rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: The impact** **<u>of trade on conflict</u>**

||(1)<br>any onset|(2)<br>any onset|(3)<br>any onset|(4)<br>any onset|(5)<br>major<br>onset|(6)<br>major<br>onset|(7)<br>major<br>onset|(8)<br>major<br>onset|
|---|---|---|---|---|---|---|---|---|
||0.040**|0.039**|0.039**|0.010*|0.012|0.008|0.008|0.003|
|Px (t)|(0.016)|(0.016)|(0.016)|(0.005)|(0.011)|(0.011)|(0.011)|(0.004)|
|P t1|-0.014|-0.014|-0.015|0.003|-0.020**|-0.016*|-0.016*|-0.003|
|x (-)|(0.020)|(0.020)|(0.020)|(0.006)|(0.009)|(0.009)|(0.009)|(0.003)|
||0.000|0.001|0.001|-0.005|0.007|0.005|0.005|-0.003|
|Px (t-2)|(0.017)|(0.017)|(0.017)|(0.005)|(0.009)|(0.008)|(0.008)|(0.003)|
|||-0.001|-0.001|0.002||-0.022|-0.022|-0.005|
|Pm (t)||(0.024)|(0.024)|(0.006)||(0.014)|(0.014)|(0.004)|
|||-0.002|-0.003|0.003||0.017|0.017|0.000|
|Pm (t-1)||(0.026)|(0.026)|(0.007)||(0.016)|(0.016)|(0.004)|
|||-0.009|-0.009|-0.001||-0.001|-0.001|0.002|
|Pm (t-2)||(0.023)|(0.022)|(0.006)||(0.014)|(0.014)|(0.003)|
|||-0.006|-0.006|-0.001||0.005|0.005|0.000|
|MP (t)||(0.023)|(0.023)|(0.004)||(0.010)|(0.010)|(0.002)|
|||-0.041|-0.042|-0.008||-0.038|-0.038|-0.005|
|MP (t-1)||(0.054)|(0.054)|(0.010)||(0.036)|(0.036)|(0.007)|
|||0.061|0.063|0.007||0.052|0.052|0.006|
|MP (t-2)||(0.066)|(0.065)|(0.012)||(0.047)|(0.047)|(0.011)|
|Trade with||-0.033|-0.031|-0.037||-0.018|-0.018|-0.018|
|neigh. (t-1)||(0.053)|(0.054)|(0.057)||(0.032)|(0.032)|(0.035)|
|Any conflict|0.179***|<br>0.183***|<br>0.185***|<br>0.177***|0.046***|<br>0.045***|<br>0.045***|<br>0.047***|
|since 1946|(0.021)|(0.021)|(0.021)|(0.021)|(0.012)|(0.012)|(0.012)|(0.012)|
|War border||-0.006|-0.007|-0.007||0.002|0.002|0.003|
|(t-1)||(0.012)|(0.012)|(0.012)||(0.008)|(0.008)|(0.008)|
||||-0.018|-0.015|||-0.001|-0.001|
|coup|||(0.016)|(0.018)|||(0.008)|(0.008)|
|Observations|3,465|3,428|3,428|3,327|3,861|3,812|3,812|3,704|
|R-sq. (within)|0.069|0.072|0.072|0.068|0.025|0.026|0.026|0.026|
|Countries|114|114|114|114|115|115|115|115|
|Shocks|logP|logP|logP|∆logp|logP|logP|logP|∆logp|
|Weight|[t-12; t-2]|[t-12; t-2]|[t-12; t-2]|[t-12; t-2]|[t-12; t-2]|[t-12; t-2]|[t-12; t-2]|[t-12; t-2]|
|<br>Time trends|<br>NO|<br>NO|<br>NO|<br>NO|<br>NO|<br>NO|<br>NO|<br>NO|
|Sum Px|0.026**|0.026**|0.025**|0.008|-0.001|-0.002|-0.002|-0.002|
|Sum Pm||-0.013|-0.013|0.004||-0.006|-0.006|-0.003|
|Sum MP||0.015|0.016|-0.002||0.019|0.019|0.001|
|_Robust standar_|_d errors in p_|_arentheses; *_|_** p<0.01, *_|_* p<0.05, *_|_p<0.1_||||
# **2.2 GIDD: Linking macroeconomic outcomes to micro survey data**

