rafmacalaba/datause-encoder-data huggingface dataset:
efficient border crossing points outside a customs union, which tend to be up to three hours. Given the cumbersome customs clearance process currently in place, this scenario assumes that not all frictions will be removed for bilateral trade and that the border crossing time would be slightly more than three times that in the most efficient border crossing points outside a customs union.

_Full integration._ The third scenario assumes complete integration of Bangladesh and India, with no restrictions on foreign truck movements. This scenario assumes that trucks do not need to stop at the border between Bangladesh and India and that trucks can use all border posts and roads in both countries. Transport costs in this scenario are the lowest of the three scenarios.

# **5 Simulation Results**

## **5.1 Gains from the Motor Vehicles Agreement**

This subsection presents the results of the model for both MVA scenarios. It starts with the aggregate results for each country and then discusses the spatial effects. The opening of new transit routes for Indian trucks travelling to and from northeast India through Bangladesh and the removal of border frictions for bilateral trade would bring significant economic benefits to Bangladesh and India. Under the inefficient MVA scenario, income is estimated to increase by 3.4 percent in Bangladesh and 1.4 percent in India; under the efficient MVA scenario, income is estimated to increase by 11.3 percent in Bangladesh and 5.6 percent in India (Table 1). The MVA would lead to reductions in travel time and transport costs for freight, with their extent varying between the inefficient and efficient MVA scenarios, which explains the differences in income gains between the two scenarios. The reduction in transport times and costs reduces the prices of intermediate and final goods.

11
|because it is unavailable|||||
|---|---|---|---|---|
|||(0.232)||(0.332)|
|**_Opportunity Cost of Labor_**|||||
|Daily Wage casual worker in<br>agriculture (log)||0.333||0.753**|
|||(0.232)||(0.332)|
|**_Competition_**|||||
|Between 1 and 5 competitors<br>(dummy)||0.007||-0.065|
|||(0.243)||(0.365)|
|More than 5 competitors<br>||0.398**||0.613*|
|(dummy)|||||
|||(0.190)||(0.320)|
|**_Shocks_**|||||
|illness||0.013||0.126|
|||(0.086)||(0.113)|
|-job loss||0.193||-0.210|
|||(0.239)||(0.439)|
|-price||-0.038||0.133|
|||(0.118)||(0.146)|
|-drought||-0.244**||-0.303**|
|||(0.098)||(0.148)|
|-flood||-0.252||-0.298|
|||(0.160)||(0.217)|
|-food||0.028||0.029|
|||(0.146)||(0.193)|
|-crop||0.100||-0.037|
|||(0.104)||(0.138)|
|-death||0.293*||0.231|
|||(0.176)||(0.197)|
|Household able to raise 100 birr||0354**||0162|
|in one week for||.||.|
|emergency needs?||(0.147)||(0.227)|
|**_Access to Credit_**|||||
|Collateral not required||0.072||0.533*|
|||(0.184)||(0.281)|
|Co-signer not required||0.266||-0.037|
|||(0.263)||(0.436)|
|Land can be used as collateral||0.275||0.433|
|||(0.325)||(0.472)|
|Constant|0.225|<br>-0.986|-0.798|<br>-5.219|
||(3.207)|(2.850)|(4.193)|(4.067)|
|R2|<br>0.283|<br>0.388|<br>0.102|<br>0.235|
|Adjusted R2|0.264|0.348|0.073|0.173|
|Observations|593|593|495|495|

_Note:_ Standard errors (in parentheses) are clustered by enumeration area. Significance at the 1%, 5%, 10% level is indicated by ***, **, *, respectively.

