rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Baseline estimations – Regulation and leverage**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Leverage|Leverage|Leverage|Leverage|Leverage|Leverage|
|Price cap|0.035|0.047|0.038|-0.008|0.033|0.033|
||(0.054)|(0.059)|(0.062)|(0.050)|(0.052)|(0.054)|
|MIG|-0.192***|-0.131**|-0.132**|-0.197***|-0.129**|-0.134**|
||(0.043)|(0.052)|(0.053)|(0.051)|(0.057)|(0.056)|
|Flexible term|0.052|0.060|0.044|0.018|0.038|0.051|
||(0.042)|(0.038)|(0.045)|(0.034)|(0.031)|(0.036)|
|Road|0.250***|0.122*|0.119*|0.279***|0.144**|0.153**|
||(0.067)|(0.065)|(0.067)|(0.089)|(0.066)|(0.067)|
|Rail|0.431***|0.292***|0.300***|0.346***|0.160**|0.172**|
||(0.116)|(0.094)|(0.091)|(0.094)|(0.081)|(0.081)|
|Air|0.138|0.043|0.031|0.193*|0.136*|0.141*|
||(0.091)|(0.093)|(0.100)|(0.099)|(0.079)|(0.083)|
|Ln (Assets)|0.007|0.024|0.018|0.024***|0.056***|0.055***|
||(0.009)|(0.022)|(0.024)|(0.007)|(0.015)|(0.015)|
|ROA|-0.313*|-0.310*|-0.325*|-0.216***|-0.205***|-0.192***|
||(0.173)|(0.172)|(0.194)|(0.065)|(0.068)|(0.069)|
|Volatility|0.334|0.386*|0.388*|0.131|0.204|0.198|
||(0.230)|(0.213)|(0.216)|(0.174)|(0.150)|(0.155)|
|GDP growth|0.676*|0.816**|0.775|0.312|0.232|0.274|
||(0.350)|(0.378)|(0.485)|(0.253)|(0.241)|(0.238)|
|Inflation|-0.200|-0.158|-0.004|-0.330|-0.233|-0.252|
||(0.313)|(0.271)|(0.361)|(0.213)|(0.213)|(0.236)|
|Bond cap|0.005|-0.004|-0.005|0.001|-0.004|-0.002|
||(0.003)|(0.005)|(0.008)|(0.002)|(0.003)|(0.003)|
|Stock market|-0.006***|-0.005***|-0.007***|-0.004***|-0.004***|-0.006**|
||(0.002)|(0.002)|(0.002)|(0.001)|(0.001)|(0.002)|
|Brazil||0.456***|0.480***||0.582***|0.602***|
|||(0.145)|(0.142)||(0.117)|(0.122)|
|Chile||0.343***|0.392***||0.276***|0.279***|
|||(0.128)|(0.150)||(0.073)|(0.081)|
|Peru||0.237|0.195||0.441***|0.439***|
|||(0.146)|(0.162)||(0.119)|(0.128)|
|Constant|0.405***|0.018|0.175|0.230*|-0.501*|-0.490|
||(0.146)|(0.367)|(0.434)|(0.139)|(0.287)|(0.311)|
|Observations|817|817|817|805|805|805|
|Projects|111|111|111|111|111|111|
|R-squared|0.07|0.08|0.09|0.16|0.20|0.22|
|Year dummies|No|No|Yes|No|No|Yes|

The dependent variable is the ratio of total liabilities to total assets (book values). Robust standard errors in parentheses. Standard errors are clustered by firm.

