rafmacalaba/datause-encoder-data huggingface dataset:
reap the largest reductions in income inequality, with declines in the Gini coefficient of 0.034 and 0.031, respectively. For the latter group, the inequality decline is almost equally attributable to larger infrastructure stocks and higher quality, while for the former it is mainly due to the improved quality of infrastructure services. In turn, the uppermiddle-income countries of Sub-Saharan Africa under this scenario experience a decrease in the Gini coefficient of 0.023, due in full to the improved quality of infrastructure services.

So far we have focused on the benefits of infrastructure development – but what about the costs? The faster pace of infrastructure development assumed in the counterfactual scenarios must surely involve extra costs too. Higher investment would be needed to acquire the additional infrastructure assets, and higher current expenditures would likewise be necessary to operate and maintain them. Assessing the magnitude of these extra expenditures is not an easy task, owing to the very limited availability of data on investment and, especially, O&M costs of infrastructure, as well as their unavoidable heterogeneity across countries. However, an illustration of the required investment can be provided using the unit capital costs reported by Yepes et al (2008) to compute a rough estimate of the additional investment needs that would be posed by infrastructure expansion.

For brevity, we focus on the first counterfactual scenario described above. It is important to stress that the lack of suitable data forces us to restrict our attention to the costs of acquiring additional infrastructure assets (roads, power generation capacity, telephone lines), ignoring the costs of maintaining them, as well as the costs of upgrading their quality as assumed in the counterfactual simulation.

Figure 6 reports the results of these calculations, expressed as percentage of GDP.<sup>24</sup> The figure shows that the infrastructure investment effort implicit in the catch-up scenario is quite considerable – as much as 15 percent of GDP in the low-income countries of East and Central Africa. The effort is more modest, but still substantial, among Southern Africa’s low-income countries, as well as upper middle-income economies, where it ranges between 7 and 8 percent of GDP. Although international data

> 24 Since the simulation assumes that Africa possesses larger infrastructure assets in 2001-2005, to take account of time-to-build the costs are expressed as percentage of average GDP over 1996-2000, assuming it takes five years for the assets to become available.

28
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16
The data include balance sheet information for each establishment’s latest fiscal year including sales, the number of full-time employees, total labor compensation (wages), investment, and energy consumption. As such, they allow us to assess the impact of climate change on a variety of firm performance measures. They also provide detailed information on establishments’ characteristics such as their year of establishment (age), the main market of their sales (international, domestic, or local), and the main economic activity (4-digit ISIC codes). Moreover, they include detailed information on the business environment, ranging from access to finance, transport, electricity, trade restrictions, business regulation, and corruption. The data are thus well-suited to test the specific microeconomic mechanisms underlying the impact of climate change on firms and to identify policy constraints that may undermine firms’ ability to adapt. Appendix subsection A.1 provides additional details on the survey design and sampling.<sup>1</sup>

# **4.2 Matched firms and climate data**

The climate data comes from the EU Copernicus satellite and ground-based measurement system, which provides, among other, highly granular geospatial information on hourly temperatures (27 x 27 km at the equator) for the entire world, from 1950–2023. The geocoded weather data include hourly temperatures from which we compute the average annual temperature for the detailed location of each firm.

We match establishments in the WBES data with their relevant weather data based on firms’ geocodes. This is done in three steps. First, we collapse daily temperatures at the yearly frequency by computing within-year measures of weather-related shocks for each firm, exactly matching each firm’s fiscal year by the day. That is, as firm-level balance sheet data refers to the last fiscal year with respect to the date of the interview, we do not aggregate weather data according to calendar years, but consider each firm’s relevant fiscal

> 1The harmonized WBES data are available online at https://www.enterprisesurveys. org/portal/index.aspx#/library?dataset=Combined. Special permission must be requested to access the masked locations of each firm in the dataset.

11
- 17 -

# Table 4

## Effect of *tE* la Autoes

(billions of 1984 dollars)