The GIDD is based on micro-simulation methodologies developed in the recent literature, including Bourguignon and Pereira da Silva (2003); Ferreira and Leite (2003, 2004); Chen and Ravallion (2003); and Bussolo, Lay, and van der Mensbrugghe (2006). The starting point for the microsimulations is the global income distribution in 2000, assembled with data from household surveys. 1.2 million households are sampled in 63 developing countries, while household information for developed countries comes from the Luxemburg Income Study dataset.<sup>9</sup> Detailed survey data is combined with more aggregate information (usually vintiles) for the remainder of the world; the final global sample covers 91 percent of the world population (see Annex 1 for a full detailed list).<sup>10</sup> The simulated 2030 distribution is then obtained by applying three main changes to the initial distribution: (a) demographic changes, including aging and shifts in the skill composition of the population; (b) shifts in the sectoral composition of employment; and (c) economic growth, including changes in relative wages across skills and sectors.

The microsimulation framework is depicted in Figure 1. Projected changes in population shares by age (upper left part of Figure 1) are taken as exogenous from the UN population projections, (fertility decisions and mortality rates are exogenous) . The changes in shares of the population by education groups due to the expected demographic changes are also accounted for (in Figure 1 arrow linking top left box with top right box). Thus, new sets of population shares by age and education subgroups are computed and household sampling weights are rescaled accordingly. In a second step, the demographic changes impact overall labor supply by age and skill groups. These changes are incorporated into the CGE model that is used to simulate overall economic growth, growth in relative incomes by education groups and sector reallocation of labor (link between the middle and bottom rectangles). The results of the CGE are finally passed-on to the re-weighted household survey (bottom link in Figure 1).

> 9 Consumption or expenditure per capita is a more reliable measure than income, and its distribution is normally more equal than the distribution of income. Nevertheless, consumption data are not available for all countries’ survey, so, to get a global picture, the study had to include countries from which only income data were available. 10 Throughout the paper, when we talk about the global distribution, we are indeed referring to the GIDD’s sample covering 91 percent of the world population.

7
dotted line C. The prevalence of malnutrition for B is the smallest followed by C and A. This argument is consistent with Deaton (2003). It is also similar to the argument made by Ravallion (1988) and Haddad and Kanbur (1990), though they dealt with welfare risk and intra-familiar distribution respectively.

To see if Figure 6 is reasonable, we also regressed average severity of malnutrition among _<u>P</u>_<sup>2</sup> malnourished children ( _i.e. P_<sup>0)on</sup><sup>_Y_ ̄andGE(0)aswellasprovincial-leveldummies.We</sup> found that the coefficient on GE(0) was neither significant for stunting nor underweight. Once we include the prevalence of malnutrition _P_<sup>0</sup> on the right hand side, GE(0) has a positive and significant coefficient for both malnutrition indicators as shown in Table 5. This means that, given the prevalence of malnutrition and the logarithmic mean of consumption, the average severity of malnutrition among malnourished children within the same province tends to be higher in communes with higher inequality. This is consistent with Figure 6, because the average severity among malnourished children tends to increase with increased level of inequality regardless of the location of the threshold X on the horizontal axis.

If the effect of consumption on the prevalence of malnutrition is linear, inequality should have no effect on _P_<sup>0</sup> of malnutrition. Hence, non-linear effects of consumption on nutrition are extremely important in understanding the relationship between consumption and malnutrition. This is particularly the case when we have only aggregate data. Prevalence is an important indicator, but we also need to look at the FGT measures with higher parameter values. DHS estimates are typically too aggregated to meaningfully investigate aggregate relationships. The small-area estimates of consumption and health are very helpful for exploring the relationship between poverty, inequality and malnutrition.

# **6.2 Decomposition analysis of health inequality**

While the relationship between consumption poverty, inequality and the prevalence of malnutrition is important, inequality of child health, as measured by nutrition outcomes, is also 21
_Table 1: Summary of the variables included in the analyses_

