rafmacalaba/data-use-mentions-extended 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
<!-- Start of picture text --> Impact of Political Risks Derived from Government Conduct , 2017<br>Share of respondents (percentage)<br>Lack of transparency and wag in dealing with public agencies 33 OL 27 4<br>Sudden change in the laws and regulations with a negative impact on the<br>company (49%) fii,Isi<br>Delays in obtaining necessaryor ope4rate government a business permits (47%) and approvals to start [iAn5 DE ss 412<br>Restrictions in the ability to transfer and convert currency (42%) 3:10|: i 11<br>Breach of contract by the government (13%) 2) [SII -- 2 0 -15 )<br>Expropriation or taking of property or assets by the government (5%) 3 33 5 40 _s 36 ;<br>0 10 20 30 40 50 #60 #70 #+%§gdo 90 = 100<br>Don't know m None m Consider delay or cancellation<br>& Significantly delay investment = Cancel planned investment Withdraw existing investment<br><!-- End of picture text -->

Source: Computationbased on the GIC Survey
---|
|Afghanistan|2011|n.a.||$1,731.70|
|Bangladesh|2010|18.51|27.49|$2,783.56|
|- Rural|2010|22.7|24.89||
|- Urban|2010|6.7|2.60||
|Bhutan|2012|2.17|0.01|$6,452.00|
|India|2011/12|21.56|239.1|$4,594.20|
|- Rural|2011/12|24.84|196.7||
|- Urban|2011/12|13.38|42.39||
|Maldives|2009|7.25|0.02|$9,714.40|
|Nepal|2010|14.89|4.20|$2,053.40|
|Pakistan|2011/12|7.93|10.26|$4,516.50|
|Sri Lanka|2012/13|1.92|0.38|$9,121.40|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). GNI per capita in USD 2011 PPP obtained from the World Development Indicators (WDI).

According to official figures, the international poverty rate is the highest in India among the eight countries in the region. India also has the largest number of extreme poor among these countries – about 268 million – which is about 28 percent of the global extreme poor.<sup>9</sup> By far, most extreme poor of India are from the rural areas. Bangladesh has the second highest number of international extreme poor in South Asia (28 million) and Pakistan the third highest number (14 million).

But India’s high rate of poverty may partly reflect methodological differences in the way in
relatively scarce literature on the impacts of climate change on the non-agriculture sectors, especially at the firm level. Despite the relatively rich literature on the impacts of climate change on agriculture, studies on the impacts on the non-agriculture sectors are relatively scarce.

We document that the effects of rising temperatures are nonlinear and uneven across climate zones, where firms in hotter zones experience steeper losses in productivity with increases in temperature, compared to firms in relatively colder zones, which tend to register productivity gains. Specifically, a one unit (1<sup>0</sup> _C_ ) increase in WBT deviation in the hottest WBT quantile 25.7<sup>0</sup> _C_ and above results

> 5The matched data on climate change and enterprises is another key contribution, since it can also be used by other researchers.
# **APPENDIX B. Additional Descriptive Statistics**

## **Table B.1.** Spatial Correlation Index (Moran’s I)

|**Country**|_Number of plots with_<br>_trees_|_Number of plots_<br>_with fruit tree_|_Number of plots_<br>_with tree cash crops_|_Number of plots with trees for_<br>_timber or fuelwood_|
|---|---|---|---|---|
|Ethiopia|0.13***|0.06***|0.13***|0.08***|
|Malawi|0.05***|0.05***||0.01***|
|Nigeria|0.08***|0.05***|0.07***||
|Tanzania|0.15***|0.11***|0.15***|0.09***|
|Uganda|0.07***|0.02***|0.08***|0.001|

_Note:_ This table presents the correlation index for key variables of interest. We estimate the haversine distance to estimate the distance matrix. While the Euclidean distance traces straight lines, haversine takes into account the curvature of earth by estimating geodesics. Since information on type of tree is not available for all countries, there are some missing values for the spatial correlation index. Standard deviation in brackets. Two-sided null hypothesis reported. * p<0.10 ** p<0.05 *** p<0.01. Data source: Authors' calculations from LSMS-ISA datasets, World Bank (2015) 37
an objective corruption measure (missing expenditures on the same project). He finds that corruption perceptions and the objective measures are only weakly correlated, with documented biases related to ethnicity and social

> 5 For the interested reader, a richer discussion of definitions of can be found in Johnston (1996).

