rafmacalaba/data-use-mentions-extended huggingface dataset:
can be used to draw synthetic cases from. This historical imputation model in vector notation is (2) _Y_ = _g_ ( _X, Z_ ) _._ In this model, _Z_ now includes the additional covariates that are historically predictive but cannot be used for projection purposes. Particularly the historical malnutrition rates, life expectancy and child mortality rates are strong predictors for historical food insecurity and allow creating good additional synthetic cases. Imputations are generated using the multiple prediction framework of Andr ́ee (2021a), which iteratively estimates fully optimized machine learning models to replace previous imputations with better predictions generated from all other been trained on data that has artificially been created from generative models (Li and Wen, 2014; Abdul Lateh et al., 2017) or even within fully simulated environments Nikolenko (2021).

> 13As a simple example, Andr ́ee et al. (2020) train a model on monthly food insecurity assessments that are published every 4 months while covariates can be observed every month. They produce simple synthetic cases by assuming that the categorical outcome of the food insecurity assessment would be the same one month prior to or after the assessment. They argue that since the official assessments themselves are not exactly timed measurements but qualitative estimates prone to error, the errors in the synthetic cases would likely be of a similar degree as the measurement error in the observations. This allows them to expand the size of the training data by a factor 3, while ensuring that all covariate values are internally consistent, and this improves prediction on holdout observations.
in the early 1960s to 17 percent in the early 1990s, and reached a high of 30 percent in 2000-2005, declining slightly to 28 percent in the last 5 years.

> 40  The first part of the table reports numbers on a commitment basis and is based on the data from the report _IDA in Retrospect_ published in 1982 on the occasion of the first twenty years of IDA.  The second part reports numbers on a disbursement basis and is based on the grant/loan database managed by the World Bank.

29
activities of US-based multinationals. Multinationals are defined a parent company, the US entity that made at least one direct investment in a foreign affiliate, defined as a foreign business enterprise. We use the data collected on majority-owned, non-bank foreign affiliates and non-bank US parents for the years from 1982 to 2002. The foreign affiliate survey forms that US multinationals are required to complete on an annual basis include detailed information on the number of employees hired abroad. In previous work we have cross-checked these data with national survey data from other countries and found the employment numbers to be remarkably similar. Using these data, we construct a panel of number of employees hired abroad by country by year.

# C. Trade Data

Our data on import penetration were made available at the 4-digit ISIC level by Bernard et al. (2006). We also include a measure of the share of import penetration from low-wage countries, also computed by these authors.

D. Data on Occupational Characteristics (United States Department of Labor, supplied by David Autor) Definitions of the Nature of Tasks, taken from Autor et al. (2003)
# 4 Conclusions

High‐growth firms have a disproportionate ability to create jobs and generate output. But there is less evidence and understanding about whether the benefits to economic growth outweigh the costs they impose on other firms. To understand the impact of HGFs on the economy as a whole, we rely on detailed administrative microdata from Hungary. We find that productivity growth of firms in Hungary is stronger if they serve industries with more HGFs. The fact that the premium in productivity growth is associated with growth in sales and that spillovers exceed local labor markets suggests that the surge in demand generated by HGFs may allow their suppliers to exploit economies of scale. Not only do HGFs have positive spillovers, but they benefit more firms in the middle of the productivity distribution. Firms in the bottom quartile of the distribution are not productive enough to take fuller advantage of HGFs demand. Firms in the top quartile of the distribution have access to wider markets and do not depend on HGFs as much. We also find that manufacturing firms are better able to benefit from the presence of HGFs than services firms. Lastly, we ascertain that the spillover effects are still present at a finer geographical level.

The results are robust to alternative definitions of HGFs, sample and estimation method. As discussed in the paper, establishing causality presents significant econometric challenges. The fact that they continue to hold when controlling for a plethora of factors and are not sensitive to several robustness checks gives us confidence in them.

The paper provides evidence for policy makers who may be concerned about directing scarce resources towards supporting HGFs, instead of using these resources for firms in other parts of the economy. It also highlights that the attention and resources given to HGFs can benefit other firms in the economy.

# References

Ackerberg, D. A., Caves, K., & Frazer, G. (2015). Identification properties of recent production function estimators. _Econometrica_ , _83_ (6), 2411‐2451.

Ács, Z. 2010. “High‐Impact Entrepreneurship”
gender of the school and separated into different grade levels. Schools are also disaggregated by urban and rural areas. The total number of schools is 60,000-62,000 from 2003-04 to 200910, however the sub-sample for this analysis is a pool of cross-sectional data that includes

> 10 The take-up rate is difficult to calculate because eligibility to the program is determined by the grade, and not the age, of girls in stipend districts. For simplicity, this analysis estimates the take-up rate as the ratio of participants (245,000 girls covered by the program in 2007) relative to the sum of the actual and potential participants (those enrolled in grade 6-8 in private schools and those whose highest grade is 5-7 but are not enrolled at the time of the survey).

