rafmacalaba/datause-encoder-data huggingface dataset:
# **1.  Introduction**

In 1960, per capita incomes in Burundi, Haiti, and Nicaragua were $347, $1512, and $2491, respectively. Despite the seven-fold difference in incomes between Nicaragua and Burundi, all three countries were unquestionably poor by developed-country standards. Nicaragua’s per capita income was just 16 percent of the level enjoyed by citizens of the United States at the time, while Burundi’s income was a paltry 2 percent of US levels. Fifty years later in 2010, per capita incomes in these three countries were basically unchanged, at $396, $1411, and $2289, respectively, reflecting negligible growth in per capita income over this period. Likewise, if one looks within developing countries, poverty appears to be persistent for some households over many years, and even generations.

There are (at least) three competing views of why poverty can be so persistent. The first, which Maitreesh Ghatak et al. (2001) term the “American Dream effect”, is that anybody can make it through hard work and thrift. This implies that households and countries should be able to gradually work their way out of poverty, and failure to do so represents lack of effort. The second view is that poverty is the result of poor fundamentals – be they poor institutions and endowments at the country level, or low skills and abilities at the individual level. As a result, policy efforts need to change the underlying fundamentals that determine the steady state, which may be difficult and slow to do. The third view is that poverty begets poverty, so that current poverty is itself a direct cause of poverty in the future. This notion is called a poverty trap, understood as self-reinforcing mechanisms whereby poor individuals or countries remain poor<sup>1</sup> , whereas other countries or individuals with the same fundamentals but also the good fortune of starting out rich will stay that way.

The idea of a poverty trap is the most compelling of the three explanations in terms of motivating policy, because it suggests that people and countries may not only be unable to climb out of poverty on their own, but also that much poverty is needless in the sense that a different equilibrium is possible, and that one-time policy efforts may have lasting effects. It lies behind calls for a Marshall Plan for Africa which argue for a massive increase in aid to act as “a “big push” designed to instigate a virtuous circle of higher rates of savings, investment and economic growth” (UNCTAD, 2006, p.3); for industrial policies designed to overcome coordination failures; for the Millennium Villages project which plans to spend

> 1 This is the definition of poverty traps offered by Costas Azariadis and John Stachurski (2005) in their extensive survey of the literature on poverty traps. Others (e.g. Chris Barrett and Michael Carter, 2013, Felix Naschold, 2013) also refer to a single poor dynamic equilibria as a structural poverty trap, but we do not use this definition in our paper.

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26 relationship is even less clear. Increases in the rate of inflation are not successful in securing more inflation tax revenues once inflation exceeds that level.

The overall evidence is consistent with the results of our estimation. The solid line in figure 4 corresponds to the fitted relation between inflation and the inflation tax using the estimates of the demand for money from the "tablital period. Steady state seigniorage is maximized at around 7.5 percent of GDP when inflation is 21.1 percent per month. The diagram suggest that the maximizing revenue could be close to 8.5 percent of GDP. a level that is still within the margin of error of our regressions.28 The evidence also indicates that in those instances in which this level was exceeded inflation displayed a tendency to accelerate. The revenue from inflation seems to fall unambiguously once inflation exceeds 22 percent.

An important finding of our study is that the inflation tax has remained close to, and even exceeded the maximum sustainable levels during most of the first half of the eighties. Indeed, this appears to have been the single most important source of revenue to the government during this period. This implies that any serious stabilization effort should find an alternative source of revenue to replace the inflation tax.

28 Fernandez and Mantel (1988) estimates indicate that the government can get up to 8.5 percent of GDP from the inflation tax.
# the impact of RTAs.<sup>8</sup>

We are also interested in quantifying the impact of CAFTA-DR on bilateral crossborder production linkages. For this purpose, we need data that distinguishes between final and intermediate goods on the one hand, and between domestic and foreign-valueadded parts of trade flows. Eora itself provides bilateral trade data in final and intermediate goods. Our value-added trade data comes from Borin et al. (2021), which is based on Eora input-output tables, and can be accessed through the World Integrated Trade System (WITS). This data provides different variables that disaggregate total bilateral trade into traditional trade and GVC-related trade. Traditional trade corresponds to the domestic value added embodied either in final or intermediate goods or services that are directly consumed by the importing economy (this value only crosses one border). Pure forward GVC-related trade is the value-added in goods and services entirely generated within the domestic chains (without any border crossing) exported to partners, which in turn reexport it to other markets. In this case, the exporting sector is engaged in GVC activities at the origin of the chain. Pure backward GVC-related trade is the value of goods and services produced with imported inputs and exported to the final destination market. In this case, the exporting sector engages in GVC activities at the end of the chain. Finally, two-sided or mixed GVC-related trade is the value of goods and services produced with imported inputs, exported by the sector to partners, which in turn re-export it to other markets. The exporting sector is located in a position of the chain that is more central. Our main variable of interest is the domestic value added to exports.<sup>9</sup> It determines the contribution of a country’s own economy to the production of exports, rather than simply counting the total gross export value. It is obtained by summing the pure forward GVC trade and the traditional trade flows.<sup>10</sup> Data on trade agreements come from the World Bank’s Deep Trade Agreements Database (Mattoo et al., 2020). It maps the coverage of 52 selected policy areas in 279 agreements notified at WTO and signed after 1958. It also includes information about the legal enforceability of each policy area.

