rafmacalaba/datause-encoder-data huggingface dataset:
# Implications of NAFTA For East Asian **Exports**

# I. Introduction

Most analyses of developing countries' export perfomance over the last two or three decades recognize the importance of the United States as a market for their manufactures exports. According to UNCTAD (1990, Table A13), approximately one-half of all developing countries' exports of manufactured goods destined for OECD markets make their way to the United States, with the European Community (EC) and European Free Trade Association (EFTA) combined receiving only about one-third.' The capacity of the US market to absorb imports is remarkable, especially when one notes that most measures of market size favor OECD Europe; _i.e.,_ the latter's population is about one-third greater than that of the United States and its gross domestic product is about 15 percent larger.<sup>2</sup> Viewed in a historical perspective, the United States was the "springboard" that newly industrialized Asian economies (NIEs) used in order to gain access to inernational markets. During the 1960s, for example, approximately three-quarters of manufactures exported from the Republc of Korea and Taiwan (China) to the OECD went to the United States. Although this proportion declined in the last two decades, it still stands at about 50 percent for the four Asian NEEs combined (Korea, Hong Kong, Sigapore, and Taiwan (China)) as well as for al the developing economies of the East Asian I With the cory into force of the Maasricht Treaty on November 1, 1993, the European Community (EC) became part of the European Union (EU). EU is now the term of choice for references to EC activities. In this text, however, the conventional acronym (EC) is used.

**2** In 1988, UNCTAD (1990, Table 7. 1) estimated that US imports of manufacts from developing countries were about 4.1 percent of apparent consumption (production minus exports plus impo,.s) compared to about 2.9 percent nthe EC. For key product gmups like cloding, 28 percent of US apparen consumption originated in developing countrics as opposed to 19 percent for the EC. Yeats (1989) found that the United States imports over 40 percent of all labor-intensive manufaum from developing countrm while the cormsponding share for dte EC was 12 percent- As a result, US per capita imports from developing countries were about S250 as opposed to $100 for the EC.
Table 1

# Distribution, Nunber, Personnel, Catchment Area and Service Provided at All Levels of the Public Health daire System

|Health<br>Center/<br>Hospital<br>Level|Number|Level of Personnel|Locatiorn<br>ImTediate<br>Catchrent<br>Area|Servic Provided|
|---|---|---|---|---|
|Type I|203|Midwife,<br>2aiHii|4,000-5,000<br>pulation|MOIC<br>hcue visits|
|Type II|89|Public<br>health<br>nurse,<br>Publichealth<br>lnspecto:RN, MD<br>"if<br>**'dentist**<br>**VIsit**|10,000-42,000<br>population|Curative,<br>pre-<br>ventive<br>aa<br>pramotive|
|Type III|782|MD, nurse practioner<br>& dentist(whoalso<br>serve Type II centers|Parish center<br>)|Curative and pre-<br>venttve at more<br>sophlsticated<br>level|
|TypeIV|2|Cambination<br>of Tvpe<br>III<br>center<br>and thie<br>parish<br>office|Parish<br>center|Curative<br>andpre-<br>ventive<br>atmore<br>sophisticated<br>level|
|Type V|2|MD, specialists,<br>nursing<br>care,<br>dentist|Undefined|Specialty<br>out-<br>patient<br>care<br>ahnd<br>ixblic health|
|type C<br>HoFspitals|11<br>|Basic<br>district<br>hospital1<br>with X-rav<br>& lab.<br>Surgeon for<br>emergency;<br>2-3MDs|Parish<br>oenter|Inpatient and out-<br>patient care in<br>medicine<br>and MCH|
|type B<br>Hospitals|4<br>|MD specialists|Urban centers|InPatient andout-<br>patient,specialist<br>service<br>atleast<br>in<br>surgery,<br>internal<br>medicine,<br>OB/GYN,<br>aand pediatrics|
|Tvpe A<br>Hospitals|53<br>|MD specialists|Kinston,<br>Montego Bay|Full range of<br>secondary and<br>tertiarycare|
|Other|44|MD specialists|Kingston|Chronic<br>or<br>specialized care|

Source: PHC Unit, Ministry of Health, 1986; in Lewis, 1988.

