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

The Great Recession has brought inequality to the forefront of the economic debate again. Does the recent rise in inequality in the United States and Europe bode well for future growth prospects? The opinions are still very much divided on this question (see for example, Krueger, 2012, and Mankiw, 2013). We will argue that it matters whose growth prospects we are talking about, and what type of inequality we are concerned with.

A variety of different channels via which inequality could affect growth have been suggested over the years. This has inspired an extensive empirical literature that dates back to the 1990s. However, the results are mixed and largely inconclusive (i.e., see Banerjee and Duflo, 2003, and Panizza, 2002). A select number of studies have proposed that this ambiguity could be due to the fact that income inequality has distinct offsetting effects on subsequent growth that may cancel out in the aggregate (Barro, 2000; Voitchovsky, 2005).<sup>2</sup> Two recent empirical studies in particular stand out. In an application to the United States, Marrero and Rodríguez (2013) find that a particular component of overall inequality, _inequality of opportunity_ (IO), has a negative effect on growth. IO arguably reduces growth as it favors human capital accumulation by individuals with better social origins, rather than by individuals with more talent. The second study, van der Weide and Milanovic (2014), also an application to the United States, “unpacks” growth. It asks whether individuals at different steps of the socio-economic ladder fare differently in societies with high (low) levels of income inequality. They find that they do, namely that income inequality is bad for the growth prospects of the poor but good for the rich.

This paper disaggregates the inequality-growth relationship in order to address two <mark>empirical questions: Can the effect of overall inequality on future income growth be attributed to IO? If indeed, is this particularly true for the poor, or does it</mark> concern households of all socio-economic classes?

We explore these questions using the IPUMS-USA database, as in van der Weide and Milanovic (2014), since it is the largest individual level database for the United States

> 2 See also Galor and Moav (2004) who have proposed that the impact of inequality on growth changes with the replacement of physical capital by human capital accumulation as a main source of growth along the process of development.

2
- Keeton, W., "Multi-office Bank Lending to Small Businesses: Some New Evidence," _Federal Reserve Bank of Kansas City Economic Review_ 80, pp. 45-57, 1995.

- Keeton, W., "Do Bank Mergers Reduce Lending to Businesses and Farmers," _Federal Reserve Bank of Kansas CityEconomicReview,_ 81, pp.63-75, 1996.

- Keeton, W., "The Effects of Mergers on Farm and Business Lending at Small Banks: New Evidence from the Tenth District States," Federal Reserve Bank of Kansas City, mimeo, 1997.

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World Bank, _Argentina Financial Sector Review,_ Washington, DC: The World Bank, 1998.

23
approximately 56 percent of the tariff margin is captured by exporters. On the other hand, the coefficient for the FTA dummy is estimated to be insignificant. These results indicate that on average the effects of FTA use on import prices are mainly based on the tariff effect, not on the RoO effect. This implies that the effects of tariff reduction on import prices may not be different between the cases based on FTA enactment and on multilateral liberalization (i.e., tariff reduction on an MFN basis).

Although it is difficult to generalize the above result from the export from Korea to Thailand, there are indeed two supporting lines of evidence regarding the insignificant effect of RoO in Korea’s export. First, the share of domestic value-added is very high. For example, according to the “Survey of Japanese-Affiliated Firms in Asia and Oceania” (henceforth “the Survey”) in 2012, administered by the Japan External Trade Organization, the average shares of labor costs, local materials/inputs, and miscellaneous expenses in Japanese affiliates in Korea are respectively 17 percent, 32 percent, and 19 percent out of total production costs. Therefore, domestic value-added occupies 68 percent, which is much higher than the usual cutoff in RVC rules (i.e., 40 percent). Second, the Survey for 2011 reports responses by Japanese affiliates in Korea to a range of problems when exporting under FTA schemes.<sup>21</sup> Consistent with the above observation concerning the high share of domestic value-added, it shows that the major problem is the time taken to obtain CoO rather than whether or not to comply with RoO.

> 21 The table is provided in supplemental appendix S3.

31
# **How Do Agricultural Policy Restrictions to Global Trade and Welfare Differ across Commodities?**

Peter J. Lloyd University of Melbourne <u>pjlloyd@unimelb.edu.au</u> Johanna L. Croser University of Adelaide <u>johlou@gmail.com</u> Kym Anderson University of Adelaide <u>kym.anderson@adelaide.edu.au</u>