> 11 This survey is part of the institutional strengthening capacity effort within the PERSP and is used for monitoring resource allocation and performance in the sector. The survey does not cover private and religious schools in Punjab; a private school census was undertaken in 1999-2000, on the basis of which it was estimated that over one-quarter of all students were enrolled in private education.  A National Education Census, also conducted in 2006, enumerated all public and private schools.

8
_67_ (1), 16–40.

- Cameron, Lisa and Manisha Shah, “Risk-Taking Behavior in the Wake of Natural Disasters,” _Journal of Human Resources_ , 2015, _50_ (2), 484–515.

- Chaijaroen, Pasita, “Long-lasting income shocks and adaptations: Evidence from coral bleaching in Indonesia,” _Journal of Development Economics_ , 2019, _136_ , 119–136.

- Christodouleas, John P., Robert D. Forrest, Christopher G. Ainsley, Zelig Tochner, Stephen M. Hahn, and Eli Glatstein, “Short-Term and Long-Term Health Risks of Nuclear-Power-Plant Accidents,” _The New England Journal of Medicine_ , 2011, _364_ , 2334–41.

- Cohen, Maurie J., “Technological Disasters and Natural Resource Damage Assessment: An Evaluation of the Exxon Valdez Oil Spill,” _Land Economics_ , 1995, _71_ (1), 65–82.

- Dell, Melissa, Benjamin F. Jones, and Benjamin A. Olken, “What Do We Learn from the Weather? The New ClimateEconomy Literature,” _Journal of Economic Literature_ , 2014, _52_ (3), 740–798.

- Donaldson, Dave and Adam Storeygard, “The View from Above: Applications of Satellite Data in Economics,” _Journal of Economic Perspectives_ , November 2016, _30_ (4), 171–98.

- Elvidge, Christopher D., Tilottama Ghosh, Kimberly Baugh, Mikhail Zhizhin, Feng-Chi Hsu, Nilo Selim Katada, Wilmon Penalosa, and Bui Quang Hung, “Rating the Effectiveness of Fishery Closures With Visible Infrared Imaging Radiometer Suite Boat Detection Data,” _Frontiers in Marine Science_ , 2018, _5_ , 132.

- EM-DAT, “EM-DAT: The Emergency Events Database - Universit catholique de Louvain (UCL) - CRED, D. Guha-Sapir - www.emdat.be, Brussels, Belgium,” www.emdat.be 2017.

- Garza-Gil, M. Dolores, Albino Prada-Blanco, and M. Xos Vzquez-Rodrguez, “Estimating the short-term economic damages from
**South Africa: Projected Cost (including associated network costs) of Electricity Production Capacity and Flows (Investments) Based on Forecasted Demand, 2003-2010 (US$ billions)**

<!-- Start of picture text --> 5<br>4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>Average annual investment flows, 2003-<br>2010: US$ 0.5 billion upper bound<br>1<br>0.5<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Long-Run PMGE forecast--production Long-Run PMGE forecast--flows<br><!-- End of picture text --> Figure 6a: 2003 – 2010 Forecast of Cost of Electricity Infrastructure Demand <!-- Start of picture text --> South Africa: Projected Cost of the Stock and Flow (Investments) of Fixed Line and Mobile Phone<br>Infrastructure Based on Forecasted Demand , 2003-2010<br>(US$ billions)<br>20<br>18<br>16<br>14<br>12<br>10<br>8<br>Average annual investment flows<br>6 2003-2010: $1.98 billion<br>4<br>2<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Long-Run PMGE forecast -- stocks Long-Run PMGE forecast -- flows<br><!-- End of picture text --> Figure 6b: 2003 – 2010 Forecast of Cost of Telephony Infrastructure Demand 17
transport sector policies, Borchert et al. (2012) go further and account for the endogeneity of policy choices.

> 31  It would be of interest eventually to take into account the impact on greenfield investment as well but data concerns prevent us from incorporating greenfield flows into the present analysis.  First, the best greenfield data with global coverage that is currently available relies heavily on _estimated_ rather than actual FDI flows, thus greenfield data is not of a quality comparable to the M&A data we use here.  Second, the sectoral breakdown is

37
acteristics. This is essential since temperatures vary significantly within countries.