- 1 CHW = ccmmunity health workers. 2 Includes Type III and IV together. 3 Includes Universi4 of the West Indies Hospital, Victoria Jubilee Maternity, and Ch.ld1y 's I &-~en I _s._

- 4 Psychiatric, Chest, Cncer, and Rehabilitation hospitals.

22
according to the 2012 census). These differences are solely driven by differences in survey design, presenting a strong challenge to both the HCES-direct and the FBS-CV methods. The FBS-CV is additionally dependent on variations in the FBS data collection, which are plausibly greater and the consequences of design variation less understood than those for surveys.

More research effort is needed to understand the impact of survey methodology on resulting hunger numbers. Note, though, that this undertaking needs to go beyond the determination of simple mean correction factors for each survey type as relative module performance in part depends on household characteristics.

While we support the calculation of hunger numbers directly from household surveys, we believe that this may only be feasible once much more effort is put into harmonizing survey design. Our findings strongly support the current drive toward such harmonization as expressed, for example, in Carletto et al. (2012).

18
of necessity, reflects foreign investors’ post-entry rationalizations, rather than their pre-entry fears. By self-selection and through the power of cognitive dissonance, the former is generally more positive than the latter. Foreign investors who had excessively negative views are more likely to have stayed away, or left early. Those with excessively positive views are more likely to stay on, and having stayed on, to rationalize their decisions _ex post_ .

Second, business-government relationships are highly heterogeneous across China. Surveys of foreign investors, who are active disproportionately in the more developed special economic zones and coastal provinces, may well have a more positive average experience than locals. (On the other hand, notice that the executive constraints and responsible government indices are for the whole of China.)

# **_One country, many systems_**

The second set of issues merits further reflection. Section 4 shows that the corruption and property rights protection vary markedly from province to province, and FDI flows into provinces with higher quality governments. It might be desirable to divide China into smaller homogeneous regions, assign different index values to these regions, and observe whether the more refined regional indexes better explain China’s regional FDI inflows. Unfortunately, measures of the rule of law, responsible government, the strength of constraints on executive power, and control on corruption in China are unavailable at the provincial level.

But there are other ways we might expand on this heterogeneity argument. Of special relevance to any discussion of FDI are different ways China’s institutions might affect foreign firms versus domestic firms.

Various levels of Chinese governments often give preferential treatment to certain multinational firms (Huang, 2003). Thus, foreign-controlled operations might actually have genuinely more developed institutional environments than Chinese controlled operations next door.

23
- 34 -

**Table 9:** Dependent variable: AMP

|Variable<br>C|oefficient|T-Statistic|
|---|---|---|
|C|14.21|4.36**|
|AMP(-1)|0.34|1.91*|
|AMP(-2)|-0.31|-1.53|
|ANFA|-0.37|-4.45**|
|ALEPPI|252.1|2.38**|
|ALRGDPSA(-3)|223.1|1.75*|
|ADEFSA|-0.60|-1.87*|
|ADEFSA(-1)|-0.70|-1.80*|
|EC(-1)|-0.03|-2.03**|
|w||0.67|
|Durbin-Watson Statistic||1.97|
|F-Statistic||15.10|
|p-value of F-Statistic||0.00|

Note: Instrumental variables used were: a constant, monthly dummies, three lags of the dependent and conditioning variables, 6 lags of NFA, uncovered interest parity and three lags of it.

Our result basically reflects that, although the Central Bank has ventured to sterilize the effect of capital inflows on the monetary base at times, this has not been the focus of monetary/domestic credit policy. The role of public sector deficit financing has been the main preoccupation of monetary policy during most of the period under study. Given the large borrowing requirement of the public sector (which is sizable with respect to the domestic financial system) the Central Bank has often been reluctant to drive interest rates up by squeezing liquidity. In the same vein, complete sterilization has been avoided due to the already high pressure of the borrowing requirement on the markets.

