rafmacalaba/datause-encoder-data huggingface dataset:
quality (at the plot level) and its price is positive and increasing with the amount of marketed land. For instance, for rural households that do not operate any plot obtained through the market, the effects of land quality on land price is a significant .116, while for rural households that operated purchased land (with title), this figure increases to .503. Analogously, the difference in the reported price of land across countries might also be due, in part, to differences in market development. In Uganda, on the other hand, in 1998 the government enacted the Land Act with the explicit aim of turning dwellers on land held under customary tenure into freeholders. Even if the policy was not fully successful (see McAuslan (2003)), the development of a land market in Uganda may help explain the disparity in valuations compared with Malawi. This issue deserves further attention beyond the scope of this paper. Nevertheless, we should keep the issue of land markets (or lack thereof) in mind when comparing the reported monetary value of land, hence, — wealth across poor countries.

Finally, we also note that the survey for Uganda does not report the value of outstanding debt, whereas the surveys for Malawi and Tanzania do so. While debt is a minor component of net worth in Malawi and Tanzania, Uganda’s wealth measure is likely to be overestimated for this reason. Also, the survey for Tanzania does not report the value of housing and other durables; thus, the monetary value of wealth reported for Tanzania is likely to be underestimated, particularly for urban areas.

# **2.4 Further Measurement Issues**

An important feature of the ISAs is that they are not top coded and there are very few missing observations. Our understanding from discussions with the World Bank survey managers is that the surveys were conducted in conjunction with national statistics offices. Ground staff were provided with official government identification badges and respondents understood the survey to be official government business. While this largely resolves survey response biases, the quality of the data merits further scrutiny. Previously, we discussed two potential sources of measurement error of annual quantities and our efforts to mitigate their effects: (i) deseasonalizing consumption (see Section 2.1) and (ii) comparing our monetary measures of wealth with physical measures (i.e., acres for land) to overcome the presence of underdeveloped markets for assets, particularly for land (see Section 2.3).

Food consumption is the lion’s share of household consumption in our settings and hence, this measurement is perhaps the most important aspect of consumption. The ISA collection of food consumption is based on a 7-day recall questionnaire. These short-recall periods tend to 14
# Table A.4.3 Panel cointegration tests

||All cou<br>Statistic|ntries<br>p-value|Intercept<br>Advanced<br>Statistic|and trend<br>economies<br>p-value|<br>EM<br>Statistic|DEs<br>p-value|
|---|---|---|---|---|---|---|
|Panel v-statistic|-1.7|0.96|2.0|0.03**|-1.2|0.88|
|Panel rho-statistic|-9.7|0.00***|1.0|0.84|-7.0|0.00***|
|Panel PP-statistic|-27.8|0.00***|-3.0|0.00***|-19.5|0.00***|
|Panel ADF-statistic|-22.4|0.00***|-3.8|0.00***|-15.7|0.00***|
|Group rho-statistic|0.3|0.60|3.1|1.00|-0.5|0.30|
|Group PP-statistic|-13.9|0.00***|-0.9|0.18|-10.8|0.00***|
|Group ADF-statistic|-11.7|0.00***|-2.7|0.00***|-10.7|0.00***|

||All co<br>Statistic|untries<br>p-value|Inte<br>Advanced<br>Statistic|rcept<br>economies<br>p-value|EM<br>Statistic|DEs<br>p-value|
|---|---|---|---|---|---|---|
|Panel v-statistic|2.4|0.01***|-0.5|0.69|1.9|0.03**|
|Panel rho-statistic|-11.5|0.00***|-0.2|0.42|-8.3|0.00***|
|Panel PP-statistic|-19.4|0.00***|-2.8|0.00***|-13.8|0.00***|
|Panel ADF-statistic|-15.2|0.00***|-2.7|0.00***|-11.0|0.00***|
|Group rho-statistic|-1.3|0.09*|1.9|0.97|-1.1|0.14|
|Group PP-statistic|-10.0|0.00***|-1.6|0.06*|-7.0|0.00***|
|Group ADF-statistic|-9.5|0.00***|-2.9|0.00***|-8.7|0.00***|

Note: Results for the full sample of 47 economies, 24 advanced economies, and 23 EMDEs, all using data for 1995-2016. ADF = augmented Dickey-Fuller unit-root test; EMDEs = emerging market and developing economies; PP = Phillips-Perron unit-root test.The null hypothesis of no cointegration is rejected at significance levels of *** p < 0.01, ** p < 0.05, * p < 0.1.