Columns (1) to (3) present the results for the full sample: (1) excludes country and year dummies, (2) includes country dummies only, (3) includes country and year dummies. Columns (4) to (6) replicate columns (1) to (3) respectively, with extreme outliers excluded. *** p<0.01, ** p<0.05, * p<0.1 18
# **Table 3**

# **Infrastructure and Economic Growth**

_Dependent Variable: Growth in GDP per capita (annual average, percent) Sample: 97 countries, 1960-2005 (non-overlapping 5-year period observations) GMM-IV System Estimation_

|**Variable**|**[1]**|**[2]**|**[3]**|**[4]**|
|---|---|---|---|---|
|_Infrastructure Development (synthetic indexes):_|||||
|Infrastructure Quantity (IK1) 1/|2.6641 **|2.1927 **|2.0260 *|1.0609|
||(1.105)|(0.981)|(1.328)|(1.403)|
|IK1 squared|..|..|-0.0403|..|
||||(0.247)||
|IK1 * Sub-Saharan Africa|..|..|..|0.2897<br>(1.450)|
|Quality of Infrastructure Services (IQ1) 2/|..|1.9581 **|1.9373 **|1.5233 *|
|||(0.549)|(0.598)|(0.800)|
|IQ1 squared|..|..|-0.0265|..|
||||(0.298)||
|IQ1 * Sub-Saharan Africa|..|..|..|1.3582|
|||||(1.281)|
|_Control Variables_|||||
|Initial Output per capita / per worker|-4.3056 **|-6.2404 **|-5.9773 **|-5.2489 **|
|_(in logs)_|(1.099)|(1.285)|(1.815)|(1.635)|
|Education|1.9914 *|2.7857 **|2.8253 **|2.9420 **|
|_(secondary enrollment, in logs)_|(1.095)|(1.160)|(1.175)|(1.376)|
|Financial Development|0.4856|-0.0147|-0.0231|-0.0489|
|_(private domestic credit as % of GDP, logs)_|(0.605)|(0.492)|(0.508)|(0.640)|
|Trade Openness|1.2705|1.0965|1.1278|0.9347|
|_(trade volume as % of GDP, logs)_|(1.053)|(1.410)|(1.380)|(1.363)|
|Lack of Price Stability|-0.0990 **|-0.0510 *|-0.0511 *|-0.0618 **|
|_(inflation rate)_|(0.036)|(0.033)|(0.033)|(0.031)|
|Government Burden|-1.3229|-1.9217 *|-2.0330 *|-1.2706|
|_(Government consumption as % GDP, logs)_|(1.274)|(1.281)|(1.297)|(1.363)|
|Institutional Quality|0.4748|-0.3029|-0.2769|0.2056|
|_(ICRG Political risk index, logs)_|(2.418)|(1.735)|(1.632)|(2.408)|
|Terms of Trade Shocks|0.0197|0.0944 *|0.0991 *|0.0768|
|_(first differences of log terms of trade)_|(0.066)|(0.051)|(0.053)|(0.055)|
|Observations|582|582|582|582|
|Specification Tests (p-values)|||||
|(a) A-B test for 2nd-order serial correlation|(0.360)|(0.482)|(0.484)|(0.481)|
|(b) Hansen test of overidentifying restrictions|(0.241)|(0.275)|(0.211)|(0.190)|
|(c) Difference-Sargan tests|||||
|All instruments for levels equation|(0.166)|(0.340)|(0.290)|(0.197)|

_Numbers in parentheses are robust standard errors. Our regression analysis includes an intercept and period-specific dummy variables._ _* (**) denotes statistical significance at the 10 (5) percent level. Standard errors are computed using the small-sample correction by Windmeijer (2005) 1/ See the notes to Table 1 for the definition of the synthetic indices of infrastructure quantity and quality._ 38
information for the three months preceding the date of the survey. Our unit of observation is the household and we obtained information on monthly income and expenditure over time for each household. The CPHS database also includes qualitative information on household borrowing, savings, and asset ownership. The CPHS provides these data at a wave frequency (four months); however, different households are surveyed in different months within a wave. _MGNREGA_ We obtained monthly data on the numbers of households that worked and those that demanded work under the MGNREGA for all the districts in Kerala, Tamil Nadu and Karnataka from the MGNREGA Public Data Portal maintained by the Ministry of Rural Development. MGNREGA is a demand-driven wage employment program. It provides at least 100 days of guaranteed employment in a financial year to every household residing in a rural area and covers all adult members of rural households who volunteer to do unskilled manual work. A surplus of households that demand work over those who work under MGNREGA reflects excess labor supply in rural labor markets. Declining excess demand for MGNREGA employment from households indicates a tightening of labor market conditions. We later examine the impact of the Kerala floods on the difference between the number of households that worked and number of households that demanded work under MGNREGA.

