rafmacalaba/datause-encoder-data huggingface dataset:
All specifications also control for the (log) distance to the closest colonial railway, as the railways had long-lasting effects on urbanization and growth in Africa (Jedwab, Kerby, & Moradi, 2017; Jedwab & Moradi, 2016), and distance to the closest explorer route. Finally, as a measure of accessibility, regressions also include a measure of terrain ruggedness, which in addition to shaping location decisions may have indirectly affected development and therefore aid allocation. There is evidence, for example, that a rugged landscape made it easier to hide from slave traders (Nunn & Puga, 2012) and enables rebel warfare (Fearon & Laitin, 2003).

The second factor to consider is the capacity to keep the settlement selfsustained for a long period of time. Self-sustainability crucially depended on access to water and suitability of land cultivation, which are likely to be important for present-day outcomes as well. Thus the regressions control for both factors, proxied respectively by the _Caloric Suitability Index_ (Galor & Özak, 2016) and the share of cell area that is within 10 km of a water source (following Nunn [2010]). Missions were also more likely to be established at high altitudes, partly to avoid diseases such as malaria, but also because of the more comfortable climate (Johnson, 1967). Regressions therefore control for average elevation and for its interaction with a dummy for the tropics. As a further control for disease environment, a measure of malaria prevalence, the _Malaria Ecology Index_ developed by Kiszewski, Mellinger, Spielman, Malaney, Sachs, and Sachs (2004), is included in the control set.

In addition, the existence of different ethnic groups may have played an important role in the missionaries’ settlement decisions. Unobserved variables at the ethnicity level may introduce biases, in light of research showing that precolonial ethnic institutions had long-lasting effects on development and public goods provision in Africa (Gennaioli & Rainer, 2007; Michalopoulos & Papaioannou, 2013). A separate dummy for each of the more than 800 precolonial ethnic homelands is included to address this concern, constructed using boundaries from Murdock’s (1959) ethnolinguistic map. Each cell is assigned to the ethnic polygon that covers the highest percentage of its surface. Regressions therefore exploit only within-ethnicity variation, and the results cannot be driven by factors varying across ethnicities.

It is also necessary to account for the main missionary purpose, namely conversion of Africans to Christianity. In particular, there is a concern that is likely to approach zero as distance increases. More specifically, the regressors have the form log(1 + distance) to avoid losing the cells at zero distance.

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**Figure 14. Poverty reduction in simulated scenarios by 2030.**

<!-- Start of picture text --> 14a. EU-IDN, FTAAP and TPP-15 scenarios 14b. RCEP and TPP-11 scenarios<br><!-- End of picture text --> <!-- Start of picture text --> Poverty reduction, change wrt baseline<br>Poverty reduction, change wrt baseline<br>3.3<br>2.5<br>2.1<br>1.8<br>1.3<br>1.0<br>0.8<br>0.3<br>0.1<br>$3.20 $5.50<br>-0.3<br>EU-IDN FTAAP TPP-15 $3.20 $5.50<br>RCEP TPP-11<br>4<br>4<br>3<br>3<br>2<br>millions millions 2<br>1 1<br>0<br>0<br><!-- End of picture text --> Incidence curves in Figure 15 below depict deviations from baseline conditions that result from each additional simulation step, by scenario. The larger net effects are caused by growth. Indeed, the EU-IDN CEPA yields the highest expected growth of income relative to the other PTAs at every point of the income distribution. The distributional effects are relatively smaller, considering that at most they account for around 1.75 percent deviations in per capita household consumption from baseline conditions, after growth. In all cases, sectoral reallocation of labor contributes only marginally to changes in the income distribution. This follows from the assumption of fixed labor, for instance PTAs are assumed not to change the willingness of people to join the labor force.

The size of the gains grows monotonically with income for the EU-IDN CEPA while the growth incidence curve has a U-shape in the case of RCEP, and a mildly inverted U-shape for the other agreements. The regressive nature of the EU-IDN CEPA is mainly due to the increase in skill wage premia spurred by the additional demand for skill intensive sectors, services in particular. Changes in wage premia are more pronounced when the effect of growth is larger, such as in the EU-IDN FTA and the FTAAP. On the other hand, the progressive effect of food prices tends to dominate in the case of RCEP, thus determining the progressive nature of the gains in the RCEP scenario.<sup>19</sup>

> 19 Note that the food price channel is always progressive except in the EU-IDN CEPA where it is slightly regressive.

