rafmacalaba/datause-encoder-data huggingface dataset:
Table 1A. House prices, equity prices and output: stylised facts (in percent)

||Mean|Volatility|Maximum|Minimum|
|---|---|---|---|---|
|**House Prices**|||||
|1971:1-2016:4|2.17|7.64|59.80|-21.77|
|1971:1-1984:4|1.24***|8.92***|59.80|-21.32|
|1985:1-2016:4|2.57|6.96|36.74|-21.77|
|**Equity Prices**|||||
|1971:1-2016:4|4.96|23.73|149.64|-65.21|
|1971:1-1984:4|-0.07***|23.85|138.50|-63.54|
|1985:1-2016:4|7.16|23.34|149.64|-65.21|
|**Output**|||||
|1971:1-2016:4|2.30|2.61|28.08|-9.26|
|1971:1-1984:4|2.60***|2.62|10.76|-4.10|
|1985:1-2016:4|2.24|2.61|28.08|-9.26|

Note: Mean indicates the average year-over-year growth rate. Volatility is the standard deviation of the growth rate. Maximum (minimum) is the maximum (minimum) growth rate. The sample consists of 18 advanced economies. *** indicates that the results for the period Q1 1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 at the 1% level.

47
**Table 14. Cross section, Alternative infrastructure indicators.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|2SLS|OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-3.083|-1.456|-3.770|-3.544|-2.077|-9.343|
||(1.624)*|(4.275)|(1.549)**|(1.627)**|(6.697)|(11.441)|
|GDPpc71|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|
||(0.000)***|(0.000)|(0.000)|(0.000)*|(0.000)|(0.000)|
|prim_enrol8403|-0.018|-0.026|||||
||(0.016)|(0.033)|||||
|second_enrol8403|0.021|-0.012|||||
||(0.011)*|(0.050)|||||
|Invt_GDP8403|0.073|-0.015|||||
||(0.377)|(0.554)|||||
|Invt_GDP8495|||0.204|0.202|0.154|0.321|
||||(0.054)***|(0.072)***|(0.143)|(0.339)|
|prim_enrol8495|||0.007|0.007|-0.010|0.061|
||||(0.012)|(0.013)|(0.058)|(0.109)|
|Govstab8495|(0.052)|(0.157)|-0.114|0.086|-0.041|0.351|
||0.412|0.488|(0.270)|(0.304)|(0.700)|(1.133)|
|Corrup8495|(0.252)|(0.573)|-0.066|-0.370|-0.489|0.196|
||-0.102|-0.155|(0.289)|(0.347)|(0.853)|(1.389)|
|telmain_8495|-0.014|-0.080|||||
||(0.008)*|(0.092)|||||
|fix+mob_9603|0.008|0.038|||||
||(0.003)**|(0.039)|||||
|roads_tot_8495|||0.000|0.000|0.000|-0.000|
||||(0.000)|(0.000)|(0.000)|(0.000)|
|pavroads%_8495|||0.028|0.007|0.057|-0.076|
||||(0.016)*|(0.021)|(0.087)|(0.175)|
|vehicles8495||||0.018||0.063|
|||||(0.009)**||(0.095)|
|Observations|46|41|53|52|41|40|
|R-squared|0.64||0.48|0.52|||
|Wu-Hausman F<br>test, p-value||0.34|||0.82|0.42|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments: see Table 3 and 4.

34
Finally, we evaluate heterogeneous responses.<sup>30</sup> It is unclear whether the impact is higher (or lower) in urban or rural areas. In principle, people tend to concentrate in urban areas, which could mean that aggregated impacts could be higher in urban areas. However, at the household level, the effects will depend on the level of assets and on the capacity to minimize the effect of adverse natural events. Dividing our sample by urban/rural our results show that the impact of major hurricanes on rural households is higher than on urban households in terms of income (both labor and total) and in terms of poverty (see Table 6). Impact in rural areas nearly doubles those than in urban areas. Results are stronger when considering MSWS or IDH. In the case of WEI, however, results are likely to be more similar by urban/rural categories.