# _Flood Relief_

We collected district-wise flood relief data from a scanned copy of a report issued by the Disaster Management (A) Department of the Government of Kerala, obtained from the official web portal of the Government of Kerala.<sup>18</sup> It contains the allotment of state disaster response funds sanctioned for 18 items - including agricultural crop loss, ex gratia, food and clothing, and repair of damaged houses - to district collectors in all 14 districts of Kerala. We normalize the relief amounts using district-wise population data from the Census. Every district in Kerala received some amount of assistance and the amount varied from around 250

> 18The report is G.O. (Rt) No. 460/2018/DMD, issued by the Disaster Management (A) Department of the Government of Kerala, dated Thiruvanthapuram, August 27, 2018.

12
# TABLE 1. Participation in _Oportunidades_ by Eligibility Status<sup>a</sup>

|Eligible|Ineligible|
|---|---|
|Level of participation (in %)<br>households|households|
|Did not know about the program<br>27.8|49.1|
|Knew about program but not where to apply<br>5.8|9.8|
|Knew where to apply but did not apply<br>5.9|9.8|
|Applied but not enrolled<br>10.0|11.5|
|Enrolled<br>50.4|19.8|

a Observations: 10,515.

_Source:_ ENCELURB, authors’ calculations.

27
We then investigate how Preferential Trade Agreements (PTAs) influence the impact of industrial policies. Since the second half of the 20th century, PTAs have been a significant feature of global trade, with their scope and depth increasing over the decades. According to the World Trade Organization, 369 regional trade agreements were in force in 2024, up from just 45 in 1995, the year the WTO was established (WTO, 2024). While pre-1995 agreements were primarily focused on reducing tariffs, more recent agreements are considered “deep” because they cover trade and investment in both goods and services as well as intellectual property rights, and a wider set of measures, including subsidies and labor and environmental standards.

When countries are involved in a PTA, the dynamics of how industrial policy affects trade flows can differ, generally fostering more positive outcomes for members. Most pertinently, PTAs can include rules on the types of industrial support allowed and the eligibility conditions (such as local content rules) that can be applied. These rules may alleviate the negative impact of industrial policies on imports from members.

We combine data for the period 2012-2022 on detailed product level bilateral trade, industrial policy (IP) announcements, and rules on subsidies in different PTAs. The econometric analysis controls for various confounding factors, and it yields three main conclusions. First, the introduction of a new industrial policy measure in a destination market reduces export growth to that market on average by about 0.28 percent. Second, exploring the interaction of industrial policy and the existence of a preferential trade agreement, we find that a newly introduced industrial policy measure reduces imports from non-members of a PTA while leaving imports from members of the PTA roughly unchanged. Third, using a measure of the depth of disciplines on subsidies in a PTA, we find that deep disciplines can lead to increased imports from members of a deep trade agreement, effectively making them beneficiaries of industrial policy. For a level of depth equal to that in the top 5 percent of the PTAs in the sample, the introduction of an industrial policy measure increases imports from other members by about 0.39 percent. These effects are robust to alternative econometric specifications, and to the exclusion of potential outliers. We suggest that PTAs provide a shielding effect against the potentially distortive trade effects of discriminatory industrial policies. This shielding effect appears to be heterogeneous across products and regions: it is more pronounced in industries such as chemicals and transport equipment, and for advanced and developing East Asian economies.