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# **6 Conclusion**

Deep non-tariff provisions in trade agreements have gained a lot of policy attention. In this paper, we revisit an old question: do RTAs lower NTBs? In our gravity analysis, we do not find a robust independent trade-creating role for them, while tariffs—once properly measured—are robustly shown to be potent inhibitors of trade. Crucially, to obtain this result, measurement error in the tariff data, which tends to inflate estimates of NTBs, must be removed. Our result suggests that, across a large set of trade agreements, tariff reductions are still quantitatively important drivers of trade creation while non-tariff provisions might be less trade-creating. This does not mean, however, that such provisions are not important as they may actually reduce distortions in bilateral trade. Moreover, they can be the price to be paid for tariff reductions.

Interestingly, we find that, in deep FTAs, non-tariff provisions create additional trade in a non-discriminatory fashion. This is welcome from a welfare-theoretic point of view as inefficient trade diversion effects can be avoided. RTAs with this function can be seen as building blocks of multilateral trade liberalization. However, if policy makers want to use trade agreements to grant preferences between geopolitically aligned countries, tariffs are the better choice.

# **References**

Aichele, Rahel, Gabriel J. Felbermayr, and Inga Heiland (2016), “TTIP and Intra-European Trade: Boon or Bane?” _Jahrbucher fur Nationalokonomie und Statistik_ 2366, 639–664.

- Anderson, James E. and Eric Van Wincoop (2003), “Gravity with Gravitas : A Solution to the Border Puzzle.” _The American Economic Review_ 931, 170–192.

- (2004), “Trade Costs.” _Journal of Economic Literature_ 423, 691–751.

Baier, Scott L. and Jeffrey H. Bergstrand (2007), “Do Free Trade Agreements actually Increase Members’ International Trade?” _Journal of International Economics_ 711, 72–95. url: https://ideas.repec.org/a/eee/inecon/v71y2007i1p72-95.html.

- Baier, Scott L., Jeffrey H. Bergstrand, and Michael Feng (2014), “Economic Integration Agreements and the Margins of International Trade.” _Journal of International Economics_ 932, 339–350. url: http://dx.doi.org/10.1016/j.jinteco.2014.03.005.

- Baier, Scott L. and Narendra Regmi (2021), “Using Machine Learning to Capture Heterogeneity in Trade Agreements.” _Mercatus Working Paper_ .

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uncertainty. Exceptions include PASEC and MLA which only have overlap with SACMEQ. To this end, scores are first made comparable to SACMEQ scores, before they are converted to our final HLO score.

Hierarchy of Tests. We construct a hierarchy of tests to facilitate year-level-subject matching between anchored and reference tests. For math and science, scores are converted to TIMSS-comparable scores at primary and secondary level. For reading, scores are converted to PIRLS-comparable scores at primary level and PISA-comparable scores at secondary level.

This hierarchy of tests can be extended to adjudicate which HLO score to use when multiple scores are available across sources tests. (e.g. a country that took part in EGRA and SACMEQ). The first HLO choice is: PIRLS, PISA or TIMSS depending on the subject and schooling level. If a country took part in PISA and not in TIMSS for math and reading, we next convert PISA scores into subject-specific secondary HLO scores. If a country took part in LLECE, we convert these scores into a subject-specific primary HLO score. Next, we take SACMEQ HLO scores, followed by PASEC HLO scores, then EGRA HLO scores, and finally by MLA HLO scores.

Measures of Uncertainty. We include measures of uncertainty to quantify the degree of confidence around our estimates. We capture two sources of uncertainty: scores on the original test and uncertainty in the calculation of the conversion factor across tests. Simplifying equations (2) and (3), the HLO for a given country on test X is: We calculate the variance of the HLO by bootstrapping. We assume country-level average scores are asymptotically normally distributed . We take 1000 draws from the distribution of subject-level-grade average test scores for each testing regime. We create doubloon indices and HLOs from each bootstrapped sample. We take the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles of the distribution and use this to construct lower and upper bounds of uncertainty for our HLO scores.