Table 6

# Heterogeneous Impacts of Hurricanes on Poverty and Income by Urban/Rural

|||Rura<br>|l<br>|||Urb<br>|an<br>||
|---|---|---|---|---|---|---|---|---|
||Labor<br>Income|Total<br>Income|Extreme<br>Poverty|Poverty|Labor<br>Income|Total<br>Income|Extreme<br>Poverty|Poverty|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MSWS|‐0.0466***|‐0.0622***|0.0219***|0.0248***|‐0.0230***|‐0.0358***|0.0152***|0.0175***|
|Standard Error|(0.0003)|(0.0003)|(0.0001)|(0.0001)|(0.0002)|(0.0002)|(0.0001)|(0.0001)|
|R‐squared|0.330|0.371|0.234|0.277|0.398|0.438|0.158|0.234|
|IDH|‐0.0644***|‐0.0831***|0.0315***|0.0368***|‐0.0201***|‐0.0336***|0.0155***|0.0170***|
|Standard Error|(0.0003)|(0.0003)|(0.0001)|(0.0001)|(0.0002)|(0.0002)|(0.0001)|(0.0001)|
|R‐squared|0.330|0.371|0.235|0.277|0.398|0.438|0.158|0.234|
|WEI|‐0.0253***|‐0.0346***|0.0115***|0.0144***|‐0.0162***|‐0.0263***|0.0115***|0.0129***|
|Standard Error|(0.0003)|(0.0003)|(0.0001)|(0.0001)|(0.0002)|(0.0002)|(0.0001)|(0.0001)|
|R‐squared|0.330|0.370|0.233|0.276|0.398|0.438|0.157|0.234|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the sub‐regional level.

We also divide our sample by the gender of the household head (see Table 7). Our results are not conclusive. Just considering MSWS and IDH, labor income tends to be more affected on female‐headed households, while total income tends to be more affected on male‐headed households. These differences vanishes when looking at poverty impacts. As in the previous table, results for WEI are more similar across all outcomes. Therefore, we cannot conclude conclusively the existence of differential effects.

> 30 Results shown in Table 6‐7 are non‐weighted. Weighted results provide similar conclusions (not shown and available upon request).

19
indicators, we could also include the EA-level means as a separate set of indicator variables in the models that follow. The EA-level means capture the between group regressions and subsequently allow us calculate the within group regression coefficients as well. Because, however, our key independent variables are at council level, we allow the EA-level variables to capture both the within and between council effects without making a specific attempt to differentiate the two. In Section 6, we also include a series of cross-level interactions to investigate how council level variables are affected by EA-level variation within council areas.

Interpreting the council-level coefficients is straightforward because there is no variation between the EA-level variables for each council-level variable. For instance, if 10 percent of local councilors in a council area report disputes with local chiefs, this is consistent for all EAlevel observations within that council. The coefficients at the council level, therefore, only describe between council differences.

We measure our three dependent variables at the EA level, and our key independent variables, councilor-chief relations, at the council level. Given the change in survey design between the 2007, 2008 and 2011 survey rounds, aggregating household level data to EA level allows us to include comparable data across multiple survey years. We cluster our 607 EA-level observations into their respective 19 local council areas and, as noted, use a random intercept design to account for unobserved variation by council. Our complete dataset has low levels of missingness (between zero and 10 percent across included variables) but we choose to use R’s Amelia function to impute missing values rather than list-wise delete missing values.<sup>12</sup> In the models that follow, we include the 2008 values of our EA-level measures of education and health care provision. Again, because the council-level variables do not vary within council areas or over time, the coefficients only explain the difference between councils. Including these baseline values allows us to capture the extent to which service provision has increased or decreased _across_ councils as a function of our council-level variables of interest and our EA-level controls, including the 2008 baseline values. We note, however, that the key findings we present below hold when removing the baseline 2008 measures – and we include the associated regression tables in Appendix 2.<sup>13</sup> We also discuss how to interpret the magnitude of

> 12 Our results, however, are robust to list-wise deletion.