> 6 The most common form of corruption is _bribery_ : the taking and/or paying of bribes by a government official at any level of government. _Extortion_ relates to the procurement of property under an unlawful threat. _Graft_ is compensation received under threat of inaction, i.e., an official exploits his or her position in order to collect rents. _Embezzlement_ is the unlawful conversion of property entrusted to the official, for private gain. _Nepotism/Cronyism_ refers to the appointment of members of the officials‟ primary group to public positions. _Patronage_ relates to the provision of benefits (including appointments) based on political ties.

> 7 Jain (2001) discusses three types of corruption, Grand, Bureaucratic, and Legislative.  Grand corruption relates to political leaders making decisions motivated by self-interest.  Bureaucratic corruption refers to activities of bureaucrats with either their political leaders or the citizenry.  The most common example of this activity is known as “petty corruption,” relating to bribery.  Legislative corruption is an action which influences the voting behavior of legislators.

> 8 The CPI compiles data from various secondary sources, to create a “poll of polls” in order to generate the perceived level of corruption within a given nation, and is the most widely used indicator in the literature.

4
than the classical “wasteful” approaches for all possible values of c. Recall the classic approach consists in using an indicator coming from a single survey, which at global level essentially means using either H<sup>ˆ</sup> M or H<sup>ˆ</sup> OD when the joint distribution is missing. Even when assuming the most extreme values for cˆ, either 0 or ∞, indicator ˆˆ ˆ ˆ 47 H yields a smaller bias than both HM and HOD. The method performs much better when assuming a value for cˆ equal to the median value for c among Nigerian states.

> 44Also see Tran et al. (2015) on the overlap between monetary poverty and the global MPI.

> 45The baseline version of this indicator satisfies the necessary assumptions for this exercise. Individuals are considered monetary poor when their consumption is below the extreme IPL, in which case they receive a weight of wM = 1/4. The identification threshold used is also k = 1/4. Note that the nonmonetary dimensions of this indicator do not correspond to those of the global MPI.

> 46For instance, this difference is equal to 2 if a state is ranked 1 by the indicator considered and ranked 3 by H<sup>ˆ</sup> .

> 47 ˆ ˆ ˆ Formally, indicator max( ˆHM , HOD) yields the same ranking as H<sup>ˆ</sup> (∞) when c tends to ∞. Also, indicator H<sup>ˆ</sup> M + H<sup>ˆ</sup> OD yields the same ranking as H<sup>ˆˆ</sup> (0) when H<sup>ˆ</sup> M + H<sup>ˆ</sup> OD ≤ 100.

26
countries, and are primarily the result of trade exacerbating income inequality. By contrast, consumption gains tend to be more evenly spread across households.

While trade-offs are prevalent, our findings also suggest that liberalization would be welfare enhancing in the vast majority of countries in our study: in a large part of the developing world, the current structure of tariff protection is inducing sizable welfare losses. Explaining what drives these patterns is beyond the scope of this paper but an interesting avenue for future research.

# **References**

Anderson, J. and P. Neary (1996). “A New Approach to Evaluating Trade Policy,” The Review of Economic Studies, vol. 63(1), pp. 107–125.

Andrew Young School of Policy Studies. (2010). Andrew Young School World Tax Indicators (Volume 1).

Antras, P., A. de Gortari, and O. Itskhoki (2017). “Globalization, Inequality, and Welfare,” Journal of International Economics, 108, pp. 387-412.

Arkolakis, C., A. Costinot, and A. Rodriguez-Clare (2012). “New Trade Models, Same Old Gains?,” The American Economic Review, vol. 102(1), pp. 94–130.

Arkolakis, C., A. Costinot, D. Donaldson, and A. Rodriguez-Clare (2015). “The Elusive Pro-Competitive Effects of Trade,” mimeo.

Artuc, E., S. Chaudhuri, and J. McLaren (2010). “Trade Shocks and Labor Adjustment: A Structural Empirical Approach,” American Economic Review Economics, vol. 100(3), pp. 1008–45.

