rafmacalaba/datause-encoder-data huggingface dataset:
in recently populated urban areas, likely due to substantial learning effects. The incidence of extreme rainfall is much lower in southern states such as Karnataka, Kerala, and Tamil Nadu (Roxy et al. 2017). There is no recent precedence for rainfall as in 2018 in Kerala, and the only other recorded floods of comparable magnitudes are those during the monsoons in 1924 and 1961 (CWC 2018). This makes the 2018 Kerala floods truly exogenous and supports our identification strategy.

# **3. Related Literature**

This section briefly discusses the literature on the impacts of natural disasters on aggregate economic activity and household finances.

# **3.1 Natural Disasters and Economic Activity**

Most papers quantifying the impact of disasters on economic activity are cross-country studies that use data on the occurrence of or damage caused by natural disasters, from the Emergency Events Database (EM-DAT).<sup>5</sup> Skidmore and Toya (2002) find a statistically highly significant positive correlation between the incidence of climatic disasters and longrun growth of real gross domestic product (GDP) per capita. They speculate that disasters may spur investment in updating the capital stock and that adopting newer technology could promote long-run growth.<sup>6</sup> Jaramillo (2009), however, finds mixed evidence on the long-run growth impacts of different disasters depending on the type of disaster, the measure, and the category of the country in terms of disaster vulnerability. Parida, Saini, and Chowdhury (2020) show that floods decrease economic growth of Indian states in the long run. Cavallo et al. (2013) find that very large disasters, if followed by radical political upheavals, lead to a

> 5EM-DAT records the incidence of more than 24,000 disaster events across the globe from 1900 to the present and contains data on the number people affected, injured, and killed in these events as well as estimates of property damage and loss of livestock. A less frequently used but similar source is NatCatSERVICE.

> 6This so-called “creative destruction” effect of natural disasters has been probed further in the literature. For example Cuaresma, Hlouskova, and Oberstriner (2008) examine if trade facilitates such a process following natural disasters but find that only richer countries benefit from capital upgrading while disasters tend to reduce technology spillovers between industrialized and developing countries.

5
cause insufficient information was available. Among the remaining 71 procedures, countries were scored 0 (worst), 1, or 2 (best). A country’s scores on the individual components were averaged within each of the 12 categories to produce policy scores at the country-indicator level.

The policy scores summarize information about 12 different areas of border policy:

- a) Information availability

- b) Involvement of the trade community

- c) Advance rulings

- d) Appeal procedures

- e) Fees and charges

- f) Formalities regarding documents

- g) Formalities regarding automation

- h) Formalities regarding procedures

- i) Border agency cooperation - internal

- j) Border agency cooperation - external

- k) Consularization

- l) Good governance and impartiality

Appendix Table A1 provides a comprehensive listing of the policy inputs, and links each to one of the 12 policy bundles. The policy bundles are each related to one or more articles of the draft negotiating text that formed the basis of the TFA.<sup>17</sup> In our analysis, we use the OECD’s reported score for each of the 12 policy groups as the quantitative measure of border policy in each category.

Before moving to our analysis we adjust one of the variables, _formalities regarding documents_ , to address a possible endogeneity concern. Four of the six inputs the OECD used to construct this variable are taken from the TAB surveys. While the variables the OECD used as inputs are not the same variables that appear on the left hand side of our estimation model, the construction of TAB data from survey responses makes it possible that our outcome variables and these data inputs are endogenous.<sup>18</sup> To address this concern we regress the _formalities regarding documents_ variable on the four TAB inputs used by the OECD, and construct a new measure of the documentation burden

> 17Mo ̈ıs ́e and Sorescu (2013), p.9 illustrates these links.

> 18Respondents that report high levels of documentation requirements might also report high numbers of days required to clear customs.

9
study. We bring country-wide data to show that fees are low for all the provinces in Pakistan, as well as for rural and urban regions within each province.