||Total|Camp|Out-of-camp|Mean|
|---|---|---|---|---|
||sample|refugees|refugees|difference|
|Impact outcome of interest|||||
|Overall quality of life (2010)|8.35|8.42|8.28|0.14|
|Overall quality of life (2015)|5.48|5.17|5.80|-0.63***|
|Risk of overcrowded dwelling|0.88|0.70|1.06|-0.36***|
|Asset index|5.99|4.49|7.46|-2.96***|
|Satisfaction with overall access to<br>service|2.84|2.52|3.17|-0.65***|
|Income per capita per month|41.14|33.65|48.53|-14.88***|
|Share of household in abject poverty|0.46|0.64|0.28|0.35***|
|Share of households that can lend 150<br>J|0.12|0.09|0.17|-0.08***|
|Control variables|||||
|Age of household head|39.99|39.14|40.82|-1.68***|
|Age of household head squared|1740|1678|1801|-123***|
|Household head is a female|0.14|0.16|0.13|-0.03***|
|Household head is currently married|0.89|0.88|0.89|-0.01|
|Household size|5.85|5.63|6.05|-0.42***|
|The household head was employed or<br>operating a business in Syria before|0.25|0.25|0.23|0.02|
|the crisis|||||
|The household head was studying and<br>working before the crisis|0.002|0.0025|0.0016|0.001|
|Household asset index in Syria|2.60|2.71|2.49|0.22***|
|Household asset index in Syria<br>squared|8.93|9.83|8.04|1.80***|
|Share of household that earn enough<br>for basic needs in 2010 (in Syria)|0.87|0.86|0.87|-0.01|
|<br>Number of months in Jordan|44.60|39.23|49.43|-10.20***|
|Number of months in Jordan squared|2182|1724|2633|-909***|
|Number of observations|2399|1191|1208||

N/B differences were tested using t-test; *** means significant at 1%, ** means significant at 5% and * means significant at 10% 18
- Bj ̈orkegren, D., Blumenstock, J. E., & Knight, S. (2020). Manipulation-proof machine learning. _arXiv preprint arXiv:2004.03865_ .

- Blumenstock, J. (2016). Fighting poverty with data. _Science_ , _353_ , 753-754.

- Blumenstock, J. (2018a). Don’t forget people in the use of big data for development. _Nature_ , _561_ , 170-172.

- Blumenstock, J. (2018b). Estimating economic characteristics with phone data. _American Economic Review: Papers and Proceedings_ , _108_ , 72-76.

- Blumenstock, J. (2020, May). Machine learning can help get COVID-19 aid to those who need it most. _Nature_ . Retrieved 2020-05-15, from `https://www.nature.com/articles/ d41586-020-01393-7` doi: 10.1038/d41586-020-01393-7

- Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone data. _Science_ , _350_ , 1073-1076.

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- Chi, G., Fang, H., Chatterjee, S., & Blumenstock, J. E. (2022, January). Microestimates of wealth for all low- and middle-income countries. _Proceedings of the National Academy of Sciences_ , _119_ (3). Retrieved 2022-01-14, from `https://www.pnas.org/content/119/3/ e2113658119` (ISBN: 9782113658118 Publisher: National Academy of Sciences Section: Social Sciences) doi: 10.1073/pnas.2113658119

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- Corral, P., Irwin, A., Krishnan, N., & Mahler, D. G. (2020). _Fragility and conflict: on the front lines of the fight against poverty_ . World Bank Publications.

20
exceed **;.** It **the** UTCS rate **Ls** above **the** critical **level a,** however, **increases** in the UTCS rate are unambiguously welfare-woreening. Moreover, the illegal trade is itself welfare-worsening in the sense that for **a** given UTCS level, welfare is lower in the presence of customs fraud.

The result that illegal trade is unambiguously welfare-worsening is In contrast to Pitt (1981), who showed that illegal trade of the type analyzed here could be welfare-improving in the presence of a combination of tariffs and export taxes. As in tho Section 3, however, the explanation is that unlike any combLnation of tariffs and export **taxes,** or any non-uniform taxcum-aubsidy scheme, the UTCS by itself is not a distortion. Illegal trade therefore has no role to play ln alleviating the distorting effects of policy. In the customs fraud case, in fact, Illegal trade actually _introducas_ a diLtortion In tho form of an "inadvertento comercial policy. This explains why customs fraud is welfare-worsening ln the UTCS context even though the activLty does not absorb domestLc resources.

Now consider what happens to the real exchange rate. For simpliclty, consider the case where b - 0, so that ; - 0 and even a very small uTCS scheme will provide incentives for illegal trade. StartLng at **a** - 0, lncreases ln a raise ox more than one-for-one and an loss than one-for-one (Flgure 3). We therefore get a rLse in the effectlve terms of trade. The demand effects of this Inadvertent co mme rcial policy wash out when there is initlally no lllegal trade, and the overall effect on q is determined on the supply side. Slnce the effective terms of trade improvemnt produces a reellocation of domestic resources towards exportables, the supply of nontradeables falls and there must be a real appreciation ln terms uf importables: 38
INFRASTRUCTURE AND GROWTH IN AFRICA Hussain, I., and R. Faruqee. 1994. _Adjustment in Africa: Lessons from Case Studies_ . Washington, DC: World Bank.