40
**Figure 5. Predicted Poverty Rates in TA 3 Based on Imputation Using Different Price Adjustments, from NPS 2020/21 to TA 3, Tanzania**

<!-- Start of picture text --> Panel 1: IMF Panel 2: WB annually<br>m1 m2 m3 m4 m8 m9 m1 m2 m3 m4 m8 m9<br>Imputation model Imputation model<br>Panel 3: WB quaterly Panel 4: WB monthly<br>m1 m2 m3 m4 m8 m9 m1 m2 m3 m4 m8 m9<br>Imputation model Imputation model<br>30 30<br>25 25<br>20 20<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>15 15<br>30 30<br>25 25<br>20 20<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>15 15<br><!-- End of picture text -->

**Note:** 1000 simulations are implemented. Models 3 and 4 use standardized variables. The dashed lines represent the true poverty rates in TA 1 using the 2020/21 poverty lines. The light grey areas represent confidence intervals of the true poverty rates in TA 1 using the 2020/21 poverty lines. The dark grey areas represent standard errors of the true poverty rates in TA 1 using the 2020/21 poverty lines.

42
# Antidumping and Competition Policy in Latin America and Caribbean: Total Strangers or Soul Mates?

## **I. Introduction**

Trade policy regimes in Latin America have been transformed since the mid 1980s, from inward oriented protectionist regimes to more outward oriented and liberal trade regimes. The extent of the transformation is seen in the reduced level of overall protection, much reduced use of quantitative restrictions compared to the past and no recourse to exchange controls in current account transactions. While the liberalization agenda is by no means complete, it is fair to say that the transformation of the trade regimes has been remarkable. Nor have the recent shocks following the Mexican peso crisis 1994 lead to reversals of the liberalized trade regimes on a wide scale, as would have been the case in the past.' There were few and relatively small reversals. Brazil raised a few tariffs, Argentina temporarily re-introduced the "statistical tax" and the Andean group's use of price bands kept domestic prices high despite a fall in world prices.

Objective indicators, such as the average tariffs, coverage of quantitative restrictions, and the openness ratio all show that Latin America is more open than any time in the post World War II period. Moreover, exports and imports have responded to trade liberalizations (see Table 1). Nevertheless, there is a need for a continuing agenda for reform in the region to sustain and further the earlier gains. Part of that agenda is the adoption of competition policies, and consistent with them, the reform of some aspects of trade policies, particularly of the antidumping practices. Both are of fundamental importance. Well designed trade (including foreign investment) and competition policies, support, complement and reinforce each other, facilitating market discipline and competitive behavior by firms, domestic and foreign. From a normative standpoint, trade and competition policy share the common economic objective of attempting to remove barriers to the competitive process and thus ensuring market access and presence, promoting efficiency. But, in practice, however, when other objectives are introduced

> **1** Braga, Carlos Primo, Julio Nogues and Sarath Rajapatirana "Latin America and the Caribbean in the World Economy," Proceedings of the Annual Conference on Development in Latin America 1995, Rio de Janeiro, June 12-13, World Bank Latin American and Caribbean Studies, World Bank 1995.
# 1 Introduction

In the early 1990s, Argentina tried to develop a homegrown auto industry, hiding behind an average tari¤ of more than 13 percent. Over the past two decades, Argentina’s auto exports have stagnated at a dismal 0.2 percent of global auto exports. Around the same period, General Motors (GM), one of the world’s largest automakers, set up GM Poland to import Opel cars for the large Polish domestic market. In 1994, production activities of GM Poland started, and today, Poland is one of the world’s major auto exporting countries. Similar to the auto industry in Poland, Vietnam’s electronics sector expanded sharply in less than a decade. Today, Vietnam is the world’s second largest smartphone exporter, producing 40 percent of Samsung’s global mobile phone products and employing 35 percent of its global sta¤. Ten years ago, Vietnam barely exported electronics products.

What sets Argentina, Poland, and Vietnam apart is their very di¤erent participation in global value chains (GVCs). In fact, the meteoric rises of Poland and Vietnam and the faltering of Argentina are not unique. China’s World Trade Organization (WTO) accession in 2001 ushered a new wave of GVCs which gave rise to “Factory Asia” (Baldwin, 2016), while a large part of the Africa, South Asia and Latin America regions is being left behind with little integration into GVCs (see Figure 1).

