rafmacalaba/datause-encoder-data huggingface dataset:
along group lines, and secondly, with widespread socioeconomic inequalities, the elites are likely to gain support among their ethnic constituencies quite easily. According to Langer (2005) the absence of political horizontal inequalities among the elites reduces the risk of violent group mobilization, even if there are severe socioeconomic divisions between different ethnic and regional groups within a country, because in such situations group leaders lack the incentives to mobilize their constituents for violent conflict. This paper tests Langer’s argument with a crossnational sample. From this follows my final hypothesis:

- H4: The conflict potential of socioeconomic horizontal inequalities increases with the level of political exclusion of minority groups in a country, ceteris paribus.

# **4 Data and Research Design**

A statistical analysis of onsets of civil conflicts in developing countries serves as the empirical test of the outlined hypotheses. The sample includes all developing countries where at least one Demographic and Health Survey (DHS) had been conducted during the period 1986–2003 and for which the data were available. In total this amounts to 123 national surveys in 61 countries. The total number of observations in the dataset add up to 1160 country-years. However, when consecutive years of conflict are removed from the analysis a maximum of 806 country-years remain.

## _4.1 The Dependent Variable: Conflict Onset_

The conflict data are derived from the Uppsala/PRIO Armed Conflict Dataset (ACD), which includes every armed conflict between a state government and an organized opposition group that caused at least 25 battle-related deaths per year (Gleditsch et al., 2002). My definition of conflict applies the ‘two-year rule’ (see e.g. Buhaug & Gates, 2002): if a conflict falls below the casualty threshold for at least two consecutive calendar years, the next observation is coded as a separate onset. I merge sub-conflicts that only differ in type (internal vs. internationalized internal conflict), and censor consecutive years of conflict.

## _4.2 Core Variables_

The estimates for socioeconomic inequalities within and between regions are based on aggregated data from 123 Demographic and Health Surveys (DHS) conducted in 61 countries during the period 1986–2003. DHS is an ongoing research project which provides data on the population, health, and nutrition of women and children in developing countries, funded primarily by USAID and administered by Macro International Inc. In a DHS survey, a sample of households is selected throughout the entire country and then interviewed using a household questionnaire to collect housing characteristics. Women between the ages 15 and 49 are interviewed by means of a women’s questionnaire to collect information mainly on background characteristics, children and women’s health and other issues, such as household assets and 11
<!-- Start of picture text --> Common index−wide spline 52259<br>20<br>10<br>0<br>−10<br>0 100 200 300<br>Days since start of year<br>Temperature<br><!-- End of picture text --> Figure 1: An example of a common index-wide spline estimate This figure presents a common indexwide spline estimate for grid index 52259 (longitude: -1.5, latitude: 63.5) over the period from January 1, 2000, to December 31, 2019. The blue points represent the original daily temperature data, and the red line shows the spline fit, which is estimated across all 365 days using 20 years of observations. The x-axis shows the days since the start of the year, while the y-axis represents the predicted temperature.

1. Country, Grid, Year Level Aggregation: At this level, the unit of observation is a unique combination of country, grid, and year. For example, one such unit could be Spain, a specific grid index, and the year 2015. It allows for an examination of how weather patterns in a specific country, grid and year affect the firms located there. It is important to note that some grids are shared by multiple countries. In our dataset, roughly 5% of the total grids span across multiple countries. Despite this overlap, we retain the country-level grouping because the behavior of firms can vary significantly based on the country, even if they experience similar weather patterns.

2. Industry, Grid, Year Level Aggregation: The second level of aggregation employs the industry classification, using the NACE industry code (Statistical Classification of Economic Activities in the European Community). We use the highest level of aggregation (level 1), which includes 21 sectors. Unlike the country-grid level aggregation, where grids are nested within the country, the industry-grid level aggregation leads to a unique combination of industries and grids, resulting in a higher number of observations (316,324). For example, in the geographical grid ‘16191,’ we observe multiple different industries each year, such as agriculture, manufacturing, etc. This level of aggregation allows us to examine industry-level heterogeneity, especially investment irreversibility (Baldwin 1982), and its influence on the relationship between temperature uncertainty and firm size growth.