-37-
|Heard/saw<br>messages<br>about ITNs|Respondent|Indicator variable equal to 1 if respondent answered yes to the ques-<br>tion ”During the last six months have you heard or seen any mes-<br>sages about insecticide treated mosquito nets?” and zero otherwise.|
|---|---|---|
|Heard/saw<br>messages<br>about<br>early<br>seeking<br>behavior|Respondent|Indicator variable equal to 1 if respondent answered yes to the ques-<br>tion ”During the last six months, have you heard or seen any mes-<br>sages about early seeking behavior for malaria treatment?”, and zero<br>otherwise.|
|Heard/saw<br>messages<br>about<br>environmental<br>management|Respondent|Indicator variable equal to 1 if respondent answered yes to the ques-<br>tion ”During the last six months, have you heard or seen any mes-<br>sages about environmental management to control mosquitoes?”<br>and zero otherwise.|
|Number of nets owned<br>byhousehold|Household|Number of bed nets reportedly owned by household, including 0 if<br>household had none.|
|Number of ITNs owned<br>byhousehold|Household|Number of ITNs owned by household, including 0 if household had<br>none.|
|Willingness to pay for an<br>ITN|Household|Reported maximum willingness to pay (in Eritrean currency, 1 US<br>dollar = 15 Nakfa) for a bed net. This question was asked only to<br>respondents who reported having no bed nets and who answered yes<br>to the question ”Would you be willing to pay for a bed net?”. An-<br>swers were recoded from missing to 0 if respondent reported having<br>no bed nets and answered no to the question ”Would you be willing<br>topayfor a bed net?”.|
|Reported net use|Individual|Indicator variable equal to 1 if person reportedly slept under a bed<br>net the night before the surveyand zero otherwise.|
|Number of observed nets<br>used the night before|Household|Number of bed nets observed during survey and reportedly used the<br>night before the surveybyat least one household member.|
|Number of observed nets<br>left unused the night be-<br>fore|Household|Difference between the total number of nets observed during the<br>survey and the number of observed nets used the night before.|
|Full net coverage|Household|Indicator variable equal to 1 if the ratio of the number of household<br>members to the number of owned bed nets is not greater than 1.5<br>and zero otherwise.|
|Adequate net coverage|Household|Indicator variable equal to 1 if the ratio of the number of household<br>members to the number of owned bed nets is not greater than 2 and<br>zero otherwise.|

32
conflicts; and for Definition 4, conflicts are preceded by 10 years free of confrontations that since their onset had reached more than 1,000 battle‐related deaths.

The control group of conflict‐free economies consists of a set of: 1) all the non‐conflict developing countries, 2) the developed countries with available macroeconomic data for each of the three variables of interest, and 3) the years in conflict countries that have been preceded by at least 10 years of peace following the specific criteria for each of the four definitions of armed conflict. A complete list of the armed conflicts considered in this study can be found in Appendix 1.

# **_III. B. Macroeconomic variables_**

As mentioned, this study focuses on the impact of the onset of conflict on public debt as a percentage of GDP, government consumption as percentage of GDP, and annual inflation rates. Other aspects of fiscal health, like primary and overall fiscal balances, were initially considered for the analysis but unfortunately are not available for the full sample of episodes of conflicts dating back to the 1950s. To obtain a constant sample for every macroeconomic variable for the 11 years of analysis centered on the dates of the onset of conflicts, only conflict countries with continuous observations for the entire episode‐period of analysis (11 years) are included in the conflict‐countries group.

In total, and based on the requirements previously described, 88 conflicts in 74 countries have complete information for the 11‐year episode analyses for at least one of the three macroeconomic variables. The starting years of the conflicts in the overall sample range from 1956 to 2012.<sup>6</sup> Table 1 shows the number of conflicts and conflict‐afflicted countries with complete information for each of the four definitions and the three macroeconomic variables. The variable definitions and their sources are detailed in Appendix 2.

> 6 Although the UCDP/PRIO Armed Conflict Dataset provides conflict information from 1939, the lack of consistent macroeconomic data available before 1950 restricted us from including in the study armed confrontations occurring prior 1956.

7
- Straub, S. (2007): “Infrastructure: recent advances and research challenges”, unpublished manuscript.

- Turnovsky, S. (1997): “Fiscal policy in a growing economy with public capital”, _Macroeconomic Dynamics_ 1, 615-639.

- Windmeijer, F., 2005. “A finite sample correction for the variance of linear efficient two-step GMM estimators.” _Journal of Econometrics 126_ , 25-51

- World Bank, 1994. _World Development Report: Infrastructure for Development_ . Washington, DC, The World Bank.

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- World Bank, 2006. _World Development Report: Equity and Development_ . Washington, DC, The World Bank.

- Yepes, T., J. Pierce and V. Foster (2008): Making sense of Sub-Saharan Africa’s infrastructure endowment: a benchmarking approach”, unpublished manuscript.

35
Kakwani, N. C. 1980. _Income Inequality and Poverty. Methods of Estimation and Policy Applications_ . Oxford: Oxford University Press.