As discussed in Antràs (2016), what distinguishes GVC trade from traditional trade are the intense ...rm-to-...rm interactions characterized by contracting and specialized products and investment. What factors determine GVC participation across countries? Do factors that a¤ect traditional trade have di¤erential impacts on GVC trade? This paper studies the determinants of GVC participation based on empirical evidence stamped from a panel data set covering more than 100 countries over the past three decades. The time period re‡ects the growing international fragmentation of production and the sample includes countries in all geographical regions and at all stages of development. This large cross-country variation makes the data set uniquely suitable to estimate the marginal impact of di¤erent potential determinants on GVC participation and assess their relative importance. While many papers explain the GVC phenomenon from various angles, the strategy of this paper is to provide a uni...ed empirical framework to test jointly the role of the di¤erent determinants highlighted 1
# "The Political Economy of Industrialization in Primary Product Exporting Countries" mimeo, 1986.

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- Mayer, W. "Endogenous Tariff Formation" American Economic Review, December 1984.

- Miliband, R. Marxism and Politics, Oxford University Press, 1977.

Myrdal, G. Asian Drama, Pantheon, 1968.

- Niskanen, W. Bureaucracy and Representative Government, Aldine, 1971.

- North, D. Structure and Change in Economic History, Norton, 1981.

- O'Donnell, G. Modernization and Bureaucratic Authoritarianism, University of Califomia Press, 1979.

- Parkinson C.N. Parkinson's Law, John Murray, 1958.

- Poggi, G. The Development of the Modern State, Stanford University Press, 1978.

- Raeff, M. The Well-Ordered Police State, Yale University Press, 1983.

- Skidmore, T. and P.M. Smith Modern Latin America, Oxford University Press, 1984.

- Srinivasan, T.N. "Neoclassical Political Economy, the State and Economic Developmene' Asian Development Review, vol. 3, no. 2, 1985.

- Stigler, G. (ed.) Clflcago Studies in Political Economy, University of Chicago Press, 1988.

- Trliy, C. "War Making and State Making as Organized Crime" in Evans fiL (eds.), 1985.

- Wellisz, S. and R. Findlay "The State and the Invisible Hand" World Bank Research Observer, January 1988.

39
# Trade and Innovation in Services: Evidence from a Develo in Econom <u>p g y</u>

_Leonardo Iacovone+, Aaditya Mattoo+, Andrés Zahler++_

**_Keywords:_ innovation; tradable services; manufacturing; exports; developing economies**

## **JEL: D22, D24, F14, F63, F60, O14, O30, O33, O54,**

+World Bank, Washington DC, USA, ++Diego Portales University, Santiago, Chile. We want to acknowledge the Chilean National Institute of Statistics and the Ministry of Trade and Industry for facilitating the database we used in our paper. We would also like to thank the participants of MEIDE 2011 at San José, Costa Rica, and an anonymous referee for helpful comments. The authors acknowledge the Nucleo Milenio Initiative NS100017 “Intelis Centre” for partial funding. This paper is part of a World Bank research project on trade in services, supported in part by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations.
37 outlays on health care follow an upward trend is due both to the way in which expenditures per treatment rise and to the swelling share of repor_.ed ilinosses which are treated as total household per capita consumption expenditures increase. On the other hand, the number of illnesses reported do not appear to vary too much with total expenditures though there is a tendency for them to diminish in urban Indonesia and to increase (more markedly) in rural areas.

An important missing factor in the preceeding analysis, which will tend to influence the true distribution of health subsidies in Indonesia, is health insurance coverage under PHB (Perum Umuim Husada Bhakti: formerly ASKES). This government run insurance scheme covers all active and retired public servants along with their spouses and up to 3 dependants. Estimates of the numbers covered under this scheme vary from around 10.5 millon to 14 million for 1986 (World Bank, 1991a). The scheme is financed through a 2 percent levy on the base salaries of all government workers and the pensizn payments of retired ones. Those covered are rarely poor.

Coverage is thought to substantially boost utilization of both primary health care centers and government hospitals where free care is accorded to cardholders. It can be presumed that PHB subscribers use facilities relatively more than others ceteris paribus. However, there is some controversy about what this implies for subsidy incidence. From existing evidence, it is probable that PHB contributions do not cover costs; what is less clear is whether those covered are subsidized more or less than those not covered. It has been claimed that PHB reimburses health care facilities at the official tariff rates (and perhaps at even lower rates); see World Bank (1991a). This would imply higher subsidy rates to PHB patients (since official tariffs are lower than average prices) and an underestimation of the regressivity of the health care sudsidy distribution. However, others claim that PHB reimbursements are actually higher than what other patients pay in
Figure 12: Reform 2 - Minimum Pension at Age 55 <!-- Start of picture text --> Earnings = 100% Average Earnings=50% Average<br>0 0<br>-0.10 -0.05 0.00 0.05 0.10 0.15 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00<br>-1<br>-2<br>-2 Risk aversion (+)<br>Discount rate (+)<br>-3 Consumption (+)<br>Prob. informal work (-) -4<br>Cost informal work (+)<br>High cost of formal -4<br>work<br>-5 -6<br>-6<br>-8<br>-7<br>-8<br>-10<br>-9<br>-10 -12<br>Change in Contribution Density (% of Time) Change in Contribution Density (% of Time)<br>Change in Retirement Age (# of Years) Change in Retirement Age (#of Years)<br><!-- End of picture text --> _Note:_ Comparisons are relative to baseline _Source:_ Simulation model.