Table 3 shows median and mean fees for private schools in the four main provinces using data from the census of private schools.<sup>19</sup> Median school fees in Pakistan are low: the highest median school fee in urban and rural areas is Rs.1757 and Rs.1265 annually, both for the province of Balochistan. Punjab, with more than 50 percent of the school going age population reports the lowest median annual fees in urban and rural regions (Rs.828 and Rs.600 respectively). In rural areas, the median annual fee translates to roughly 80 cents a month, or a nickel a day. Based on expenditure data from the PIHS, the mean tuition fee in Punjab represents 1.7 percent of average household expenditure in rural and 2.1 percent in urban areas. A family with 4 children in an urban area will spend 8.4 percent of their household budget on school tuition fees if all their children are enrolled in an average private school.<sup>20</sup> The consistently higher mean compared to the median shows that the distribution is skewed to the left, with a high concentration of schools around _lower_ school fees. The maximum inter-quartile range, which shows the range of fees between the schools at the 75<sup>th</sup> and 25<sup>th</sup> percentiles of the fee distribution, is Rs.1200 (urban Balochistan): in rural regions, 50 percent of all schools are concentrated in a tight band ranging from Rs.900 to Rs.1500.

A striking aspect of Table 3 is that provinces with more schools report lower median fees. If private schools respond to higher local demand for quality education, we expect the opposite relationship to hold—private schools arise where people are willing to pay more for education; therefore, the extent of private schooling should be _positively_ correlated with fees. The data is more consistent with the hypothesis that the existence of private schools is constrained not by the demand for such schooling, but by the availability of teachers. Private schools arise where teachers are available. Since the presence of private schools is a response to an increase in supply, fees are lower in areas where their penetration is higher.

# <u>Teachers (and Their Wages) in Private Schools</u>

Table 4 is a first look at the importance of teachers in the private schooling story. The first row in the table compares salaries in the private and public sector using data on 4,890 teachers in the 800 public and private schools of the LEAPS study. The difference in pay between a teacher in a

> 19 We eliminate a small number of NGO schools that are subsidized by donors and may charge lower fees. 20 Note that total expenditure in schooling includes more than just tuition fees and these numbers therefore represent an underestimate of the total costs of schooling.

13
Month FE YES YES YES YES State FE YES YES YES YES Controls YES YES YES YES Interacted Controls YES YES YES YES Robust standard errors in parentheses (White, 1980): *** p<0.01, ** p<0.05, * p<0.1. Column (1) is measured on the household level. Columns (2‐4) refer to per capita OECD adult equivalents.

Regression techniques, which are based on assumptions for large samples drawn from finite populations, are often not suitable in the context of randomized experiments (Heß, 2017). The uncertainty is in this case not coming from the sampled units observed, but from the fact that we can only observe one of the potential outcomes, which is due to the treatment applied to the different units (Athey & Imbens, 2017). One approach would be to take the randomization explicitly into account and follow R.A. Fisher’s idea of statistical inference via permutation tests of treatment allocation (Young, 2016). The idea is to assume uncertainty about the treatment allocation and compare the actual treatment allocation to re‐randomizations. The results of this exercise are depicted in Table 11, underscoring the robustness of the main results.

_Table 9: Results from baseline estimation, model (2), with random‐inference based p‐values._

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|ln(Cons.<br>Num.)|ln(Cons.<br>Num.)|ln(Cons.<br>Quant.)|ln(Cons.<br>Quant.)|ln(Cons.<br>Val.)|ln(Cons.<br>Val.)|ln(Cons.<br>Cal.)|ln(Cons.<br>cal.)|
|Treatment|0.0348**<br>(0.0200)|0.0614**<br>(0.0560)|0.0281<br>(0.1300)|0.1374***<br>(0.0020)|‐0.0178<br>(0.2820)|0.0812**<br>(0.0340)|0.0189<br>(0.4980)|0.0007<br>(0.9940)|
|Observations|3,995|3,995|3,995|3,995|3,995|3,995|3,995|3,995|
|R‐squared|0.0012|0.2744|0.0003|0.0805|0.0006|0.0725|0.0001|0.1232|
|State FE|NO|YES|NO|YES|NO|YES|NO|YES|
|Month FE|NO|YES|NO|YES|NO|YES|NO|YES|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|
|Controls<br>Interacted|YES|YES|YES|YES|YES|YES|YES|YES|

Robust standard errors in parentheses (White, 1980): *** p<0.01, ** p<0.05, * p<0.1.