- Lederman, D., W. Maloney, and L. Servén. 2005. _Lessons from NAFTA for Latin America and the Caribbean_ . Palo Alto and Washington, DC: Stanford University Press and World Bank.

- Loayza, N., P. Fajnzylber, and C. Calderón.  2005. _Economic Growth in Latin America and the Caribbean: Stylized Facts, Explanations and Forecasts._ World Bank Latin American and Caribbean Studies. Washington, DC: World Bank.

Sánchez-Robles, B. 1998. “Infrastructure Investment and Growth: Some Empirical Evidence.” _Contemporary Economic Policy_ 16: 98–108.

Wheeler, D. 1984. “Sources of Stagnation in Sub-Saharan Africa.” _World Development_ 12: 1–23. World Bank. 1994. World Development Report. Washington, DC: World Bank.

21
This paper is structured as follows. The next section describes the data. In the third section, we unbundle the firm and present a series of stylized facts based on firm-product level information. The following section documents product heterogeneity within a firm. In section five, we show a set of stylized facts consistent with recent trade models characterized by information asymmetries, trust and search costs. Then we present some novel findings on export discovery and decompose the Mexican export boom using our microlevel data. Finally, the conclusions on the implications of these stylised facts close the paper.

# 2 Data

Our analysis is based on firm-product-level data from the Encuesta Industrial Mensual (EIM) and the Encuesta Industrial Anual (EIA) administered by the Instituto Nacional de Estad ́ısstica Geograf ́ıa e Inform ́atica (INEGI) in Mexico.

The Encuesta Industrial Anual is an annual industrial survey that covers about 85% of Mexican industrial output, with the exception of “maquiladoras.” The EIA was originally started in 1963 and then expanded in subsequent years, with the last expansion taking place in 1994 after the 1993 census. In our analysis, we use the information for the 19932002 period.

The unit of observation is a plant described as “the manufacturing establishment where the production takes place.” Each plant is classified in its respective class of activity (clase) based on the basis of its principal product. The clase or class of activity is equivalent to the 6-digit level CMAP (Mexican System of Classification for Productive Activities) The post-1993 EIA includes 6,867 plants spread across 205 classes of activity. The sampling framework is based on the 1994 industrial census. In each of the selected 205 clases the survey samples the largest firms until the coverages reaches 85% of the sectoral output. In sectors with fewer than 20 plants, all entities are surveyed. Moreover, all plants with more than 100 employees are automatically included in the sample.

The Encuesta Industrial Mensual (EIM) is a monthly survey that is collected by INEGI 5
> **POLICY RESEARCH WORKING PAPER 3060**

# Abstract

Albuquerque, Loayza, and Serv6n analyze the unparalleled increase in foreign direct investment to emerging market economies in the past **_25_** years. Using a large cross-country timeseries data set, the authors evaluate the dependence of foreign direct investment on global factors or worldwide sources of risk (that is, factors that drive foreign direct investment across several countries). They construct a globalization measure that equals the share of explained variation in direct investment attributable to global factors. The authors show that the globalization measure has increased steadily for industrial and developing countries. For the full sample of countries, the globalization measure rose eightfold from **1985** to **1999.** Furthermore, in recent years developing countries' exposure to global factors has approached that of industrial countries, particularly for Latin America. Finally, the globalization measure correlates strongly with measures of capital market liberalization. Overall the authors find strong support for the hypothesis of increased market integration which implies a greater role for worldwide sources of risk. They discuss the implications of the results for public policies regarding capital market liberalization and policies directed at attracting foreign investment.

This paper-a product of Macroeconomics and Growth, Development Research Group-is part of a larger effort in the group to understand international capital flows. Copies of the paper are available free from the World Bank, **1818** H Street NW, Washington, **DC** 20433. Please contact Tourya Tourougui, room **MC3-301,** telephone 202-458-7431, fax **202-5223518,** email address ttourougui@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at albuquerque@simon.rochester.edu, nloayza@worldbank.org, or Iserven@worldbank.org. May **2003.** **_(51_** pages)

> _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._

Produced **by** Partnerships, Capacity Building, and Outreach
portfolio investment securities (that is, the stock of bilateral investments). In principle, holdings information could be used to estimate the investment flows. However, according to the CPIS guide, flows reflect changes associated with both transactions and other flows (IMF, 2002). The latter covers changes that are recognized under three broad sub-categories: “revaluations due to changes in exchange rates,” “revaluations due to price changes,” and “other changes in volume.” The CPIS does not contain enough information to distinguish between transactions and other flows and, therefore, cross-border securities transactions can be derived from the CPIS only with significant noise. These caveats notwithstanding, we compute a proxy for transactions using the CPIS holdings and measure the correlation between this variable and the flows covered in the BoP database. Given that the CPIS database does not have information on revaluations caused by price changes, the proxy variable simply computes the difference between the holdings at the end of the period and the holdings at the beginning of the period. Despite these shortcomings, the correlation between the two variables is significant (0.69 for outflows and 0.82 for inflows).

