rafmacalaba/data-use-mentions-extended huggingface dataset:
influence of outliers. We further impose the following five restrictions on regression coefficients to ensure homogeneity of degree one in input prices: We then use the coefficient estimates from the previous regression to estimate marginal cost for bank _i_ in calendar year _t_ : The Lerner index is then computed as: Above, _Pit_ is the price of assets and is equal to the ratio of total revenue (sum of interest income, commission and fee income, trading income, and other operating income) to total assets.

In section 3.3, as a robustness check, we use bank asset concentration as an alternative proxy for bank competition.<sup>8</sup> Untabulated results show that country level bank concentration, as measured by the three bank asset concentration ratio, is correlated with the country level average Lerner index with a Pearson correlation coefficient of 0.077 (p-value < 0.0001). The three bank asset concentration ratio is also highly correlated with the Hirschmann-Herfindahl index of bank assets concentration with a Pearson correlation coefficient of 0.798 (p-value < 0.0001).

# **2.4. Institutional and Regulatory Variables**

As mentioned in the introduction we are also interested in the impact of the larger regulatory and institutional framework on the competition and systemic stability relationship. The main regulatory and institutional variables used in this study come from the three surveys conducted by Barth, Caprio, and Levine (2008). The surveys were conducted in the years 1999, 2002, and 2005. Since country level regulations change slowly over time, we use the previously available

> 8 We use the three bank asset concentration ratio from the Financial Structure Dataset (Beck, Demirguc-Kunt and Levine 2010) as a measure of bank concentration.

9
the most attention given to how GQ traits are developed using project-level records and then paired to district-level conditions.

Empirical specifications use a non-parametric approach with respect to distance to estimate treatment effects. We define indicator variables for the shortest distance of a district to the indicated highway network (GQ, NS-EW) being within a specified range. Most specifications use four distance bands: nodal districts, districts located 0-10 km from a highway, districts located 10-50 km from a highway, and districts over 50 km from a highway. Of our 311 districts, 9 districts are nodal for GQ, 69 districts fall within 0-10 km of GQ, 37 districts fall within 10-50 km, and 196 districts are 50 km or more from GQ.

Our focus is on the non-nodal districts of a highway. We measure and report effects for nodal districts, but the interpretation of these results is difficult as the highway projects are intended to improve the connectivity of the nodal districts. For the GQ network, we follow Datta (2011) in defining the nodal districts as Delhi, Mumbai, Chennai, and Kolkata. In addition, Datta (2011) describes several contiguous suburbs (Gurgaon, Faridabad, Ghaziabad, and NOIDA for Delhi; Thane for Mumbai) as being on the GQ network as 'a matter of design rather than fortuitousness.' We include these suburbs in the nodal districts. As discussed later when constructing our instrument variables, there is ambiguity about whether Bangalore should also be considered a nodal city. The base analysis follows Datta (2011) and does not include Bangalore, but we return to this question. For the NS-EW network, we define Delhi, Chandigarh, NOIDA, Gurgaon, Faridabad, Ghaziabad, Hyderabad, and Bangalore to be the nodal districts using similar criteria to those applied to the GQ network.

# 4 Empirical Analysis of Highways' Impact on Loan Activity

## 4.1 Econometric Methodology

Long-differenced estimations compare district x industry loan activity in 1999, just prior to the start of the GQ upgrades, with loan activity in
changing program targeting and payment rates. However, in isolation, offering reduced payments will simply attract more applicants at low risk, resulting in less avoided deforestation. To address adverse selection, it is essential to combine payment adjustments with targeting to higher risk areas. This conclusion is broadly applicable to a variety of cash transfer situations – in the event that a behavior is costly, reducing the compensation for changing that behavior will reduce program effectiveness on aggregate, unless there is also a mechanism encouraging enrollment of those who are likely to engage in behavioral change.

Finally, this paper makes a methodological contribution to the stated preference literature by using information provided by households to support the use of a community leader statement regarding willingness to accept. Although the specifics of Mexico’s communal governance forests are rather unique, a recent global assessment found that at least 18 percent of the world’s total land area is held communally or by indigenous groups (Rights and Resources Initiative 2015). Thus in both land conservation and other realms, situations in which individuals are given voice by a leader are important, and we hope that our theory and analysis linking household stated preference to leader behavior on a referendum question can prove useful in other contexts.

# References

- Alix-Garcia, J., De Janvry, A. & Sadoulet, E. (2008), ‘The role of deforestation risk and calibrated compensation in designing payments for environmental services’, Environment and Development Economics 13(3), 375–394.

- Alix-Garcia, J. M., Shapiro, E. N. & Sims, K. R. (2012), ‘Forest conservation and slippage: Evidence from Mexico’s national payments for ecosystem services program’, Land Economics 88(4), 613–638.