Lambert, P. 1993. _The Distribution and Redistribution of Income_ . Manchester: Manchester University Press.

LeGrand, J. 1978. “The distribution of public expenditure: The case of health care.” _Economica_ 45: 125-42.

Lindelow, M. 2000. “Health Care Demand in Rural Mozambique: Evidence from the 1996/97 Household Survey.” FCND Discussion Paper 126. International Food Policy Research Institute, Washington, D.C.

Mackenbach, J. P., and A. E. Kunst. 1997. “Measuring the Magnitude of Socio-economic Inequalities in Health: An Overview of Available Measures Illustrated with Two Examples from Europe.” _Social Science and Medicine_ 44(6): 757-71.

Makinen, M., H. Waters, M. Rauch, N. Almagambetova, R. Bitran, L. Gilson, D.

McIntyre, S. Pannarunothai, A. L. Prieto, G. Ubilla, and S. Ram. 2000. “Inequalities in health care use and expenditures: empirical data from eight developing countries and countries in transition.” _Bulletin of the World Health Organization_ 78(1): 55-65. Montgomery, M. R., M. Gragnaloti, K. Burke, and E. Paredes. 2000. “Measuring Living Standards with Proxy Variables.” _Demography_ 37(2): 155-74.

Morris, S. S., C. Calogero, J. Hoddinot, and L. J. M. Christiaensen. 2000. “Validity of rapid estimates of household wealth and income for health surveys in rural Africa.” _Journal of Epidemiology and Community Health_ 54: 381-87.

Ministry of Planning and Finance. 1998. “Understanding Poverty and Well-Being in Mozambique: The First National Assessment (1996-97).” Ministry of Planning and Finance (Mozambique), Universidade Eduardo Mondlane, International Food Policy Research Institute.

Murray, C. J. L., E. E. Gakidou, and J. Frenk. 1999. “Health inequalities and social group differences: What should we measure?” _Bulletin of the World Health Organization_ 77(2): 537-43.

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Sahn, D. E., and D. C. Stifel. 2000. “Poverty Comparisons Over Time and Across Countries in Africa.” _World Development_ 28(12): 2123-55.

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Samuelson, P. A. 1975. “Complementarity - An essay on the 40th anniversary of the Hicks-Allen revolution in demand theory.” _Journal of Economic Literature_ 15: 2455.

23
7. Do you currently have a bank loan?

8. Do you currently have a bank overdraft facility?

The questions underlying each of the indicators appear in Appendix A. Note that there is not a product-based indicator for _indirect access_ since there was only one question on that topic and it was asked in the same way in both the product-based and institution-based questionnaires. That indicator is therefore dropped from subsequent tables.

We focus on individual use of financial services so as not to conflate the effects of the method of eliciting household usage information (informant versus full enumeration) with the effects of asking product versus institution-based questions. And again, while we acknowledge some degree of subjectivity in selecting the questions underlying our product-based indicators of usage, we made a concerted effort to adapt those questions to the country context. We also selected from the questions that have been used in past FinScope surveys. We hope, therefore, that this constitutes a fair test of the importance of asking product-based questions in the sense that it represents well the most advanced surveys undertaken to date.

Table 7 shows that the product- and institution-based questions produce very similar usage percentages for basic services, such as _banked_ and _formal non-bank saving_ . The same is also true for _informal credit_ , suggesting that respondents understand well the meaning of informal credit without needing to be cued about specific types of arrangements. By contrast, the product-based questions yield much higher usage percentages than do the institution-based questions for _formal credit_ (2.8% vs. 0.8%), _informal savings_ (18.8% vs. 8.9%), and _insurance_ (18.4% vs. 5.7%), and all of those 21
|Main Data||
|---|---|
|Urban area population|5,101,300 households|
|Fraction of ground surface devoted to housing|0.45|
|Households average income|56,098<br>~~C~~|
|Transport times and costs in Paris urban area|cf. Appendix D.2|
|Interest rate|δ=5%|
|Built capital depreciation time|ρ=0_._5%|
|Calibrated paramet|ers|
|Households utility function parameter|β=0_._3<br>|
|Coeffcients of construction cost function|_A_=1_._57_∗_10<sup>_−_3</sup>and_a_=0_._07|
|Maximum foor-area ratio|cf. Appendix D.2|
|Cost associated with travel time|cf. Appendix D.2|
|Rent determining city border|_R_0=12<br>~~C~~_/m_<sup>2</sup>|