Artuc, E., D. Lederman, and G. Porto (2015). “A Mapping of Labor Mobility Costs in the Developing World,” Journal of International Economics, vol. 95(1), pp. 28–41.

Atkin, D. and D. Donaldson (2015
|***|
|Change in land market (sales/purchases) participation<sup>a</sup>|0.011|0.498|0.022|0.511|-0.008|0.477||
|Change in the size of land traded in hectares<sup>a</sup>|0.005|0.421|0.021|0.380|-0.020|0.477|**|
|Number ofparcels|63|27|36|34|26|93||

**Source:** Own computation from 2010 World Bank Land Tenure Regularization Survey

**Note:** Significance levels are reported for t-tests of the equality of the means for each of the variables between control and treatment areas (* significant at 10%; ** significant at 5%; *** significant at 1%). aHousehold level outcome variables.

20
19 Argentina and Colombia the TOT shocks have an effect of intermediate significance, while they appear relatively unimportant in Brazil, Chile and Mexico. From the impulseresponse functions (Appendix 4), the following multipliers can be obtained: In Argentina, a one standard deviation shock in the index will raise growth by 1.3% during twelve months. In Brazil a similar shock will increase growth by 0.2% during 18 months. In Chile a one standard deviation shock in the terms of trade variable increases growth 0.3% during 17 months. In Colombia, a shock of similar size raises growth by 0.4% during 21 months, while in Mexico it raises growth by 0.2% during two years.

Table 4 summarizes the sensitivity of output growth to shocks in different variables, as well as the persistence of the shocks in each of the five countries under consideration. This information was calculated on the average behavior of the system for the sample period 1992-1998 and will be used in the next section to examine 1998-1999 in more detail.

# **IV. The Role of External and Domestic Factors on the 1998/1999 Slowdown**

In this section we aim at accounting for the 1998-1999 slowdown in a sub-set of economies of the region (Argentina, Brazil, Chile, Colombia and Mexico). Based on the generalized impulse response functions estimated for the whole period and the shocks that took place during 1997-1999, we calculate approximately the importance of each variable explaining growth rate changes during 1998 and 1999.

|**Sensitivity of Industrial Out**<br>**Average change in output grow**|**Table 4**<br>**put Growth to shoc**<br>**th due**to**a historic**|**ks and persistence of the**<br>**al one standard deviatio**|**effects**<br>**n shock in:**|
|•<br>Perceived levels of respect with which members of<br>one caste/ethnicity are treated by members of other<br>castes/ethnicities<br>•<br>Perceived degree of security relative to other<br>castes/ethnicities<br>•<br>Perceived levels of opportunity for improvement<br>relative to other castes/ethnicities<br>•<br>Respondent is restricted from entering certain<br>public areas, such as village district office<br>•<br>Perceived levels of improvements in the status of<br>indigenous people’s rights since 1990||||

> 32 Asset indicators are listed in the shaded boxes, opportunity structure indicators in the non-shaded ones.

48
|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|260|260|260|260|260|260|260|260|260|
|R-squared|0.947|0.934|0.979|0.446|0.897|0.844|0.751|0.913|0.988|

_Note:_ Dependent variables at the 2-digit industry level are, for each column, entry rate, exit rate, share of employment in firms up to 5 years of age, standard deviation of employment growth rate, gross job creation, gross job destruction, net employment change, job reallocation, and the Herfindahl–Hirschman index (HHI). The share of employment in SOEs is the fraction of employment in all SOEs relative to total employment. All models include year and sector-fixed effects. The sample is restricted to competitive sectors. Data includes private sectors from RAIS, 2016-2019. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.
3 Given the above mentioned concerns, this paper investigates, by way of econometric techniques, the effects of the changes in age structure on the central government budget deficits of national governments. Following conventional wisdom, I argue in this paper that increases in the population share of the elderly will tend to increase the budget deficit and estimate fixed-effects panel regressions using data for 55 countries to verify the validity of this postulation.

# **2. Population Age Structure Changes and the Budget Deficit**

## **2.1 The Effects of the Elderly Share on the Budget Deficit**

The above changes in the age structure of the population bear significant impact on the level of public social expenditure and consequently, holding other things constant, the size of the budget deficit.

