rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 6. Effects of Standards on Firm Export Participation, Entry, and Exit</u>**

||||<br>Dependen|<br> <br>t Variable:|||
|---|---|---|---|---|---|---|
||Export<br>Participation|Export<br>Entry|Export<br>Exit|<br> <br>Export<br>Participation|Export<br>Entry|Export<br>Exit|
||Dummyc,i,k,d,t<br>(1)|Dummyc,i,k,d,t<br>(2)|Dummyc,i,k,d,t<br>(3)|<br>Dummyc,i,k,d,t<br>(4)|Dummyc,i,k,d,t<br>(5)|Dummyc,i,k,d,t<br>(6)|
|Rel_restrictivenessc,k,d,t|‐0.074<br>[0.011]***|‐0.032<br>[0.016]**|0.009<br>[0.031]|‐0.425<br>[0.036]***|‐0.237<br>[0.049]***|‐0.098<br>[0.182]|
|Log 1+Tariffc,k,d,t|‐0.030<br>[0.005]***|‐0.025<br>[0.007]***|0.015<br>[0.008]*|‐0.192<br>[0.011]***|‐0.195<br>[0.013]***|0.053<br>[0.046]|
|Log GDP per capitad,t|0.260<br>[0.036]***|0.008<br>[0.047]|‐0.549<br>[0.095]***|1.621<br>[0.091]***|0.494<br>[0.105]***|‐5.863<br>[0.465]***|
|Log Total Exportsc,k,t|0.047<br>[0.002]***|0.045<br>[0.002]***|‐0.071<br>[0.005]***|0.276<br>[0.005]***|0.256<br>[0.006]***|‐0.352<br>[0.021]***|
|Exporting Firm‐Importing<br>Country‐Product Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Estimation method|Linear<br>Probability<br>Model|Linear<br>Probability<br>Model|Linear<br>Probability<br>Model|Conditional<br>Logit|Conditional<br>Logit|Conditional<br>Logit|
|Observations|796,636|535,056|240,074|634,930|396,153|93,606|
|R‐squared|0.351|0.242|0.703||||
|Pseudo R‐squared||||0.013|0.031|0.302|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Dependent variables are defined as follows: export participation equals one if there is positive trade between countries and zero otherwise; entry equals one if firm _i_ exports product _k_ to destination _d_ in year _t_ but did not do so in year _t-1_ and zero if the firm did not export product _k_ to destination _d_ in year _t-1_ and does not start to do so in year _t_ . Similarly, exit equals one if firm _i_ does not export product _k_ to destination _d_ in year _t_ but did so in year _t-1_ and zero if the firm exported product _k_ to destination _d_ in year _t-1_ and continues to do so in year _t_ .

34
subject to the same barriers, if any (i.e., nondiscrimination or most-favored-nation (MFN) treatment.). Liberalization then refers to actions that reduce discrimination. However, reducing discrimination may not result in a greater volume of international transactions. Thus, if government policies support a monopoly, nondiscrimination will have no effect, as entry is barred to all potential sources, whether foreign or domestic. More generally, even if entry is feasible, policy may affect the operating costs of a foreign entrant differentially from domestic firms. As a result of these considerations, the literature on services trade policies argue that a distinction needs to be made between policies that discriminate on the basis of nationality of ownership of factors of production, and whether policies affect _entry_ into a market – through whatever mode of supply, including mode 3 (establishment/FDI) – and/or the _operation_ of firms (see, e.g., Warren and Findley, 2000; Deardorff and Stern, 2006). This can alternatively be thought about in terms of whether policies affect fixed or variable costs, and, whether they create rents for incumbent (domestic) firms. Table 1 illustrates the typology of various possibilities.

The availability of information on policies that restrict international trade in services is limited.<sup>29</sup> In large part this reflects the immense difficulty of identifying and quantifying barriers to services trade, which in turn is a reflection of the historical nontradability of most services. Detailed data on goods trade flows exist because these flows are taxed – the data are an ancillary product of the application of tariffs on imports imposed at the border. As services are not observed crossing borders – at best factors of production (labor, suppliers) may be observed – governments need to survey economic activity in order to get a picture of trade in services.<sup>30</sup>

**Table 1. A Typology of Policies Affecting Trade in Services**

||Impact on entry/establishment|Impact on operations|
|---|---|---|
|Non-discriminatory|E.g., A limit of two mobile phone<br>providers permitted to operate in<br>the country;|E.g., all retail banks must have<br>personnel on call to monitor and<br>service ATMs|
|Discriminatory|E.g., nationality requirements for<br>senior managers of affiliates;<br>maximum equity ownership limit<br>for foreign investors;|E.g., car and fire insurance subject<br>to additional capital requirements;<br>cross-border provision of<br>insurance services subject to price<br>regulation|

Two different approaches have been taken to assess the magnitude and impact of policy barriers to trade. The first involves collection of information on applied policies, converting these into coverage/frequency indicators and using the resulting indices as regressors to explain

> 29 UNCTAD and World Bank (1994) and Hoekman and Primo Braga (1997) are surveys of the policy literature.

> 30 See for example, Stern and Hoekman (1987), Karsenty (2000) and Whichard (2000).

