precipitation. The analysis showed that consecutive months are highly correlated and performed poorly in the models, so seasonal averages were used instead. A season was defined as an average of three months. Winter was defined as December through February, spring as March though May, summer as June through August and fall as September though November. In our analysis, the linear and quadratic terms of temperature only were included in the analysis. The seasonal wetness index was excluded from the model because it violates the condition of the OLS (Ordinary Least Squares) models, which must have a value for the F-stat to ensure that the model is a full rank. Because precipitation levels are very low in Egypt, all the farmers use irrigation for their crops.

# _Soil data_

Soil data for the 20 governorates under study were provided by the FAO. These data provide information about the major and minor soil types. Similar soil types were grouped into 12 sets and used in the analysis to obtain the best results in terms of significant and positive effect on revenue. Insignificant soil types were excluded from the analysis. Of the 12 sets, only two soil types were found significant and used in the analysis: soil_rc, calcaric regosols, and soil_z, solonchaks.

# _Socioeconomic data_

Socio-economic data were obtained from the survey, such as household size, farm size, distance to nearest market to buy inputs, total cost for farm labor, amount of crops sold, amount of crops consumed by livestock, quantity of light machinery and a control variable for livestock.

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# **A Descriptive Statistics**

Tables 3 and 4 report the mean values of the variables used in the model by year, for urban and rural areas. The descriptive statistics generally reflect India’s rapid economic development. Average log per capita consumption, in real terms increased from 4.5 to 4.74 between 2004-05 and 2011-12. When expressed in levels, per capita consumption grew approximately 24% growth over the seven-year period, or 3.1% a year. Household size steadily fell during this time. In the decade between the first and last survey, the share of the population living in households with 4 or fewer persons rose from 39 to 47 percent in urban areas, and from 29 to 35 percent in rural areas. A byproduct of smaller households is a drop in the share of household members that are children ages 0-14, which fell by 5 percentage points in both urban and rural areas. Regular wage work became slightly more prevalent in urban areas, and the share of rural workers working in agriculture declined 3 percentage points, as industrial work became more common in rural areas.

Average spending on transport declined in 2014-15 after a consistent rise between 2004-05 and 2011-12. In contrast, expenditure on household miscellaneous services and recreational spending showed a continual strong increase from 2004-05 to 201415. The drop in transport expenditures may in part result from differences in the components of transport expenditure in 2014-15.<sup>24</sup> To the extent this is causing the drop in reported transport expenditure, the model may underestimate poverty reduction. We therefore also consider a model that only includes whether households reported any consumption on these three items in the past month, rather than including the amounts, as a robustness check to see how much including expenditure amounts matters.

The final rows of Tables 3 and 4 report the population-weighted mean rainfall, across districts, in terms of deviation from historical district means. Monsoons in India typically occur between June and October, making the third quarter rainfall important for agricultural production. Table 4 indicates that in rural areas, rainfall was below average in the second half of 2004, the third quarter of 2009 and the first two

> 24While the recall periods are the same and most of the categories of transportation expenditure are similar across surveys, in the NSSO72 the surveyors were asked to exclude expenditure on fuel for one’s own transport. The consumption expenditure surveys do include expenditure on petrol and diesel for vehicles. Also, in the NSSO72 survey questions regarding transportation expenditure were asked separately for overnight and non-overnight journeys. We include the expenses for both types of journeys as well as expenditure on services incidental to transport to make it comparable to the consumption expenditures surveys where the questions do not make any distinction on the type of journey and also include expenditures on services incidental to transport.

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are larger in wealthier areas than in poorer ones, suggesting that the price‐wealth gradient does not only represent cross‐subsidization.

A few other studies have documented positive relationships between patients’ wealth and the quality of health services received in low‐ and middle‐income countries. Sharma et al. (2017) show that in Kenya, health facilities located in more impoverished areas have worse infrastructure and provide lower quality antenatal and delivery care. In Mexico, Barber, Bertozzi, and Gertler (2007) find that poorer women reported having received fewer procedures in their antenatal consultations, and that indigenous women receive particularly low levels of care. Barber, Gertler, and Harimurti (2007) find that in Indonesia the poor and the wealthy have access to providers of similar knowledge levels, but that poorer women report receiving worse quality of prenatal care. The authors conclude that these results might suggest discrimination against the poor due to informal payments or social and educational differences between providers and patients. Das and Mohpal (2016) show that socioeconomic status is correlated with access to and use of more knowledgeable health care providers in the Indian state of Madhya Pradesh. However, this relationship was driven by differences across villages; within villages, better‐off and worse‐off households use the services of equally knowledgeable providers.

This paper contributes to the literature in several ways. First, to our knowledge, this is the first study that formally decomposes quality differentials into facility selection and within‐facility discrimination mechanisms. Second, this is the first study to combine direct clinical observations of patient‐provider interactions with maternal recall data. The combination of three data sources—clinical observations, patient exit interviews conducted immediately following the observations, and household‐based interviews with women who recently gave birth—allows us not only to measure differential access, but also to measure objectively the quality of services received. Third, the exit interviews that were collected allow us to assess directly the out‐of‐pocket payments and their relationship to the quality of care received as well as patients’ socioeconomic status. To the best of our knowledge, Sharma et al. (2017) is the only other study in this literature to use data from direct observations. Our data allow us to link direct observations to data on patients’ socioeconomic background and the fees they paid. Finally, we add to the literature on health and health system challenges in the DRC, which remains responsible for a disproportionately large proportion of global maternal and neonatal deaths.

4
INTRODUCTION IxxXi Table 20 Stationaiy Population, Momentum, and Year When Net Reproduction Rate Reaches 1 for All Countries and Economies

|Stati<br>popul|onary<br>ation||||Stati<br>popul|onary<br>ation|||
|---|---|---|---|---|---|---|---|---|
|||Poula-|||||||
|||p<br>ti|<br> <br>||||Popula-<br>||
|||on|<br>Year||||tion|Year|
|Country<br>or ecomomy<br>Millions<br>|Ratio<br> <br>to 1985|<br>momen<br> <br>tun|when<br>NRR=1|Country or economy|Million|Ratio<br>s to 1985|<br>momen<br> <br>tuLn|when<br>NRRg1|
|world<br>11514|<br>2.4|1.4|2060|Gabon|6|6.2|1.4|2045|
|||||Gambia, The|5|<br>6.9|1.4|<br>2050|
|Africa<br>3049|5.5|1.6|2060|Ghana|61|4.8|1.6|<br>2035|
|||||Guinea|33|5.4|1.3|2045|
|East Africa<br>1424|<br>6.2|<br>1.6|2055|Guinea-Bissau|4|<br>4.8|<br>1.3|2045|
|Botswana<br>3|2.7|1.8|2010|Liberia|11|<br>4.9|1.5|2035|
|Burundi<br>29|6.2|1.5|2045|Mali|57|7.7|1.5|2050|
|Comoros<br>3|6.5|1.6|2040|Mauritania|13|<br>7.1|1.4|2050|
|Djibouti<br>2|5.8|1.5|2040|Niger|72|10.9|<br>1.5|2060|
|Ethiopia<br>246|5.8|1.5|2045|Nigeria|509|<br>5.1|1.6|2035|
|Kenya<br>200|9.8|1.C|2050|Sao Tom6 and Principe|0|3.4|1.5|2025|
|Lesotho<br>6|3.7|1.5|2030|Senegal|41|6.3|1.5|2045|
|Madagascar<br>49|4.8|1.6|2035|Sierra Leone|23|6.2|1.3|2050|
|MaLawi<br>75|10.2|<br>1.5|2055|Togo|16|5.1|1.6|2035|
|Mauritius<br>2<br>|1.7|1.5|1985|Other west Africa (a)|0.01|<br>1.9|1.6|2000|
|Mozaffbique<br>86|6.3|1.5|2045||||||
|Namibia<br>5|4.4|1.6|2030|North Africa|435|3.5|1.6|2050|
|RIunion<br>1|1.7|1.5|1995|Algeria|84|3.9|1.7|2025|
|Rwanda<br>64|10.6|1.6|2055|Egypt,<br>Arab Rep. of|132|<br>2.8|1.5|2020|
|Seychelles<br>0|1.8|1.6|2005|Libya|32|8.5|1.6|2050|
|Somalia<br>38|7.0|1.5|2050|Morocco|66|3.0|1.6|2020|
|South Africa<br>91|2.9|1.5|2020|Sudan|102|<br>4.7|13|2035|
|Swaziland<br>4|5.3|1.7|2035|Tunisia|17|2.3|1.6|2010|
|Tanzania<br>160|7.2|1.6|2045|Other North Africa (a)|1|6.3|1.4|2050|
|Uganda<br>87<br>|5.9|1.6|2040||||||
|Zaire<br>196|6.<br>4|1.5|2045|America|1201|<br>1.8|1.4|2030|
|Zambia<br>51<br>|7.6|1.6|2045||||||
|Zimbabwe<br>27|3.3|1.7|2015|Latin America and Caribbe|an 872|2.2|1.6|2030|
|||||Antigua and Barbuda|0.1|1.8|1.8|2030|
|West Africa<br>1190<br>|5.8|1.5|2060|Argentina|49|1.6|1.3|2005|
|Angola<br>61|7.0|1.4|2050|Bahamas|0.5|_2.0_|1.6|2000|
|Benin<br>20|4.9|1.6|2035|Belize|1|3.3|1.8|2020|
|Burkina Faso<br>46|6.1|1.4|2045|Bolivia|25|4.0|1.5|2030|
|Cameroon<br>68|6.7|1.6|2045|Brazil|280|2.1|1.6|2005|
|Cape Verde<br>1|3.5|1.9|2025|Chile|21|1.7|1.5|2000|
|Central<br>African<br>Rep.<br>11|4.4|1.5|2035|Colombia|57|2.0|1.6|2005|
|Chad<br>27|5.3|1.4|2045|Costa Ricp|5|_2.1_|1.7|2005|
|Congo.<br>People's<br>Rep. of the<br>17|8.8|1.6|2050|Cuba|13|1.3|1.4|2030|
|C6te d'lvoire<br>85|8.3|1.5|2050|Dominica|0|1.8|1'5|2000|
|Equatorial<br>Guinea<br>1|3.8|1.3|2035|Dominican<br>Rep.|14|2.2|1.7|2010|
the extensive margin of regional integration remains sparse (Kosar and Fajgelbaum, 2014; Emran and Shilpi, 2019). In the absence of data on the universe of trucking transactions, we developed an empirical methodology to define this margin by using observable route characteristics to predict the expected number of trips for all potential routes and using a cut-off consistent with the maximum distance traveled in our data set. Simulation results for the post-lockdown transport cost shock highlight the need for considering the extensive margin. This margin shrank significantly, affecting the remoter states disproportionately.