An unexpected rise in the population share of the elderly due to improved longevity tends to increase the budget deficit because this will increase the amount of social security benefits that are paid out to the elderly by the government. Such social programs are largely financed by taxes and social security contributions collected from the working population. Since the size of the elderly population relative to that of the working population also increases with the elderly share, holding other things constant, the benefits that are paid out will tend to exceed the revenue collected for such programs. This consequently means that the government, in order to keep such social programs viable, must increase the level of taxes and social security contributions being collected from the working population or decrease the level of social security benefits being paid out to the elderly, or run a budget deficit by financing some of the social security benefits by bond issuance<sup>2</sup> .

Population aging also tends to lead to an increase in the government deficit by increasing the government's expenditure on health care. Surveys conducted in OECD countries show that public expenditure on health care is significantly higher for older persons than for the remainder of the population. This is predominantly due to health problems and costly medical technologies being concentrated
.

# **IV. SECTORAL DISTRIBUTION OF** FDI

Akin to the geographic distribution of FDI in China, the distribution of FDI across the country's industrial sectors is also skewed. This phenomenon became pronounced during the first half of the 1980s, when there was a concentration of FDI in the tourist industries, such as hotel and catering services, and more recently in real estate. Surprisingly, little effort has been made toward a rigorous quantitative analysis as to the determinants and impacts of the sectoral distribution of Chinese FDI or to put them into international perspective.

We base our analysis on data compiled by the United Nations in its World Investment Directory series. Table 10 displays the current landscape of FDI distribution by sector. The mean of the share of FDI flows into each industry is calculated for (i) the global economy as a whole, and for (ii) the developed countries, (iii) the Asian and Pacific countries, and (iv) the Latin American and Caribbean countries separately.

## **The Global Trend**

The sectoral distribution of FDI varies greatly from country to country, and from year to year. Many factors that are unique to a particular region during a particular period of time work together in determining how FDI flows into the various sectors of an economy. Table 10 shows that the sectoral distribution of FDI is quite different between the three regions. The sector that has the most uniformity is the manufacturing sector, which attracts about 42 percent of global FDI on the average. In Asia, almost half of the FDI flowing into the region is concentrated in this sector. The proportion of FDI attracted to manufacturing in Latin America and the Caribbean is the smallest among the three, although it is still the largest sectoral share of FDI in that region.

Within the manufacturing sector, data on FDI stocks are disaggregated into twelve categories. Food, beverages, tobacco, chemicals and mechanical and electrical equipment industries are those that attract the most foreign capital. The chemicals industry, in
15 Participation”, and then dividing this difference by overall GVC participation. This measure, when applied to individual country-pair-industries and GVC-trade, can be written as follows: where the vector **P** _sr_ measures the “forwardness” of country _s_ exports to country _r_ , for each sector _n_ , and each of its elements is bounded between -1 and 1. Forwardness at the country level can be obtained simply by summing across sectors and bilateral partners both the numerator and the denominator.<sup>14</sup> In the same way, a natural measure of the type of participation in output of countries and sectors in GVC can be straightforwardly obtained as At the global level, _P_ is equal to zero both for trade and output, given pure backward and pure forward participation are equal at that level of aggregation,<sup>�</sup><sup>_G_</sup> _s_<sup>_Ps_= 0.Thus,any</sup> index different from zero at any level of aggregation might be interpreted as a deviation from the world aggregate average.

The forwardness index offers a synthetic means of evaluating the primary mode through which a country or a sector engages in GVCs. Previously, a separate strand of the literature has developed specific indicators, such as “upstreamness”, “downstreamness” and “positioning”, to characterize more accurately the participation mode (Antr`as and Chor, 2019 and Wang et al., 2017; see Mancini et al., 2024 for a comprehensive database of such measures based on the most popular Inter-Country Input-Output tables). It is reassuring to see that the forwardness index proposed above shows a strong positive correlation with those measures of positioning.<sup>15</sup> Incorporating both the dimensions of the extent of GVC participation and the primary participation mode within a unified framework provides a coherent and easily comprehensible method for a thorough evaluation of a country’s or a sector’s engagement in GVCs. Additionally,
displacement: Legal empowerment and refugee engagement in transitional processes_ , Refugee Survey Quarterly, 35, 1-25.

