rafmacalaba/data-use-mentions-extended huggingface dataset:
known that high temperatures can reduce wheat yields, very little is known about the level of basis risk in high temperature indexed products.

# <u>Weather Indices for Pests and Diseases</u>

Indices have been developed in India to try to capture exposure to pestilence or disease, such as aphid infestation or potato blight and are typically based on relative humidity (see Table 12), or a combination of relative humidity, temperature and rainfall (Tables 13 and 14). Such indices are typically complex and, to an even greater degree than other indices, there has been little analysis of the degree to which such indices capture yield shortfalls.

# _Basis risk_

One key requirement of any index-based approach to agricultural insurance product design is that the claim payments from the indexed product are sufficiently correlated with yield losses of individual farmers, particularly in those years with significantly poor yields. In other words, products must have limited basis risk between the index and the actual individual yield loss.

Preliminary statistical analysis has been conducted for the relationship between average subdistrict yields and weather indexed claim payments for all 270 WBCIS products sold in one state in one year, spanning 13 districts and 12 crops. Applying the product characteristics to available historical weather data we obtain the claim payments that would have been paid in each of the nine years from 1999 to 2007. These historical burn costs may then be compared with subdistrict average yields, where each yield measurement is normalized by the average yield between 1999 and 2007 for the respective crop in the respective subdistrict.

8
not include the financial sector indicators in the regressions, since unlike for the other variables, there is a strong case for bi-directional causality, which might bias the OLS coefficients and renders interpretation problematic.

> 15 This interpretation has to be taken with a grain of salt since the correlations might also indicate that sectors that provide greater outreach are more competitive and therefore margins are lower as a result.

> 16 Only when we control for communication infrastructure (Table IX), does economic size turn insignificant.

> 17 For an overview of the importance of legal institutions for financial development, see Beck and Levine (2005).

16
Instruments for migration were obtained from probit regressions of _M h_ on household characteristics _Z h_ and the number of household members involved in each migration type (international and internal) in 1990, 12 years prior to the time at which household expenditures are observed. The latter were the key identifying variables used to obtain the migration instruments. The predicted probabilities of migration obtained from these probits, rather than observed migration, were used in the expenditure system estimation.

The system of expenditure equations was estimated jointly for the full household sample using three-stage least squares to exploit the information contained in the crossequation error correlations. To improve efficiency, we estimated the system using iterative three-stage least squares. Both an “unrestricted” and a “restricted” expenditure system were estimated. The unrestricted system includes the migration variable and its interactions with the logarithm of total expenditure ( _Ph_ ,<sup>_E_</sup> _h_ ) . The restricted system omits the migration variable and its interactions. A log likelihood test was used to test whether expenditure patterns are significantly different for migrant and non-migrant households, taking into account both migration and its interactions with the logarithm of total expenditure in the demand system.

# **Data**

Data to estimate the model are from the Mexico National Rural Household Survey (ENHRUM). This survey provided detailed data on assets, socio-demographic 17
For advertising we employ the 1982, 1987, and 1992 input-output tables from the Bureau of Economic Analysis (BEA). Two input industries are used to calculate advertising: advertising, and signs and advertising displays. We supplement the advertising data from input-output tables with data from Schonfeld & Associates.

# 7 Empirical Findings

We ...rst focus on assessing the impact of tangible sunk costs. Table 1 presents the ordinary least squares (OLS) estimates of equations (15) and (18), along with the values of the ordinary and adjusted R<sup>2</sup> statistics. As noted above, to estimate (15) we employ the four-...rm concentration ratio, thus setting m = 4. Separate regressions were estimated for the census years 1982, 1987, and 1992. Overall, these results strongly support the hypothesis that sunk costs in‡uence the size distribution of ...rms.