# 6. Discussion and Conclusion

The conflict in South Sudan displaced circa two million persons, constituting more than 15 percent of the country’s population (UN OCHA, 2017). Moreover, the majority of population is living in extreme poverty (The World Bank, 2016). Humanitarian crises like the one in South Sudan ask for well targeted policy responses, which address the population strata with the highest need first. This, however, is no arbitrary task as aid allocation mechanisms might set adverse incentives to underreport. Even given 19
not more likely as the neoclassical framework would suggest. This result is concentrated among households that are unfamiliar with the vendor, BASIX, or do not use other types of insurance.

We interpret these finding to suggest that many households are uncertain about the insurance product itself, leading risk-averse households, households with higher costs of evaluating new technologies, and households that are less familiar or place less trust in the insurance provider, to eschew purchasing insurance. This interpretation is also consistent with qualitative evidence: lack of understanding about the product was the most commonly cited explanation for not purchasing insurance, while a significant fraction of purchasers cite “advice from others” as a reason for their decision to buy.

These results represent an early step towards understanding barriers to household participation in “micro-insurance” contracts, and should be viewed as a progress report of our research to date. A new survey implemented during the 2006 Kharif, involving a randomized field experiment, will provide more detailed and robust results about the determinants of participation, as well as the impact of insurance participation on other household decisions.

Section 2 of this paper outlines the concept of index insurance. Section 3 describes the insurance contract features and related institutional details. Section 4 discusses theoretical determinants of insurance participation, and states hypotheses to be tested. Section 5 discusses our survey, and presents summary statistics. Section 6 presents empirical results. Section 7 concludes.

# **2. The Promise of Index Insurance**

Index insurance provides a payout based on the realization of a publicly-verifiable aggregate index, such as rainfall at a local rain gauge or an area-wide measure of crop yields. The goal of such insurance is to insulate household income and consumption against aggregate shocks that are plausibly exogenous to the household unit.

A properly designed index insurance policy minimizes or eliminates moral hazard and adverse selection problems that otherwise distort behavior in insurance markets. This is because 3
health, namely that different elements of subsidy may accrue to different income groups if user fees and out-of-pocket payments vary with income. This is much easier to tackle, since from household surveys one can often establish the amount paid out of pocket by each user, and by implication how much was financed by the government out of public revenues.

There are a few studies to date examining inequalities in the receipt of specific services in developing countries. The World Bank study discussed above includes, in fact, distributions of utilization of key MCH services as well as key MCH outcomes. **Figure 15** is based, however, on another recent study, this time on immunization in India (Pande and Yazbeck 2001). It shows concentration indices for fully immunized children grouped into wealth quintiles along the lines of the World Bank study above. Despite the Indian government's commitment to achieving 85 percent coverage against the six immunizable diseases by 1990, and despite the fact that the service is free at the point of use, some states apparently do far better than others in immunizing poor children. There is comparatively little inequality in immunization coverage in states like Andhra Pradesh, Kerala and Tamil Nadu. But in states such Assam, Bihar and Uttar Pradesh, poorer children are far less likely to be fully immunized that better-off children. As the authors of the study note, the challenge is to find out which factors enabled the low-inequality to states to reach the poor to see if there are lessons to be leamed for the delivery of immunization services in the high-inequality states.

**Figure 15. Inequalities in immunization coverage in India**

<!-- Start of picture text --> 0.40 -<br>0.35-<br>0.30 -<br>*~0.25<br>.2 O3rural<br>i~0.20- ura<br>0.15<br>0.05~~~~~~~~~~~.<br>tu "I 0~~~~t<br><!-- End of picture text --> _Source:_ Pande and Yazbeck (2001).