> 46 Fagen, P.W. (2009). _Peace processes and IDP solutions_ . Refugee Survey Quarterly, 28(1), pp.31-58.

47 Ruta, C., Ruaudel, H. and Bongard, P. (2018). The Guiding Principles and armed non-State actors. Forced Migration Review, (59), pp.37-39.

48 Examples of direct IDP participation in track-one peace processes are rare, since they are typically difficult to access and displaced persons are further disadvantaged by lack of resources, education, political skills or influence, and the lack of a formal interlocutor to support their rights. See: Brookings Institution, Addressing Internal Displacement in Peace Processes, Peace Agreements and Peace-Building, September 2007. Available at: https://www.brookings.edu/wpcontent/uploads/2016/06/2007_peaceprocesses.pdf 49 IDPs have participated in track-two initiatives in a number of settings, joining a broader coalition that can offer them greater leverage, and also in track-three process that aim to resolve conflict and encourage reconciliation at the community level, and thereby support IDP return. See: Cohen, R. and Deng, F. (1998) Masses in Flight, Washington DC: Brookings Institution Press, p.259 8
**Figure 4:  Health Indicators**

Panel A: Adult Survival Rate <!-- Start of picture text --> Adult Survival Rate<br>LBRNERMWICODMDGGNBCOMTGOETHGINMLIGMBHTIBFARWAAFGBENKIRTLSSLBYEMNPLTZASENVUTKENTJKBGDFSMKHMMRTSTPDJIKGZGHAPNGSDNSYRCOGPSEHNDNICPAKMDATONWSMINDVNMMMRLAOBOLCPVGTMGUYPHLMARJAMAGOBTNSFJIBPRYARM L UZBXKXUKRVZGEOVCTDMAJORLCATUNEGYIDNECUBIHLKACUBALBPERMNGGRDCOLDZAIRQLBNBRAMKDDOMCHNSRBVENSURGABBRBTHAMDVCRIAZEMNEBWAMEXIRNBLRBGRMUSARGTURURYPANCHLBHSATGROUTKMHRVRUSKAZMYSLVAGRCPOLSYCHUNPRTESTCYPLTSVKSVN T ISRCZEMLTPRINZLUOESPJPNITAKORABWOMNFRAFINGBRBHRCANAUSTWNBELDNKDEUISLSWEAUTSAUNLDHKGUSACHEKWTIRLBRNNORARESGPMACLUX QAT<br>BDI MOZ SSDUGA MHLZMB NAMZAF<br>ZWE CMR GNQ<br>TCD NGA<br>CAF SLE CIV<br>SWZ<br>LSO<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>Panel B: Fraction of Children Under 5 Not Stunted<br>Fraction of Children Under 5 Not Stunted<br>LCA CHL KORAUSUSA<br>LBR SLEGNBCOMTGOGINMLIGMBHTIBFASSDZWEUGABENSLBTZASENLSOCMRVUTKENTJKMRTKHMSTPDJITUVKGZGHACIVSYRCOGPSEHNDNICMDATONWSMVNMMMRLAOBOLGUYPHLMARJAMBTNSWZFJISLVBLZPRYARMGEOJORTUNEGYIDNECUBIHLKANAMALBPERMNGZAFNRUCOLDZAIRQBRAMKDDOMSRBCHNVENSURGABBRBTHAMDVCRIAZEMNEBWAMEXIRNTURURYPANTKMKAZMYSSYCTTOGNQJPNOMN KWTBRN<br>CAFNERMWIMOZCODETHRWATCDAFGNPL BGDZMBSDNPAKNGAINDAGO<br>YEM GTM<br>MDG TLS PNG<br>BDI<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>1<br>.9<br>.8<br>.7<br>.6<br>Adult Survival Rate (Age 15 to 60)<br>.5<br>1<br>.8<br>.6<br>Fraction Not Stunted<br>.4<br><!-- End of picture text --> Notes: Adult survival rates are estimated by the UN Population Division and refer to the fraction of 15 year‐olds who survive to age 60. Stunting rates are taken from the WHO‐UNICEF‐World Bank Joint Malnutrition Estimates and refer to the fraction of children under 5 who are more than two reference standard deviations below the reference median height for their age. Data are supplemented with estimates provided by World Bank staff. The graph reports the complementary proportion of children who are not stunted. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing