rafmacalaba/datause-encoder-data huggingface dataset:
G6mez-Ibafnez, J.A. and J.R. Meyer (1993), _Going Private: The International Experience with Transport Privatization,_ The Brookings Institution, Washington D.C.

Gonzrlez A. , Hinojosa S. and R. Mufioz (1 999), 'Un andlisis de incentivos por Diferencias de Tasa de Descuento en la Licitaci6n por MVPI (LPVR)', _Cuadernos de Economia,_ Facultad de Ciencias Econ6micas y Administrativas, Universidad de Chile, _Sanfiago,forthcoming._ Hinojosa S. (1998), "Infrastructure Concessions in Chile", _Infrastructure and Sustainable Development,_ Proceedings of the 1998 APEC Public-Business/Private Sector Dialogue, APEC Economic Committee, May, Taipei.

Highways Agency and Private Finance Panel (1997), _DBFO-Value in Roads: A case study on the first eight DBFO road contracts and their development,_ Central Office of Information, London.

Irwin, T., M. Klein, G. Perry and M. Thobani (eds), _Dealing with Public Risk in Private Infrastructure,_ The World Bank, Washington, D.C.

Jiles, J. (1998), 'Aspectos Econ6mico Financieros y Microecon6micos del Programa de Concesiones para una Etapa de Consolidaci6n', Ministerio de Obras Puiblicas, Santiago, October.

Klein, M. (1997), 'Los requisitos de una politica global de infraestructura vial', Estudios Publicos, N.65, 2 15-223.

Lewis, C.M, A. Mody and D.G. Mountain (1996), 'Alternative Structures for Budgeting and Accounting for central Government Guarantees in Colombia', The World Bank, mimeo.

Marshall, B. (1989), 'Financing the Second Severn Crossing', Bank of America, mimeo.

Ministry of Public Works and Ministry of Finance (1998), 'Bono de Infraestructura', joint publication by the Ministry of Public Works and the Ministry of Finance, January.

Mody, A. and D.K. Patro (1996), 'Methods of Loan Guarantee Valuation and Accounting', in Mody, A. (ed.), _Infrastructure Delivery: Private Initiative and the Public Good,_ EDI Development Studies, Economic Development Institute, The World Bank.

Tirole, J. (1997), 'Comments to the proposal by Engel, Fisher and Galetovic related to highway franchising', Estudios Puiblicos, N.65, 201-214 [in Spanish].

51
Consequently, the welfare results reported in Table IX might overstate the true cost of trade liberalization. We therefore also calculate the welfare effects for an economy in which the average income risk, _σε_<sup>2,andallchangesinincomerisk,∆</sup><sup>_σ_</sup> _ε_<sup>2,arescaleddownbya</sup> factor of 0 _._ 7. The factor 0 _._ 7 is derived from the fact that the estimate of income risk, _σε_<sup>2,</sup> obtained by Carroll and Samwick (1997), Gourinchas and Parker (2002), Hubbard, Skinner and Zeldes (1994), and Storesletten, Telmer and Yaron (2004) using income data with a very long panel dimension is roughly 70 percent of our estimate of income risk using a much shorter panel dimension (see our discussion in Section II.5 for details). Using the scaled down values for income risk and income risk changes, we find the following welfare cost of a five percent tariff reduction for a degree of risk aversion of _γ_ = 1. First, the one-year increase in income risk immediately following the tariff reduction is equivalent to a decrease in lifetime consumption by _._ 68 percent. Second, a five percent decline in GDP growth leads to an increase in income risk that is equivalent to a loss of lifetime consumption of _._ 27 percent before the tariff reduction, and this loss increases to _._ 68 percent after the tariff reduction (that is, the difference is _._ 41 percent). Thus, although the welfare cost of trade liberalization are somewhat smaller than for the baseline case, they are still quite substantial.

# **VI. Conclusions**

This paper studies empirically the relationship between trade policy and _individual_ income risk. The analysis proceeds in three steps. First, longitudinal data on Mexican workers are used to estimate individual income risk in various manufacturing sectors. Second, the variation in income risk and trade barriers _−_ both over time and across sectors _−_ is used to arrive at estimates of the relationship between trade policy and individual income risk. Finally, using the estimates of this relationship between trade policy and income risk, a simple dynamic general equilibrium model with incomplete markets is used to obtain estimates of the corresponding welfare effects.

33
**Table 1** Relative Consumption Volatility: Selected Emerging Economies This table shows the reform date and the volatility of consumption relative to that of output in the pre- and post-reform period for a set of emerging economies.

||Relative consumption vo<br> <br>|latility<br>|
|---|---|---|
|Region & reform|Pre-reform<br>Post-reform|Change|
|date<br>Latin America: 19<br>|90<br> <br>||
|Chile|1.10<br>1.26|⇑|
|Colombia|0.97<br>0.85|↓|
|Mexico|0.94<br>1.45|⇑|
|Peru|1.09<br>1.72|⇑|
|East Asia: 1996<br>|||
|Indonesia|2.45<br>1.01|↓|
|Malaysia|1.36<br>1.52|⇑|
|Philippines|0.73<br>1.06|⇑|
|Korea|0.93<br>1.69|⇑|
|Taiwan|1.84<br>0.80|↓|
|Thailand|0.88<br>1.00|⇑|
|East Europe: 1990<br>|<br> <br>||
|Turkey|1.07<br>1.09|⇑|
|Poland|0.92<br>1.45|⇑|
|Hungary|1.01<br>1.50|⇑|
|South Asia|||
|India: 1992|083<br>123|⇑|
|<br>Africa|.<br>.||

40
local level, under increasingly more decentralized fiscal arrangements. At the level of aggregation at which such studies are typically conducted, a lot of the variance in project aid, and responses to it, will be lost. For one, the situation in the absence of aid is not observed. This undoubtedly leads to a loss in the ability to identify the key parameters of interest. In contrast, since external aid projects can’t be implemented everywhere, there will be places that don’t get them. Observations on places without the project aid provide us with useful extra information that other studies have not had.

A second difference concerns the degree of heterogeneity that can be allowed for in our approach. Differences in government preferences will be difficult to take into account in cross-country regressions. One has a better hope of identifying the displacement of aid using country-specific data. But country studies have also tended to focus only on central government behavior. A finding of no fungibility at the central level could well be compatible with widespread displacement at lower government levels. Our approach allows us to explore whether displacement is found in local government behavior ─ at the level where the key decisions are made. Against that, our approach has the drawback that we may be unable to detect displacement that occurs at the highest level of government. In the case of the rural roads project studied here, displacement could occur either at the local or higher levels. Vietnam’s central government may well reduce its budget allocation to the Ministry of Transport (MOT) as a response to the World Bank’s project funding. We test for displacement at lower levels of government, but are unable to do so at the central level.

Funding from intermediate levels of government (district or province) that had been slated for road improvements in the project communes may get diverted to communes not benefiting from the donor-financed project, or to other basic commune infrastructure that they also typically fund. We think this is the most likely way that displacement occurs in this setting. In principle, the local commune authorities could divert resources that would have been spent on roads to other commune-level needs or to reduce the tax burden, for example by reducing residents’ labor burden.<sup>3</sup> However,

> 3 Able-bodied adults in Vietnam are expected to contribute 10 labor days yearly (or the cash equivalent) to finance small commune infrastructure construction and maintenance.

5
**Table 3: Probit estimation of determinants of being an RSE applicant, and of an applicant being selected** Marginal effects shown for probit estimation on 21 to 58 year olds

||Sele|ction into Ap|plying|Selection am|ong Applicants|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||All|Males|Females|All|Males|
|Male|0.368***<br>(0.032)|||0.188**<br>(0.089)||
|Age|0.0337***|0.0233|0.0281***|0.0301|0.0330|
||(0.012)|(0.018)|(0.0086)|(0.025)|(0.029)|
|Age Squared|-0.000403**|-0.000304|-0.000335***|-0.000410|-0.000519|
||(0.00016)|(0.00024)|(0.00011)|(0.00034)|(0.00038)|
|Married|-0.0864*|0.0112|-0.113*|-0.0591|-0.0346|
||(0.051)|(0.066)|(0.058)|(0.081)|(0.087)|
|Literate in English|0.146***|0.240***|0.0217|0.131|0.119|
||(0.030)|(0.053)|(0.020)|(0.099)|(0.099)|
|Has Primary schooling or Less|-0.0142|0.0524|-0.0624**|0.0794|0.181*|
||(0.034)|(0.052)|(0.029)|(0.086)|(0.098)|
|Has beyond Form 4 schooling|-0.101**|-0.0877|-0.0418***|-0.0996|-0.135|
||(0.048)|(0.088)|(0.015)|(0.15)|(0.15)|
|Worked for pay in first half of 2007|0.0257|-0.0231|0.0424|0.0393|-0.0288|
||(0.036)|(0.056)|(0.033)|(0.058)|(0.068)|
|Had a health compliant in last 6 months|-0.0278|0.00323|-0.0435***|-0.176|-0.0812|
||(0.052)|(0.084)|(0.015)|(0.12)|(0.12)|
|Currently smokes|0.0254|0.0433||0.0635|0.0620|
||(0.049)|(0.060)||(0.086)|(0.089)|
|Drank Kava or Alcohol in last month|-0.0952**|-0.137**|-0.0318|-0.136|-0.155*|
||(0.043)|(0.058)|(0.022)|(0.085)|(0.082)|
|Household Size|-0.0298***|-0.0291***|-0.0154***|-0.0196|-0.0248|
||(0.0067)|(0.010)|(0.0053)|(0.017)|(0.019)|
|Household Asset Index|-0.000229|-0.00836|0.00329|0.0385*|0.0207|
||(0.0072)|(0.011)|(0.0039)|(0.020)|(0.022)|
|Log per capita weekly income|-0.00608|0.0200|-0.0167*|0.0254|0.0168|
||(0.015)|(0.023)|(0.0091)|(0.032)|(0.034)|
|Number of Cattle|-0.00404|-0.00336|-0.0138**|-0.0149|-0.0167|
||(0.0036)|(0.0053)|(0.0067)|(0.012)|(0.012)|
|Number of Chickens|-0.0000645|0.000170|0.000254|-0.00357|-0.00228|
||(0.0011)|(0.0015)|(0.00075)|(0.0023)|(0.0023)|
|Tanna|0.0886*|0.111|0.0358|0.0772|-0.0538|
||(0.049)|(0.072)|(0.032)|(0.097)|(0.11)|
|Ambrym|-0.0641|-0.0444|-0.0448*|-0.329***|-0.454***|
||(0.043)|(0.068)|(0.024)|(0.11)|(0.11)|
|Has a family member in New Zealand|0.143**|0.159*|0.0898*|0.157*|0.270***|
||(0.068)|(0.093)|(0.055)|(0.091)|(0.065)|
|Observations|969|517|445|277|229|
|Pseudo R-squared|0.198|0.073|0.239|0.259|0.304|

Notes: Robust standard errors in parentheses clustered at the household level, *** p<0.01, ** p<0.05, * p<0.1 There are too few female applicants to look at selection among female applicants.

- 36 -
Our paper differs from the literature in the sense that the contracting right and property right environment is broadly stable in the sample. All our specifications include country fixed effects that absorb time invariant determinants of bank credit and allow to exclusively exploit variation over time within a country, and the impact of time varying factors on the maturity of bank credit.

# **3. Data and Summary Statistics**

This paper empirically examines the determinants of domestic credit levels across 26 EU countries: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden. Our definition of advanced economies and emerging market economies is based on the classification of the IMF.

Table 1 presents the list of the main variables we use as well as the sources of information from which we obtained each variable. We construct quarterly data series for bank credit variables and macroeconomic variables for EU member countries. Data comes from three major sources: ECB Statistical Data Warehouse, the International Financial Statistics (IFS) and Global Financial Stability Report (GFSR). Additional data on institutional characteristics described in Table A2 are from various editions of the World Bank Doing Business database and from the International Country Risk Guide database.

The information on bank credit of different maturities for EU member countries is from the ECB Statistical Data Warehouse, which contains: the total outstanding amount of bank credit to non-financial corporations with maturity of i) less than 1 year; ii) over 1 year and less than 5 years; and iii) over 5 years. We define these three types of loans as short-term, medium-term, and long-term loans, respectively. Credit flows are corrected for break in the series and other 8
Firm-Level Climate Change Adaptation: Micro Evidence from 134 Nations Claudia Berg<sup>1</sup> , Luca Bettarelli<sup>2</sup> , Davide Furceri<sup>3</sup> , Michael Ganslmeier<sup>4</sup> , Arti Grover<sup>5</sup> , Megan Lang<sup>1</sup> , and Marc Schiffbauer<sup>1</sup>

> 1 _1: World Bank, 2: University of Palermo, 3: International Monetary Fund, 4: University of Exeter, 5: IFC_

_Keywords: Climate adaptation, firms, temperature, resilience JEL Codes: D22, Q56, Q54, O12, O14_ ©2024 The World Bank and International Monetary Fund.
null hypothesis that the OLS and the IV estimates are statistically equal (Cameron and Trivedi, 2005).<sup>33</sup> The p-values generated from these tests are all above 0.5, indicating a failure to reject the null hypothesis. The IV results thus render support to our county-year FE results of significant and positive effects of access to plant water on education.<sup>34</sup> In light of the Hausman test results we shall stick to the county-year FE specification in all future specifications since it is more efficient.

# **Controlling for More Village Characteristics**

When choosing the baseline specification, we opt to control for the county-year FEs rather than the village FEs for the following key reason. With the slow rollout of the water program, only 45 out of 174 villages in the sample experienced changes in their treatment status during the sample period. When we use village fixed effects, our identification comes from the before-after changes in the outcomes for these 45 villages (after controlling for other covariates) when Water Plant changes from zero to one. This strategy thus risks throwing the baby out with the bath water: the variations in education and water quality for the always-treated and the never-treated villages (within a county-year cell), though quite informative, are totally ignored in identifying the treatment effects.

However, it is still a legitimate concern that omitted characteristics at the village level can cause a spurious correlation between Water Plant and education. To check this possibility we proceed in three steps. _First_ , we control for more village characteristics, including the average income and the Gini coefficient of the village. This partially deals with the issue of omitting key village characteristics while preserving the full sample. The results are in Table A2. The estimated treatment effects are qualitatively similar to the baseline ones. The magnitudes drop slightly. The effect of Water Plant changes from 1.08 in the baseline specification to 0.96 here.

> 33 The bootstrapped Hausman tests are conducted as follows: (i) estimate OLS and an IV estimates from a bootstrap subsample with the village as the resampling cluster; (ii) repeat this process 1000 times to calculate the standard errors of those estimates; (iii) conduct the Hausman test by using the estimated coefficients using the whole sample and the standard errors obtained in step (ii).

> 34 We have also tried using the non-flat dummy interacted with all wave dummies as instruments. The qualitative results are similar to the results with the simple non-flat dummy IV, with significant treatment effects on grades completed (2.22 years) and the dummy variable of graduating from middle school (34 percentage points).  However, the first-stage F statistics become smaller (10.11) which is over 10 but less than the Stock-Yogo weak ID test critical value (20.53) for 10% maximal IV size.

20
would be neither practical nor normatively feasible. Since the impact of person _i'_ s actions on person _j_ may be observable only to _j_ (and possibly to _i_ ), this kind of Coasian solution simply will not work.

However, social scientists have become aware of the more complex nature of many social interactions. Ostrom (1990) has shown that societies sometimes do design cooperative equilibria using repeated games to ensure that it is in the individual’s _selfish_ interest to be _other-regarding_ . Her work is consistent with the large literature that shows how cooperative equilibria can emerge in the context of games in which a group of players interact repeatedly. In the indefinitely repeated game, it can be in the self-interest of individuals to take into account how their actions affect others. Norms to support this can be established, which are sometimes referred to as _social capital._ It can even be in the self-interest of individuals to punish norm violators, including free-riders who shirk by not punishing others who violate norms.<sup>15</sup> In these models, individuals are selfish, but the repeated game provides incentives to act cooperatively. Societies that have constructed repeated games to sustain welfare-enhancing cooperation are said to have high levels of social capital.<sup>16</sup> But a disturbing implication of the social capital literature is that essentially any Pareto-efficient equilibrium can be supported, including equilibria entailing the exploitation of one group—such as African-Americans under Jim Crow. Those who violated the Jim Crow norm were punished. Discriminatory equilibria can persist unless there is an exogenous intervention.<sup>17</sup> Policy restricting the set of variables that can be used as the basis of hiring can move the economy from one equilibrium to another. Such a policy entails very limited intervention by government and at very low cost: all that is required is to prohibit certain actions. In the new equilibrium, it is not in anyone’s interest to undertake the actions.<sup>18</sup> Social context in these models is important—far more important than in the case of standard models. The repeated “games” change the _consequences_ of individual actions, thereby affecting incentives and the nature of the equilibria that emerge. These models are not about changing individual preferences.

> 15 Abreu (1988). This section draws heavily on Kraft-Todd et al. (2015).  See also the references they cite.

> 16 There is a large formal and informal literature on social capital; see, e.g., Dasgupta and Serageldin, eds. (2000). 17 See Dasgupta (2011) and Acharya, Blackwell, and Sen (2014).  These results are markedly different from those suggested by Becker (1957).

> 18 See, e.g. Stiglitz (1974).

15
population around the moderate poverty line is substantially less than the density around the extreme poverty line. As a result, fewer additional households slip into moderate poverty than into extreme poverty; this is particularly true of households who earn their primary income from farming.

**Table 6 Biofuels impact on consumer prices and real income**

|(percent change in 2010 relative to n|on-biofuels s|cenario)<br>**_Consu_**|**_mer price index_**|||**_Rea_**<br>|**_l income_**<br>||
|---|---|---|---|---|---|---|---|---|
||||||% cha|nge|$2004|million|
||Ai|Pd|Ail|All d|||||
||gr-<br>culture|rocesse<br>food|grcuture<br>and food|goos<br>and services|Households|National|Households|National|
|United States|3.4|1.0|1.3|0.1|0.0|-0.3|-3,919|-43,864|
|Canada|2.8|1.0|1.3|0.2|-0.1|-0.5|-631|-6,206|
|Japan|0.6|0.3|0.3|-0.1|-0.1|-0.1|-1,730|-2,779|
|Rest of high income|2.6|0.7|1.0|0.0|-0.1|-0.1|-1,134|-3,268|
|EU 27 and EFTA|1.6|0.3|0.5|0.1|-0.1|-0.3|-11,553|-41,170|
|China|2.9|1.4|2.3|0.7|-1.1|-1.1|-9,933|-30,231|
|Indonesia|10.3|4.4|6.1|1.2|-1.4|-3.0|-2,905|-10,075|
|Rest of developing East Asia|4.7|1.5|2.2|0.4|-0.3|-0.6|-919|-3,099|
|India|19.8|5.2|13.5|5.7|-3.9|-5.5|-21,512|-54,105|
|Rest of South Asia|2.6|1.2|1.9|0.6|-0.5|-0.7|-1,026|-1,821|
|Russia|3.0|1.5|2.0|0.5|-0.5|-1.2|-2,002|-10,305|
|Rest of Europe and Central Asia|4.9|1.4|2.9|0.7|-0.5|-1.2|-2,027|-9,199|
|MENA Energy exporters|3.3|1.3|2.1|0.4|-0.4|-0.5|-2,477|-7,938|
|Rest of MENA|5.0|1.9|3.2|0.9|-0.8|-1.9|-1,065|-4,035|
|Argentina|12.9|6.5|7.1|-0.5|-0.7|-4.8|-839|-10,080|
|Chile|5.8|1.4|1.8|0.1|-0.1|-0.4|-55|-424|
|Brazil|11.0|4.7|5.8|0.0|-1.3|-5.1|-5,068|-37,377|
|Colombia|3.9|1.7|2.2|0.3|-0.2|-0.5|-125|-657|
|Mexico|6.2|2.0|3.6|-0.1|-0.4|-1.1|-2,155|-9,148|
|Peru|8.1|2.0|3.8|0.8|-0.6|-1.2|-382|-1,175|
|Venezuela, R.B.|5.2|1.6|2.5|0.3|-0.4|-0.7|-386|-1,258|
|Bolivia and Ecuador|5.6|2.1|2.9|0.7|-0.1|-1.2|-35|-615|
|Paraguay and Uruguay|10.7|4.8|5.9|0.8|-1.0|-6.3|-200|-1,744|
|Central America|5.1|1.6|2.4|0.6|-0.3|-1.2|-235|-1,415|
|Caribbean|4.9|1.7|2.6|0.0|-0.4|-0.7|-746|-2,147|
|Sub Saharan Africa|9.0|1.9|4.9|1.8|-1.4|-2.5|-6,455|-19,170|
|High income countries|1.9|0.5|0.7|0.1|-0.1|-0.3|-18,967|-97,287|
|East Asia and Pacific|3.5|1.9|2.7|0.7|-1.0|-1.2|-13,757|-43,405|
|South Asia|16.1|4.1|10.6|4.4|-3.0|-4.4|-22,538|-55,926|
|Europe and Central Asia|4.2|1.4|2.5|0.6|-0.5|-1.2|-4,028|-19,504|
|Middle East and North Africa|3.7|1.4|2.3|0.4|-0.4|-0.7|-3,543|-11,973|
|Sub Saharan Africa|9.0|1.9|4.9|1.8|-1.4|-2.5|-6,455|-19,170|
|Latin America and the Caribbean|7.2|3.1|4.1|0.0|-0.6|-2.4|-10,227|-66,040|
|Developing countries|7.6|2.4|4.7|1.0|-1.0|-1.8|-60,548|-216,018|
|World total|5.6|1.0|2.2|0.2|-0.3|-0.6|-79,516|-313,305|