Table D.3: Summary of main data and calibration parameters Π is the profit per land unit, ρ represents the joined effect of real estate capital depreciation and annual taxes payed by land owners on the real estate capital, and δ the interest rate. The metropolitan area boundary is defined by a rent _R_ 0, below which it is not profitable to build housing building (this value corresponds both to other use of the land like agriculture and to transaction costs in the building and renting process). Developers build to maximize their profit: at each point of the metropolitan area they construct, i.e. choose _K_ , to maximize Π under the constraint that<sup>_<u>H</u>_</sup> _L_<sup>�ratio is</sup> limited by a land-use constraint (see detail below).

# _Appendix D.2. Calibration_

Tab. D.3 presents the numerical data we used in our simulations. In absence of adequate data for some parameters, for instance the cost of time and construction costs, these parameters have been calibrated on the Paris structure in 2006. A detailed comparison of model results with available data is provided below, and shows a good agreement on the model with observed urban evolutions.

_Maximum floor-area ratio in the center of Paris._ We include in the model a constraint on the maximum density allowed. This constraint reflects actual land-planning constraints which prevents building which are too high. Calibration process leads to 1.5 as the value of this maximum floor-area ratio. In the simulations, this constraint is only binding in the center of Paris.

This value may seem low as most buildings in Paris have approximately 6 floors, which would induce a ratio of about 6 at the center of Paris. However, our ratio is only taking into account housing surface, and not the total built surface, and the discrepancy is simply caused by built surface intended for purposes other than housing (it includes, on the one hand, corridors and lobbies in buildings dedicated to housing and, on the other hand, all buildings not dedicated to housing: offices, shops, museums, train stations, office buildings, schools, universities, etc.).

35
**Table A4. (Continued) First stage for baseline regressions, export rank 76-100**

||l|og avg. dest. incom|e|export|share|log avg.<br>dest. distance|
|---|---|---|---|---|---|---|
|Instrument|(1)|(2)|(3)|(4)|(5)|(6)|
|El Salvador|-0.00|-0.06|-0.04|-0.10|-0.07|0.79*|
|Armenia|-0.08|-0.10|-0.12|-0.04|-0.07|-0.82|
|Malawi|0.01|0.04|0.04|0.05|0.07|0.34|
|Jamaica|0.02|0.02|0.03|0.00|0.01|0.31|
|Yemen|0.06|0.04|-0.04|-0.03|-0.21|-4.51|
|Gabon|-0.25**|-0.36***|-0.34**|-0.22*|-0.17|1.20|
|Cameroon|-0.89*|-1.47***|-1.45***|-1.14***|-1.10**|0.91|
|Tanzania|-0.09**|-0.10**|-0.10**|-0.03|-0.03|0.05|
|Costa Rica|0.03|0.06|0.05|0.05|0.04|-0.27|
|Qatar|0.09|0.06|0.06|-0.05|-0.05|-0.05|
|Indonesia|-0.06*|-0.05|-0.04|0.03|0.04|0.26|
|Honduras|-0.00|-0.03|-0.02|-0.05|-0.03|0.51*|
|Kazakhstan|0.02|0.02|0.03|-0.01|0.02|0.67|
|Zambia|-0.03|-0.05|-0.04|-0.05|-0.01|0.82|
|Burkina Faso|0.50*|1.12***|1.23***|1.22***|1.48***|6.38**|
|Bangladesh|0.05|0.03|0.01|-0.06|-0.10|-0.99*|
|Madagascar|0.01|0.08|0.07|0.12|0.11|-0.13|
|Georgia|0.08|0.02|0.00|-0.12|-0.16|-0.97|
|Albania|0.01|0.01|0.00|-0.00|-0.02|-0.39|
|Togo|0.37|0.32|0.39|-0.10|0.05|3.56|
|Benin|-0.60|-1.43*|-1.55**|-1.65**|-1.92**|-6.55|
|Dem. Rep. of Congo|0.06|0.06|0.06|-0.00|0.00|0.05|
|Rep. of Congo|-0.42***|-0.22|-0.22|0.38**|0.40*|0.50|
|Mauritania|-0.15**|-0.10|-0.11|0.11*|0.07|-0.80|
|Niger|0.96**|1.07**|1.01**|0.22|0.08|-3.55|

Notes: See above.
paper, we focus our analysis on the political connection that firms have with senators chairing the most relevant committees. These chairmen in principle have access to more political resources and are perceived by banks as important political figures.