10
_Ingo Borchert and Mattia Di Ubaldo_

# **2 Related literature**

Services are an increasingly important part of a country’s production and trade, as reflected by the growing attention to services in domestic regulation, as well as multilateral and bilateral policy instruments.

From a general equilibrium perspective, entering a services PTA is likely to generate welfare gains akin to those arising from entering PTAs liberalizing trade in goods, as the PTA is expected to lower services input prices, final goods prices, and allow more consumption than would otherwise be possible. Further, as barriers to services trade typically consists of regulatory measures which act behind a country’s border, their removal does not come at the expense of a loss in tariff revenues, making services PTAs even more desirable (Egger and Shingal, 2020).

The economic literature on how PTAs affect services trade is a great deal less abundant than work on goods trade. Partly the reason is data availability: it is only with the publication of the OECD database on bilateral services trade in the early 2000s that a literature attempting to estimate determinants of bilateral services trade rapidly emerged, finding generally a positive and significant impact of PTAs (Ceglowski, 2006; Kimura and Lee, 2006; Walsh, 2008; Marchetti, 2011; Shingal, 2010; Egger et al., 2012).<sup>3</sup> Another reason why analyses on PTAs and services are scant, is that most of the PTAs explicitly featuring services chapters have been negotiated in the last two decades. For this reason, early work investigated the impact of goods PTAs, rather than services PTAs.<sup>4</sup> More recently, work focusing explicitly on the impact of services PTAs confirms

> 3Authors also investigated whether the standard gravity equation can explain bilateral services trade patterns, some confirming that proximity (geographical and linguistical) is a key determinant of services trade (Ceglowski, 2006; Kimura and Lee, 2006), other concluding that the impact of distance is negligible (Walsh, 2008; Kandilov and Grennes, 2012).

> 4Authors have also detected complementarities between goods and services trade i.e. an increase in goods trade being associated with an increase in services trade (Ceglowski, 2006; Kimura and Lee, 2006), or a larger overlap of goods-trade network leading to a larger volume of services trade (Egger et al., 2017).

9
patent database, Pat _il_ equals zero.<sup>15</sup> This dummy variable is interacted with the post reform dummy variable.

Changes in the value of licensing payments could reflect changes in the volume of technology transferred or merely changes in the price charged for that technology. Analyzing changes in the R&D expenditures of affiliates is helpful in distinguishing between these two possibilities. There is a considerable body of work that details the relationship between affiliate and parent-firm R&D.<sup>16</sup> It is clear that U.S.-based multinationals undertake basic and applied research abroad, as well as product development. However, to the extent that it is done outside the United States, research tends to be concentrated in other developed countries, such as Germany, where the local scientific and engineering community is highly accomplished and the infrastructure for frontier research is well-developed. R&D conducted by affiliates in developing countries, which account for most of the countries in our sample, tends to be focused on the modification of parent firm technology for local markets.<sup>17</sup> The literature review presented in Kuemmerle (1999) makes the point that a number of studies suggest that the co-location of R&D with foreign manufacturing facilitates the “transfer of knowledge and prototypes from the firm’s home location to actual manufacturing.”<sup>18</sup> Viewed in this light, affiliate R&D and technology transfers from the parent can be considered complements. Given this complementary relationship, IPR reform should also prompt an increase in R&D spending.

> 15 Alternative ways of specifying Pat _il_ yield similar results.  A four year window is used in these calculations because the sample begins in 1982 and the first reform occurs in 1986.

> 16   For an early study on this topic, see Mansfield, Teece, and Romeo (1979).  Kuemmerle (1999) provides an excellent survey of the literature as of 1999.

> 17   Mansfield, Teece, and Romeo (1979) were perhaps the first scholars to make this observation.

> 18   Studies by Bartlett and Ghoshal (1990), Hakanson (1990), Vernon (1966), Fors (1997), Clark and Fujimoto (1991), Hayes and Wheelwright (1988), Nonaka and Takeuchi (1995), and von Hippel (1988) generally support this view.