> 8Other international input-output tables are also available but have either fewer countries or fewer years. The global Eora MRIO tables with a harmonized 26-sector classification across countries (also known as Eora26) can be accessed from http://worldmrio.com/simplified/. For further description of the data, its sources, and construction, see Lenzen et al. (2012, 2013).

> 9While the domestic value-added of trade is our main variable of interest, we study the impact of CAFTADR on all the GVC variables available from Borin et al. (2021). 10See the appendix for more details.

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and ultimately converged with those for regression. Therefore, the number of poverty classes selected entails a tradeoff between the accuracy and the granularity of the prediction.

**Result #2: In Guatemala CDRs predict urban and total poverty more accurately than rural poverty**

In both the regression and classification models, rural poverty rates were consistently more difficult to predict than either total or urban poverty rates. R<sup>2</sup> values fell to around 0.25 for rural poverty, implying that CDR data could explain just 25 percent of the variation in rural poverty rates. The accuracy of the classification models dropped to between 0.3 and 0.65 depending on the specification. Two hypotheses could explain this phenomenon. First, cellular penetration rates in urban areas tend to be higher, and thus CDR-based analysis provides more robust modeling signals in urban areas, where they represent the behavior of a larger share of the population. In rural areas, fewer phones and fewer calls weaken the signal that can be extracted from CDR data, and their predictive power decreases. Second, urban areas tend to have more cellular antennae per square kilometer, which results in smaller polygons. The much larger polygons in rural areas may tend to reduce the granularity of the data by aggregating behaviors, weakening the predictive power of the algorithm. Testing these hypotheses will require further research.

**Result #3: Further analysis will be necessary to determine the extent to which CDR-based models based on past poverty data can be used to predict future poverty dynamics**

CDR-based models could potentially be used for temporal prediction or time extrapolation (see Boxes 2 and 3), but no research project has yet been able to prove that models trained on past poverty values can be used to predict future poverty levels. Such an analysis would require an extremely large and detailed dataset. For example, predicting future poverty values in Guatemala would require CDR data from 2006 and 2011, as well as corresponding poverty survey data for the same time periods. However, this analysis was based on CDR data from 2013 and rural poverty levels for 2006 and 2011, and no national or urban poverty data were provided. As a result, the predictive model was trained with the 2013 CDR data and the 2006 rural poverty data, and the 2013 CDR data were used to predict rural poverty levels in 2011. While this is the best methodological approach given the data constraints, the preliminary results showed low R<sup>2</sup> values at around 0.29 for regression and maximum F1 scores of 0.6.

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high transport costs. As a result, a significant part of the economy was effectively an ‘autarkic village economy’ with little or no long-distance (international) trade exchanges. We develop a spatial equilibrium model that allows for the co-existence of an integrated segment characterized by sectoral (agriculture vs. manufacturing) specialization and a subsistence-based autarkic hinterland within the same country. The model includes two trade routes in a country: a route affected by the shock directly, called the “Abidjan route”, and a second route for trade through alternative ports, called the “non-Abidjan route”.

The focus of our analysis is on spatial heterogeneity and persistence in the effects of the temporary trade cost shock on different routes. We consider spatial heterogeneity at two margins of adjustment along a route: the spatial margin of market integration (henceforth SMI) and the spatial margin of specialization (henceforth SMS). The SMI separates the subsistence-based autarkic region from the integrated segment of the economy characterized by specialization according to comparative advantage driven by inter-regional and international trade. The SMS, on the other hand, separates the zone of manufacturing specialization from that of agricultural specialization within the integrated segment of the economy. To understand whether the reallocation triggered by a temporary trade shock can lead to persistent effects, we analyze irreversible investments in built-up density (housing and manufacturing structures) by taking advantage of the recently available satellite data on global human settlement. In addition, we provide evidence on the role played by reallocation of skilled labor (agglomeration externalities) and public provision of complementary inputs such as electricity (using night-lights density as an indicator).