The next simulation increases the eligibility age for the minimum pension to 65 (Reform 3). Again, there are no major changes for average earners – except that a few might delay retirement and reduce contribution densities thus becoming eligible for the minimum pension. Among low income workers, on the other hand, the effects are large. First, not surprisingly, the minimum pension at age 65 creates strong incentives to delay retirement and the majority of workers do. Contribution densities also decrease in all cases but most of the time the effect is small – less than 5 percentage points. In fact, there is a very strong negative correlation (-0.9) between the increase in the contribution density and the increase in the retirement age. Those individuals who delay retirement until age 65 leave unchanged or increase little the contribution density (i.e., lower pension wealth from the minimum pension provides some incentives to earn more and save more). On the other hand, those workers who do not increase retirement ages (mainly because retirement ages are already high) “accommodate” the subsidies by reducing contribution densities.

Figure 13: Reform 3 – Minimum Pension at 65 <!-- Start of picture text --> Earnings = 100% Average Earnings=50% Average<br>6 8<br>7<br>High cost of formal 4<br>work Risk aversion (+)Discount rate (+) 6<br>2 Consumption (+)Prob. informal work (-) 5<br>Cost informal work (+)<br>0 4<br>-0,10 -0,05 0,00 0,05 0,10 0,15 0,20 3<br>-2<br>2<br>-4 1<br>0<br>-6 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00<br>-1<br>-8 -2<br>Change in Contribution Density (% of Time) Change in Contribution Density (% of Time)<br>Change in Retirement Age (# of Years) Change in Retirement Age (#of Years)<br><!-- End of picture text --> _Note:_ Comparisons are relative to baseline _Source:_ Simulation model.

31
well be a less optimal way of pollution control, specifically if it is not sufficiently progressive.<sup>11</sup> In these cases, lowering household expenditures interferes with the overall effectiveness. Taxbased policies may possibly be made more effective if the tax revenues are in turn invested in the poor.

Importantly, we see that the economic impacts of pollution growth are higher in polluted areas. Combined, the evidence points toward a pro-active stance towards both poverty reduction and pollution abatement as early in the development process as possible. A grow first, solve later, attitude leads in either case to the lesser effective growth strategy. Letting pollution increase, results in increasingly higher damages. Both in a cumulative, but also in a marginal sense. Slowed growth of the poor prolongs poverty, which in turn slows down a potential pollution decline. The narrative of pollution naturally reducing as development occurs is a decades-old concept, and has been surrounded by controversy and debate related to its implications for development (see Stagl (1999) and Soumyananda (2004) for examples). The so-called “clean-up phase” that historically accompanied middle- and late-stage income growth has long been misinterpreted as a justification for knowingly developing through “dirty” means and neglecting to establish policy interventions that would curb early-stage pollution. We hope our evidence contributes to an ending of this unjustified and harmful interpretation that can only lead to bad economic outcomes. This conclusion has been put forward also by others, already in earlier literature (Panayatou, 1997; Lee, 2012).

# **References**

- Andree, B. P. J., Blasques, F., and Koomen, E. (2017). Smooth Transition Spatial Autoregressive Models. _Tinbergen Institute Discussion Papers_ .

- Andree, B. P. J., Spencer, P., Chamorro, A., and Dogo, H. (2018). Penalized Kernel Learning of Deforestation, Pollution and Carbon. _World Bank Policy Research Working Papers, forthcoming_ .

- Bao, Y. and Ullah, A. (2007). Finite sample properties of maximum likelihood estimator in spatial models. _Journal of Econometrics_ , 137(2):396–413.

- Beenstock, M. and Felsenstein, D. (2007). Spatial Economic Analysis Spatial Vector Autoregressions Spatial Vector Autoregressions. _Spatial Economic Analysis_ , 2(2):167–196.