**Key words:** Distorted commodity markets, agricultural price and trade policies, trade restrictiveness index

**JEL codes:** F13, F14, Q17, Q18

**Author contact:**

Kym Anderson School of Economics University of Adelaide Adelaide SA 5005 Australia Phone +61 8303 4712 Fax +61 8223 1460 kym.anderson@adelaide.edu.au
|Cultural Heritage Project|Thomas Blinkhorn|
|---|---|
|Cultural Heritage Project|Thomas Blinkhorn|
|Cultural Heritage Project|Thomas Blinkhorn|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|China Pension Reform Project|Yvonne Yee Mun Sin|
|PolicyReform Support Loan Project(PRSL)|Amine Khene|
|PolicyReform Support Loan Project(PRSL)|Amine Khene|
|Health Sector Development|Lingzhi Xu|
|Health Sector Development|Lingzhi Xu|
|Health Sector Development|Lingzhi Xu|
|Health Sector Development|Lingzhi Xu|
|Health Sector Development|Lingzhi Xu|
|Health Sector Development|Lingzhi Xu|
|Health Sector Development|Lingzhi Xu|
|Health Sector Development|Lingzhi Xu|
|Health Sector Development|Lingzhi Xu|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Integrated Marine and Coastal Resources Management Project|John Virdin|
|Agricultural Services Innovation and Reform|Wahida Huq|
|Agricultural Services Innovation and Reform|Wahida Huq|
|Agricultural Services Innovation and Reform|Wahida Huq|
|Agricultural Services Innovation and Reform|Wahida Huq|
|Liepaja Region Solid Waste Management Project|Barbara Letachowicz|
|Liepaja Region Solid Waste Management Project|Barbara Letachowicz|
|Liepaja Region Solid Waste Management Project|Barbara Letachowicz|
|Liepaja Region Solid Waste Management Project|Barbara Letachowicz|
|Liepaja Region Solid Waste Management Project|Barbara Letachowicz|
|Liepaja Region Solid Waste Management Project|Barbara Letachowicz|
20 we additionally exclude female, part-time, self-employed and public sector workers, the value becomes 0.58 (specification 2). Thus, the relationship appears to be moderately monotonic.

Excluding countries in the top 5% in past child or adult mortality rate (averages in the 1950s-2010s) or HIV prevalence rate (1990s-2010s) increases the values for the two specifications to 0.63 and 0.60, respectively. If we also exclude ex-Communist countries, the correlations become 0.75 and 0.57, respectively, indicating a strongly monotonic relationship. The relationship is still not _fully_ monotonic, which suggests non-linearities. These come from developing countries, not developed countries.

If we focus on specifications 1 and 2 and exclude ex-Communist and high-mortality countries, the correlations respectively increase to about 0.8 and 0.6 if we additionally exclude low-income countries (not shown). If we exclude high-income countries instead, the correlations respectively decrease to about 0.3 and 0 (ibid.). Thus, the relationship is barely monotonic among developing countries, which we discuss next.

**Income Group-Specific Returns.** We modify equation (2) to obtain the mean returns to experience for low-income countries, middle-income countries and high-income countries in 2017 according to the classification of the World Bank. As seen in Web Appx. Table A.17, with specification 1 or specification 2, returns are slightly higher for middle-income countries than for low-income countries (at 1.8-2.0 vs. 1.60.8), but not significantly so. Simultaneously excluding high-mortality/HIV countries and ex-Communist further increases the gap between low-income and middle-income countries. However, the difference is only significant in specification 2.

# **5. Variation in Returns to Work Experience Over Time**

Returns to experience are available for 951 country-years for the time period 19902016. Using this data, we investigate using panel regressions how the returns vary with economic development, recessions and economic transitions as well as existing stocks of human capital. While estimates derived are not causal, the aim is to highlight novel stylized facts about how returns to experience vary across countries over time.

More precisely, for countries _c_ and years _t_ , we estimate the following model: with Var. of Interest being the variable explored as a potential predictor of the returns
2

- the creation of new RTAs, or the relaunching of RTAs that had effectively been dormant. The most publicized of these new RTAs has been Mercosur, but other examples include the Central American Common Market (founded in 1960, but effectively dormant until 1993) and the South Asian Preferential Trading Agreement established in 1993 with the aim of forming a Common Market between Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka.

At the same time the international trading system has also deepened and widened. GATT discussions have extended for the first time into trade in services, intellectual property rights, and prospectively other industries like telecommunications; again this deepening has been signaled with a name change, as GATT has become the WTO. The widening has occurred largely as a consequence of the collapse of the formerly centrally planned economies; and this is also reflected in the fact that all but nine of the 33 agreements notified to GATT since 1990 have been concluded with Central and East European countries.<sup>2</sup> The search for an economic explanation for this new proliferation of RTAs begins with a number of negatives. The intellectual case for free trade is as strong as it has always been - which is to say fairly strong - but not obviously any stronger, although the rediscovery of possible externalities in the growth process and the ability to model these rigorously (e.g. Rivera-Batiz and Romer (1991a,b), and Grossman and Helpman (1991)) has added intellectual respectability to the traditional arguments linking trade liberalization and growth. Still, it would be difficult to believe that the growth in RTAs is the policy counterpart to the proliferation of academic papers on trade and growth. Nor has the debate as to whether RTAs are to be preferred on theoretical or empirical grounds to an admittedly imperfect global trading system been resolved in such a way so as to pave the way for an explosion in the number of RTAs. In fact, the debate remains lively but unresolved, though lately with the Uruguay round concluded, all parties seem to be swaying towards accepting RTAs as a useful accompaniment to the WTO. But in any case, the amount of time and effort expended by policy-makers and bureaucrats in concluding trade agreements seems disproportionate to the gains or losses that, at least on the face of it, could be anticipated from most RTAs. NAFTA, for example, involved