9
- Amuedo-Dorantes, Catalina and Cynthia Bansak (2011) “The Impact of Amnesty on Labor Market Outcomes: A Panel Study Using the Legalized Population Survey,” _Industrial Relations: A Journal of Economy and Society_ , 50 (3), 443–471.

- Amuedo-Dorantes, Catalina, Cristina Borra, and Noelia Rivera-Garrido (2022) “Fertility .

- implications of family-based regularizations,” _Journal of Economic Geography_

- Amuedo-Dorantes, Catalina and Sara De La Rica (2007) “Labour Market Assimilation of Recent Immigrants in Spain,” _British Journal of Industrial Relations_ , 45 (2), 257–284.

- Avitabile, Ciro, Irma Clots-Figueras, and Paolo Masella (2014) “Citizenship, Fertility, and Parental Investments,” _American Economic Journal: Applied Economics_ , 6 (4), 35–65.

- Bahar, Dany, Bo Cowgill, and Jorge Guzman (2022) “The Economic Effects of Immigration Pardons: Evidence from Venezuelan Entrepreneurs,”Technical report, National Bureau of Economic Research.

- Bahar, Dany, Ana Mar ́ıa Ib ́a ̃nez, and Sandra V Rozo (2021) “Give me your tired and your poor: Impact of a large-scale amnesty program for undocumented refugees,” _Journal of Development Economics_ , 151, 102652.

- Bailey, Martha J. (2012) “Reexamining the Impact of Family Planning Programs on US Fertility: Evidence from the War on Poverty and the Early Years of Title X,” _American Economic Journal: Applied Economics_ , 4 (2), 62–97.

- Baker, Scott R. (2015) “Effects of Immigrant Legalization on Crime,” _American Economic Review_ , 105 (5), 210–13.

- Becker, Gary (1960) “An Economic Analysis of Fertility,” in _Demographic and Economic Change in Developed Countries_ , 209–240: National Bureau of Economic Research, Inc.

- Becker, Gary and H Gregg Lewis (1973) “On the Interaction between the Quantity and Quality of Children,” _Journal of Political Economy_ , 81 (2), S279–88.

17
# **5.1 Estimation of** _σE_ **,** _σY_ **and** _ρ_

Let us first recall that _E_ and _Y_ are uncertain because there is no single model that would accurately predict their value based on observables. The existing models that project future output and future emissions are themselves based on parameters that are unobservable and uncertain, such as the rate of autonomous technical change. In addition, there are competing models that project future output and future emissions.

To estimate _σE_ , _σY_ and _ρ_ , three main techniques are available. First, one can take sets of projections generated by one model (e.g. the scenarios of the US Energy Information Administration), and use the difference between the high and the low scenario as a proxy for uncertainty. Data points in this method are often too few to allow for the estimation of _ρ_ . Second, one can compute the variance of projections originating from different models, as listed for example in the IPCC Special Report on Emissions Scenarios (Naki ́cenovi ́c and Swart, 2000). The internal consistency of the scenarios is lost because different models are involved, but larger data sets allow for the estimation of _ρ_ . Finally, one can compute historical forecast errors, and take them as a proxy for the accuracy of today’s forecasts for emissions and GDP during a post-Kyoto period.

Each approach has limitations. Using scenarios may lead to an overestimation of the actual variance because scenarios are often built to explore a wide range of plausible futures, and thus are not intended to be interpreted probabilistically. Historical forecast errors, on the other hand, suffer from data scarcity, especially when considering long-range forecasts. In this method therefore, linear correlation coefficients can often be computed only from forecasts for the same time horizon, but for different countries (assuming forecasts as independent). This approach provides a cross-country average value for _ρ_ , but individual countries may have higher or lower coefficients depending on their particular relationships between emissions and GDP.