> 13 For online appendix, see here:

> http://cega.berkeley.edu/assets/cega_hidden_pages/5/Clayton_When_Elites_Meet.pdf

21
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 12: Regional Formal Establishment Entry and Exit - 1992-2010 <!-- Start of picture text --> 5<br>4<br>3<br>2  Entry<br>1<br>0<br>1992  1993  1994  1995  1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010<br>-­‐1<br>-­‐2<br>-­‐3<br>Exit<br>-­‐4<br>-­‐5<br>-­‐6<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the rate of establishment entry or exit from 1991 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects and pre-trend control. Positive entry estimates imply larger declines in firm entry rates in regions facing larger tariff declines. Negative exit estimates imply larger increases in firm exit rates in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

51
positively associated with the presence of foreign banks. If the potential advantages of foreign bank entry - improved sector efficiency, a subset of domestic banks forced by competitive pressures into new market niches, and new credit scoring technologies - outweigh the general tendency of large foreign banks to eschew SME lending, borrowers should rate access to credit (both quantities and terms) as easier in countries with relatively high levels of foreign bank penetration. Thus, our analysis should help to sort out whether the benefits of foreign bank entry found in other cross-country studies accrue to all borrowers or only to a lucky subset.

The remainder of the paper is organized as follows. Section II describes the data used in this study. Section III discusses the estimation method. The empirical results are presented in section IV. Section V concludes.

# **II.** DATA

The data used in this paper come from two main sources: the World Business Environment Survey (WBES) and a database on bank regulation and supervision compiled by Barth _et al._ (2001b). These data are supplemented with macroeconomic data from the World Bank's World Development Indicators. Table I provides a list of the 38 countries included in the sample, which is restricted to developing and transition economies. Table II provides summary statistics for the main variables used in this analysis.

The World Business Environment Survey (WBES) is a major cross-sectional survey of industrial and service enterprises conducted in developing and transition countries in 1999 by the World Bank and several other agencies.<sup>10</sup> The main purpose of the WBES is to identify perceived constraints on enterprise performance and growth in developing and transition 6
For gender inequality, we use year 2005 values of the United Nation’s Gender Inequality Index ( _GII_ ). The index quantifies gender inequality in three dimensions – reproductive health, empowerment, and the labor market. The reproductive health dimension is measured by a country’s maternal mortality ratio and adolescent fertility rate, while the empowerment dimension combines the share of parliamentary seats held by each sex with female/male attainment levels in secondary and tertiary education. Finally, the labor dimension is calculated from female/male labor market participation rates. The index varies between 0 and 1, with 0 implying no gender inequality and higher values implying greater gender inequality or increasingly less favorable treatment of women vis-à-vis men.<sup>2</sup> In our sample, _GII_ varies between .065 (Sweden) and .73 (Niger). The mean value of _GII_ equals .39 and the standard deviation is .19.

For income, we use lagged values (year 2005) of (log of) GDP per capita (PPP adjusted and at constant 2005 International $) taken from World Development Indicators, World Bank ( _Income_ ). In our sample, _Income_ varies between 6.4 and 11.1. The mean value of _Income_ is 8.9 and the standard deviation is 1.3.

The interaction term mentioned above is obtained by multiplying the gender inequality index with the income measure ( _GII_ * _Income_ ).

# 2.3 _Other explanatory variables_

As mentioned above, reverse causality is unlikely to be an issue with our estimation results since we use lagged values of all the explanatory variables. Similarly, spurious correlation or omitted variable bias problem is also a less serious problem for us than is typical of cross country

> 2 We note that gender inequality as measure by _GII_ never implies that women perform better than men; that is, gender inequality or _GII_ >0 implies less favorable treatment of women relative to men.