The results of equation (15) indicate that the intensities of the rental and secondhand� markets� and the rate at which capital depreciates are signi...cant determinants <u>Q4i � Qi</u> of ; the di¤erence in mean size between the four largest and all ...rms in Qi industry i. We ...nd that the stronger is the second-hand market for the capital employed in an industry, the faster is the rate at which such capital depreciates, and the easier is the leasing of that capital (i.e., the lower is the sunkenness of the capital employed in the industry), the smaller is the dispersion in ...rm size within the industry. Moreover, the parametric restrictions implied by the model are not rejected by these data, which provides a measure of con...dence in the model and the measure of sunkenness proposed in (8). The likelihood ratio test indicates that the maintained hypothesis H01 : b1 = b2 is not rejected at the 5 percent level
*egro**|
|Owner-occupied|56.0|47.7|51.7|84.7|73.9|86.8|
|Other tenure|44.0|52.3|48.3|15.3|26.2|13.2|
|Public rental|40.7|32.6|43.1|3.9||5.8|
|Private rental|3.0|19.4|4.7|7.1|18.1|4.9|
|Free orother|0.4|0.4|0.6|4.4|8.1|2.5|

_Source_ : Household surveys (Table 1).

*Lithuanian Free Market Institute (2002) _._

_Note_ : Urban apartment sector, except Lithuania (entire housing sector). Private rental includes company housing and excludes in-kind and free housing. Age definitions: starters, 16–29 years; young families: 30–44 years (with at least one child). Lithuania Household Survey age definitions: starters, all under 20 years; young households, 30–39 years (regardless of number of children).

> 50  Sub-renting is typically at market rents, while formal renting is often subject to rent controls—or at least to rent review controls, as in Poland.

28
subforests between 2014 and 2019, we regress _αsrt_<sup>November</sup> on all village characteristics from the LSMS survey and pre-treatment average of November fire occurrences using the following random effects panel model: Here, _Y_<sup> ̄</sup> _srNov,t≤T_ 0 is the average pre-intervention level of November forest fire occurrences in subforest _s_ in region _r_ , _Hs_ denotes the set of normalized (and time-invariant) characteristics of the village matched with subforest _s_ , _νs_ is the subforest random effect, _δr_ 28
can serve, and the issuance of an instruction to regional governments to address teacher distribution problems. But much analysis

> 3 The amendment was made in 2002.

> 4 We use the terms ‘regional’ and ‘local’ interchangeably throughout this report. In both cases, they are used to refer to districts ( _kabupaten_ ) or municipalities ( _kota_ ) and, depending on the context, provinces as well.

> 5 Figures are from the World Bank’s World Development Indicators database.

> 6 For an analysis of the budgetary impact of teacher salary increases, especially ones stemming from the certification program, see Chang _et al_ (2014: 161-162).

8
|

Notes: The dependent variable is the common factor from the full sample estimates in Table 3. ∆TFP is the first difference of total factor productivity (TFP), Uncertainty is the log of the U.S. economic policy uncertainty index taken from Baker, Bloom and Davis (2016), Real interest rate is the U.S. real short-term interest rate. T-statistics in brackets computed with heteroskedasticity and autocorrelation consistent (HAC) standard errors. The regressions include a constant.

To assess the covariates of the global business cycle in our much broader country sample, Table 4 presents regressions of the estimated common factor on total factor productivity, policy uncertainty, and the U.S. short-term interest rate, taken as a measure of global monetary conditions. The upper panel reports the results obtained using as dependent variable the common factor derived from the model using the trade weight matrix, and the bottom panel reports the results obtained with the factor estimated when using the bilateral distance weight matrix.

The univariate regression results show that total factor productivity is positively correlated with the common factor, corroborating the findings of Crucini, Kose, and Otrok (2011) using data for G-7 countries. Next, the uncertainty index shows a negative sign with a statistically significant coefficient, showing that the adverse effect of uncertainty on output found by Baker, Bloom and Davis (2016) among major advanced countries also holds in our larger country sample. In turn, the U.S. short-term real interest rate is negatively correlated with the global factor under both 20
two‐thirds of the countries (against one‐third previously), and for four‐fifths of the bottom quintile (against half previously). These results are consistent with much greater reliance on home‐grown food by the poor than the rich.

Table 8 reproduces Table 6 based on total household cash expenditures. In Burkina Faso and Rwanda, only about one out of every eight households not connected to the grid would find 30 kWh of electricity a month affordable, compared to one out of every four in Table 6. Out of the 15 countries for which full information was available, more than half of non‐connected households would not find electricity affordable in six countries.