Our view is that, having a more defensive stance on the exchange rate in the pre-crisis period, the Central Bank engaged in relatively more active sterilization and pursued a tighter stance in the pre 1993 period. The 1994 crisis revealed the fragility of the system. With direct cash credits to the Treasury being constrained, purchase of foreign exchange became the main instrument through which the market was funded, facilitating the rollover/placement of domestic debt. There had been some sterilization attempts in the first three quarters of 1995 due to strong short term inflows, but they were unsuccessful in the shallow financial system. There have been periods when the Central Bank eased the market, for example by providing liquidity to the banks through the interbank market in late 1997 and early 1998. During other episodes, such as in the second quarter of 1998, massive purchases of foreign exchange were partly sterilized. The IMF Staff Monitored Program of July 1998, however, envisions a target for net domestic assets, which implies that the Central Bank will not be sterilizing reserve flows to a great extent.
Figure 5 Reserve Money <!-- Start of picture text --> 60 4.5<br>4.0<br>50<br>3.5<br>40 3.0<br>2.5<br>30<br>2.0<br>20<br>1.5<br>10 1.0<br>0.5<br>0<br>0.0<br>-10 -0.5<br>Growth in Reserve Money (%) Change in Reserve Money (% of GDP, right scale)<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text --> Both the level and volatility of these indicators have declined sharply after 1999. This in turn could have contributed to lower macroeconomic uncertainty and inflation, lowering interest rates. Likewise, Figure 6 shows that the effective import tariff rate and the share of import duties in total revenues, which were at peak levels after Goldenberg, have both come down sharply. The immediate impact of cutting import tariffs would be to increase the primary deficit (as we have seen) and lower the cost of imported machinery and raw materials as well as the tax on exports by Lerner symmetry. In combination with cuts in income tax rates, which raise the private returns to capital, the cut in import tariffs would increase competition from imports, spurring both more investment and higher productivity in firms, improving growth prospects.

Figure 6 Import Duty and Effective Tariff Rate <!-- Start of picture text --> 17.5<br>15.5<br>13.5<br>11.5<br>9.5<br>7.5<br>5.5<br>3.5<br> Effective Tariff Rate (%) Import Duty (% of Revenue)<br>1.5<br>-0.5<br>1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 18 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07<br><!-- End of picture text -->
from January 2015 in half-day sessions over a period of 15 to 20 days, so that entrepreneurs could complete the training while continuing to attend to their businesses.

From January to March 2016, approximately one year after the treatment group were offered the training, the research team resurveyed the 799 entrepreneurs. Follow-up I survey was conducted with 729 female entrepreneurs who were tracked out of the entrepreneurs already interviewed at baseline. An additional follow-up II survey for 726 entrepreneurs was administered in February-March 2017, approximately two years after the training. The panel of firms with data collected across the three survey rounds is 680 firms. The follow-up survey questionnaires elicited business performance, business practices plus additional entrepreneurial and psychological characteristics of the female business owners. The main results in this paper are an intention-to-treat (ITT) estimation, i.e. the impact of being offered training.

# **3.1.2** **_PI/BSED study_**

For the PI/BSED training experiment, the impact evaluation team drew upon the WEDP registration database of clients in Addis Ababa only and randomly assigned 2,001 women entrepreneurs to the different treatment arms. The research team randomly assigned WEDP clients into a treatment group who were offered the PI training (747), a treatment group who received BSED training (757) and a control group (497) who were not offered training for at least one year.<sup>7</sup> From the registered WEDP clients in Addis Ababa the research team excluded all those who were already part of the overall WEDP program impact evaluation and those who had reported that they already received some form of business training in the registration database. The research team randomly selected the original 2,000 names from the WEDP registration database in Addis Ababa using Stata in November 2015 when the random sampling for the experiment was initiated.

> 7 Since the survey firm faced issues with locating all the women in the original WEDP registration list, the survey firm was provided with replacement names and instructed to survey until they reached a 750 PI, 750 BSED and 500 Control sample size.
11 can rank phenomena, only with cardinal data can numbers be tabulated and values of the phenomena being measured be compared directly. Most common statistical techniques Oike correlations or linear regression) cannot be applied to ordinal data<sup>7</sup> .

However, the data used in this analysis created by the systematic case review method are subjective, yet cardinal. Our data on rural water projects is doubly subjective: the original project evaluator subjectively assessed and described the amount of participation in each project; a coder later read the evaluator's report and subjectively assigned a level of participation to that project. If this process generated ordinal data, empirical analysis would be difficult. But note that in everyday life, we observe many events which generate subjective, cardinal data. Contests are the most obvious example. When hogs, figure skaters, or bodybuilders compete judges assign cardinal scores to subjective criteria: 'quality of coat" for hogs; 'artistic impression" for figure skaters; and 'muscle tone" for body builders. Grades for acadeniic papers are another familiar example: a professor's subjective evaluation of a humanities paper is given a cardinal score. In each case, these subjectively assigned scores are added, averaged, tabulated in ways only appropriate to cardinal data. Of course, the judging and grading criteria same preferences.