We find small uncertainty intervals overall, with an average of 3.5 points and ranging from 1 to 11 points (see Figure 6.0). This is consistent with original standard errors from each respective testing regime. We find larger uncertainty for HLO scores relative to original scores when testing regimes have fewer doubloon countries, such as SACMEQ and PASEC. By quantifying this uncertainty, we can reliably bound our estimates.

16
25 same results as when relying on production ((3)-(4)). Lastly, for both cement production and consumption, if we use 10-year panel regressions with several lags of the variables of interest, we do not find significant differences between the types of countries.<sup>33</sup> Therefore, although NRXGDP countries have more tall buildings, in particular concrete towers, they do not use more cement, which might imply eviction effects within the construction sector. Of course, with economic development and rising land values, tall buildings replace short structures (Jedwab et al., 2020b). However, we here compare NRXGDP and MFGSERV countries with _similar_ levels of urban economic development, thus focusing on “excesses” that might lead to “gaps” in the rest of the urban sector.

To study urban construction more generally, we turn to information on the value added of the construction sector (in PPP; constant 2005 international dollars) from 1970 to date.<sup>34</sup> We use the long-difference model and consider as the dependent variable the log sum of construction GDP over the period 1970-2020 while simultaneously controlling for log urban population in both 1970 and 2020, thus capturing _construction GDP per urban capita_ . Again, we observe no significant difference ((5)-(6)). If we use 10-year panel regressions with several lags of the variables of interest, we also do not find significant differences between the types of countries (not shown). However, one caveat with these analyses is that both cement use and construction include infrastructure.

Finally, if residential construction is indeed crowded out in NRXGDP countries, one might see higher slum shares there. However, to project prestige NRXGDP may more willingly adopt slum clearance policies. Data on slum shares are only available for the most recent years (United Nations, 2020b). If we use the cross-sectional version of the long-difference model (thus not controlling for slums c. 1960), we find smaller slum shares in MFGSERV and NRXGDP countries when not controlling for urban economic development ((7)). Consistent with the results on tall building construction, when MFGSERV or NRXGDP countries develop and urbanize, whether due to industrialization broadly defined or resource windfalls, their urban sector also develops, which leads to some forms of formalization. But if we control for urban economic development ((8)), we find no correlations between the slum share and the variables of interest. Therefore, the slum share is not higher in NRXGDP countries. As a result, if any crowding takes places

> 33These results are not shown, but are available upon request.

> 34From (United Nations, 2020c) we know the GDP share of construction over time. We then obtain GDP using data from Bolt and van Zanden (2014), Gollin et al. (2016) and World Bank (2021).
- 15 -

traded product with few countries being so abundant in the raw materlal<sup>that</sup> there **ia** a local cost advantage). In countries whlch dld not<sup>opt for nuclear</sup> power durlng this twenty year period there has certainly been substantlal lnter fuel substltution. For example, Australla decreased the<sup>shareof</sup> oll and increased that of gas, whlle Denmark and Greece switched from<sup>**a**heavy reliance</sup> on oil to coal and solid fuel. The picture from O.B.C.D.<sup>countries is that</sup> wlthln the homogeneous power generatlon sector not only<sup>is fuel substitution</sup> posslble, but that it actually took place and that in some countries<sup>there was</sup> an extraordinary shift in the fuel mix used.

Fuel Shares in Various Industries Ln the O.R.C.D.

As argued above the LndustrLal sector is the major user of energy<sup>and</sup> fuel substitution ln thLs area is of great importance for pollcy purposes. However the figures for the aggregate sector are not a complete guide to fuel choice since different industries have very different patterns of fuel choice. Hence shifts in the relatlve importance of an indlvidual<sup>sub-sector (e.g. the</sup> decline in lron and steel ln many LndustrialLzed countries) wlll produce changes ln aggregate fuel mix which are unrelated to the possLbilitLes for<sup>fuel swLtchLng</sup> to produce a glven level of output.