Table 8: Expenditure share of monthly electricity consumption of 30 kWh by grid connection status and current spending, based on total household cash expenditures

|Country|Sh<br>All|are of hou<br>Expendi<br>electr<br>Yes|sehold ex<br>ture on<br>icity<br>No|penditur<br>Gr<br>conne<br>Yes|e<br>id<br>ction<br>No|of<br>All|househol<br>Expendi<br>elect<br>Yes|ds for whi<br>ture on<br>ricity<br>No|ch share<br>Gr<br>conne<br>Yes|5<br>id<br>ction<br>No|
|---|---|---|---|---|---|---|---|---|---|---|
|Botswana|4.2|1.4|5.6|1.8|6.3|83|98|76|96|72|
|Burkina Faso|13.0|5.0|14.
a period of physical growth during the conict years, the latter cohort would have already gained full adult height (detail discussion is presented in empirical strategy section 4). On average, women in the sample are 151.6 centimeters tall, with the younger treated cohort 0.46 cm taller on average. Similarly, compared to the control cohort, treated cohorts are less likely to have had any incidence of pregnancy loss, report having had fewer live births, were slightly younger at rst birth, attained more years of education, and were less likely to be employed at the time of the survey. However, there is no dierence in economic status (wealth index) between the two groups.

By construction, the older cohort faced zero level of conict during the rst 15 years of life (Panel B.1). Balance in lifetime exposure to conict between the two sets of women (Panel B.2) is reassuring for the empirical strategy used in section 4, which implies that the two groups do not come from dierent types of sampled clusters. On average, women are 33.4 years old with treatment and control cohorts being 27.9 and 41.8 years old respectively (Panel C). Women in treatment groups are more likely to live in a household with a female head, are more likely to be of lower caste, and are less likely to be from Eastern Development region. All other controls are balanced between the groups.

Summary statistics of the children sample is presented in Table 3. Children are divided into treated and control group by their mother's age at the start of the conict. 47% of the children are girls and on average tend to be the second child (Panel C). There is no dierence in control variables (Panel C) and health outcome variables between the two groups except treated children are slightly taller. Panel B reports mother's exposure to conict. Again, it
education, which may lead to biased estimates of causal relationships between violence and other factors. When such surveys are available, most are cross-sectional and are a limited tool to assess longitudinal analyses of economic and behavioral change.

Researchers may use administrative data to deal with some of these concerns. Police records have been used occasionally (for example, Mele 2009), but few victims report to the police. In Italy, only 7 percent of cases of IPV were reported to the police (UNSD 2014). Thus, health data, such as hospitalizations and homicides, can be preferred proxies for IPV. After all, globally, almost half of the homicides against women in 2012 (47 percent) were carried out by intimate partners or family members, in contrast to less than 6 percent of homicides against men (UNODC 2011), and it is nearly impossible to underreport a death. Aizer (2010) uses hospitalizations by assault in California, and, in the context of Brazil, Perova and Reynolds (2017) use data on female homicides as a proxy for IPV. Limitations remain, however, in that only the severe physical IPV is measured because the administrative data do not provide information on emotional and economic IPV or instances of IPV that do not require medical treatment.

**Background: Empirical Evidence**

There is an extensive literature on the relationship between women’s income and IPV. Depending on the theoretical framework adopted, the relationship is hypothesized to be either negative or positive. The Nash bargaining framework suggests that higher income is likely to trigger a reduction in IPV (Manser and Brown 1980). A reduction in stress because of a relaxation of the budget constraint regardless of who generates more income may also reduce violence (Buller et al. 2018). Models of asymmetric information (Bloch and Rao 2002) or models that explicitly assign value to traditional gender identity (for instance, Akerlof and Kranton 2000) suggest that an increase in women’s income may be associated with a higher risk of violence
TRANSDOC database; and the TRB Transportation Research Information Services database (TRIS). Special emphasis is on road transportation and transportation economics. Coverage: 1988 to the present.