In this paper, we pursue all three approaches to make our estimates of the uncertainties as robust as possible. The upper section of Table 3 shows our estimates for the normalized standard deviation (also called coefficient of variation) of GDP and BAU CO2 emissions in 2015, as inferred from the US Energy Information Administration’s (EIA, 2005) low, mid and 20
dominated by few countries on both export and import flows, trade flows are quite subjective to shocks on either the supply or demand side. More specifically, six import markets (Japan, Russia, European Union, Hong Kong (China), Mexico and the states in the Arabian Peninsula) account for more than 60 percent of all imports. Five exporters (Brazil, European Union, USA, China and Thailand) account for about 90 percent of total exports.

Table 2 reports the evolution of imports in the major import markets from 1995 to 2006. With the exception of Japan and Hong Kong (China), most countries have seen a rapid increase of imports of unprepared poultry during the last 10 years. Imports have grown most substantially in Russia, Mexico, in the states in the Arabian Peninsula<sup>14</sup> , and in the early year in the European Union. Increases in imports are largely due to a greater consumption of poultry meat in developing countries, where increases in living standards drive upward the demands for meats.<sup>15</sup> In general, the trade data shows very little evidence of the effect of avian influenza on overall imports. Slight decreases in imports are observed only for 2004 and only for countries, such as Japan and Hong Kong (China), which relied heavily on imports from countries affected by HPAI. By 2006, with the exception of Japan, imports of unprepared poultry products exceeded the levels of 2004. At the aggregate level, total trade in unprepared poultry meats was stable between 2003 and 2004 and increased thereafter. This suggests that, on the demand side, the impact of avian flu has been modest and temporary.

> 14 Bahrain, Kuwait, Oman, Qatar, Saudi Arabia. United Arab Emirates and Yemen.

> 15 Consumption demand of poultry meat grew in most countries even in 2004 and 2005 (FAO 2005).

10
31 identified for any given country, we find that firms tend more to identify groups of constraints as posing serious problems. At least in Africa, there may be systematic relationships between countries’ level of development or income and the types of constraints most often seen as binding.

At the lowest level, the most serious constraints as seen by the firms tend to be those closely related to the ability to plan work and produce at all—macroeconomic stability, electric power, and finance. Concerns in these areas decline as income rises, to be replaced by a second set of constraints. These new constraints relate to the quality of governance and the capability of the state to provide important services. Concerns include corruption, the level of taxation and quality of tax administration, and security. These in turn ease as countries move to middle-income status and governments develop greater capacity and regulatory competence. But this opens the way to a third set of obstacles—business will not be the only political constituency. Organized labor may be powerful in some countries. In others, business might feel constrained by environmental regulations. Moreover, in these countries the state has a stronger capacity to implement regulation, so that policies in these areas may have more impact on business than they would in many low-income countries.

Individually binding constraints are therefore more likely at the two ends of the income spectrum. At the low end, easing some specific infrastructural constraint—such as electric power in East Africa—may have a huge payoff in business opportunities. At the higher end, surveys may distinguish a few policy areas of particular concern to business. Things are more difficult in the middle of the spectrum, where weak governance and low state capacity cause firms to experience a wide range of business climate impediments.

But even in cases where particular constraints emerge as serious, there is no magic bullet. Efforts to improve the business climate need to be implemented across a broad front, with physical investments in infrastructure complemented by regulatory reforms and also by the careful monitoring of service delivery to increase pressure for actual changes on the ground. Firms’ views can help to prioritize reforms across broad areas. Finer
the largest human migrations in history. Beginning in the 1950s, China implemented a registration system, referred to as _hukou_ , which shares many features with the propiska system in Uzbekistan. In 2000, before reform, the ratio of the income per worker in China between provinces in the 90th and 10th percentile was 3.2, compared to around 1.5 in the United States. Tombe, and Zhu (2019) find that reforms to the _huhou_ system played key role in quickly reducing this ratio by generating rapid income growth between 2000 and 2005. This growth contributed to the unprecedented poverty reduction achieved during this period. The findings suggest that over the relevant window, reforms allowing greater domestic mobility accounted for more economic growth than international trade liberalization. The authors estimate that registration changes alone increased aggregate productivity and welfare by 12.1 percent and 7.3 percent, respectively. Overall, reductions in domestic migration and trade costs likely account for close to half of China’s aggregate labor productivity growth from 2000 to 2005 (Tombe, and Zhu, 2019).