The theory shows that a trade shock to one route affects resource allocation across routes and also along a given route at different locations differentiated by the initial (prewar) market access. An important insight is that a temporary trade shock shifts both spatial margins (SMS and SMI) along a given route leading to ambiguous overall effects on employment structure; but it also leads to well-defined heterogenous effects with respect to the initial (pre-war) extent of market access of a location. In particular, the areas with the poorest market access in each route are not affected by the trade shock which plays an important role in our research design.

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require a critical stock of equipped human capital that can trigger technological progress and innovation.

When juxtaposing African countries to the Asian Tigers and large emerging countries, one can see that the pace of change in SSA, although headed in the right direction, has been far slower. South Africa experienced a fertility decline as large as India or Malaysia and huge progress in education, yet improvement in life expectancy is little, the labor force participation rate remains low, and investment has been stagnant. With respect to demographic structure, Ethiopia, Ghana, and Nigeria today look similar to the conditions of economic takeoff of other countries: TFR between four and five (similar to the Tigers), life expectancy similar to that of Korea in 1966 or India in 1990, and substantial progress in education and growth in investment already seem to be taking place in Africa.

# **7. Concluding Remarks**

Can Africa’s economy take off as the East Asian industrial countries did in the 1960s and the large emerging countries like China and India did in the early 1990s? This paper provides some answers by examining the sources of growth, particularly focusing on the turnaround of economic performance in the mid-1990s in SSA. An optimistic outlook emerges in that since the mid-1990s SSA has experienced unprecedented growth performance, in large part due to favorable demographic transition, capital accumulation particularly through FDI, and TFP progress.

Moreover, a few human capital indicators suggest that the conditions in SSA may be ripe for rapid growth, as they are similar to the initial conditions of the countries that experienced growth earlier.

Our examination of sources of growth, of course, may have overlooked potentially important factors that could reverse the economic outlook. In particular, the lack of data prior to 1995 presents limitations. Financial sector development, for instance, accelerated in the late 2000s. This may have affected capital accumulation and investment as private sector obtained better access to credit.<sup>44</sup> However,

> 44 See Iossifov and Khamis (2009).

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# **2 Context, data and methodology**

## **2.1 Data source**

Our analysis draws from data collected between April and June 2021, from 4,459 individuals spread over 40 communities in three Tanzanian cities: Dodoma, Dar es Salaam, and Iringa. The dataset constitutes the baseline of a randomized control trial of an SES training intervention, conducted in collaboration with the international development organization BRAC.<sup>3</sup> The analysis in this paper focuses specifically on the baseline information collected on individual respondents’ demographics, own and parental education history, SES, cognitive ability, social desirability, and gender attitudes.

## **2.2 Measuring socio-emotional skills**

A measurement team including some of the authors of this paper developed a framework — of 14 SES (Appendix Figure A1) informed by existing frameworks, consultations with psychologists, focus group discussions, literature on which SES matter for labor outcomes, and theory on gender gaps in levels and returns to SES (Delavallade et al., 2020). The list of skills includes four “awareness” SES (emotional awareness, self-awareness, listening, and empathy) and 10 “management” SES (emotional regulation, self-control, perseverance, personal initiative, problem-solving and decision-making, expressiveness, interpersonal relatedness, influence, negotiation, and collaboration). An alternative way to categorize the skills is that “intra-personal” skills are composed of self-awareness and self-management while “inter-personal” skills are composed of social awareness and relationship management. Throughout our analysis, we present results for these two alternative classifications of SES. Examples of the measures used for each skill can be found in Appendix A6

> 3The RCT study, forthcoming, tests the impact of trainings focused on different sets of SES. It is designed to examine which SES are most teachable to vulnerable male and female youth, and which matter most for economic empowerment. The results are expected to inform updates to one of BRAC’s flagship programs, the Empowerment and Livelihood program (ELA), which provides training and mentorship via safe spaces.

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**aisunct** penod not aiiectea by the cnsis, we separate the pre-cnsis penoa from me cnsis penoa Dy three years. We define the pre-crisis period to be (t-8, t-4), if t-8 is available, otherwise as many years towards t-8 as possible, where t is the first crisis year reported in Caprio and Klingebiel (2002). This restricts the pre-crisis period to a maximum of 5 years. We only allow for one crisis period in a country, which is the first crisis mentioned in Caprio and Klingebiel (2002). to avoid identification prbe. **1?** case **_o~f_ recp;r,-,** cr.ses.oo "J*^1%Jarnao ;i **ea0%** t%f* . **AA*r %I* flj0.a**