||||-----Endoge|nous<br>Wage Distor<br>|tion */----<br>|
|---|---|---|---|---|---|
||No Wag.<br>Distortion|ExogenousWag*<br> <br>Distortion|Union Wags<br>Weight r.2|Union Wage<br>Weight**U.5**|Union Wage<br>Weight Z.$|
|1. Distortionary<br>Costs of<br>VERo_k/_||||||
|a. CRTS|1.96|1.78|1.79|1.84|1.88|
|b. Contectable<br>Markoto|0.91|0.82|0.85<br>|0.98<br>|1.08<br>|
|c. Monopolistic<br>Competition<br>J|1.63 (.9|52)<br>1.49 (.952)|<br>1.48 (.9|54) 1.43 (.966)|1.38 (.977)|
|2. Capture Rentsh/||||||
|a. CRTS|7.87|7.8|7.86|7.67|7.87<br>|
|b. Contestable<br>Markets|7.87|7.86|7.86|7.37|7.87|
|c. Monopolistic<br>Competition|7.89|7.88|7.88|7.83|7.89|
|3. Total Benefits of VER Removal<sup>b/</sup>||||||
|a. CRTS|9.62|9.64|9.65|9.71|9.76<br>|
|b. Contestablo<br>Markets|8.79|8.68|8.71|8.84|8.96|
|c. Monopolistic<br>Competition|9.62|9.U63|9.366|9.31|9.27|
|WagePremium_O/_||||||
|a. CRTS|0.0|0.27|0.28|0.21|0.16|
|b. Contostable<br>Markets|0.0|0.27|0.26|0.20|0.14|
|c. Monopolistic<br>Competition|0.0|0.27|0.26|0.20|0.16|
|Change In Employmnt_/_||||||
|**a.**CRTS|-36.2|-36.2|-38.9|-21.2|-8.7|
|b. Contestable<br>Markets|-21.7|-21.7|-20.2|-12.6|-6.1|
|c. Vonopolistic<br>Competition|-19.5|-19.5|-18.3|-11.6|-4.9|

Notes: All simulations are with ondogenous economywide labor supply.

- */ Weight on wagon Is given by 7 and in all cases 7 + 6 * 1.0.

- b/ EV meaoure In billions of 1984 dollars.<sup>The distortionary</sup> cost Is calculated **as** the difforenco between the total costs of VERo<sup>and the rants.</sup>

- _/ The number In parentheses is the<sup>proportion</sup> of firms which remain in the industry after firm exit due to VER removal.

- J Porcentage **wage** distortions In **autos.**

- */ Change in employment In **autos**<sup>inthousands of work-years.</sup>
"dynamic' effects that many have argued to be important [e.g. Balassa (1961)], even though they are difficult to quantify. Also, like scale efficiency effects, they are not particular to RI arraingements. However, these effects are likely to be more important than the static efficiency effects alluded in sections m and IV. For example, if increased competition from trade puts pressures on domestic firms to adopt new technologies at a faster rate than in an environment sheltered from competition. An enlarged market also increases the stimulus to investment to take advantage of the enlarged market and to meet the expanding competition. As has been emphasized in the new growth literature, one would expect a catch-up to occur on the part of the least-developed partners. However, in past RI schemes among developing countries this dynamic effect was probably not fully exploited because of frequent barriers to the purchase of foreign technology.

Economic cooperation in areas where significant externalities and public goods (education, research and development, infrastructure, environment) exist, is also a potentially important area for dynamic benefits. The potential benefits of cooperation have been emphasized by Langhammer and Heimenz (1990, 1991) and Foroutan (forthcoming). Cooperation can take many different forms ranging from the simple exchange of information through the provision of joint training facilities to the mutual recognition and adaption of rules and regulations, to the implementation of joint policies and the establishment of joint institutions with quasi-legislative powers. In the sample of schemes described in table 1, only ASEAN and SADCC emphasized cooperation.

Can one discern any of these "dynamic" effects? To do so would require in-depth micro-level information, extending beyond this paper. Less ambitiously, we examine GDP and investment data for the group of regional arrangements identified in table 1 to see if there were any apparent changes in the value of these variables following integration. While we are aware that any observed changes is also likely to capture a host of other effects, one would hope that if integration changed the climate 31
<!-- Start of picture text --> 100<br>80<br>< 60<br>o<br>oO<br>240 ee<br>° ee ee<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>—Ghana —SSA, average —LMICs, average<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>80 oN<br>< 60 7<br>o<br>oO<br>o 40<br>20<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>—Ghana SSA, average LMICs, average<br><!-- End of picture text -->
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- **Harvey, Ross** , “Risks and Fallacies Associated with Promoting a Legalised Trade in Ivory,” _Politikon_ , 2016, _43_ (2), 215–229.

25
_15_

**_7he Madab Projec_**

four different age groups of children in the Treatment and Comparison Areas from 1975 to 1984 are presented in Table 4.