7 For instance is x were an ordinal measure of participation then estimating the linear model y=p ***x** could produce different results than estimating _y=P *(x)_ , JO a monotone transform of x, even though x and Ax) would represent exactly the same information Therefore any statistical procedure that relied on summing observations (or any comparison of the magnitude of the distance between observation) would be invalid for ordinal data.
Does the middle class expand or contract as an economy grows? Plainly that depends on whether growth is more effective in reducing poverty by the standards of poor countries, or poverty by US standards. Empirically, the answer is clear: the pace at which the middle-class expands tends to be higher in more rapidly growing economies. The correlations are higher for the proportionate changes in the middle’s population share; the annualized change in the log of the ratio of the proportion living below $13 to that below $2 has correlation coefficients of 0.55 and 0.37 with the growth rates in the survey means and national accounts (NAS) consumption per capita respectively. The corresponding correlations with the annualized changes in the levels of the population shares living between $2 and $13 are 0.28 and 0.19. Figure 7 plots the proportionate middle-income bulges ( _gi_ (<sup>_R_</sup> _it_ ) ) against the growth rates in the means,<sup>_g_</sup> _i_ (<sup>μ</sup> _it_ ) . The regression coefficient in Figure 7 is 1.248 with a robust standard error of 0.212 (n=91) and R<sup>2</sup> =0.333. Evidently this regression coefficient is the difference between the regression coefficient of _gi_ (<sup>_F_</sup> _it_ (13)) on<sup>_g_</sup> _i_ (<sup>μ</sup> _it_ ) and that for _gi_ (<sup>_F_</sup> _it_ (2)) on<sup>_g_</sup> _i_ (<sup>μ</sup> _it_ ) . Thus the positive growth effect in Figure 7 can be interpreted as saying that the growth elasticity of poverty reduction is greater (in absolute value) for the lower poverty line. The regression coefficient of _gi_ (<sup>_F_</sup> _it_ (2)) on<sup>_g_</sup> _i_ (<sup>μ</sup> _it_ ) is -1.372 (s.e.=0.231; n=91) while the corresponding regression coefficient for _gi_ (<sup>_F_</sup> _it_ (13)) is -0.124 (s.e.=0.036), although it is higher if one uses all the observations (including those for which _gi_ (<sup>_F_</sup> _it_ (2)) cannot be calculated given that one or both poverty rates are zero); for that sample the coefficient is -0.20 (s.e.=0.05; n=99).

There is no sign at country level of a non-zero mean distributional effect on the size of the middle-class. This is evident in Figure 7, on noting that the regression line passes though the (0,0) point, implying that the ratio of the population share under $13 to that under $2 is roughly stationary at zero growth. (The intercept of the regression in Figure 7 is 0.003 with a standard error of 0.006.) So the overall distributional effect on the expansion in the middle class for the developing world as a whole is driven largely by the between-country effect rather than withincountry. Even so, changes in distribution (in both directions) within countries have contributed to the changes in the size of their middle class. This is suggested by the fact that the growth rates in the mean only explains one third of the variance in Figure 7, although measurement errors are likely to be playing a role and it is difficult to distinguish these from idiosyncratic distributional shifts. It is at least suggestive that if one adds to the regression in Figure 7 a control for changes 12
_32_ S. P. (2017). Delivering education to the underserved through a public-private partnership program in Pakistan. _Policy Research working paper; No. WPS 8177; Impact Evaluation series._

- Bau, N. and Das, J. (2016). The Misallocation of Pay and Productivity in the Public Sector: Evidence from the Labor Market for Teachers. _Working Paper_ .

- Baum, D., Lewis, L., and Patrinos, H. (2013). Engaging the private sector: What policies matter? a framework paper. _SABER Working Paper Series_ .

- Beck, T. (2007). Financing Constraints of SMEs in Developing Countries : Evidence , Determinants and Solutions. _Financing Innovation-Oriented Businesses to Promote Entrepreneurship_ , (April):1–35.

- Bruhn, M. and McKenzie, D. (2009). In pursuit of balance: Randomization in practice in development field experiments. _American Economic Journal: Applied Economics_ , 1(4):200–232.