To formally verify the importance of chairing a commission and validate our classification of influential commissions, we evaluate three indicators of the political importance of the three types of senators. The first measure indicates whether the senator headed their party’s state list for the senate elections. The second measure is an indicator of whether the senator held an important political position in the past, like state governor, federal senator, federal deputy, ambassador or secretary at the state or federal level. Finally, the third measure is an indicator of whether the senator will hold an important political position after chairing the commission, including state governor, federal senator or ambassador. The results are displayed in Table A3. All measures support our classification and corroborate the relative importance of chairing a commission. In the second line, we find that only 44 percent of type I senators headed their party’s list for the senate elections, contrasted with 67 percent and 81 percent of type II and III senators respectively. Regarding their previous occupations we find in the third line that only 4 percent of type I senators had relevant past positions, compared with 30 percent and 56 percent of type II and type III senators respectively. Finally, we find that once their term as senators ends, chairmen of important committees are also more likely to hold important federal or state appointments in the future. In line four, we find that 5 percent of both type I and type II senators end up with important political positions, compared with 39 percent of type III senators.

# **III. Data and Methodology**

## **_III_ .A. Data**

The data in our paper come from four primary sources. The first is the loan-level data for the universe of commercial lending in Mexico from 2003 to 2012. The second is the monthly bank balance sheet data set provided by the Bank of Mexico. The third is hand-collected data on personal and professional information on all 256 senators elected in 2000 and 2006. This information includes all political occupations as well as the commission memberships. Finally, and to document variation in real economic outcomes, we use yearly information on firms’ balance sheets using the Orbis data set compiled by Bureau van Dijk.

The loan-level data are a supervisory data set on the entire universe of commercial loans in Mexico from September 2003 to March 2012. The data set was compiled by the Mexican Banking and Securities Commission (CNBV) in its role as bank supervisor. The data come from regulatory reports (known as R04) sent monthly by every commercial bank to the CNBV. Reports are mandatory, updated electronically, and include detailed characteristics of all new and continuing 9
Such an adjustment can be done for a particular year, or by taking the average ratio for a country over a longer time period. We estimate such country specific IPLs for use with national accounts for 1990, ensuring that poverty estimates from national accounts and surveys are aligned in that year. The results for global poverty are shown in Panel C of Figure 5. While the poverty estimates are equal (by design) in 1990, there is considerable variation over time, and, in global measures the rate of decline is much larger, due to the faster growth rates of national accounts means compared with survey means.

Across all the methods which involve substituting survey means with national accounts means, poverty is estimated to be lower and falling faster when compared to traditional survey measures. The more rapid decline seen in the poverty measures using national accounts points to a concern with current use of national accounts growth rates in extrapolating household survey estimates for years with missing surveys. Even if household survey means are used for poverty estimation for survey years, national accounts growth rates are used to interpolate such estimates to non-survey years and for nowcasts and projections of poverty in the future. Current methods use actual or projected national accounts growth rates to align poverty estimates to non-survey years for global aggregation. Removing the bias implicit in this method suggests a slower global decline in poverty than the World Banks official poverty numbers.

Because India, Indonesia and China, countries which historically have been the home of a large share of the global poor, typically have household surveys for most reference years for which the World Bank reports poverty, the effect on global numbers is of less concern. However, the lack of recent surveys available for India, the home to a large share of the world’s poor, has generated greater uncertainty about poverty estimates from national accounts based extrapolation of the latest available survey.<sup>31</sup> Extrapolations or projections of poverty beyond the World Bank’s latest reference year that use national accounts growth rates commonly use an adjustment factor to adjust for the discrepancy in growth rates between national accounts and surveys. But even if the systematic bias and overall error is reduced by applying the adjustment

> 31 In recent years no survey has been available for India, making global poverty estimates for these years highly uncertain. Nowcasting approaches that take into account the limited pass through of national accounts growth to household consumption growth, have been tested. For a closer assessment of the current method applied by the World Bank for the situation in India see Newhouse and Vyas (2019).

24