25
of DFS users through consumer protection initiatives and, especially, protection of their personal data.

The Findex survey continues to be an indispensable source of data on usage of financial services throughout the world. Ensuring that future Findex rounds are fielded should be a priority for development practitioners and donors with interest in promoting financial inclusion. By asking the same questions in future rounds, for example on digital merchant payments, Findex data can provide valuable information on trends in DFS usage over time and whether gains in DFS usage during COVID-19 were permanent or transitory. Developing new questions that can better summarize the digital borrowing and saving activities of respondents can offer a more complete picture of the benefits (and risks) of DFS for households. And perhaps the largest value added from additional data work would be to expand the country coverage for all the datasets used in this analysis. While the data sources exploited in this paper have yielded useful insights, our sense is that this is the tip of the iceberg in terms of what researchers will learn about the importance and drivers of DFS for households across the globe.

# **_References_**

- Abbasi, Tariq and Hans Weigand. 2017. "The Impact of Digital Financial Services on Firm's Performance: A Literature Review," Papers 1705.10294, arXiv.org

- Agur, Itai, Soledad Martinez Peria, and Celine Rochon. 2020. “Digital Financial Services and the Pandemic: Opportunities and Risks for Emerging and Developing Economies.” IMF Research Special Series on COVID-19. July. IMF: Washington, DC.

- Aker, Jenny, Rachid Boumnijel, Amanda McClelland, and Niall Tierney. 2016. “Payment Mechanisms and Antipoverty Programs: Evidence from a Mobile money Cash Transfer Transfer Experiment in Niger.” _Economic Development and Cultural Change_ 65(1): 1-7.

- Alber, Nader, and Mohamed Dabour. 2020. “The Dynamic Relationship between FinTech and Social Distancing under COVID-19 Pandemic: Digital Payments Evidence.” _International Journal of Economics and Finance_ 12(11): 109-117.

26
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Because of inequitable access to power and water services in the CAR, subsidized tariffs are highly regressive. Close to 100 percent of people with electricity or piped water connections belong to the top 20 percent of the expenditure distribution; such connections are nonexistent for poorer households (figure 25). Only the richest quintile has access to piped water. Most of the poorest quintiles still rely on surface water. This inequitable distribution of connections virtually guarantees that any price subsidy to these services will be extremely regressive.

**Figure 24. Underpricing of power and water in the Central African Republic and comparator countries**

Financial burden of underpricing, as percentage of GDP <!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power Water<br>CAR LIC-Fragile<br>Percenatage of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

**Figure 25. Consumption of infrastructure services is highly differentiated by budget, 2006**

<!-- Start of picture text --> a. Mode of water supply, by income quintile b. Prevalence of connection to power grid among population, by<br>income quintile<br>80% 30%<br>25%<br>60%<br>20%<br>40%<br>15%<br>20% 10%<br>5%<br>0%<br>0%<br>Q1 Q2 Q3 Q4 Q5<br>Q1 Q2 Q3 Q4 Q5<br>Piped water Stand posts<br>Power<br>Wells/boreholes Surface water<br>% population<br>% population<br><!-- End of picture text --> _Source:_ Banerjee and others 2009. _Note:_ Q1 – first budget quintile, Q2 – second budget quintile, and so on.

## **Reallocating existing spending within sectors**

About $5 million more than the estimated O&M requirements in the power sector is spent each each year. This could be better allocated toward the expansion and rehabilitation of the existing network. According to estimates, the CAR needs to spend around $5 million per year on power O&M (see table 15) but in fact is spending about $10 million (recall table 20).

# Annual funding gap

The CAR’s infrastructure funding gap amounts to $183 million per year, or about 9 percent of GDP, once efficiencies are captured. While much of this gap is found in the transport and ICT sectors, accounting for about 75 percent, the water supply and sanitation sectors account for 17 percent of the funding gap and the power sector for the remaining 7 percent (table 22).

46