> 11This does not imply that pollution taxes are not effective. In fact, multiple studies have shown the effectiveness of tax-based approaches in curbing pollution (Deschenes et al., 2012; Shapiro and Walker, 2016).

32
Table 11 Backcasted estimates using different pass-through rates and sectoral GDP data at the US$1.90 and US$3.20 poverty lines

||Pover<br>|tyrate at US$|1.90povertyl<br>|ine<br>|Pover<br>|tyrate at US$ <br>|3.20povertyl<br>|ine<br>|
|---|---|---|---|---|---|---|---|---|
||Backcast,<br>pass-<br>through=0.<br>42|Backcast,<br>pass-<br>through=0.<br>75|Backcast,<br>pass-<br>through=0.<br>87|Backcast<br>, pass-<br>through<br>=1|Backcast,<br>pass-<br>through=0.<br>42|Backcast,<br>pass-<br>through=0.<br>75|Backcast,<br>pass-<br>through=0.<br>87|Backcast<br>, pass-<br>through<br>=1|
|2009|42.2|44.5|45.3|46.3|73.5|75.3|75.8|76.5|
|2010|40.7|41.9|42.4|42.8|72.0|72.9|73.1|73.5|
|2011|40.1|41.1|41.5|41.8|71.6|72.1|72.1|72.3|
|2012|39.5|39.9|40.1|40.3|71.2|71.3|71.3|71.3|
|2013|38.9|38.9|39.0|38.9|70.6|70.2|70.0|69.8|
|2014|38.3|37.7|37.5|37.4|69.8|69.1|68.6|68.3|
|2015|38.0|37.4|37.1|36.8|69.8|68.9|68.5|68.0|
|2016|38.6|38.2|38.1|38.0|70.4|70.1|69.9|69.7|
|2017|38.9|38.7|38.7|38.5|70.7|70.6|70.5|70.4|
|2018|39.0|39.0|39.0|39.0|70.9|70.9|70.8|70.8|
|2019|39.1|39.1|39.1|39.1|71.0|71.0|71.0|71.0|

Note: the table shows different backcasted series for different values of the pass-through rates and different values of the poverty line. Estimates use sectoral GDP growth rates mapped to household’s head sector of employment to backcast household consumption from the 2018/19 NLSS over the previous decade.

36
# _2.2. Performance during the recovery_

Annual GDP data show a large bounce-back effect in economic activity: economies that suffered greater collapses were typically those that enjoyed larger growth recoveries defined as GDP growth in 2010 minus that in 2009 (Figure 1 and bottom panel of Figure 2).<sup>9</sup> Only a few economies suffered a decline in growth rates between 2007 and 2009 and a consecutive decline between 2009 and 2010.<sup>10</sup> Nevertheless, the degree of cross-country heterogeneity observed during the recovery has been greater than that of the downturn. In particular, as the effects of the global systemic shock faded, the relative strengths of individual economies might have started to play a more fundamental role. Heterogeneity in the rebound is observed not just between advanced and emerging economies, but also across emerging regions (Figure 1). Even though advanced economies and economies in the Eastern Europe and Central Asia region suffered large collapses, their recovery was not as strong as that observed in other emerging regions.

This last observation predicts that, by 2010 or 2011, not all economies would recover all the losses derived from the crisis; and that is what we observe. One way of capturing this cost is assessing where economies stand relative to some reference level of GDP. To do this, we consider two reference levels for GDP: (i) the GDP level that was reached in 2008 (i.e., the precrisis annual peak), and (ii) the level that would have been reached in 2010–2011 had economies maintained their average growth rates registered during 2000–2007. The latter reference level is an estimate of trend GDP.

Consider first the expected level of GDP in 2010–2011 relative to that of 2008 (Figure 4).

In 2010, the average levels of GDP in advanced economies and in Eastern Europe and Central

> 9 We use Consensus Forecasts or World Economic Outlook estimates when actual 2010 GDP data are not available. 10 These economies are Albania, Azerbaijan, Equatorial Guinea, Djibouti, Greece, Guyana, Haiti, Kyrgyz Republic, Pakistan, The Gambia, Uganda, and Vanuatu.

12
- 17 -

FIGURE 2: MEXICO - RELATIVE PRICE OF NEW CAPITAL GOODS <!-- Start of picture text --> 225-<br>200- UNITED p.<br>STATES' IN |<br>175- MEXICAN,<br>PESOS<br>150-<br>125<br>1001<br>1960 1965 1970 1975 -1980 71985<br><!-- End of picture text -->