**Related Literature** Our work connects to several strands of the literature. Re- cent papers have studied the measurement of industrial policies and their

4
CO meetings.<sup>14</sup> We aggregate the CO level credit and social outcomes at the FA-month level using information from the FA-CO panel. We calculate the performance of an FA before and after the bonus, by taking the average across the 9 months prior to the bonus announcement and the 15 months during the bonus period, respectively. Data from March 2005 (the month when the bonus was announced but not yet implemented) is dropped from the analysis.

In addition, CO members from a subset of COs managed by our study-sample FAs were interviewed in November 2006 (5 months after the end of our study) as part of a baseline survey for another study (see Gin ́e and Mansuri (2017)). It covered 11 FUs, and interviewed 1691 CO members from 214 COs managed by 57 (out of 162) study-sample FAs. The survey asked CO members about any changes since around the time the bonuses were introduced in their COs’ activities—such as discussions of non-credit related social issues during CO meetings, member’s ability to speak freely and actively participate in CO decisions, willingness to seek advice from CO leaders outside of CO meetings, and collective action taken by CO members to jointly purchase agricultural inputs or sell the harvest. We use these data to construct subjective quality measures of a CO as reported by its members.

Lastly, we interviewed supervisors in June 2006 (last month of the bonus). The survey asked supervisors about the performance of all FAs working under them on various credit and social outcomes in the previous month. They also reported their subjective evaluation of each FA since the study began. We use the data from the supervisor survey to construct measures of supervisory effort, and of their assessment of FA performance.

# 4.1 Baseline characteristics and balance tests

Columns 1, 2, and 3 of Table 1 report means of FA characteristics measured at baseline in the control, credit bonus, and social bonus groups, respectively. Columns 4, 5, and 6 report the p-value from the t-test of the difference in means between control FAs and credit bonus FAs (Cr-FAs); control FAs and social bonus FAs (Soc-FAs); and Cr-FAs and Soc-FAs, respectively. Across all the reported variables, we cannot reject that means are equal for any pairwise comparison at conventional levels of statistical significance. As indicated by the p-value of F-test at the bottom of Columns 4, 5, and 6, we again cannot reject that all covariates are not jointly different from zero in a regression where the dependent variable takes the value one if the FA is in the control group using the sample of FAs in the control and credit bonus groups in Column 4 and FAs in the control and social bonus groups

> 14The random visits were carried out by the Credit Officer (CrO) to whom FAs report all issues related to microcredit. All FAs working from the same FU report to the same CrO.

15
microgrammes per litre of arsenic. At the time of mass installation of tube wells in Bangladesh, during the 1980s, the aquifers were not tested for arsenic. Arsenic was first discovered in 1993. Realising seriousness of the crisis, a mass-detection campaign was undertaken during late 1990s. A systematic census of tube wells in suspected areas was followed by comprehensive awareness campaigns. As part of a multi-million dollar donor financed project, 5 million of the country's 11 million tube wells were tested in arsenic-prone areas. 1.4 million (or roughly 29 percent) were found to be contaminated (Rohde, 2005). According to government sources, there are 271 arsenic prone sub-districts (upazilas) in Bangladesh (BAMWSP, 2005) and 31% of all tube wells in affected regions have arsenic above WHO permissible level; a total of 32 million people drink from such tube wells (Chakraborti et al., 2004).

Arsenic toxicity has been recognized as an acute national problem. Intake of arsenic over the permissible level over a period of several years leaves a person at risk of developing arsenic related diseases. So far, 40,000-100,000 people have already developed visible pigmentation of skin, skin lesions, swollen limbs, warts, gangrene, and cancer) and/or invisible (damage to lungs, kidneys, and other internal organs; loss of feeling in the hands and legs). Prolonged exposure to inorganic arsenic can lead to hallucinations, agitation, emotional lability, memory loss, gangrene and skin as well as internal (lungs, bladder and kidneys) cancer (ATSDR, 2005).