_33_
# **OECD Imports: Diversification and quality * search**

April 2010 Olivier Cadot<sup></sup> Céline Carrère<sup></sup> Madina Kukenova<sup></sup> Vanessa Strauss-Kahn<sup>§</sup> Keywords: Import diversification, international trade, trade quality JEL classification codes: F1, O11

> * Research on this paper was supported by a grant from the World Bank. Without implicating them, the authors would like to thank Daniel Lederman, William Maloney, Marcelo Olarreaga, and an anonymous reviewer for useful comments.

>  World Bank, HEC Lausanne, CERDI, CEPR and CEPREMAP.

>  CERDI-CNRS, Université d’Auvergne.

>  HEC Lausanne.

> § ESCP-EAP  and CEPR.
**Table 5.B. Model comparison: Root mean squared error of aluminum price forecasts**

(U.S. dollar per metric tonne)

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**<br>|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus<br>|3<br>|170.3|...<br>|...|...|...|...|
|Futures|3||146.6|...|...|...|...|
|Bivariate correlations|3|*|*|47.1|...|...|...|
|BVAR|3||*|*|232.4|...|...|
|OEM|3|||*|*|109.5|...|
|Machine learning|3|*|*||*|*|67.3|
|Consensus|6|175.8|...|...|...|...|...|
|Futures|6||133.1|...|...|...|...|
|Bivariate correlations|6|*|*|37.3|...|...|...|
|BVAR|6|*|*|*|265.8|...|...|
|OEM|6|||*|*|109.3|...|
|Machine learning|6|*|*|*|*||75.1|
|Consensus|9|190.4|...|...|...|...|...|
|Futures|9|*|123|...|...|...|...|
|Bivariate correlations|9||*|265.3|...|...|...|
|BVAR|9|*|*||314.5|...|...|
|OEM|9|||*|*|133|...|
|Machine learning|9|*||*|*||96.3|
|Consensus|12|202.6|...|...|...|...|...|
|Futures|12|*|117.3|...|...|...|...|
|Bivariate correlations|12|*|*|567.4|...|...|...|
|BVAR|12|*|*|*|305.7|...|...|
|OEM|12|||*|*|174.7|...|
|Machine learning|12|*||*|*||133.4|
|Consensus|15|218.1|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15|*|...|...|409.8||...|
|OEM|15||...|...|*|209.2|...|
|Machine learning|15||...|...|*||173|
|Consensus|18|232.9|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|BVAR|18|*|...|...|373.3|...|...|
|OEM|18||...|...||221|...|
|Machine learning|18||...|...|*||205.5|
|Consensus|21|246|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21|...|...|...|...|...|...|
|BVAR|21||||3538|||
|OEM|21||...<br>...|...<br>...|.|...<br>232|...<br>...|
|Machine learning|21||...|...|*||232.6|
|Consensus|24|249.3|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24||...|...|358.1|...|...|
|OEM|24||...|...||275.7|...|
|Machine learning|24||...|...|||257.8|

_Source_ : World Bank.

_Note_ : The forecast period is 2015Q1-2022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate average RMSE estimates for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to an F-test (RMSE).

41
of Americans answered that the effects of global warming had already begun, with this figure rising to 60 percent in 2007. Other survey trends show that only about a third of the public believe that global warming will pose a threat within their lifetime.

A recent comparative study on concern takes a different angle. Hanno Sandvik (2008) published some very interesting findings based on a cross national sample of data on public concern for climate change from 46 countries. Sandvik builds on work by Norgaard and others to assert that public awareness and concern regarding climate change is not a function of scientific information alone, but psychological and sociological factors as well. Sandvik reports a negative association between concern and national wealth and carbon dioxide emissions, and a “marginally significant” tendency that nations’ per capita carbon dioxide emissions are negatively correlated to public concern. Sandvik writes, “these findings suggest that the willingness of a nation to contribute to reductions in greenhouse gas emissions decreases with its share of these emissions” (333). He concludes that such a relationship apparent is “in accordance with psychological findings, but poses a problem for political decision-makers.” Although Sandvik is the first to explicitly test a relationship between wealth and concern, his findings are in accordance with earlier work across spatial scales from the individual to the nation-state. For example, Zahran et al.. (2006) found that citizens residing in U.S. states with higher emissions of climate gasses are somewhat less likely to support climate change policies. O’Connor et al.. (2002) found that higher income negatively affected participants’ wiliness to take some voluntary actions such as driving less (this finding did not hold across all actions). Similarly, an inverse relationship between wealth and concern is also reported in Dunlap’s 1998 cross national research, but with a smaller sample of nations. Although he does not give this relationship much discussion, Dunlap notes that “Quite importantly, despite the lower levels of understanding among citizens of the poorer nations in our study (Portugal, Brazil and Mexico), residents of these nations typically express more concern over global warming than do those in the more affluent nations (Canada, U.S., Russia)” (488). Furthermore, there are no examples of the reverse relationship, in which higher income is positively correlated with concern or support of climate protection policy. Especially because these findings are counter-intuitive to many, we will return to these findings in our section on policy considerations.