8
income on the utilities. The implication is that any social policy aimed at subsidizing the cost of a subsistence basket of services need probably only focus on the poorest of the poor.

2.15 Regarding expenditure on public transport services, it is important to note that comparisons between the 1997 and 2002 household expenditure surveys are complicated by the fact that the former survey lumps together expenditure on public and private transportation (Figure 3). Nevertheless, since vehicle ownership is concentrated in the upper end of the income distribution, this need not represent a major problem for comparing the expenditure patterns of the poor. A more serious limitation is that the 2002 survey only reports transport expenditure for commuting journeys of the head of household, and thereby represents only a lower bound on public transport spending. A comparison between the two surveys shows that the budget share of public transport services more than doubled between 1997 and 2002. It rose from an average share of 4.3% in the 1997 survey to 8.1% in the 2002 _(even though the latter only includes commuting journeys for the head of household)_ . This jump is largely explained by the fact that public transport tariffs rose between 35% and 40% in real terms since 1997, while per capita household income fell by a third over the same period.

**Figure 3: Affordability of public transport in the Metropolitan Area**

_Source:_ Family Expenditure Survey, May 1997 and OPSM, November 2002 <!-- Start of picture text --> 70.0 12.0<br>Public Tranport<br>Expenditure,<br>60.0 Buenos Aires<br>10.0 1997<br>50.0<br>8.0 Public<br>Transportation<br>Expenditure,<br>Buenos Aires<br>40.0 2002<br>6.0<br>30.0 Public<br>Transportation<br>Expenditure as %<br>4.0 of total - ENGH1997<br>20.0<br>Public<br>2.0 Transportation<br>10.0 Expenditure as<br>percentage of<br>total, 2002<br>EOPSM<br>0.0 0.0<br>1 2 3 4 5<br>Absolute Expense (Argentine Pesos/month)<br><!-- End of picture text --> <!-- Start of picture text --> 2.16 Nevertheless, the situation of the first quintile stands out as being somewhat<br>different, given that the expenditure share did not change significantly, due to a fall of<br>30% in the absolute level of expenditure. Survey evidence shows that this reduction is<br>associated with substitution away from public transport towards ‘free’ modes of travel,<br>such as walking and cycling. Indeed, in the Metropolitan Area only 28% of first quintile<br>households commuted on public transport, as against 60% that used bicycles or<br>motorcycles (Figure 4).<br><!-- End of picture text --> 11
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 10: Formal/Informal x Tradable/Nontradable Share of Regional Employment - 2000, 2010