There are a number of studies documenting the adverse effect of arsenicosis on physical wellbeing of adults. There is evidence that chronic arsenic exposure causes adverse pregnancy outcomes in terms of spontaneous abortion, still birth and pre-term birth rates (Ahmad et al. 2001). Ehrenstein et al. (2006) use primary data on 200 mothers from rural West Bengal and find that exposure to high concentrations of arsenic during pregnancy was associated with a six-fold increased risk of stillbirth after adjustment for potential confounders. Smith et al. (2006) find significant increases in mortality from lung cancer and bronchiectasis in persons with probable exposure to high concentrations of arsenic in drinking water _in utero_ and early childhood. However, studies documenting costs of health care and inability of affected persons to engage in productive activities are missing.

4
Pelling, M. (2003). _The Vulnerability of Cities: Social Resilience and Natural Disaster_ . London: Earthscan.

Plate, E. J. (2002). Flood risk and flood management. _Journal of Hydrology_ , 267(1): 2–11.

- Saltelli, A., K. Chan, and E. M. Scott (eds.) (2000). _Sensitivity Analysis_ , Wiley Series in Probability and Statistics. New York: John Wiley & Sons, Ltd.

- Smith, K. (2012). _Environmental Hazards: Assessing Risk and Reducing Disaster_ , 6<sup>nd</sup> edn. London: Routledge.

- Tierney, K. (2002). Methods of Disaster Research, Xlibris Corporation, chapter The Field Turns Fifty: Social Change and the Practice of Disaster Fieldwork, pp. 349–374.

- Skidmore, M. and H. Toya (2007). Economic development and the impacts of natural disasters. _Economics Letters,_ 94(1): 20-25.

- UN (2012). _World Urbanization Prospects_ 2012 Revision, United Nations Population Division, New York.

- UN-ISDR (2005). Hyogo Framework for Action 2005-2015: Building the Resilience of  Nations and Communities to Disasters, United Nations International Strategy for Disaster Risk Reduction, Geneva.

- UN-ISDR (2009). Global Assessment Report on Disaster Risk Reduction: Invest Today for a Safer Tomorrow, United Nations, Geneva, Switzerland.

- UN-ISDR (2011). Global Assessment Report on Disaster Risk Reduction: Revealing Risks, Redefining Development, United Nations, Geneva, Switzerland.

- White, G.F. (1973). Natural hazards research. In _Directions in Geography_ , ed. R.J. Chorley, 193-216. London: Methuen.

Wisner, B. (2003). Building Safer Cities: The Future of Disaster Risk. In Disaster Risk Reduction in Megacities: Making the Most of Human and Social Capital. World Bank Disaster Risk Management Series, No. 3, 181–196. Washington, DC: The World Bank.

- World Bank (2006). Hazards of Nature, Risks to Development: An IEG Evaluation of World Bank Assistance for Natural Disaster. Washington, DC: World Bank.

- World Bank (2009). _Reshaping Economic Geography_ . World Development Report 2009. Washington DC: World Bank.

41
**Table 3: Impact of savings on investment (GMM 2-step results)**

||_Total domestic_<br>_Investment_|_Private investment_|_Public investment_|
|---|---|---|---|
|Lagged investment|0.617|0.610|0.698|
||(0.00)|(0.0)|(0.00)|
|Saving|0.109|0.073|0.027|
||(0.00)|(0.00)|(0.00)|
|GDP growth|0.115|0.011|0.089|
||(0.00)|(0.09)|(0.00)|
|Sargan test|37.3|27.4|29.8|
||(1.00)|(1.00)|(1.00)|
|2<sup>nd</sup>order|-1.14|0.42|-1.16|
|autocorrelation|(0.25)|(0.68)|(0.24)|
|Observations|1125|715|703|

Notes: Saving and investment are in % of GDP and numbers in parentheses are p-values We will nevertheless use this estimated link between investment and saving to counterfactually illustrate the impact of capital flight on investment. Based on these results, we simulate the effects of capital flight repatriation by assuming that, first the repatriated capital constitutes an increase in domestic saving, and second that the increase in savings will lead to an increase in investment at a rate of 0.109 as estimated in the regression analysis.