Finally, the supply chain disruptions due to the COVID-19 pandemic have been a major cause of inflationary pressure around the world in recent months.<sup>7</sup> There is also considerable press coverage of the disproportionate burden of inflation falling on the poor. The evidence on the extensive margin of trade shows that the burden of the rise in transport costs also falls disproportionately on the poorer and remoter states in India. Given that enforcement of COVID-19 restrictions in India was more frequent in major cities which also experienced more frequent outbreaks, this result suggests spillovers from these restrictions in major cities to relatively poorer areas through the transport and trade networks. Any economic recovery policy in India should take account of these potential spillover effects.

The rest of the paper is organized as follows. We start with a discussion of transport platform data in section 2. Section 3 presents the evidence on the size and determinants of transport cost followed by its behavior during the pandemic and post-lockdown periods. Section 4 is devoted to the estimation of trade elasticity. Section 5 discusses the counterfactual results. Section 6 concludes the paper.

> 7 Though the incidence of the pandemic was much milder in India before March 2021, the national lockdown created serious disruptions in the Indian economy during this time. The transport of essential goods was exempt from mobility restrictions during lockdown to minimize supply chain disruptions. Yet, supply disruptions ensued because of the way the lockdown was implemented. As opposed to the developed world where supply chain disruptions did not emerge until the Christmas season of 2021, India experienced higher food prices and major disruptions in its supply chain during the early days of the pandemic due to inter- and intra-state travel/transport restrictions which were in place until July 2020 (Tomar and Mahajon, 2020).

6
(∆τi−k) , as well as how its inclusion may affect the estimates of our main explanatory variable of interest, L.∆Prefik . We use the export supply elasticities estimated by Nicita, Olarreaga and Silva (2015) that are provided for each country at the 6-digit Harmonized System level. Because the elasticity estimates are at more disaggregated level than our data, we concord them to the 4-digit ISIC level by using the median of the 6-digit HS level elasticities within each 4-digit ISIC industry.<sup>43</sup> Since some of the theoretical predictions that we have described relate to the level of an importing country’s market power, we sometimes utilize measures of the level of the inverse foreign export supply elasticity. However, the elasticity estimates are known to be imprecise; thus we construct a medium-high inverse export supply elasticity indicator, which equals one for the two-thirds of products with the highest inverse elasticity values in the given country.

In some specifications we consider measures of the _change_ in the importing country’s import market share resulting from the formation of the customs union. We proxy for the change in market power resulting from the customs union formation by first computing the minimum of the export supply elasticities faced by the four MERCOSUR members, and then measuring the change in the inverse export supply elasticity, from the importing country (either Brazil or Argentina, depending on the specification) to the inverse of the minimum export supply elasticity of the MERCOSUR members. And we again use a medium-high increase in the market power indicator.

# **5.2 Estimation results**

Table 6 provides the IV-ordered probit estimates for Argentina and Brazil that explore the inclusion of different measures of import market power for during the three different MERCOSUR periods of the FTA only (1990-1994), the customs union only (1995-2001), and the entire sample (1990-2001). With the exception of the newly added measures of market power that we describe in more detail below, otherwise the IV-ordered probit model is estimated just as presented in Tables 3 and 4.

The results obtained for Argentina when adding the inverse elasticity indicator in period 1 are shown in columns 1-3 in panel A of table 6. Column 1 corresponds to the non-IV specification from column 1 of table 4 but also includes this additional variable to capture the effect of the level of market power during the FTA period. The results for the preferential tariff change variable are qualitatively and quantitatively similar to those from column 1 of table 4. Moreover, the inverse export supply elasticity

> 43 The results using the mean instead of the median for the concordance are qualitatively similar.

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