rafmacalaba/datause-encoder-data huggingface dataset:
use our baseline sample of countries and the longer sample period. In this case, _β_ increases further to _−_ 3 _._ 3.

Column 4 excludes applied tariffs from equation (4), because we exclude those from our baseline estimation of the annual effect in the next subsection. The coefficient on the pre-PNTR dummy is now _β_ = _−_ 3 _._ 6, which is only a small increase from our benchmark specification ( _β_ = _−_ 3 _._ 3). Column 5 shows the result is similar when we use the NNTR rate instead of the NNTR gap, which is the result of the good-year fixed effects absorbing the variation in MFN rates. Finally, column 6 uses the applied NNTR rate instead of the NNTR gap in 2001. As is evident in Figure 5, the NNTR statutory tariffs (a part of _Xg_ ) are correlated with the applied tariffs ( _τg_ ). The correlation between the mean SITC applied tariff rate over the period of 1974 to 1979 and the median NNTR rate is 0.78. The effect of the pre-PNTR dummy is even stronger when we use applied NNTR rates instead of the scheduled NNTR gap. Overall, these findings indicate the effect of NNTR rates on China’s exports to the United States after its PNTR access becomes significantly larger as the sample period increases.

# **3.3.2 The Annual Effect of the NNTR Gap and Chinese Exports**

We now explore the time path of the effect of the NNTR gap on China’s exports to the United States by estimating Note this equation is the same as equation (4), except that the effect of the NNTR gap is now interacted with a year dummy, and we exclude the tariff rate because it is highly correlated with the applied NNTR rate prior to 1980. Therefore, its inclusion would undermine the explanatory power of the gap during these early years.<sup>19</sup> This issue was less of a concern when we considered only the shorter time period. The MFN rate in the gap measure, however, is not an issue. The sample is restricted to NTR countries, so the good-year fixed effects control for changes in scheduled MFN rates. Finally, note the elasticities _{βt}_<sup>2007</sup> _t_ =1974<sup>capture</sup>

> 19We report estimates of equation (5) that include tariffs in Table 3. The _βt_ coefficients in the earlier years are, in fact, smaller than those from the specifications that do not include tariffs.

14
**Table 9 and Figure 14** shows the changing inequalities in and levels of child mortality in the MCH and non-MCH areas for the years 1982 and 1996.10 In the area selected for the MCH program, inequalities were higher at the start of the study period (five years after the start of the MCH program) than in the area not selected for the program. The overall child mortality rate (CMR) was lower, however. During the period 1982-96, both areas saw a reduction in their CMRs by a little over 40 percent, the reduction being slightly higher in the control area. The spread of reductions across income groups was quite different, however. The MCH area saw the largest reductions among the poorest group, the next largest among the middle group, and the smallest among the richest group, the latter seeing a very much smaller percentage reduction than the two poorest groups. By contrast, in the control area, the biggest percentage reduction was in the richest group, with the poorest group securing the next largest reduction-almost as high a percentage as that secured by the richest group. The middle group in the control area, by contrast, showed only a very small percentage reduction. The concentration curves thus tell an interesting story-in the MCH area, the concentration curve moved everywhere closer toward the line of equality, and the concentration index fell dramatically. In the non-MCH area, by contrast, the concentration curve moved closer to the line of equality at the _bottom_ of the income distribution but then moved outside the original curve, the net effect in terms of the concentration index being a rise in inequality. In both areas, the achievement (or rather _disachievement)_ index fell (despite the rise in inequality in the control area), but the bigger reduction was in the MCH area. Indeed, the inequality in the MCH area was so low in 1996 that the disachievement index was lower there than in the control area, despite the higher average MCR. This study seems to be a success story not only in terms of study design and data collection, but also in terms of shedding light on policies that can reduce levels of and inequalities in child mortality.