Evidently, it is difficult if not impossible to repatriate the totality of capital flight in the short term. Thus we will assume that only a fraction of the stock of capital flight is repatriated, intermediated into the financial system as savings, and eventually allocated to financing investment. Table A1 in the appendix shows for each country the current investment to GDP ratio and the ratios that would result from the repatriation of 25% of the stock of capital flight as estimated in 2004. We use average investment and saving to GDP ratios for the period 2000-2004.

The results show clearly that SSA would generate tremendous benefits from capital flight repatriation. If only 25 per cent of the stock of capital flight accumulated up to 2004 was repatriated the average ratio of gross domestic investment to GDP for the sub-continent would increase from the current 18.5 per cent to 29.6 per cent (Table A1). This result is significant on two accounts. First, it implies that through repatriation of only a quarter of capital flight stock, the sub-continent is likely to go from trailing other developing regions to leading in terms of domestic investment (Figure 3). Second, repatriation of this small fraction of capital flight would allow the sub-continent to move very close to the minimum threshold of investment that is conventionally considered to be necessary to achieve the required high growth needed to achieve the millennium development goals. Indeed the UNECA estimated in 1999 that Africa needed to achieve an investment/GDP ratio of 34 per cent in order to record the growth rate of 7 per cent per annum that is needed to halve poverty by 2015 (UNECA 1999).

In general, debates on accelerating growth through domestic investment have focused on mobilization of external private capital flows and increasing domestic resources especially by deepening financial intermediation to increase savings and by raising tax revenues [UNECA (2006), UNCTAD (2007)]. In

**15**
Supplementary grants. In addition to the VAT sharing and interstate equalization scheme, the federal government offers additional grants to the states. These include: grants to lift up financially weak states (east and west) to 90 percent of the average fiscal capacity, about DM 5 billion in 1996; grants to Eastem States at a minimum of DM 14 billion a year, until the year of 2004; grants to some financially weak Westem States to compensate partly for the revenue losses due to the integration of the Eastern States into the interstate equalization scheme, at DM 1.2 billion each year, for a ten year period; grants to the States of Bremen and Saarland to help them deal with debt service problems, at DM 3.4 billion, from 1994 to 1998; grants to some smaller Eastern and Western states at DM 1.5 billion each year.

Upon the German unification, in 1990, the states in the Western Germany refused to accept the Eastem states to join the interstate equalization program. Accepting the Eastern states meant all the recipient states in the west would become contributing states. As a compromise, the German Unity Fund was established to assist the poor Eastern states. This fund had DM16.1 billion and was distributed to the Eastern states during 1990-95. Sources of the fund include contributions from the federal government (5 billion), the states' budgets (1.6 billion), and borrowing from the capital market. The fund is distributed to the states based on an equal per capita basis, and 40 percent of these distributions must be further distributed to the municipalities (Spahn 1995). This temporary program was terminated by the end of 1995 and currently all the Eastern states are incorporated in the standard equalization schemes.

The following table shows the significant equalization effects of the three transfer schemes. According to an estimate for 1996, the Western states' per capita own revenue will be DM 3705, while that of the Eastern States will be DM 2030. After equalization, the Western States' per capita revenue will be DM 5510, and that of the Eastern States will be DM 5190.

T able 2.3. Per Capita Revenue Relative to the National Average before and after Transfers: Estimates for 1996

|Ow|n Revenue|After|Interstate|Supplementary|
|---|---|---|---|---|
|||VAT sharing|Equalization|Grants|
|Western States|11%|5%|2%|1%|
|Eastern States|-39%|-18%|-7%|_-5%_|

Source: Data provided by Prof. Wolfgang Fottingger.

# 2.5. The United Kingdom

Unlike federally structured states such as American, Canada, and Gennany, the United Kingdom is a unitary state in which local governments derive their powers and functions from the central government. The central government can, at any time, by the ordinary process of legislation, change the powers of local authorities or abolish them altogether.

14