- Carneiro, P. and Heckman, J. J. (2002). The evidence on credit constraints in post-secondary schooling*. _The Economic Journal_ , 112(482):705–734.

- Das, J. and Zajonc, T. (2010). India shining and bharat drowning: Comparing two indian states to the worldwide distribution in mathematics achievement. _Journal of Development Economics_ , 92(2):175–187.

- de Mel, S., McKenzie, D., and Woodru↵, C. (2008). Returns to capital in microenterprises: Evidence from a field experiment. _The Quarterly Journal of Economics_ , 123(4):1329–1372.

- de Mel, S., McKenzie, D., and Woodru↵, C. (2012). One-time transfers of cash or capital have long-lasting e↵ects on microenterprises in sri lanka. _Science_ , 335(6071):962–966.

- Dhaliwal, I., Duflo, E., Glennerster, R., and Tulloch, C. (2013). Comparative cost-e↵ectiveness analysis to inform policy in developing countries: a general framework with applications for education. _Education Policy in Developing Countries_ , pages 285–338.

- Epple, D., Romano, R. E., and Urquiola, M. (2015). School vouchers: A survey of the economics literature. Technical report, National Bureau of Economic Research.

- Evans, D. and Popova, A. (2015). What really works to improve learning in developing countries? an analysis of divergent findings in systematic reviews.

- Hoxby, C. M., Murarka, S., and Kang, J. (2009). How new york city’s charter schools a↵ect achievement. _Cambridge, MA: New York City Charter Schools Evaluation Project_ , pages 1–85.

- Hoxby, C. M. and Rocko↵, J. E. (2004). _The impact of charter schools on student achievement_ . Department of Economics, Harvard University Cambridge, MA.

- Hsieh, C.-T. and Urquiola, M. (2006). The e↵ects of generalized school choice on achievement and stratification: Evidence from chile’s voucher program. _Journal of public Economics_ , 90(8):1477–1503.

- Jensen, R. (2012). Do labor market opportunities a↵ect young women’s work and family decisions? experimental evidence from india. _The Quarterly Journal of_
<u>Caveats:</u> Given the lack of a standardized official source on Chinese credit events, our tally of Chinese debt restructurings is likely to be incomplete. An omission that deserves particular attention is restructurings of recipient country SOEs and SPVs for which hardly any data is publicly available and which constitute and increasingly important share of China’s outbound lending (Horn et al. 2021; Malik et al. 2021). In the same way that off-balance sheet debt of these entities often remains “hidden” from official debt statistics, debt restructurings with these entities are also likely to go unreported. Our tally of credit events with the Chinese government and its state-owned entities therefore needs to be regarded as a lower bound.<sup>12</sup>

**Data on credit events with Paris Club creditors** : To add restructurings with the Paris Club, we use data from Cheng et al. (2019) until 2015 and update their database by using the Paris Club website and World Bank information on the implementation of the DSSI. As for Chinese creditors, we also exclude Paris Club reschedulings with DSSI eligible countries that are not considered to be “in debt distress” or “at high risk of debt distress” under the World Bank and IMF debt sustainability framework. To distinguish between Paris Club agreements that entailed only rescheduling and agreements that also implemented face value reductions, we additionally draw on the data set by Das et al. (2012), which we again update using the Paris Club website. Figure A3 shows our tally of Paris Club restructurings between 1970 and today.

**Data on debt restructurings with private creditors:** To identify restructurings with private external creditors (foreign bondholders and banks), we use the database by Asonuma and Trebesch (2016). Since 1975, their data identifies a total of 192 debt restructurings with 74 developing and emerging countries. To assess the scope of debt relief, we add information from Cruces and Trebesch (2013) and from the updated data set of Asonuma et al. (2017) and Asonuma and Trebesch (2016). Specifically, we use their dummy variable on whether debt restructurings involved face value debt reductions in contrast to agreements that only reschedule debt payments. Figure A4 shows the resulting data and depicts the number of countries that completed a debt restructuring with private creditors in any given year since 1975.

> 12 Data on principal and interest arrears from the World Bank’s International Debt Statistics offers corroborative evidence: In several countries that have large bilateral debts to China, arrears to official creditors have been rising sharply, although no credit events or restructuring agreements have been made public.

18