**Table 9. Child survival inequalities and Bangladesh's Matlab MCH program**

|_Incomegroup_|_1982_<br>_MCH_|_1996_<br>_MCH_|_Percent_<br>_change_|_1982_<br>_non-MCH_|_1996_<br>_non-MCH_|_Percent_<br>_change_|
|---|---|---|---|---|---|---|
|1|147|60|-59|173|81|-53|
|2|124|71|-43|151|126|-17|
|3|64|52|-19|98|41|-58|
|Average|101|59|**41**|131|75|43|
|**Cl**|-0.1944|-0.0442|77|-0.1347|-0.1677|-25|
|Achievement|120|62|49|148|87|41|

_Source:_ Bhuiya and others (2001).

'° The wealth groups in the study are not of equal sized, and furthermore the sizes changed over time. For reasons indicated above in connection with the World Bank 42-country study, one would like to take group sizes and their changes into account. I am grateful to Abbas Bhuiya for providing me with the group sizes, which were not reported in the original article, to construct the concentration curves, concentration indices, and averages reported in Figure 14 and Table 8.

_31_
Figure 2. ICA data on Firms’ Financing by Capital Origin <!-- Start of picture text --> Firm Financing by Type<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2003 2008 2003 2008 2003 2008 (blank)<br>Large (>500 Medium (100 to Small (<100 (blank)<br>emplyees) 500 employees) employees)<br>Private Domestic Private Foreign Government<br><!-- End of picture text --> Table 3 shows the main financing constraint of firms that declare to have been denied a loan request. The most important source of financial constraint is “Lack of collateral” (38%), followed by “Poor Credit History” (26%). Among the financially constrained firms, the majority are small firms (82%). For small firms, the problem of lack of collateral is even more binding, being the cause of 42% of the financial constraints. However, for medium and large firms this is not the case: the most important financial constraint is “Poor Credit History”. When looking at industry types of the financially constrained firms, the sectors most affected by this restriction are Garments (34%) and Furniture (32%). For both these industries, the main reason for the restriction was “Lack of collateral”.

Regarding the contribution of the various financial sources to new investments and working capital, for the former, most of the funding comes from internal sources; while for the latter most of the funding is from external sources. This could be in line with banks’ reluctance to fund riskier projects, accounted for under “New Investments”, rather than safer bets like “Working Capital” (cash, inventories, etc.). For both working capital and new investments, most of the external financing comes from Commercial Banks. However, for new investments, the picture is more even and Trade Credits and Investment Funds are also relevant actors in the funding scheme.

12
literature is the construction of asset and liability positions for each country vis-à-vis the rest of the world, using cumulative gross capital flows from balance of payments data (Lane and Milesi-Ferretti 2001, 2007). This strand of the literature shows that external assets and liabilities grew rapidly over time until the 2007–08 global financial crisis (GFC) (Gourinchas, Rey, and Govillot 2010; Obstfeld 2012; Lane and Milesi-Ferretti 2018). Since then, the growth in gross external positions overall has stagnated, reflecting a retrenchment of cross-border banking activity and the growing weight of the less integrated South in world GDP.

Third, several studies analyze bilateral international investments, as we do in this paper. Most of these papers focus on a single investment type and use gravity models to estimate the role of various determinants of capital flows and international investments (Portes and Rey 2005; Aviat and Coeurdacier 2007; Stein and Daude 2007; Lane and Milesi-Ferretti 2008; Coeurdacier and Gibaud 2011; Aggarwal, Kearney, and Lucey 2012; Hale and Obstfeld 2016; Hellmanzik and Schmitz 2017; Brei and von Peter 2018). A few papers combine data for different investment types to study the role of information frictions and institutions, investments between different regions before the GFC, and their network structure (Daude and Fratzscher 2008; Milesi-Ferretti, Tamirisa, and Strobbe 2010; Kubelec and Sa 2012).

In this paper, we use rich bilateral data sets with ample coverage across investment types, countries, and years, to analyze the rise of the South in global finance. Relative to studies that rely on the aggregate investment positions of countries vis-à-vis the rest of the world, our bilateral data shed light on the source _and_ destination of international investment. For example, our more granular data allow us to show that South-to-South investments have grown the fastest. We ensure that data on investment types are comprehensive and mutually exclusive. For example, our bilateral data on bank loans and deposits improve over previous studies by stripping out banks’ portfolio holdings, because these are already included in countries’ aggregate data on portfolio investment (CPIS). Relative to 5
has the implication that relaxing the constraints on these firms is likely to produce the largest increases in output and that scarce resources available to policy makers would be better directed toward easing such bottlenecks than in focusing on the bottlenecks reported by poorly performing firms.