10
|_Regions:_|||||||
|---|---|---|---|---|---|---|
|Chisinau|14.3|17.6|39.7|7.9|24.4|28.5|
|Tighina|8.5|17.9|5.4|8.3|2.0|11.7|
|Ungheni|4.6|9.7|12.1|13.6|19.9|13.1|
|Chisinau county|6.0|4.6|5.0|5.7|8.1|6.8|
|Balti|18.5|15.3|10.6|6.5|3.4|5.1|
|Cahul|8.5|2.6|5.6|3.6|1.6|2.6|
|Edinet|8.2|2.7|0.9|11.9|14.2|7.4|
|Gagauzia|11.0|4.3|6.3|13.9|16.3|7.5|
|Lapusna|2.4|6.4|4.7|10.3|7.1|7.0|
|Orhei|5.8|7.1|2.5|6.5|0.0|3.9|
|Soroca|12.3|11.7|7.2|11.9|3.0|6.5|

Source: Calculations based on HBS data.

33
randomly distributed, we expect that accounting for the informal sector will systematically alter the structure of the observed network. A first piece of suggestive evidence is that regions with higher levels of informality have fewer observed firm-to-firm links in the VAT data, even after controlling for population and travel time to metropolitan areas (Table A1).

# **Informal-sector shares correlate negatively with regional economic size and income.**

First, we explore the spatial distribution of informal firms, which we find predominantly reside in smaller markets. Figure A4 plots the distribution of formal sector shares across counties, measured using both value-added and employment metrics. The graph shows that in most counties, the formal sector accounts for less than 20% of economic activity.

We find a strong correlation between a county’s formal sector share and both its economic size (measured by Gross County Product) and income level (measured by Gross County Product per capita). As shown in Figure A5, economic size alone explains between 35% and 52% of the variation in formal sector shares across counties. This pattern is consistent across all three measures of economic activity: employment, value added and the number of firms.

To validate that this positive correlation between market size and formal sector share is not merely an artifact of the administrative data, Figure 5 presents correlations between Gross County Product and three additional employment-based formality measures that do not rely on the administrative data. Notably, while more stringent definitions of informality yield flatter slopes, the _R_<sup>2</sup> remains stable. This consistency suggests that economic size explains similar proportions of county-level informality variation regardless of the measurement approach.

# **The incidence of informality systematically varies across sectors.**

Beyond geographic patterns, informality also varies systematically across sectors. Figure 6 compares a sector’s value added (from administrative data) with its contribution to Kenya’s GDP (from national accounts). Manufacturing and business services show the closest alignment between these measures, which suggests that the bulk of economic activity in those two sectors takes place in the formal economy. This pattern is consistent with the fact that both sectors rely predominantly on inputs from other firms and tend to sell to other businesses (Figure A1).

In contrast, downstream sectors closer to final consumers exhibit larger disparities between value added and GDP contributions (Figure A8). This pattern aligns with weaker self-enforcement in consumer-facing sectors inherent to most VAT systems (Pomeranz, 2015; Naritomi, 2019). We observe similar patterns in both the extensive margin of informality (comparing firm counts across data sources, Figure A6) and the intensive margin (comparing formal and informal em15