We started with the Caprio and Kiingebiei (2002) data set of systemic banking crisis countries. This data set includes 113 banlcing crises from 93 countries since the 1970s. Due to data constraints we need to drop a large number of countries. First, we do not have data on sectoral value added for many crisis countries. Second, we exclude countries for which we do not have data for both the pre-crisis and the crisis periods. This excludes, for example, Poland for which we do not have **LIaVY%** data **IWL" IVAl** _fo;_ +'ke U1%1 **_FL"-"'_** p-.4e=cii **_0 1 0_** period. **_j)I.LLlJ%A._** We **_TV_ %,** also "low'. **'.&Aj)** _A-o_ o-le for whic we **ZVJUU~II YWIUY%.., WV,** dop **UVF llIVJ** + hae-e-vau **114z** ' **V** --- **,i ,LLt.JL** +-- **V4LUv,** added data for at least five sectors. Tlhis excludes Argentina, for which we have only data available for four sectors during the pre-crisis period. The final data set includes 19 crisis countries, including both developing -and developed countries. Table 2 presents a list ofthese countries. For each country, the table also shows the average real growth in value added and the number of sectors during both the Dre-crisis and crisis period. We do not investigate the post-crisis periods, because we do not have suicieIntIL **UaLa** on pos-crisis yea.-s **foI** illaly **fU** the '-Lu s Hill **Ukvu baLuplv.** The number of sectors varies widely across countries from 10 sectors in Hungary to 28 sectors in Chile, Finland, Israel or Sweden. To ensure consistency in a country across periods, we examine the same sectors in both the pre-crisis and crisis periods. This excludes a number of sectors for several countries for which we could obtain data in only one sub-period. We note that this setup 11
practical matter, it turns out that we remove extreme rainfall observations from the sample (see section 4.1), hence the concern is mitigated.

The idea of (13) is to investigate how output growth affects the level of capital inflows? A positive γ implies that higher GDP growth will attract a higher level of capital inflows, something one would predict for FDI and sovereign borrowing, because the return for capital is expected to be higher in good times, and because countries have a pro-cyclical access to capital markets (Kaminsky, Reinhart and Vegh, 2004). However, it is more difficult to predict the impact of GDP growth on aid inflows. On the one hand, there is less need for aid in good times. On the other hand, countries can be rewarded with more aid when growth is higher. For instance, Pallage and Robe (2001) find that foreign aid is procyclical. This finding suggests that aid rewards good economic performance. Nevertheless, their paper only examines the correlation between aid and growth, and is silent on the causality between them, a point our paper is trying to address.

# **IV.4 Estimate of the Impact of Capital Inflows on Real per Capita GDP Growth**

After estimating the impact of real per capita GDP growth on capital flows, we will estimate the impact of capital inflows on GDP growth. Before we do that, consider an OLS regression between real capital inflows and real per capita GDP growth where ai is a set of country fixed effects , bt is a set of time fixed effects , and timetrendi is a set of 1 2 3 country-specific time trend, and xi,t, xi,t, xi,t are the log of real aid, FDI and sovereign borrowing capital inflows per capita. The idea is to investigate how each type of capital inflows affects output growth. Theory predicts that foreign aid would help growth because it alleviates capital constraints. On the other hand, foreign aid might have no effect in the presence of bad policies (Burnside and Dollar, 2000). The same argument can apply to FDI and sovereign borrowing. If foreign capital is not effectively used, the negative effects associated with foreign capital might outweigh the positive effects. Note that we use the 14
# **5.3 Product-level estimation**

To test that our conclusions based on aggregated trade flows are not spurious, we run product-level estimations. These regressions analyze whether the aggregate changes observed in North-South and South-South trade when standards are harmonized are driven by products affected by NTM harmonization.

Product information is usually not available in the legal texts of EIAs. Therefore, to perform this investigation, we rely on the NTM dataset used by Disdier et al. (2008), which is relevant for our sample period. The dataset provides all NTMs notified by WTO members at the WTO up to 2004 at the HS6-digit level. Our estimation strategy is as follows:

- Our sample is restricted to the sub-sample of Southern exporter countries with an EIA with a Northern partner.

- _For North-South trade_ , a dummy is defined and equal to one for HS6 products affected by at least one NTM in the Northern country. This dummy is interacted with our two main variables of interest: “N-S EIA with standards harmonization & promotion of regional standards” and “N-S EIA with standards harmonization & promotion of regional standards”;

- _For South-South trade_ , a dummy is defined and equal to one for HS6 products affected by at least one NTM in the North. It is interacted with our main variable of interest: “N-S EIA & standards harmonization with the North for the exporter”.

Estimated coefficients on the interaction terms are reported in Table 4.<sup>10</sup> Column (1) presents the results for South-South trade, and the coefficients for North-South trade are reported in column (2). The results confirm and strengthen our previous findings. For NorthSouth trade, our estimated coefficient is negative and significant when harmonization takes

> 10 To save space, only estimated coefficients on the interaction terms are reported. Detailed results are available from the authors.

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