Data is available for a sub-set of the O.B.C.D. on<sup>fuel use by</sup> industrLal sub-sectors. Table 2.8 shows fuel shares for O.E.C.D.<sup>Europe for</sup> thirteen industrles in 1988.

These sectors, which vary considerably ln the technologLes<sup>used, show</sup> conslderable variations in their fuel mix. Excluding<sup>chemLcal feedstock, in</sup> whlch the oil and gas are converted into other products,<sup>there is stillagreat</sup> variation from the very coal intensive iron and steel<sup>industry to the electrlclty</sup> intensive non-ferrous metals lndustry. This table<sup>does not of course show the</sup> possibilitLes of fuel substitutlon, sLnce the technologies<sup>used are often</sup> industry specific; it does however show the dangers of using trends ln aggregate industrlal fuel shares to lndicate substitutLon posesbilities, Changes in industrial mlx could obviously bring about large changes in aggregate fuel mlx without any fuel substitution taklng place at a plant level. Of course factors
WPS4260 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 17</mark>

# **Civil War Risk in Democratic and Non-Democratic Neighborhoods**

Clionadh Raleigh Centre for the Study of Civil War, (PRIO) University of Colorado at Boulder World Bank Project on Political Institutions and Conflict

## **Abstract**

This study questions the extent to which domestic conflict is influenced by national, regional, and international relationships. It is designed to answer specific questions relating to the effects of neighboring characteristics on a state’s risk of conflict and instability: what is the interaction between neighboring conflict and political disorder? Do democratic neighborhoods have different conflict trajectories than non-democratic neighborhoods and if so, where and why? Given that most poor countries are located in poor and conflictual neighborhoods, to what extent is there a relationship between poverty and political disorder in different regime neighborhoods? Using spatial lag terms to specify neighboring regime characteristics and multilevel models to differentiate between explanatory levels, this study reiterates the importance of domestic and neighboring factors in promoting or diminishing the risk of instability and conflict. However, the pronounced negative effects of autocratic and anocratic neighborhoods are mitigated by a growing domestic GDP. This study also finds that democratic neighborhoods are more stable, regardless of income level. Research presented here is unique in its contribution regarding how regime type as a significant development indicator, which in turn is salient in determining the risks of civil war across states.

## World Bank Policy Research Working Paper 4260, June 2007

_The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post-Conflict Transitions Project can be found at http://econ.worldbank.org/programs/conflict_ ). _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 these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._ 1
# **Table 2:  Distribution of Data Points by Type of Data in 2006 WGI**

||**Commercial**<br>**Business**<br>**Information**<br>**Providers**|**Surveys of**<br>**Firms or**<br>**Households**|**Non-**<br>**Governmental**<br>**Organizations**|**Public Sector**<br>**Organizations**|**_Total_**|
|---|---|---|---|---|---|
|**_Number of Data Points_**||||||
|Voice and Accountability|497|340|684|324|1845|
|Political Stability|1027|179|0|227|1433|
|Government Effectiveness|845|371|315|314|1845|
|Regulatory Quality|795|206|277|343|1621|
|Rule of Law<br>|960<br>|371<br>|410<br>|655<br>|2396<br>|
|Control of Corruption|959|439|133|314|1845|
|**_Total_**|5083|1906|1819|2177|10985|
|**_Shares of Total for Each In_**<br>|**_dicator_**<br>|||||
|Voice and Accountability|0.27|0.18|0.37|0.18|1.00|
|Political Stability|0.72|0.12|0.00|0.16|1.00|
|Government Effectiveness|0.46|0.20|0.17|0.17|1.00|
|Regulatory Quality|0.49|0.13|0.17|0.21|1.00|
|Rule of Law|0.40|0.15|0.17|0.27|1.00|
|Control of Corruption|0.52|0.24|0.07|0.17|1.00|
|**_Total_**|0.46|0.17|0.17|0.20|1.00|
|**_Weighted Shares of Total f_**|**_or Each Indicator_**|||||
|Voice and Accountability|0.35|0.03|0.54|0.08|1.00|
|Political Stability|0.82|0.04|0.00|0.14|1.00|
|Government Effectiveness|0.65|0.12|0.09|0.14|1.00|
|Regulatory Quality|0.59|0.09|0.12|0.19|1.00|
|Rule of Law|0.59|0.12|0.13|0.15|1.00|
|Control of Corruption|0.59|0.20|0.06|0.16|1.00|
|**_Total_**|0.60|0.10|0.16|0.14|1.00|

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other way around by imputing from Sample 2 to Sample 1 and then compare with the true poverty rate in Sample 1.