The structure of the paper is as follows. In Section 2, we summarize the analytical framework used in the rest of the paper and the predictions it generates about how a firm’s evaluation of the costs of constraints imposed by its external environment would vary with its characteristics; the model is set out in detail in Appendix 1. Section 3 describes the Enterprise Survey data available for the SAR countries and how the variables in the model map onto the data. In Sections 4-6 we analyze the survey data from formal firms in the region. As expected, firms that had created jobs in the preceding three years reported higher costs of constraints than did other firms. Reported costs – shadow prices – varied in the expected way with a number of other firm characteristics such as size and location. In Sections 7-8, we use country averages of costs of constraints reported by formal sector firms to assess which elements of the external environment are more problematic for firms across the region and in each country, and in Section 9 we compare these country averages for the SAR countries with those reported by formal sector firms in other countries at a similar level of development. Section 10 compares business environment constraints reported by formal sector firms in the region with those reported by rural and informal sector firms. Section 11 concludes.

# **2.  Modeling framework**

Our modeling builds on the framework in Carlin et al. (2006, 2010, 2012). The framework allows us to interpret clearly the different business-environment related measures collected in the Enterprise Surveys and to formulate hypotheses as to how they relate to firm efficiency and growth. Our modeling framework predicts that well-performing firms report _higher_ costs of constraints. This insight has the implication that relaxing the constraints on these firms is likely to produce the largest increases in output and that scarce resources would be better directed toward easing such bottlenecks than in focusing on the bottlenecks reported by poorly performing 3
8 Table 2. Income Elasticities of Motor Vehicle Ownership and Usage

|_Study_<br>_Sample_|_Cross section_|_Time series_|_Panel data_|
|---|---|---|---|
|_Vehicle ownership at the national level_||||
|Silberston (1970)<br>38 free-market countries, 1965, cars|1.14|||
|38 free-market countries, 1965, total vehicles|1.09|||
|46 countries, 1965, cars|1.21|||
|Wildhorn and others (1974)<br>United States, 1950-73||0.88||
|Sweeney (1978)<br>United States, 1950-73||0.82||
|Pindyck (1979)<br>11 Western countries, 1955-73|||0.30|
|Wheaton (1982)<br>25 countries, early 1970s, cars|1.38|||
|25 countries, early 1970s, total vehicles|1.19|||
|42 countries, early 1970s, cars|1.43|||
|Kain (1983)<br>23 OECD countries, 1958|1.95|||
|23 OECD countries, 1968|1.59|||
|98 non-communist countries, 1977|1.30|||
|Button and others (1993)<br>58 developing countries, 1968-87, cars|||0.53- 1.12|
|29 developing countries, 1968-87, commercial ve|hicles.||0.84 - 1.50|
|Kain and Liu (1994)a<br>52 countries, 1990, cars|1.58|||
|52 countries, 1990, commercial vehicles<br>Chile, 1960-90|1.15|0.56- 0.92||
|Ingram and Liu (1997, 1998)<br>50 countries, 1970, 1980, 1990, cars|1.02- 1.21||1.12|
|50 countries, 1970,1980, 1990, commercial<br>hid|0.64- 0.94||1.12|
|vees<br>50 countries, 1970, 1980, 1990, total vehicles|0.90 - 1.09||0.98|
|Johansson and Schipper (1997)<br>12 OECD countries, 1973-92|||0.75- 1.25|
|_Vehicle ownership at the urban level_||||
|Beesley and Kain (1964)<br>45 U.S. cities, 1960|0.73|||
|Kain and Liu (1994)a<br>60 world cities, 1980, cars|1.02|||
|Chin and Smith (1997)<br>Singapore, 1968-89||0.53 - 0.61||
|Ingram and Liu (1997)<br>35 urban areas, 2 points in time, cars|0.50 - 0.91||1.83- 1.88|
|35 urban areas, 2 points in time, conmmercial<br>vehides|0.37 - 0.60||0.53|
|35 urban areas, 2 points in time, total vehicles|0.52 - 0.83||1.01- 1.03|
|_Vehicle usage at the national level_||||
|Pindyck (1979)<br>11 Western countries, 1955-73|||0.66|
|Wheaton (1982)<br>25 countries, early 1970s, cars|0.54|||
|<br>25 countries, early 1970s, total vehicles|0.53|||
|42 countries, early 1970s, cars|0.33|||
|Button and others (1993)<br>58 developing countries, 1968-87, cars|||0.71|
|<br> <br>29 developing countries, 1968-87, commercial ve|hicles||0.52|
|Johansson and Schipper (1997)<br>12 OECD countries, 1973-92|||-0.10- 0.35|
|_Vehicle usage at the household level_||||
|Mannering and Winston (1985)<br>U.S., more than 1,000 households, 1978-80||||
|households with one vehicle|0.06|||
|households with two vehides|0.11|||
|Train (1986)<br>California, 105 households, 1976-80|0.29|||
|<br>Hensher and others (1990)<br>Sydney, 1172 households, 1981-82|0.05- 0.14|||