> 3See Winters (1996) for a recent survey of this literature.

**2** Figures from WTO (1995).
with the residency definition only in specific cases when information is available, as acknowledged in the LIC DSF Guidance Note.<sup>32</sup> In fact, debt denominated in foreign currency is still used as a proxy of external debt in 43 percent of the 2019 DSAs.

# _4.3. Recording errors_

Data collected in over 20 WB-led missions on Medium-Term Debt Management Strategy (MTDS)<sup>33</sup> in LIDCs between 2010 and 2020 reveal severe recording mistakes in approximately 30 percent of debt databases. The quality of DRS data assessed by the WB on an annual basis confirms these findings: of 74 LIDCs, 15 (20%) do not submit comprehensive reporting templates or, if they do, the quality is not deemed satisfactory. Among those who provide a complete template, 28 percent are affected by at least one type of recording mistake (Table 2).

**Table 2. Assessment of the 2020 reports by IDA countries to DRS**

||**No / partial /**<br>**unsatisfactory**<br>**reporting**||**Complete reporting**||
|---|---|---|---|---|
|**Number**<br>**of**<br>**countries(%)**|20%|of which 28% af|80%<br>fected byone or more of t|he errors below|
|**Error types**||_stocks/flows_<br>_inconsistencies_|_missing information_<br>_on new commitments_|_transactions not_<br>_reported_|
|**Frequency (%)**||15%|17%|17%|

Source: WB Development Data Group. Notes: author’s elaboration These problems are mostly driven by the debt recording process itself, which is still a very manual exercise involving limited or no cross-validation with creditor records during the lifetime of debt instruments. In fact, two parallel debt recording processes coexist for each instrument, with very limited systematic validation across them (Figure 9).

On one hand, the debtor manually records debt transactions in its DMRS as follows:<sup>34</sup> (i) loan terms are extracted from hard copies of the agreements; (ii) disbursement information is shared with creditors by letter/email; (iii) debt service is entered after reception of the creditor's billing advice. The operational

> 32 “ Because of difficulties in record keeping (for example due to secondary market trading and data limitations in LICs), and where non-resident participation in domestic debt market is not significant, debt denominated in foreign currency can be used as a proxy for external debt”. (WB-IMF, 2018d)

> 33 Prior to any MTDS mission, a data validation is conducted to verify that (i) financial terms for each debt instrument are present and (ii) debt stock-flows are consistent. Inconsistencies of type (i) are regarded as “minor”, while those of type (ii) are regarded as “severe” if the error is higher than 1% of the total debt stock.

> 34 Some debtors use integrated financial management systems, which eliminate some of the manual steps, though straight-through processing is still a very rare exception.
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27
changes in sector and location in driving changes in individual incomes and household poverty over time. The paper is structured as follows: section 2 describes the conceptual framework and reviews the relevant theoretical and empirical literature; section 3 describes the data, shows the aggregate picture of sectoral and locational movements in employment, and provides a decomposition of income growth and poverty transitions according to sector and location; section 4 analyzes the micro-determinants of income and poverty changes using the IFLS panel data, before concluding with implications for policy in the final section.

# **2. CONCEPTUAL BACKGROUND AND REVIEW OF THE LITERATURE**

Since the early works of Lewis (1954) one of the main questions in development economics has been the nature of the “structural transformation” that takes place as countries develop and how it influences growth and poverty reduction in the long term. Figure 1 presents a simple conceptual framework for how structural transformation translates into poverty reduction. We use this framework to organize our review of the literature and our later empirical work.

There are arguably two paths out of poverty. First is the move from low to high productivity farming (Pathway 1). This includes both intensification through raising productivity of traditional crops, as well as diversification into higher value crops, whether food or non-food. This pathway also includes those individuals who exit poverty by gaining better paid employment on higher productivity commercial farms. Thus staying in agriculture does not necessarily mean staying as a self-employed farmer.

Second, some people exit poverty because of increases in the productivity and profitability of their non-farm enterprises, or by finding better-paid employment in such enterprises (often more formal) owned by others (Pathway 2). This pathway applies to both rural and urban non-farm activities.

3