We consider three model specifications based on different sets of regressors for further comparison. Specification 1 employs the variables that are only available in the PG data set (PGspecific variables), which include case (household) size and the PA’s demographic and employment characteristics (age, gender, different levels of education achievement, occupation group, marital status, religion, and the governorate or city of original residence in the Syrian Arab Republic).<sup>9</sup> Specification 1 also includes variables related to the PA’s immigration status such as the type of border crossing point and the legal status of entry. It is the main model specification. Specification 2 adds to Specification 1 several variables that are only available in the HV data and that are related to household assets, utilities, and the physical characteristics of the house. These variables include the quality status of the kitchen, electricity access, and the ventilation system, the living area of the house (as measured by the number of square meters per person), whether the house is made of concrete, and the availability of tap water and piped sewerage system. Specification 3 further adds to Specification 2 HV-specific variables related to the household’s shock-coping strategies (i.e., whether receiving humanitarian assistance, help from the host family, or from the host community), whether the household has a valid certificate of asylum, and whether the household receives UNHCR financial assistance.

We are particularly interested in examining whether adding HV-specific variables to the main specification in Specification 1 can improve the accuracy of the estimates. If we find that some key predictors of household expenditure—that are not available in the PG data—can improve the accuracy of the poverty predictions significantly, this provides a strong argument for collecting

> 9 We consider the following five levels of education achievement: 1) below six years of schooling, 2) 6-8 years of schooling, 3) 9-11 years of schooling, 4) 12-14 years of schooling, and 5) university education or higher.

11
many institutions that provide sharia-compliant consumer loans, and so the search for a suitable partner was nontrivial.

# 3. Experimental Design

# _Partner Institution and Financial Product Descriptions_

The research team partnered with a Jordanian microcredit institution, Tamweelcom, to evaluate market demand for sharia-compliant loans. Tamweelcom was established in 2000 as a non-profit and is one of the country’s largest and fastest growing microcredit institutions. As of December 2014, when the experiment began, it had over 56,000 active borrowers, more than 94 percent of whom were women.<sup>11</sup> The average loan size in 2014 was 315 Jordanian Dinars (approximately 445 USD) and the repayment rate exceeded 95 percent.

Tamweelcom focuses on individual lending. Most new accounts (outside of this experiment) were by individuals who had heard of Tamweelcom and went to ask for a loan directly. They would determine loan eligibility by looking at proof of income. Individuals also had to secure a guarantor for the loan. As mentioned above, the public credit agency only covers 2% of the population, and so most people do not have a credit history.

At the time of the experiment, Tamweelcom was in the process of establishing a subsidiary entity that would provide sharia-compliant products to the market. This entity eventually became the first sharia-compliant microcredit institution in Jordan. At the time, they were interested in knowing how the level of demand for sharia-compliant loan products compares with the demand for the conventional products they had been providing to the marketplace for over a decade.

The lender designed a sharia-compliant product similar in price and terms to the conventional product, but different in its contractual form. The two products shared many features including the type of asset allowed to be financed (i.e. household assets like stoves and refrigerators), the range of loan sizes (300-1500 JD), the allowable loan term (a maximum of 20 months), and the price (1.9% interest per month for the conventional loan and a 1.9% per month _murabaha-fee_ which was calculated in the same way as the interest rate). The lender also dealt with late payment and default the same way, using a daily fee structure for each day late, as opposed to compounding interest. The main difference between the two products was that in the sharia-compliant case the lender would pay the retailer of the household asset directly (and so would be providing the loan in-kind), whereas in the conventional product the

> 11 Tamweelcom Annual Report 2015

10