a. Kain and Liu (1994) used per capita GDP at purchasing power parity exchange rates.
Having said this, the five case studies discussed in this paper suggest that the most restrictive criterion for a rule of origin is domestic ownership and control. Ignoring implementation challenges, it is not clear whether a domestic ownership and control criterion would satisfy the disciplines of GATS Article V. This will depend on whether the FTA in question would be an agreement involving developing countries only.<sup>17</sup> In addition, a restrictive rule of origin would come with certain economic costs: it may promote the entry of second-best service providers or force first-best service providers to depart from their most efficient global corporate structures. These outcomes would diminish the gains from service market opening in services.

While the studies reviewed in this paper present insightful evidence on an understudied topic, more research is needed to provide better guidance to policymakers on the benefits and costs of different rules of origin in services trade. From an economic perspective, the choice of rule of origin is closely linked to the broader question of the dynamic effects of trade policies in services. The limited evidence available suggests a higher overall growth potential for economies with open service markets (Mattoo et al., 2001). But little is known about the relevance of learning-by-doing effects in specific service sectors and their potential size in relation to the efficiency costs of trade protection. Equally, little is known about the relevance and size of trade diversion costs from preferential treatment in services. Surveys of multinational companies could shed greater light on the tax implications and business transaction costs associated with different geographical structures of corporations. In addition, case studies of past preferential treatment in services could shed light on whether a first-mover advantage has affected the performance of service markets even after entry was liberalized on an MFN basis.

> 17 A critical question in this respect is whether Singapore could be considered a developing country under the GATS.  The WTO has a formal list only of least-developed countries (LDCs), based on United Nations criteria.  In principle, non-LDC WTO members can decide for themselves whether they are to be considered ‘developed’ or ‘developing’ countries.  In this context, it is interesting to note that Singapore has already entered into one FTA hat has incorporated an ownership and control criterion (see footnote 10).  However, a member’s decision on developing country status may be challenged by other members.  So far, there has been no jurisprudence that could give guidance on what criteria would be used to resolve a disagreement on this question.

18
two psychosocial stimulation arms into a single treatment group (N=64) and combine the nutritional supplementation only group with the pure control group into a single control group (N=65). Henceforward we use the term stimulation effects of stunted participants to designate the analysis that compares groups 1 and 3 against groups 2 and 4.

# **3 The New Survey**

We resurveyed both stunted (experimental) and non-stunted (comparison) study populations in 2007 and 2008 some 20 years after the original intervention when the participants were approximately 22 years old.<sup>16</sup> We attempted to find all of the study participants regardless of current location and followed migrants to the the US, Canada, the UK and the Caribbean. When we could not find a participant in Jamaica, we contacted relatives for further information to find the participants.