Firms may change their locations over time to adapt to the impact of rising temperatures and new firms may choose locations less vulnerable to climate change. This can affect the estimated impact of climate changes on firms if those firms also happen to be more productive, resulting in selection bias. Descriptive evidence from the WBES mitigates concerns about selection bias. First, around 70% of firms in the WBES were established before 2005, when awareness of climate change risks were absent or low. As such, initial firm location decisions for a large majority of firms in our sample were likely not informed by future climate risks. Second, the WBES data in India allow us to track a panel of firms over time. Among the 4,066 panel firms interviewed in 2014 and 2022, only 0.5% changed their location between survey rounds, suggesting that a small share of firms in our sample are relocating over time. Third, Table 1 shows that correlations are low between temperature differences and a set of firm characteristics that are stable over time.<sup>4</sup> However, since some of the covariates are statistically significantly correlated with the temperature shocks we use to identify effects, there is some evidence of selection. We therefore show that our core results are robust to controlling for additional firm characteristics.

Finally, measuring the impact of changes in temperatures from a location’s long-term average may neglect effects of firms’ past adaptive decisions. Specifically, firms in locations with higher average temperatures may have taken measures to reduce their exposure to heat. Neglecting the effects of past adaptive actions biases our estimates away from finding effects of higher than average temperatures on firm performances. This implies that our results, if anything, underestimate the impact of temperature shocks on firms.

> 4In addition to being stable over time, we include only covariates that are nonmissing for at least 95% of our sample.

15
~<br>~~Access to Sanitation (WHO/UNICEF)~~<br>~~•~~<br>~~Access to Improved Sanitation (WHO/UNICEF)~~<br>**Health Outcomes Sub-Index**<br>•<br>Child Mortality (IGME)<br>•<br>Maternal Mortality (MMEIG)<br>•<br>Undernourishment (WB)<br>•<br>Disease (WHO)<br>Malaria (WHO)<br>•<br>Tuberculosis (WHO)<br>•<br>~~Open Defecation Sanitation (WHO/UNICEF)~~|

_Source: Mo Ibrahim Index of African Governance Indicators, 2017_ 37
visibility in the market could lead to more frequent solicitations and higher distortions. We also present results with the sample including firms without any transaction to show whether the relationship between bribery payments and sales growth is sensitive to the composition of the sample. We make the assumption that firms who did not conduct a business transaction were not faced with bribes.

The correlation table for the variables used in the estimation is presented in Table 6. Collinearity between the control variables does not seem to be a concern for the estimation. Table 7 shows IV estimates for the full sample (29 LAC countries). In this table we use the Bribery Indicator ( _BI_ ). The country-location-sector average of the indicator ( ) is used as instrument for the firm’s response. The average is interpreted to represent the likelihood of 16
3 accentuated where there is a third party picking up part of the cost, especially one that is simply reimbursing (a fraction of) the costs incurred by the provider. Self-regulation by the Chinese medical profession is limited, and while the Chinese government has identified the delivery of unnecessary and poor quality care as a matter for concern, there are no formal complaint procedures for patients who feel that they have been over-treated. In such a setting, it seems perfectly plausible that at least some patients may end up not only getting more care than would have been the case if they had been uninsured but also paying more out of pocket. Insurance in such a setting may, in other words, actually _increase_ the probability of large out-of-pocket payments and hence exposure to financial risk.

There have already been some studies that claim to explore the extent to which health insurance reduces financial risk or ‘financial protection’. Mostly, these do so by looking at the effects of health insurance on _expected_ health care payments.<sup>2</sup> However, this does not get at the question of risk. Indeed, in the classic textbook case of actuarially fair insurance, expected health care payments, gross of the premium, are the same with and without insurance. A better approach, but one adopted in only one paper to date to our knowledge, is to model the impact of insurance on the probability of individuals incurring ‘large’ out-of-pocket payments.<sup>3</sup> One could define ‘large’ either in relation to the household’s own per capita income (or consumption), or in relation to the sample average income (or consumption). We employ both approaches, using data from three separate surveys spanning the period 1991-2004 and covering half of China’s 32 provinces.

> 2  See, for example, the studies in Preker and Carrin (2003).

> 3  O’Donnell et al. (2005) explore the factors influencing the probability of a household having catastrophic
where the same parcel is sold multiple times during the period under concern are retained as different observations.

> 21 Extreme values due to very small plot size that result in very high per-ha prices are likely due to data entry error and removed via trimming.

> 22 OSM data was downloaded from <u>https://www.openstreetmap.org. Equivalent and possibly richer data on access to infrastructure could in principle</u> be obtained from official topographic maps that were not accessible to us.

> 23 Downloaded from https://ukraine-cropmaps.com.

> 24 Downloaded from <u>https://files.isric.org/soilgrids/latest/data. Systematically comparing</u> _bonitet_ to these publicly available data would be one area for improving the data base used for estimation.