Another relevant experience in managed migration comes from Vietnam. There, a system known as _ho khau_ and patterned off the _hokou_ example in China in the 1950s, was used both to register people and control migration flows. During the period of central planning until 1986, access to food rations, land, housing, education, health, and employment was managed through the _ho khau_ system. In rural areas, farmers were registered to cooperatives through _ho khau_ that linked workers’ membership with access to food and rural employment. (Liu and Dang, 2019; Demombynes and Vu, 2016). Market reforms in the 1980s led to significant changes. Rationing and cooperative-membership were undone leading to an increased flow of rural-to-urban migration beginning in the early 1990s. A new land law in 1993 also contributed to the rate of migration by granting individuals and households the right to transfer, exchange, mortgage, lease, and inherit land.

The _ho khau_ system was subsequently reformed twice to further relax restrictions on domestic migration (Liu and Dang, 2019; Demombynes and Vu, 2016). A series of empirical studies on the effects of these reforms find declining poverty rates and positive returns to migrants, their families, and host communities in urban areas (Coxhead, Cuong, and Vu, 2015; Liu and Dang, 2019; Demombynes and Vu, 2016). Also consistent with HT models and the new economic geography literature, the shares of the industrial and service sectors in Vietnam’s GDP rose quickly following the introduction of market reforms in 1986. According to the World Bank’s WDI database, the share of employment in agriculture stood at more than 66 percent in 1994, falling to less than 40 percent by 2018. At the same time, output per agricultural worker (in constant 2010 USD) rose from $494 to 21
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Benin > Road investments > Macro results**

||||Reduction in<br>|Roads (pe|rcent varia<br>|tion)<br>||
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
|Ym|Aggregate household income|1,250,000.00|0.60|0.73|0.73|0.74|0.74|
|EV|Equivalent variation||0.61|0.68|0.69|0.75|–1.53|
|s|Wage|1.00|–3.22|4.27|4.60|4.88|5.33|
|yg|Government income|214,000.00|1.77|13.53|13.53|13.53|13.53|
|g|Total government expenditure|132,000.00|–9.08|10.05|10.05|10.05|10.05|
|ye|Firm income|135,000.00|1.20|–0.03|–0.09|–0.12|–0.21|
|It|Total private investment|463,000.00|0.79|0.66|0.47|0.17|–1.12|
|GDP|GDP|1,320,000.00|0.67|0.66|0.67|0.66|0.67|
|e|Nominal exchange rate|1.00|1.10|1.79|–0.69|–4.85|0.79|

# **Benin > Road investments > Sectoral results**

||||Reduction in||Roads|||
|---|---|---|---|---|---|---|---|
||||other ublic||Imort|Forein|Income|
|Variable|Sector|Base|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
||Crop agriculture|461,000.00|1.03|0.82|0.80|0.79|0.74|
|Va|Export agriculture|95,000.00|1.50|1.24|1.19|1.21|1.16|
|Value added or|Industries|190,000.00|1.75|0.56|0.26|0.87|0.47|
|output|Private services|477,000.00|1.24|–0.61|–0.44|–0.83|–0.46|
||Public services|93400.00|–7.05|6.05|5.96|6.64|5.94|
||Crop agriculture|1.01|–0.02|0.00|–0.03|–0.61|–1.97|
||Export agriculture|1.00|0.03|–1.65|–2.56|–2.17|–3.34|
|pq<br>Market prices|Industries|1.10|0.89|2.30|3.06|–1.68|0.66|
||Private services|1.06|–0.37|2.87|0.85|–0.01|–0.09|
||Public services|1.00|–2.19|3.77|3.86|3.20|3.88|
||Crop agriculture|1.00|0.98|0.73|0.23|0.25|–1.33|
|r<br>|Export agriculture|1.00|1.43|–1.11|–2.68|–1.39|–2.81|
|Rental rate of<br>capital|Industries|1.00|3.26|3.77|2.29|6.29|4.28|
||Private services|1.00|1.34|–2.16|–0.84|–2.88|–0.24|

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