|||20|00|||20|10||
|---|---|---|---|---|---|---|---|---|
|Change in share of employment||OLS||IV<br>1980 share||OLS||IV<br>1980 share|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Panel A: Formal tradable share|||||||||
|Regional tariff change|0.461***|0.433***|0.386***|0.162***|0.521***|0.560***|0.452***|0.205**|
||(0.082)|(0.044)|(0.040)|(0.051)|(0.117)|(0.077)|(0.051)|(0.085)|
|Dependent var. pre-trend (80-91)|||0.180*|1.033***|||0.396***|1.302***|
||||(0.099)|(0.163)|||(0.145)|(0.205)|
|State fixed effects (26)||✓|✓|✓||✓|✓|✓|
|R-squared|0.454|0.683|0.698||0.303|0.575|0.620||
|First stage partial F-Statistic||||32.81||||30.36|
|Panel B: Formal nontradable share|||||||||
|Regional tariff change|0.009|0.121***|0.070|-0.158***|-0.057|0.113*|0.102|-0.080|
||(0.041)|(0.042)|(0.060)|(0.044)|(0.053)|(0.063)|(0.094)|(0.084)|
|Dependent var. pre-trend (80-91)|||0.105|0.579***|||0.022|0.401***|
||||(0.075)|(0.073)|||(0.115)|(0.118)|
|State fixed effects (26)||✓|✓|✓||✓|✓|✓|
|R-squared|0.000|0.376|0.390||0.006|0.590|0.590||
|First stage partial F-Statistic||||166.1||||178.9|
|Panel C: Informal tradable share|||||||||
|Regional tariff change|-0.622***|-0.571***|-0.627***|-0.101|-0.803***|-0.771***|-0.825***|0.041|
||(0.053)|(0.044)|(0.051)|(0.206)|(0.079)|(0.078)|(0.096)|(0.277)|
|Dependent var. pre-trend (80-91)|||-0.069*|0.573**|||-0.065|0.981***|
||||(0.042)|(0.244)|||-0.058|(0.320)|
|State fixed effects (26)||✓|✓|✓||✓|✓|✓|
|R-squared|0.535|0.704|0.708||0.486|0.682|0.684||
|First stage partial F-Statistic||||15.08||||16.01|
|Panel D: Informal nontradable share|||||||||
|Regional tariff change|0.011|-0.045|-0.060|-0.440**|0.073|-0.023|-0.135*|-0.879**|
||(0.052)|(0.047)|(0.048)|(0.224)|(0.103)|(0.073)|(0.073)|(0.368)|
|Dependent var. pre-trend (80-91)|||-0.076|-1.969*|||-0.542***|-4.142**|
||||(0.090)|(1.059)|||(0.086)|(1.722)|
|State fixed effects (26)||✓|✓|✓||✓|✓|✓|
|R-squared|0.000|0.329|0.332||0.008|0.454|0.534||
|First stage partial F-Statistic||||4.351||||4.985|

Decennial Census data. Positive (negative) coefficient estimates for the regional tariff change imply larger decreases (increases) in the relevant share in regions facing larger tariff declines. 411 microregion observations. Standard errors (in parentheses) adjusted for 112 mesoregion clusters. Weighted by the inverse of the squared standard error of the estimated change in the relevant employment share. Pre-trends computed for 1980-1991 period. To address endogeneity of pre-trend due to overlap with dependent variable in 1991, pre-trends are instrumented using the corresponding 1980 employment category share. First-stage partial F-statistics reported for instrumental variables specifications. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

72
among others, increasing energy grid efficiency, coal generation efficiency and renewable and hydro energy expansion. In addition, Pakistan levies various taxes on fuel – including petrol and gas levies, customs duties on import and sales tax - which have the potential to provide a disincentive for burning fossil fuels. However, according to IMF (2018), there was a gap of USD 18.56bn (6.82 percent of GDP) between the fuel taxes justified by global external costs and the fuel taxes that Pakistan actually levies (only USD 4.8 billion is the global warming externality, the rest are domestic externalities). Most of this tax gap comes from low oil prices (8.41bn), followed by natural gas (5.55bn), coal (1.94bn) and electricity (2.57bn).

Figure 1: Electricity generation by source in Pakistan <!-- Start of picture text --> 70.0% Coal Oil<br>Gas Nuclear<br>60.0% Hydro Other RE<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Source: (IEA 2021)

# Existing efforts to adapt to climate change in Pakistan

The extent of Pakistan’s vulnerability to climate change induced disasters emphasizes the need for adaptation measures, which are guided by the National Disaster Risk Reduction Policy (2013). The policy advocates creating resilient communities with disaster risk reduction interventions at the local level, including the need to build links, information exchange and communication channels between community-based actors, local governments, and District Disasters Management Authorities/Provincial Management Authorities. The Government of Pakistan has also recently passed the Climate Change Act 2017, which establishes the Climate Change Authority and Climate Change Fund to advance the climate change agenda, tasked with contributing toward prevention and mitigation of disaster and climate risks.

# Future mitigation and adaptation options in Pakistan

Going forward, multiple pathways exist for Pakistan to increase its mitigation and adaptation efforts. As concerns mitigation. For one, fuel taxation that equalizes the marginal cost paid by fuel consumers with the internal and external costs generated by burning fuels can play a central role in reducing emissions 6
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