rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9. Comparative Statistics, over time** _2010-2014_

|||GRO|WTH|CAPI|TAL|T|FP|
|---|---|---|---|---|---|---|---|
|||**10-year**|**20-year**|**10-year**|**20-year**|**10-year**|**20-year**|
|Trade openness|SSA|0.29%|0.41%|0.11%|0.16%|0.21%|0.31%|
||SSA oil|-0.20%|0.20%|-0.08%|0.08%|-0.15%|0.15%|
||SSA metal|0.33%|0.35%|0.13%|0.14%|0.24%|0.26%|
||SSA non-RR|0.22%|0.22%|0.09%|0.09%|0.16%|0.17%|
||DevelopingexcludingSSA|0.11%|0.36%|0.04%|0.14%|0.08%|0.26%|
|Product concentration|SSA|0.37%|0.63%|0.37%|0.64%|0.25%|0.43%|
||SSA oil|0.35%|0.58%|0.36%|0.59%|0.24%|0.39%|
||SSA metal|-0.09%|0.62%|-0.09%|0.63%|-0.06%|0.42%|
||SSA non-RR|0.74%|0.80%|0.75%|0.82%|0.50%|0.54%|
||DevelopingexcludingSSA|-0.02%|0.05%|-0.02%|0.05%|-0.02%|0.03%|
|Natural Resource|SSA|0.02%|0.27%|0.02%|0.21%|0.01%|0.09%|
||SSA oil|-0.01%|0.06%|0.00%|0.05%|0.00%|0.02%|
||SSA metal|-0.09%|0.29%|-0.07%|0.24%|-0.03%|0.10%|
||SSA non-RR|0.06%|0.17%|0.05%|0.14%|0.02%|0.06%|
||DevelopingexcludingSSA|-0.20%|0.02%|-0.16%|0.02%|-0.07%|0.01%|

# **5 Conclusions and Policy Implications**

This paper aims at understanding the relationship between international trade integration and growth by assessing the impact of different dimensions of trade integration —namely, trade openness, trade diversification, and export dependence on natural resources— with a special focus on the Sub-Saharan Africa region. Overall, we conclude with the following five takeaways: First, the Solow decomposition shows that growth of output per worker over the long-term has been overwhelmingly attributed to factor accumulation —especially physical capital accumulation and, to a lesser extent, human capital. The contribution of human capital has been fairly constant in Sub-Saharan Africa regardless of the country groups under analysis. In contrast, the contribution of TFP growth to economic performance over the longterm is detrimental —and, negligible, at best. This may point to a severe misallocation of resources across countries in the region. The relative importance of the source of growth has changed over time, with the contribution of TFP growth becoming positive during the period 1996-2014. However, this contribution might be mis-represented due to the omission of natural capital as an additional input —especially when calculating the sources of growth for resource rich countries in Sub-Saharan Africa. Our calculations show that accounting for natural resource wealth in the production function will over-estimate the contribution of TFP to economic growth.

Second, the extent of international trade integration has increased in the region. Trade volumes have expanded, there is some progress in terms of export product and market diversification, and the dependence on natural resources has slightly declined over time. However, there are two caveats: (a) there is still ample room for improvement in the way that the region is inserted in world trade —particularly if one gauges the distance to the rest of the developing world _page 30_
to ensure that the project is on-track, as well as of the outcome indicators linked to disbursements.

Finally, the use of governance and service delivery indicators for project monitoring can have the additional benefit of building the measurement capacity of countries. The inclusion of these indicators into results frameworks means that countries will have to collect this data over time. This provides a window of opportunity for incorporating governance indicators into administrative data and on-going surveys that can exist beyond the life of the project.

# **A Framework for Measuring Governance and Quality of Service Delivery**

While there is great interest in defining and measuring the role that governance and quality of service delivery play in human development outcomes, there is limited consensus on how this complex relationship works in practice. Ultimately service delivery is only part of the story. Health status is influenced by outside factors, such as the quality of the water supply and infrastructure, while educational outcomes are influenced in part by family characteristics such as the level of parents’ education. Behaviors of service users (e.g., adherence to medical treatment or time spent on homework assignments) are also critical in determining final outcomes. Similarly, governance is only one factor influencing the quality of service delivery. For example, the quality of medical care or teaching is influenced by factors as varied as the availability of technologies and the overall functioning of the labor market; for the most part, we would consider these factors to be independent of governance arrangements, at least as defined in this paper.