# _<u>UN Economic Commission of Latin America and Caribbean (ECLAC)</u>_

The international transport database covers annual trade and transport statistics of eleven Latin American countries - Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Venezuela. The document consists of two main parts: first, the Introduction explains the scope and content of the BTI (International Transport Database) maintained by ECLAC’s Transport Unit. It covers annual trade and transport statistics of each country. The second part of the document contains detailed tables and charts about the value and volume of imports and exports of the eleven Latin American countries covered by the BTI. It also includes information about the use of different transport modes, the costs of international freight and insurance, and the traded commodities. Data is annual, for the year 2000, and grouped by the Standard International Trade Classification (SITC) codes.

It also contains a compendium of publications drawn from various sources on ports, maritime transport, links for statistics on LAC, as well as information and useful tools for researchers on 24 __________________________________________________________________________________________________________
' 2030 Agenda and its Sustainable Development Goals (SDGs). Specifically, they are embodied in "SDG 1.2.2" which assesses the proportion of people of all ages, both men and women, living in poverty across all dimensions as defined by each nation's unique criteria.<sup>15</sup> The monetary approach depends on information collected from household surveys that track household spending and incomes.

**One approach to address the multidimensional aspect of poverty is to evaluate the extent of deprivation within each dimension separately, thereby constructing a comprehensive set of indicators.** This approach yields a set of poverty indicators that not only includes the traditional income poverty rate but also encompasses deprivations in various other dimensions of wellbeing. These may include statistics on undernourishment, illiteracy, and the lack of access to improved sanitation. This approach offers valuable insights across multiple dimensions of poverty but often falls short of revealing the degree of overlap between different deprivations or the joint distribution of disadvantages. The ambiguity arises because these indicators alone cannot distinguish between scenarios where no one experiences multiple deprivations or scenarios where a large share of the population experiences all deprivations concurrently.

## **A different approach goes further and constructs multidimensional poverty measures, with the advantage of allowing the identification of multiple overlapping deprivations** . While the

> 15 SDG Indicator 1.2.2 (National Multidimensional Poverty) distinguishes itself from other indicators within the Global SDG Indicator Framework by not imposing a universal methodology. Instead, it empowers each country to establish its national multidimensional poverty measure. This approach acknowledges the diversity of circumstances and priorities across nations, allowing for a more nuanced and context-specific assessment of poverty and well-being.

6
officials) see McChesney (1997). There is a huge literature on the dynamic costs of rent seeking (Tullock rectangle), and one recent estimate puts the costs of rent seeking in Western Europe at 7% of GDP (Angelopoulos et al, 2009).

> 3 The first comparative and systematic data on the level of political corruption and rents and corruption was compiled by PRS Group beginning in 1986. Other prominent measures include Transparency International’s ‘Corruption Perceptions Index’begun in 1995- (data beginning 2011) and the World Bank’s Graft indicator-begun in 1998- (2011).

> 4 See Treisman (2007) for an extensive review and testing of the theories on the determinants of rents and rent extraction. Seldadyo and de Haan (2005),  Alt and Lassen (2008), also find several other variables as being very robust in explaining reduced (-) or increased (+) the incidence of rent extraction–namely population density (-), Scandinavian legal origin (-) and ethnic conflict (+) and to a lesser extent literacy (+), primary school enrolment (+), public sector salaries (+), dependence on fuel exports (-), parliamentary form of government (+) and female labour force participation (+). In their study of US states Maxwell and Winters (2004) find that the size of government (+), population size (+), social homogeneity (-) and citizen education and engagement (-) are robust predictors of rent extraction. Glaeser and Saks (2006) using Maxwell and Winters data (2004) but with a less ‘noisy’ identification strategy find that historical income (-), education (-), income inequality (+) and racial heterogeneity (+) are robust determinants of rent extraction.
# **4. Assessing the process of creative destruction**

## **_The distribution of firms by size: sector specialization or framework conditions_**

The first step in our analysis of creative destruction is to look at the distribution of firm by size across countries and industries. Firm size is an important dimension in our analysis for several reasons. As discussed above, small firms seem to be affected by greater churning, but also have greater potential for expansion. Thus, a distribution of firms skewed toward small units may imply higher entry and exit, but also greater post entry growth of successful firms. Alternatively, it may point to a sectoral specialization of the given country towards newer industries, where churning tends to be larger and more firms experiment with different technologies. However, as for all our firm-level indicators, any observed difference in one single indicator – like firm size -- cannot, per se, be taken to indicate differences in the magnitude or characteristics of creative destruction. The distribution of firm by size is likely to be influenced by the overall dimension of the internal market – especially for non-tradeables – as well as the business environment in which firms operate that can discourage firm expansion (see below). So, the analysis of firm size should be taken as one of the aspects that together with the others on firm demographics and the productivity decomposition will enable to identify a coherent story about cross-country differences in creative destruction.