13
WPS3741

# Can Insurance Increase Financial Risk? The Curious Case of Health Insurance in China

## by

## Adam Wagstaff and Magnus Lindelow

The World Bank, Washington DC, USA

### **Abstract**

The most basic argument for insurance is that it reduces financial risk. But since insurance opens up new opportunities for consuming expensive high-tech care which permits health improvements that are valued by the insured, and because in many settings the provider is able and has an incentive to exploit the informational advantage she has over the patient, it is not immediately obvious that insurance will in practice reduce financial risk. We analyze the effect of insurance on the probability of an individual incurring ‘high’ annual health expenses using data from three household surveys—one a cross-section, the other two panels. All come from China, a country where providers have until recently largely been paid fee-for-service (often according to a schedule that encourages the overprovision of high-tech care and the underprovision of basic care) and who are only lightly regulated. We define annual spending as ‘high’ if it exceeds 5% of average income in the sample and as ‘catastrophic’ if it exceeds 10% of the household’s own per capita income. Our estimates of the effect of insurance on financial risk allow for the possible endogeneity of health insurance in the panel datasets by allowing for a time-invariant fixed effect capturing unobserved risk that may be correlated with insurance status, and in the cross-section dataset by using instrumental variables, where we use availability of and eligibility for health insurance as instruments. Our results suggest that during the 1990s China’s government and labor insurance schemes increased financial risk associated with household health care spending, but that the rural cooperative medical scheme significantly reduced financial risk in some areas but increased it in others (though not significantly). From our results, it appears that China’s new health insurance schemes (private schemes, including coverage of schoolchildren) have also increased the risk of high levels of out-ofpocket spending on health. Where
( _spatial adjustments_ ) after floods because these events destroy local suppliers and raise the cost of sourcing local inputs (Kato and Okubo, 2018). Nevertheless, these firms are also larger and better managed and hence, conceptually, they could be more resilient. There are not many studies that have considered this distinction in a systematic manner.

By comparison, single-person firms appear to be more resilient to crises. They are more likely to reopen in the first 3 months after the shock partly because (i) employment is flexible with family participating in business operations (ii) absence of other means of income opportunities for the owner and family members; and (iii) ability to react faster when making such decisions than larger firms. This was substantiated in a study of 673 Mississippi establishments that were tracked weekly in the year following Hurricane Katrina, (LeSage et al., 2011). Nevertheless, given that these firms are, by definition, small they are the hardest hit by the shock. This could be because the firms that disappear are not the same as those that emerge after the crisis.

# _Informality_

Poor people who run informal firms are the most likely to set up production in risky places because of the low demand for locating on such land parcels. Clear tradeoffs emerge between encouraging entrepreneurship while protecting people from engaging in excessive risk taking. Lowering the costs of entry for firms fosters competition and can contribute to achieving upward income mobility for those entrepreneurs who succeed. At the same time, informal production areas are most likely to emerge in areas exposed to flood risk and mudslide risk.

With the ever improving access to administrative data, we foresee that researchers in more and more developing countries will be able to recreate firm demographic longitudinal data to observe firms being born and closing. Empirical benchmarks of a failure to adapt include firm closings, new formal business formation, firm growth and a lower average product of labor in areas facing more weather shocks. Evidence from India using data on both formal and informal firms suggests that floods
- 8 -

The difference between CDP and GNP consists of net factor income payments. Consequently, the greater the change in share of factor income payments in the product, -he greater the difference in growth rate of the two aggregates for a given councry. It is probable that in the late 1970s and early 1980s the gap was appreciable and varied with the evolution of interest payments (and of income remittances of migrant workers who had remained residents of their country of origin).

Table 2 compares average annual growth rates of CDP and GNP calculated on the basis of the World Bank's World Tables. The absolute gap between the two rates exceeds 0.52 in only 9 cases out of 85 during the period 1970-81, and 15 cases out of 86 in 1974-81; it exceeds 1% in t.nly 3 cases and 5 cases, respectively, over these two periods. The difference is therefore less important than might have been supposed. This result is perhaps due to poor estimation of the trend of factor income payments.

The illustrative model was estimated using' the two definitions of product (cf. results, table 3, equations 1 and 2). The model explaining GDP growth is seen to be a little better than the model explaining GNP growth. This may result from the greater risk of error in GNP estimates due to poor knowledge of international factor income payments or to the fact that the independent variables are less well adapted to explanation of CNP growth than of CDP growth.

# 1.1.2 Data sources

Most of the studies use World Bank data to measure product. However, a few use IMF data (Fry and Lilien 1986, Kormendi and Meguire 1985, Landau 1986, Lanyi and Saracoglu 1983, Rana 1987) and others, United Nations data (Venieris and Gupta 1983). Some authors have also used the data of the project of international comparison of products on the basis of purchasing power parities (Landau 1983, based on Kravis 1979, and Ram 1986, based on Summers