9
is problematic. Another is that the demographic characteristics of the eBay users may be online-specific and not representative of the offline world. A further shortcoming is that international search costs may be very different from those within the US. Hence their sample may not be fully appropriate to study the “death of distance” in international trade.<sup>1</sup> Our dataset allows us to overcome these criticisms and compare the distance effect on eBay and offline trade considering the same set of countries and goods. It covers all eBay transactions, disaggregated into 40 product categories, between 62 countries (representative of 92% of total world trade) during 2004-2007. To create the best-possible comparison groups, we match eBay product categories to product descriptions from the 6-digit level HS classification to build a comparable basket of goods. We also drop from our eBay data all transactions that were concluded via auctions (60 percent of eBay traded value), as well as those sold by consumers, so that our eBay data reflect offline practices. Prima-facie evidence (Figure 1) indicates that the relationship between trade flows and distance is indeed more flat-sloped on eBay (left panel) than offline.

To identify as precisely as possible the effect of distance we use a gravity framework (Anderson and van Wincoop 2004), controlling for other standard gravity trade costs such as the absence of a common language, a common legal system, a border, a colonial history, or a free-trade agreement. We find the distance effect to be 65 percent smaller online than offline. This difference in distance coefficients is statistically significant at the 99 percent level, robust to using OLS or Poisson pseudo-maximum likelihood estimations as well as to various aggregations of goods online and offline, including using disaggregated data. We also run our gravity model by product category and show that distance matters less online for all products.

This supports the prediction of Chaney (2011), namely that in a world where search costs are greatly reduced, the role of distance in explaining trade flows is smaller. However, even though the importance of distance is 65 percent smaller on eBay, it still matters significantly. According to the literature, distance may capture three types of frictions, namely (i) shipping

> 1Horta ̧csu et al. (2009) do provide international evidence using MercadoLibre, another online market, though it only covers 12 Latin American countries.

3
informality exist in the literature (e.g., Ulyssea, 2018), the practicalities of data collection oblige a clear and workable definition of the population of interest. The surveys used in this analysis employ a definition of _legal_ informality (World Bank, 2020), i.e., informal businesses are those that lack sufficient registration or licensing requirements to operate. This notably excludes activities that produce goods or render services that are deemed illegal, such as narcotics. The specific details of what registration entails, e.g., which government agencies, were adjusted to cater to each country's regulatory framework.<sup>5</sup>

# **2.1 Data Collection**

The data was collected through face-to-face interviews of owners and managers following an application of the Adaptive Cluster Sampling (ACS) methodology (Thompson, 1990) to study informal businesses (for more details on the adaptation, see the companion paper Aga et al., 2022). Thus far, the study of the informal sector has primarily come from household surveys, notably Labor Force Surveys, where questions on employment and income from the informal sector are added. While this approach provides valuable information, these are not surveys of businesses, and at best, they are a second-best approach to study informal business activity. Other common approaches to collect information about informal businesses rely on building area-based frames, either using the household surveys, or extensive enumeration of economic activities, or a combination of the two. Depending on the details of implementation, these methods either do not necessarily result in representative samples of informal businesses or are prohibitively expensive to implement.

The ACS is a data collection methodology that provides representative estimates of the population of informal businesses, with the efficiency gain obtained through taking advantage of the feature that informal business activity is clustered in certain areas. This method is primarily applied to study populations whose individuals tend to cluster and are usually hard to reach (Turk and Borkowski 2005, Heckathorn 1997). For such populations, standard random sampling methods are likely to result in large variances of population estimates due to likely misses of many clusters (Seber and Thompson 1994). Businesses, including those in the informal sector, may cluster in certain areas for multiple reasons, such as input-output linkages, social networks, or

> 5 Appendix Table A1 lists specific definitions of informality used in each country during the respective Informal Sector Enterprise Survey implementation.

5
Table 2: Marginal Effective Corporate Tax Rates for a Profit-Making Pioneer Firm, 1983

||Pre-1989 Budg|et Reform||
|---|---|---|---|
|Profile|Debt Financing|Retained Earnings|Debt and Ret. Earn|
|Yr 1|-.0118|-.0135|-.0124|
|Yr 2|-.0158|-.0176|-.0165|
|Yr 3|-.0204|-.0219|-.0209|
|Yr 4|-.0255|-.0263|-.0258|
|-<br>S.S.|-.0436|-.0112|-.0223|

||Post-1989 Bud|get Reform||
|---|---|---|---|
|Profile|Debt Financing|Retained Earnings|Debt and Ret. Earn.|
|Yr 1|.0302|.0365|.0325|
|Yr2|.0301|.0353|.0320|
|Yr3|.0300|.0339|.0315|
|Yr 4|.0296|.0324|.0307|
|S.S.|-.0331|.0073|-.0173|

Source: Boadway, Chua, and Flatters (1989) 86
**Table 4: Average Annual Agricultural Output Growth Rates, 1990s and 2000s**

||Industrial|Developing|
|---|---|---|
|Period|Countries(%)|Countries(%)|
|1990-91/2000-01|0.7|1.9|
|2000-01/2005-06|-0.2|2.1|

Source: FAO web data on Agricultural Production Index.