The previous discussion alluded several times to the critical importance of the farm/nonfarm distinction to the poverty outcomes. Compared with the baseline, in which the urban wage premium of unskilled workers in developing countries reduces by 8 percent between 2004 and 2010, the same wage premium is reduced by 24 percent in the biofuels scenario. On the other hand, these income gains are offset by the increase in the cost of 21
**15**

**Table 7:** Income-earning in off-farm activities: complements

||(1)<br>Income|(2)<br>-earning i|(3)<br>n off-farm|(4)<br>activities|
|---|---|---|---|---|
||Fe|male|M|ale|
|Self-esteem (fem)|0.03<sup>_∗∗_</sup>|0.02<sup>_∗∗_</sup>|0.01|0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Self-esteem (male)|0.01|0.00|0.03<sup>_∗∗_</sup>|0.03<sup>_∗∗_</sup>|
||(0.02)|(0.01)|(0.01)|(0.01)|
|Income-earning in off-farm activities (male)||0.15<sup>_∗∗∗_</sup><br>(0.03)|||
|Income-earning in off-farm activities (female)||||0.15<sup>_∗∗∗_</sup><br>(0.03)|
|Outcome mean|0.29|0.29|0.30|0.30|
|R-squared|0.059|0.080|0.112|0.132|
|Observations|1510|1510|1510|1510|

Note: Robust standard errors are reported in parentheses. All regressions include village fixed effects and the controls listed in Table 1. Self-esteem measures are corrected for acquiescence bias. Significance levels are denoted as follows: * p<0.10, ** p<0.05, *** p<0.01.

women (in Table 8 for women and in Table 9 for men) in export-oriented agriculture using the same specification as in Table 4, but adding separately in each column potential confounders of the "battle of the sexes". In the third column of both tables, we find that rubber agriculture knowledge, the least rivalrous of all inputs to production, does not drive the "battle of the sexes". Similarly, credit access and ownership of agricultural assets (Columns 4 and 5) also cannot fully explain this relationship.

The only variable that mitigates the correlation between self-esteem and control over income is control over land. In both Tables 8 and 9, the coefficients for self-esteem in column 6 are significantly different at the 1% level from those in column 2, as indicated by the p-value of the coefficient test at the bottom of Tables 8 and 9, indicating that control over land is a mediator for the battle of the sexes. In addition, the negative correlation between male self-esteem and female income-earning in export-oriented agriculture disappears entirely when controlling for whether the woman manages any plot of land in the household. These results provide additional evidence that self-esteem operates through the capture of fixed inputs such as land, rather than more variable inputs such as knowledge, liquidity or assets. Couple members with higher self-esteem may be able to exert higher control over fixed resources, and thus enter into commercial agriculture. To investigate this pattern further, we make the distinction in subsection 4.2.4 between land that was actually acquired by the household, and land that was not.