It should be stressed at the outset that our analysis is affected by the different thresholds used on firm size. For most countries the data cover all firms with at least one employee. But the cutoff size is 5 employees in South Korea and Venezuela (with a random sample of smaller),<sup>8</sup> 10 employees in Chile, Colombia and Indonesia. Second, even amongst the countries for which data cover all firms with at least one employee, data may be at the establishment level instead of the plant level, and the definition of both may
_Table 5: Determinants of energy poverty (Marginal Effects) (measured via the energy poverty index)_

||(1)|
|---|---|
|VARIABLES|EnergyPoverty|
|Members|-0.135***|
||(0.000569)|
|Adult equ.|0.139***|
||(0.000554)|
|Female-headed|0.0402***|
||(0.000409)|
|HH with +1 unemployed|0.203***|
||(0.000641)|
|Single-elderly HH|0.197***|
||(0.000907)|
|HH with +1 child|-0.161***|
||(0.000795)|
|HH with +1 disabled|0.0705***|
||(0.000723)|
|HH with +5 members|0.0480***|
||(0.00105)|
|HH with +1 pensioner|0.00778***|
||(0.000631)|
|Social-aid recipient HH|-0.0316***|
||(0.000831)|
|Urban|-0.0425***|
||(0.000422)|
|Female|0.0197***|
||(0.000434)|
|Unemployed|0.0653***|
||(0.000981)|
|Age|-0.000325***|
||(1.90e-05)|
|Elderly|0.0230***|
||(0.000995)|
|Child|-0.0220***|
||(0.00116)|
|Disabled|0.0411***|
||(0.000823)|
|Pensioner|0.0190*
**Figure 4:** Pollution estimates by region

<!-- Start of picture text --> Pollution density<br>ón Aisén del Gral.Carlos Ibáñez del Ca Región de Antofagasta Región de Arica y Parinacota Región de Atacama<br>250<br>200<br>150<br>100<br>50<br>0<br>Región de Coquimbo Región de La Araucanía Región de Los Lagos Región de Los Ríos<br>250<br>200<br>150<br>100<br>50<br>0<br>ión de Magallanes y de la Antártica Chi Región de Ñuble Región de Tarapacá Región de Valparaíso<br>250<br>200<br>150<br>100<br>50<br>0<br>Región del Biobío ión del Libertador Gral. Bernardo O’Hig Región del Maule Región Metropolitana de Santiago<br>250<br>200<br>150<br>100<br>50<br>0<br>0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30<br>Mean PM2.5<br>Count<br><!-- End of picture text --> Source: Authors’ calculations using van Donkelaar et al. (2016)

# **5.2 City-level productivity data**

Our economic dataset consists of region/city-level data, which contains several productivity indicators that are relevant for our the analysis. For each of the 346 cities ( _i_ ) there are 21 sectors and 183 sub-sectors ( _j_ ). For each of the indicators within each city, the annual data ( _t_ ) span from 2005 to 2016. From the city level data we construct real output per worker, our proxy for labor productivity.<sup>3</sup> The data tracks nominal sales of firms within each city. We have estimates of city-level sales values, but not for
|Property size (ha)|0.055|0.058|0.059|
|<br>Years dwelling has been occupied|7.451|7.245|7.185|
|Improved dwelling†|0.397|0.393|0.385|
|<br>Has formal document to non-agric. land†|0.065|0.035|0.030|
|No. of households|1,354|7,099|6,239|

_Source:_ Own computation from 2015/16 Malawi IHPS and IHS4.

_Note: ‘_ IHS4 restricted’ refers to the subsample of respondents reporting to be owners or co-owners of the parcel or dwelling. † Underlying variable is dichotomous variable.

16