Table 4. Infant and child mortality rates In the Matlab Treatment and Comparison Areas: 197S-1983.

||||_Infant M_|_ortality_|||
|---|---|---|---|---|---|---|
||_Neo_|_natal_|_Post_|_natal_|_All In_|_fants_|
|_Year_|_Theatment_<br>_area_|_Comparl-_<br>_son area_|_Treatnent_<br>_area_|_Compari-_<br>_son area_|_Treatment_<br>_area_|_Compari-_<br>_son area_|
|1975|64|79|91|94|161|168|
|1976|57|69|54|46|112|113|
|1977|75|63|36|41|112|105|
|||_MCH-_|_FP Intervend_|_ons Begin_|||
|1978|67|75|37|42|107|117|
|1979|72|73|45|_.52_|119|124|
|1980|59|76|30|39|89|114|
|1981|71|69|39|46|111|114|
|1982|62|68|51|52|111|119|
|1983|_58_|71|40|43|101|113|
|1984|59|72|54|53|114|124|
||||_C_ildM_|_ortality_|||
||_Tod_<br>_(1 Y_|_dler_<br>_ear)_|_Late_<br>_(2-4_|_Ci_<br>_Years)_|_All Ch_|_ildren_|
|_Year_|_Teaonent_<br>_area_|_Compari-_<br>_son area_|_Deatment_<br>_area_|_Compari-_<br>_son area_|_Treatment_<br>_area_|_Comparl-_<br>_son area_|
|1975|_55_|S0|31|31|38|36|
|1976|42|49|24|25|28|31|
|1977|31|24|_15_|19|19|20|
|||_{MC_|_H-FP_<br>_Interventi_|_ons Begin_|||
|1978|36|29|17|19|23|22|
|1979|29|36|17|22|21|26|
|1980|23|33|_15_|22|17|25|
|1981|30|37|17|23|20|27|
|1982|29|37|17|23|20|27|
|1983|36|46|18|31|22|35|
|1984|38|_55_|19|33|24|39|

**Sowrc: Phippet** al. **(1987).**
# **WPS3497**

**_What Does Regional Trade in South Asia Reveal about Future Trade Integration?_**

## **_Some Empirical Evidence_**

#### **_Nihal Pitigala*_**

### World Bank Policy Research Working Paper 3497, February 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> *Consultant, International Trade Team (DECRG). I am particularly grateful to Garry Pursell, Alexander Yeats, Simon Evenett and seminar participants at the World Bank for comments and suggestions for improvements.
that a 10 percent increase in exports raised employment rates by 0.23 percentage point on average.

The weak employment impacts may be explained by the fact that exports led to a labor supply shock by raising the number of net migrants, thereby increasing both the numerator and the denominator of the employment rate. The analysis investigates this issue by examining the impacts on total labor incomes, that is, the sum of all labor incomes in the municipality. The impacts of exports on total labor income at the local level are statistically significant. A 10 percent rise in exports per worker raised total labor incomes by 2.4 percent on average (panel c, column 6). This is significant in economic terms as well, considering that total labor incomes in real terms grew by 9.2 percent during the period. Despite the increase in total labor incomes, labor income per worker did not rise in response to export growth. The OLS and IV effects are not statistically significant (panel d).

# **4.3. Migration and Remittances**

The period under analysis includes the financial crisis (2008–09), that is, the first year in which the stock of Mexican migrants to the United States started to decline because of a lower number of arrivals to the United States and a higher number of Mexican returnees. The number of unauthorized migrants from Mexico to the United States fell from the peak of 6.9 million in 2007 to 5.6 million in 2015 (Gonzalez-Barrera and Krogstad 2019). The total stock of people born in Mexico and living in the United States fell from about 11.7 million to 11.2 million in 2010–18.<sup>9</sup> Local exports played an important role in determining which municipalities were less likely to send emigrants to the United States and which ones were more likely to absorb returnees. Internal migration flows were also responsive to changes in local exports.

Table 8 shows the impacts of exports on migration. A 10 percent rise in exports per worker raised the number of people who had migrated in the previous five years to the municipality by about 7.1 people (panel a, column 1). This represents about 4.6 percent of the number of recent migrants for the average municipality in 2005. Most of this immigration response was generated by internal migrants (panel b). Indeed, a 10 percent increase in exports per worker raised the number of immigrants from other Mexican municipalities by about 4.9 people. The corresponding estimate for external migrants was only 2.2 people (panel c). The inflows of

> 9 See “U.S. Immigration Trends,” Migration Policy Institute, Washington, DC, https://www.migrationpolicy.org/programs/data-hub/us-immigration-trends#source.