- Alix-Garcia, J. M., Sims, K. R., Orozco-Olvera, V., Costica, L., Medina, J. D. F., Romo-Monroy, S. & Pagiola, S. (2018
To further illustrate, we plot in Figure 1 poverty and inequality against temperature at the subnational level for India, a populous country with a major share of the global poor. This figure shows large degrees of subnational variation in both poverty and inequality. Poverty, as measured by the headcount poverty rate at US$ 1.90 a day, ranges from being relatively low in the Northern regions (lowest rate of 0.5 percent) to quite considerable in the Central and Eastern regions (highest rate of 52.8 percent) (Panel C). A similar pattern is seen with inequality, as measured by the Gini index, which ranges between 18.9 percent and 47 percent (Panel D). Within the country, average temperature also widely varies between 4.3<sup>◦</sup> C and 28.7<sup>◦</sup> C (Panel E). Such wide-ranging subnational variations are not revealed by simply looking at India’s country-level averages of poverty, inequality, and temperature (16 percent, 31 percent, and 22<sup>◦</sup> C, respectively), suggesting that an accurate assessment of the effects of global warming on poverty and inequality would require data analysis at the subnational level.

In this study, we find strong and statistically significant global effects of both higher and lower temperature on poverty and inequality, employing different identification strategies on a novel global database of subnational poverty and inequality. Our (preferred) subnational fixed effects model shows that a one-degree Celsius (i.e., 1<sup>◦</sup> C) annual increase in temperature causes headcount poverty increases of 0.9, 1.8, and 2.3 percentage points, respectively, using the daily poverty lines of $1.90, $3.20, and $5.50 (which correspond to 9.1 percent, 9.0 percent, and 6.8 percent increases). The corresponding
# **4.2 Projecting the Pollution Impact of Alternative Policies**

China affords a unique opportunity for projection of alternative futures, because for many parts of China the future already exists. As Figures 8, 13 and 14 indicate, China's provinces and major urban areas exhibit great disparity in the present strictness of environmental regulation, the share of production in large enterprises, and the degree of state ownership of industry. Shanghai, for example, has a high proportion of production in large facilities, a low proportion in state enterprises, a high (although unfortunately, falling) degree of regulatory strictness and, as a result, low levels of pollution intensity per unit of industrial output. Sichuan's statistics are the converse in most cases.

For much of China, it will be many years before current conditions in Shanghai are replicated. Thus, although we cannot predict the future path of technological progress with any accuracy, we can develop conservative projections for much of China based on actual conditions in the more advanced provinces. And even among the latter, there is sufficient diversity of characteristics to permit use of econometrically-estimated relationships to estimate the consequences of moving toward leading-edge status in the dimensions which matter most for pollution intensity.

China's second advantage is its wealth of data. Large databases made available to us by NEPA have enabled us to base our entire forecasting exercise on the econometric estimation exercises summarized in this paper. To our knowledge this has not previously been possible, in China or any other country. We have used the NEPA databases in three related studies: (1) determinants of industrial air and water pollution intensity; (2) the impact of air emissions on atmospheric pollutant concentrations; and (3) the cost of pollution abatement. For our air pollution scenarios, we have completed the forecasting exercise by joining our estimates to the empirical findings of Xu. et. al. (1994) on the health impact of air pollution.

# **4.3 Projecting Pollution Damage**
In Section 4 we complement this analysis with a non-structural empirical investigation using a cross section of 62 countries. In practice net foreign assets can be predicted fairly well based on a country’s wealth, population, and a measure of institutional quality that we take as an underlying determinant of productivity. Net foreign asset positions are positively related to wealth per capita and, controlling for that, negatively related to institutional quality. In particular, developing countries with better institutional quality (higher productivity) attract more capital inflows and retain more of their own people’s savings. In this empirical implementation, China is a large outlier, with a more positive net foreign asset position than would be predicted by its characteristics. That finding is consistent with the calibration result.

In Section 5 we then use the calibration and empirical models to investigate a number of plausible scenarios for the future. There are certainly different directions in which China’s economy could go, and quite a large existing literature with different views on just how significant productivity growth has been during China’s reform. One plausible scenario is that economic reform continues, including the shift from state to private ownership and deepening of financial sector reform. We draw on recent empirical analyses of productivity growth in Chinese industry that find significant productivity growth resulting in part from reallocation from state to private as well as significant further scope for this reallocation: for example, nearly two-thirds of the industrial capital stock is still under state control. This high reform scenario we model as continued improvement in China’s productivity relative to the rest of the world together with some moderation of the very high savings rates of recent years. Improvements in consumer finance, pension instruments, and health insurance should enable Chinese households to save less and increase welfare. The high reform scenario suggests that China will emerge by 2025 as a significant net debtor, and that a linear path of current account balances to achieve this would result in average deficits in the 5% of GDP range.