# **_Agricultural Trade Shares: Direction of Aggregate Trade flows_**

A trade flow matrix for the years 1990/91, 2000/01, and 2006/07 shows the details of nominal agricultural trade flows among different groups of countries (table 5).<sup>8</sup> The European Union (EU 15) is the largest trader, with exports of $377 and imports of $420 billion. Developing countries as a block are the second largest trader, with exports of $420 and imports of $368 billion.

> 8 In reality, total merchandise imports and exports do not equal each other in world trade. First there are the differences where exports are valued at FOB, while imports are valued at CIF. In addition, the data here is derived from “mirror” accounts because not all countries report their trade accounts on time and fully. In table 5, the “real” numbers are the exports, and imports are adjusted to equal these export numbers. In a sense the import data is the residual. Despite these problems, we think the errors would be small and the data very close to reality.

7
findings and the extent to which our findings for formal manufacturing firms are likely to be relevant for other industries. Political connectedness among manufacturing firms is on average slightly lower, with an average score of 2.88, than political connectedness in the financial and services sectors where firms on average have scores of 3.37 and 3.48 respectively (see Table B2).

To build confidence in our data and assess the importance of survivor bias, we first replicate the key regressions from Fisman’s (2001) seminal paper using our smaller sample. The results are shown in Table B3. While the pattern of the coefficient estimates is qualitatively similar to the results in Fisman (2001), quantitatively the estimates are somewhat different.<sup>29</sup> Perhaps the biggest difference is that the interaction between the return on the Jakarta Stock Exchange Composite Index net of broader Southeast Asian Effects (referred to using _NR JCI_ ) and the measure of political connectedness, though positive, is not statistically significant. This could be due to sample selection and/or differences in the way the _NR JCI_ proxy is constructed.<sup>30</sup> The data we use in our paper is restricted to manufacturing firms, so to assess whether political connections might be especially valuable (or not) for manufacturing firms, we run a regression in which we add two crude sector dummies and interact them with the _POL_ measure. The results are presented in column 9: we cannot reject the null hypothesis that political connections are less valuable in manufacturing 29 For example, using the sample of surviving firms, we find no evidence of connections playing a significant role in the response of firm’s share prices to rumors about Suharto’s health On April 1-3, 1997, even though the estimated coefficient estimate is negative (-0.53).

30 The net return measure is calculated as follows; first, a “market model” for daily returns is estimated: ii mm ii ii RR (JJCCHH) = αα+ �ββ RR (mm) + εε where is the return on the Jakarta Composite on day t, mm ∈MMmm ∈MM where is the return on the Jakarta Composite on day t, mm ∈MMmm ∈MM is the return on market index m and M is the set of ASEAN market indices (including Tokyo’s Nikkei 225; Hong Kong SAR, China’s Hang Seng; Bangkok’s SET; Taiwan, ii ii RR (JJCCHH) RR (mm) China’s Weighted and the Philippines Composite; but not Singapore’s Straits Times, Kuala Lumpur’s Composite or Seoul’s Composite, which Fisman did use in his 2001 paper, simply because we lack the relevant data). For each episode the net return for the JCI is calculated as Note that our measure of _NR JCI_ is slightly different from the one used in the original Fisman paper not only because we use a more limited set of ASEAN market indices but also because of survivor bias; we can only use data on surviving firms.

44
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This equation in levels includes country fixed effects, _αc_ , which are differenced away in the first-difference transformation (1). Identification of the parameters is hence based on variation within countries over the different time periods.

Causal identification of all the model parameters in such a macroeconomic crosscountry panel data set appears futile. Subtle identification strategies with external instruments can sometimes be found for individual parameters, but they offer no promising chance for the wide range of model parameters in which we are interested. Some researchers have reverted to internal instruments (system generalized method of moments (GMM)), but the results by Kraay (2015) suggest that this approach frequently provides weak identification in the first stage.

For our preferred baseline results, we rely on identification with least squares dummy variable (LSDV) estimation of equation (2). In our view, this provides the most transparent estimation approach and avoids common pitfalls such as unobserved cross-country heterogeneity (through inclusion of fixed effects) and weak or overfitted first stages (which are common in System-GMM estimation). This is particularly true since we are not interested in deep structural identification of the ultimate growth drivers but simply want to detect variables that are correlated with economic growth in a wide range of countries. The downward bias for estimates of the autoregressive coefficient θ in small-T samples with fixed effects is also increasingly neglected in the empirical growth literature (e.g., Brueckner and Lederman 2018).