11
# **4 Design and Methods**

## **4.1 Experimental Design and Sample**

The evaluation sample comes from 80 villages in four districts of Balkh province. Households were selected based on a wealth ranking of the entire population of households in these villages, conducted through a Participatory Rural Appraisal (PRA), and resulting in an eligible UP group of slightly under 6% of the population, or the census of ultra-poor households in study villages. A baseline survey was conducted from February to April 2016. A public lottery was then held in May 2016 where 1,219 UP households in the study villages were randomly assigned into one treatment group (491 households) and one control group (728 households).<sup>11</sup> Additionally, approximately 20 households in each of the study villages were randomly drawn at baseline (excluding TUP-eligible households), which allows us to have a representative benchmark for the TUP sample, referred throughout the paper as the non-ultra-poor (non-UP) sample. Further details on the sample are described in Bedoya et al. (2019).

The randomization was stratified by PRA groups, with larger villages split into multiple PRA groups, typically defined by the catchment area of a _masjid_ (mosque). Starting in May 2016, the treatment group received the TUP package, and the control group did not receive any of the components. A midline survey was conducted from July to October 2018, approximately two years after the asset transfer, and an endline survey took place over several short data collection rounds between January and June 2021, approximately 5 years after the asset transfer. Due to the COVID-19 pandemic and the escalating conflict in Afghanistan, the endline survey was conducted by phone, with the implication that only households with a working phone number could be surveyed. This covered 69% (839/1,219) of randomized UP households. Additionally, data were collected from 1,081 non-UP households at endline, which represents 64% (1,081/1,679) of non-UP households surveyed at baseline.

In this paper, we present estimates of the impact of the program on UP households with a working phone, comparing the treatment and control groups five years after the asset transfer. As Table 1 shows, the sample interviewed at midline is similar to the endline sample across all dimensions measured: there are no statistically significant differences between the two groups.

> 11The initial design considered adding a second treatment arm, but it never materialized, which explains why the control group is much larger than the treatment group.

11
Table 5: Trade, Legal Origins and Control of Corruption, Baseline Results Dep. Var.: log of average trade flow

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|IV|OLS|IV|
|Different Legal Origin|-0.08|-0.09|-0.19|-0.18|
||(-1.70)|(-1.78)|(-4.13)|(-3.83)|
|Control of Corruption|0.27|0.28|0.17|0.15|
||(25.11)|(12.64)|(15.80)|(6.71)|
|average weighted tariff|||-0.02|-0.02|
||||(-13.12)|(-12.60)|
|log distance|-1.08|-1.08|-0.97|-0.98|
||(-37.03)|(-36.23)|(-32.82)|(-31.90)|
|log product of GDP|1.13|1.12|1.08|1.09|
||(108.66)|(96.35)|(112.99)|(106.44)|
|log product of land|-0.21|-0.21|-0.19|-0.19|
|area|(-23.48)|(-22.33)|(-22.14)|(-21.69)|
|no. of landlocked|-0.35|-0.35|-0.40|-0.40|
||(-7.84)|(-7.84)|(-9.32)|(-9.25)|
|no. of islands|-0.03|-0.03|-0.04|-0.03|
||(-0.55)|(-0.64)|(-0.83)|(-0.72)|
|common border|0.91|0.91|0.92|0.92|
||(6.33)|(6.33)|(6.21)|(6.10)|
|common language|0.62|0.62|0.63|0.63|
||(11.05)|(11.06)|(11.64)|(11.64)|
|currency union|2.15|2.15|1.37|1.36|
||(8.59)|(8.60)|(5.66)|(5.58)|
|Observations|77594|77594|32752|32752|
|R^2|0.67|0.67|0.73|0.73|
|First stage R^2||0.40||0.43|
|% increase in trade<br>due to 1s.d. institution|70%|74%|40%|36%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant and year dummies are not reported. IV estimators use the sum of absolute latitude as an instrument for the sum of control of corruption for both countries in a trading pair. The sum of control of corruption ranges from 0 to 12 with mean 7.012 and standard deviation 1.972. The percent increase in trade due to 1s.d. institution (control of corruption) is calculated as follows: exp(coef*sd)-1.

39
<!-- Start of picture text --> Cyclical frequency: 6-36 Cyclical frequency: 12-48 Cyclical frequency: 6-24<br>1.00 1.00 1.00<br>0.75 0.75 0.75<br>0.50 0.50 0.50<br>0.25 0.25 0.25<br>0.00 0.00 0.00<br>-0.25 -0.25 -0.25<br>-0.50 -0.50 -0.50<br>-0.75 -0.75 -0.75<br>-1.00 -1.00 -1.00<br>1984 1987 1990 1993 1996 1999 2002 2005 2008 1984 1987 1990 1993 1996 1999 2002 2005 2008 1984 1987 1990 1993 1996 1999 2002 2005 2008<br><!-- End of picture text --> Figure 3: Rolling correlation between government spending and business cycle fluctuations