The opposite of the high reform scenario
# **1. Introduction**

With the onset of the global financial and economic crisis in late 2008, concerns were immediately raised over the vulnerability of infants and young children and the likelihood of increases in infant mortality (Friedman and Schady, 2009). Short of increasing mortality, business cycle fluctuations may have considerable impact on early child well-being. Indeed, even in less extraordinary times, it is common for negative aggregate shocks to be associated with increased infant mortality across the developing world, particularly for girls (Baird et al 2007). A growing number of studies establish a link between malnutrition during early childhood and slower physical growth, delayed motor development, lower IQ, and low educational achievement. Improving health and nutritional status, on the other hand, is frequently associated with improvements in longer-term outcomes, including reduced likelihood of chronic disease, increases in educational attainment, and higher subsequent returns in the labor market.<sup>1</sup> The apparent lack of deterioration in child well-being in Indonesia in the wake of the 1997/98 East Asian Financial Crisis stands out as a striking example of the potential capacity for insuring infants and children against the most extreme consequences of adverse economic shocks.<sup>2</sup> Missing from research on the Indonesian case, however, is evidence as to whether policy responses to the crisis can be credited with helping to avert the negative consequences that have been experienced after similar crises elsewhere the developing world. Existing research does not separate the impacts of policy from private family based consumption smoothing efforts, and if policy

> 1See, for example, Alderman et al 2001; Alderman, Behrman and Hoddinot, 2005; Alderman, Hoddinot and Kinsey 2006; Glewwe, Jacoby and King 2001; Glewwe and King 2001; Maluccio et al 2005; Martorell 1999; and Strauss and Thomas, 1998.

> 2Studies using the Indonesian Family Life Survey (IFLS) and rural Central Java survey data have found that there was no significant decline in the health and nutritional status
# **Administrative data**

# <u>Method</u>

Administrative records are data collected for carrying out governmental programs or processes. These include:

- Records related to regulation of the flow of goods and people across borders.

- Data from legal requirements to register events (births, marriages, among others).

- Benefits and obligations administration records (taxation, pensions, health insurance).

- Records related to administration public institutions (schools, health institutions, prisons).

- Data arising from government industry regulation (banking, transportation, telecommunications).

- • Records from electricity, water services or telephony utilities (Brackstone, 1987).

More broadly, administrative data can also include private companies’ records or any entities’ historical data on their assets, people, or resources. Schools, hospitals, and other organizations routinely collect data to serve students, clients, or stakeholders. In other words, administrative data cover a wide variety of areas, and are relatively inexpensive to collect since they are already available or captured as part of governmental or private companies’ processes.

Using administrative data also minimizes burdening participants with more surveys and avoids some data quality loss due to misreporting. Evidence shows that an important source of misreporting is the unwillingness to provide socially undesirable answers. Stigma is often associated with participation in welfare programs, for instance (Celhay, Meyer & Mittag, 2018). Criminal records or violence data are other examples of data that can be difficult to obtain with a survey or other method.

The versatility and availability of administrative data allows researchers to address a variety of important questions and areas, including long-term effects of past policies. Administrative data can provide accurate details related to policy costs and benefits (Penner & Dodge, 2019). For example, evaluators stop collecting data at the end of their study in most impact evaluations. However, participants of policy interventions or projects (the treatment group) remain captured in administrative records, which can provide valuable information to estimate long-term effects.
s center are classified as “pond”. In the second approach, all squares that contain ponds anywhere are classified as “pond”. Overall, the first approach can be expected to underestimate the total pond area while the second approach is more likely to overestimate. Thus, this analysis uses both approaches, determines flood exposure for both scenarios, and then averages the results.

> 7 Erosion data is available between 1990 and 2015 for Vietnam’s northern and central shores and between 1988 and 2015 for the coastal provinces in the Mekong River delta including Ho Chi Minh City and Ba Ria-Vung Tau province

32
The Importance of Existing Social Protection Programs for Mental

# Health in Pandemic Times<sup>∗</sup>

Yoonyoung Cho<sup>†</sup> Teresa Molina<sup>‡</sup> _JEL Codes: I38, I31, H12_ _Keywords: social protection, cash transfers, crisis, mental health_

> ∗We thank Jeff Bloem, Pamela Medina Quispe, and seminar participants at the Philippine Economic Society Annual Conference, IFPRI, Ateneo de Manila, the Asian Economic Development Conference, Kyoto University, ANU-ADBI Workshop on Development in the Pacific, and UH Manoa for helpful comments. Laila Mostello-Wetherbee provided excellent research assistance. The study benefited from the generous financial support of the Department of Foreign Affairs and Trade (DFAT) of the Australian government. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent. The data underlying this article cannot be shared publicly due to the privacy of individuals that participated in the survey of the study.