## **3.1 The Experimental Sample**

We were able to find and interview 105 out of the original 127 (83%) stunted participants who completed the program. For this sample, Table 1 reports the baseline means for the treatment and control groups, the difference in the means of the two groups, and _p_ -values for two sided permutation tests of equality of means. We observe significant differences in 3 out of 19 variables. Mothers of children in the treatment group were more likely to be employed and have completed less schooling than mothers of children in the control group, and children in treatment group had lower weight for height than children in the control group. These imbalances are already present in the full baseline sample of 127, which suggests that they

> was more intensively shared with other family members in Jamaica, the formula provided in the Jamaica intervention had fewer micronutrients, and the supplement was a smaller share of the total food budget in Jamaica (Hoddinott et al., 2008; Walker et al., 1992, 1990.)

> 16The survey received ethical clearance from the IRB of the University of the West Indies in Kingston Jamaica.

7
We find that young plants are much smaller, since on average they employ one third of the labor and produce one fifth of the output of old plants. While the use of skilled labor by young and old plants is similar, the wages in the former are about one fourth lower than those in old plants. Young plants invest substantially more than old plants, with an investment/output ratio about 70% higher. Consequently, their capital is of a much newer vintage. This is perhaps the reason why young plants’ TFP is only about 7% lower than that of old plants, although their labor productivity is 35% lower. Finally, while young plants are half as likely to participate in export markets, when they do so, their average exports are only 20% less than those of old plants.

# **5. Main Results**

Some initial insights on the relationship between productivity and exporting can be gained from Figure 1 that shows levels and growth rates of plant TFP before, during, and after the year of entry into export markets. More specifically, the figure plots in bold lines the estimated coefficients τ ' τ of the dummy variables _Dit_ in regressions of the form _Yit_ = α + β _Z it_ + ∑ τδτ _Dit_ + ε _it_ , where 0 _a Dit_ = 1 if plant _i_ enters the export market at time _t_ ; _Dit_ = 1 ( _a_ <0) if plant _i_ will enter the export _b_ market _a_ years after time _t_ ; and _Dit_ = 1 ( _b_ >0) if plant _i_ has entered the export market _b_ years before time _t_ . _Z it_ contains year, industry, and region dummy variables, and _Yit_ is alternatively the level (in logs) and the average annual growth rates of plant _i_ ’s TFP at time _t_ over horizons of one, three, and five years. The thinner lines show 95% confidence intervals around the parameter estimates.

Panel A of Figure 1 shows that the levels of TFP jump up at the time of entry into export 19
100 _k=1_

- 6) Take the average of the mean difference over all sous-branches and all years.

- 7) Divide by the average value of exports in year t-1 over all sous-branches and years to calculate the percentage change in the mean difference.

- 8) Finally, divide by the percentage change in the exogenous variable z to yield the arc elasticity.

By simulating the model one hundred times, one can calculate not only the mean elasticity but also a standard error of the simulation. For over 90 percent of the sousbranches and years, one can not reject the hypothesis that the distribution of the one hundred solutions of the endogenous variables is normal. Thus, these standard errors can be used to form confidence intervals for the simulated elasticities.

Table III presents arc elasticities based on simulations of the model with supply equations only and non-constant returns to scale for the sample aggregated over all firms. The reported elasticities capture the partial effects of a change in one exogenous variable, holding all other exogenous variables constant. In particular, it is important to note that the elasticities with respect to changes in export prices, labor costs, and intermediate goods costs were obtdined holding domestic prices constant. These elasticities pertain to the short run, keep-ng capital fixed. Standard errors of the simulations are presented in parentheses. There is very little dispersion around the mean effect. The low standard errors reflect the fact that the price variables that are changed in the simulations were precisely estimated in Tables I and II.

Table III indicates a positive export supply response to increases in export prices. Given the estimated elasticity of 0.34, a 40 percent increase in export prices (of the order of magnitude contemplated in the subsidy cum tariff program) would be expected to lead to a 12 percent increase in exports. This is very close to the value of the shortrun own price export supply elasticity obtained by Diewert and Morrison (1988) using aggregate U.S. data. Using a very different methodology and aggregate quarterly data for Greece, Balassa et al (1986) find a short-run elasticity of around 0.6. Their estimated long run elasticities are considerably higher.

The low output supply elasticity (0.03) and the negative domestic supply elasticity (-0 11) with respect to an increase in export prices, suggests that the increase in exports comes about primarily from a decrease in sales to the domestic market. - 20 -