We additionally provide results from System-GMM estimation, which allows using “internal instruments” (that is, lagged level values and differences) for potentially endogenous series.<sup>16</sup> We treat period dummies as strictly exogenous variables (ivstyle) and use “collapsed” lags 1 to 5 for most of the variables as instruments in the difference equation, except inflation and changes in the terms of trade. Since those variables are less persistent, we use (non-collapsed) lags 1 and 2 in both equations (the difference and levels equations). For the lagged dependent variable, we use collapsed lags 1 and 2 as instruments in the equation in levels.

> 16 We use the xtabond2 module in STATA 17.

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Fasih, Tazeen, Harry Patrinos, and Najeeb Shafiq. 2009. “The Impact of Financial and Macroeconomic Crises on Education and Labor Markets Outcomes”. World Bank, mimeo.

Feenstra, Robert C and Gordon H. Hanson. 2003. “Global Production Sharing and Rising Inequality: A Survey of Trade and Wages”. In Handbook of International Trade, vol. 1, ed. E. K. Choi and J. Harrigan Malden, Mass: Blackwell, 146-85.

Gautier, Pieter, Gerald van den Berg, Jan van Ours, and Geert Ridder. 2002. “Worker Turnover at the Firm Level and Crowding Out of Lower Educated Workers”. _European Economic Review_ 46:523-538.

Gilles, Saint-Paul. 1996. “Unemployment and Increasing Private Returns to Human Capital”. _Journal of Public Economics_ 61:1-20.

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Kaymak, Baris. 2008. “Enssays in Education, Selection and Wage Dynamics”. Doctoral Thesis, University of Rochester, New York Keane, Michael, and Esward Prasad. 1993. “Skill Levels and the Cyclical Variability of Employment, Hours, and Wages”. _International Monetary Fund Staff Paper_ 4(4):711-743.

Khalifa, Sherif. 2009. “Heterogeneous Workers and Occupations: Inequality, Unemployment, and Crowding Out”. _Southern Economic Journal_ 75(4):1141-1164.

King, Randall. 1980. “Some Further Evidence on the Rate of Return to Schooling and the Business Cycle”. _Journal of Human Resources_ 15(2):264-272.

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Krebs, Tom, Pravin Krishna and William Maloney. 2010. “Trade Policy, Income Risk and Welfare”. _The Review of Economics and Statistics_ 92(3):467-481.

Kydland, Finn. 1984. “Labor-Force Heterogeneity and the Business Cycle”. _Carnegie-Rochester Conference Series on Public Policy_ (21):173-208.

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18
2009, p. 87). Forced migration can also contribute to urbanization. War, environmental degradation, and economic crisis lead to large population movements from rural areas into cities where people take refuge. There are allegedly 2 million Somalis in Mogadishu, mostly refugees escaping the fighting in their villages and seeking protection of the NGOs or the warlords (Lassailly-Jacob _et al. 2006_ ). The rapid expansion of cities combined with weak local administration or lack of migration policies have led to desperate actions by the host countries officials. In Dhaka, Bangladesh, 60,000 people were forcibly cleared from the slums in early 2007; in Jakarta, Indonesia, migrants are required to show proof of employment and housing to enter the city; and in Zimbabwe (internal) migrants were evicted from shanty towns around Harare in 2005 (UNDP 2009, p. 87). Migration is only a part of the urbanization challenge, but the interaction between migration and rapid urbanization is likely to be important for policy in the destination countries in the South.

# 4. Conclusion

Before presenting the policy recommendations, it would be useful to point out a few emerging themes in the literature and public debates on the impact of migration on climate change, political institutions, fertility and national security.

The Intergovernmental Panel on Climate Change (IPCC) noted already in 1990 that the greatest single impact of climate change could be on migration (IOM 2008). It predicted that as many as 200 million people may be forced to migrate by 2050 because of climate change (Myers 2005).<sup>16</sup> Dasgupta and others (2007, 2010) estimate that rising sea levels, tidal waves and storm surges could displace millions of people in Bangladesh, some of whom might seek refuge in neighboring countries. The **effects of migration on climate change** , on the other hand, are less understood. It is generally assumed, that short term and circular migrants make little investment in maintaining the ecological stability of their destination while long-term immigrants have an incentive to invest in

> 16 Identifying adaptation and resettlement strategies have been called for to deal with the emigration pressure imposed by the climate change (Martin 2010).

15