> †World Bank, IZA; ycho1@worldbank.org

> ‡University of Hawaii at Manoa, IZA, UHERO; tmolina@hawaii.edu
Figure 9: Arithmetics and Politics <!-- Start of picture text --> EAS ECS LCN<br>MEA SAS SSF<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Povery Eradication Capacity Index, I(P~p) Povery Eradication Capacity Index, I(Pr)<br>)Π 1 )Π 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>Political Influence Concentration Index, I( .5 Political Influence Concentration Index, I( .5<br><!-- End of picture text --> quadrant they belong to in Figure 9. The figures show that countries with higher capacity (higher poverty eradication capacity index, quadrants _ne_ and _se_ ) are also those where tax revenue is a larger share of GDP. Moreover, among countries with higher arithmetical capacity to mobilize resources, countries with lower concentration of political influence ( _se_ quadrant), have a higher probability of collecting higher taxes than countries with a higher concentration of political power ( _ne_ quadrant), particularly when the arithmetic capacity of DRM of a country is measured starting from the normative statement that only the rich are responsible to transfer resources to the poor (Figure 10, right panel).

20
proportion of domestic services or inputs in total outputs tends to rise (McCaig 2013 [6]; Ghosh 2021). At first glance, this might suggest that a larger proportion of employment remains unaffected by trade, given the larger non-tradable component of the services sector. However, more trade can even indirectly influence non-tradable industries that serve as inputs to tradeable sectors.

This paper focuses on estimating the total effect on labor market outcomes in response to an export shock at the provincial level in Viet Nam from 2009 to 2019. In order to fully capture the overall impact of trade, we need to consider crucial supply chain linkages previous research has overlooked. Our analysis goes beyond solely focusing on directly exporting industries and accounts for industries indirectly affected by the rising demand for exports.

To accomplish this, we adopt a multi-stage approach. Initially, we employ an instrumental variable (IV) methodology to isolate an exogenous component of trade driven solely by foreign demand. The chosen IV is the proportion of a given trading partner country’s share in a specific commodity relative to Viet Nam’s total export value in that commodity, adjusted by the observed GDP growth of the partner country. This IV demonstrates a strong predictive capacity for Viet Nam’s export values while maintaining plausible independence from any supply-side determinants of exports.

Subsequently, leveraging the predicted export exposure obtained in the first stage, we construct a matrix delineating the direct and indirect exposure of each industry in each province. This is accomplished by utilizing input-output tables that document the flow of intermediate goods among different sectors in the economy. The predicted export exposure calculated earlier is then distributed proportionally to the labor share of each industry within each province.

Finally, we estimate the direct and indirect effects of exports by conducting a regression analysis of these computed exposures against various labor market outcomes of interest at the provincial level: wage levels, income disparities, the premium on college education wages, gender wage differentials, employment rates, rates of inactivity, informality in employment, and female employment rates
- 18 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

- **H4** : Regular urban structures (associated to low values of _Circuity_avg_ ) are usually associated with more efficient and shorter trips, which reduce agglomeration costs and may lead to higher levels of productivity.

- **H5** : An increase of either _Intersection_density_ or _Street_density_ implies higher levels of street coverage that facilitates the mobility of people and products increasing the connectivity levels, and therefore, leading to higher productivity levels of the city.

Finally, as shown in Fallah et al (2011), the consideration of the distributional aspects of the population within the urban extent provides information on the land use pattern of the city. The authors proposed a measure of urban sprawl that allows differentiating between cities with an even distribution of its population, from cities with highly concentrated population. For completeness, we present in equation (8) the measure proposed by Fallah et al (2011), and adopted in this work: Dividing the urban extent in small areas, L% is the share of the urban population living in a small area with density below the median density calculated for the entire set of analyzed urban extents (in our case the 940 LAC cities). Since our population data are in raster format, we considered each 250 m pixel of the GHS layer as a small area. This measure ranges from 0 to 1, with 1 indicating a greater level of sprawl. Fallah et al (2011) find that higher levels of sprawl are associated with lower levels of productivity.

- **H6** :  The distribution of population density within a city affects its economic performance. According to Fallah et al (2011), an uneven distribution of population density, associated with high levels of _Sprawl_ , can be linked to deteriorating socioeconomic outcomes, inefficient provision of public goods, and lower productivity levels.

As a summary, Table 2 presents the