17
The lower panel of Table 4b shows that part of the increased earnings is likely to have been spent on non-food items given the positive and significant impact on this expenditure measure in the ANCOVA specification. We do not see any increase in other expenditure categories (in general, however, the sign of the estimated coefficients on treatment is positive). This is consistent with Getahun and Villanger’s (2018) finding of large industrial job effects on non-food expenditure largely driven by the improved spending of young female workers on clothing and shoes.

|**Table 4b: In**|**come and Expenditures**|||||
|---|---|---|---|---|---|
||||Mean (Control|||
|Dependent var|iables|Obs.|<br>group,follow-up)|ITT Difference|ITT ANCOVA|
|**Earnings**|Total cash earnings in the past 7 days (in|827|148|45.9***|40.0**|
||birr)||[203]|(15.6)|(15.6)|
|||||{0.008}***|{0.026}**|
||Total cash earnings in the past 4 weeks (in|827|610|209***|194***|
||birr)||[763]|(62.2)|(61.8)|
|||||{0.007}***|{0.013}**|
|**Expenditure**|Food expenditure in past 7 days (in birr)|827|125|4.17|9.92|
|**& Savings**|||[125]|(10.5)|(9.43)|
|||||{0.30}|{0.214}|
||Non-food expenditure in past month (in|827|340|42.5|46.2*|
||birr)||[349]|(27.9)|(26.9)|
|||||{0.158}|{0.13}|
||Savings from earnings in past 7 days (in|827|32.1|5.80|7.93|
||birr)||[89.2]|(10.6)|(10.7)|
|||||{0.30}|{0.298}|
||Savings from earnings in past 4 weeks (in|827|68.6|26.3|25.3|
||birr)||[211]|(17.7)|(17.9)|
|Notes: The tab|le reports the intent-to-treat (ITT) estimates of|the effect o|f the job facilitation i|{0.158}<br>ntervention on inco|{0.188}<br>me and|

Notes: The table reports the intent-to-treat (ITT) estimates of the effect of the job facilitation intervention on income and expenditure. Standard deviations in brackets. In the last two columns, we report standard errors in parenthesis and _q_ -values in curly brackets underneath each estimated coefficient. _q-values_ are calculated following the sharpened procedure proposed by Benjamini et al. (2006). *** p<0.01, ** p<0.05, * p<0.1.

In addition, we find that the standardized strenuosity score increased significantly which mirrors the adverse health impacts reported by Blattman and Dercon (2018).<sup>15</sup> Table 4c shows that the two strenuosity scores we construct are positively affected by treatment. Both indices are based on

> 15 In a five-year follow-up study recently published, Blattman et al., (2019) find that the adverse health effects observed after one-year follow-up dissipate over time.
explanation is worrisome. Determining whether and which of these two possible explanations applies in each of the countries concerned is beyond the scope of this paper, but is clearly an important question for further research.

It should be stressed that the differences in the content of the coverage of the two indices used make our findings merely suggestive. Clearly much more research and effort to “map” actual policies is required. For example, it is not clear whether our finding is a reflection of the methodology underlying the indices used, noncompliance with multilateral commitments, or the limited commercial relevance of the type of commitments that are required under the GATS. With the information available it is not possible to go beyond identifying the questions. However, insofar as our finding is not simply a reflection of the underlying data weaknesses that affect the level and change over time of the indices, there may be significant limitations to the relevance of the WTO for small (poor) countries as a commitment device. An element of a possible solution could be to devote more attention and resources to multilateral monitoring of compliance with commitments, the actual policies that are applied and their effects.

# **References**

- Bagwell, Kyle, and Robert Staiger. 2002. _The Economics of the World Trading System_ . Cambridge, MA: MIT Press.

- EBRD (European Bank for Reconstruction and Development). 2004. _Transition Report 2004_ . London: EBRD.

- Eschenbach, Felix, and Bernard Hoekman. 2005. “Services Policy Reform and Economic Growth in Transition Economies,” World Bank Policy Research Working Paper 3663, Washington, DC.

- Hoekman, Bernard. 1996. “Assessing the General Agreement on Trade in Services,” in Will Martin and Alan L. Winters (eds.), _The Uruguay Round and the Developing Countries_ , Cambridge: Cambridge University Press, 88-124.

- Hoekman, Bernard, and Michael M. Kostecki. 2001. _The Political Economy of the World Trading System_ . Oxford: Oxford University Press.

- Hoekman, Bernard, and Patrick Messerlin. 2000. “Liberalizing Trade in Services: Reciprocal Negotiations and Regulatory Reform.” In _GATS 2000_ : _New Directions in Services Trade Liberalization_ , ed. Pierre Sauvé and Robert M. Stern, Washington, DC: Brookings Institution, 487508.

- Langhammer, Rolf. 2005. “The EU Offer for Service Trade Liberalization in the Doha Round. Evidence for a Not-Yet-Perfect Customs Union,” _Journal of Common Market Studies,_ Vol. 43, 2: 311-325.

- Tumlir, Jan. 1985. “Trade Policy in Democratic Societies.” American Enterprise Institute, Washington, DC.

20
non-SUSETI districts. Second, in many developing countries and in particular in Indonesia, a number of social safety nets target those same types of households.<sup>12</sup> Although the SUSETI sample is not statistically representative of the whole country (or even the given districts), it has several unique features that make our results internally valid in terms of the primary goal of evaluating and decomposing the targeting performance of the UDB. First, the survey incorporated a rigorous matching process to enable the identification of households registered in the UDB. Desk-based matching was first conducted using the names and addresses of household heads and spouses, and the matching results were also verified in the field. Out of the 5,682 households surveyed in the SUSETI, 2,444 or 43 percent are registered in the UDB with an additional 1,048 households on the pre-listing but not ultimately registered in the UDB. This coverage compares favorably to the overall share of these six districts’ population registered in the UDB (41 percent). The matching process and its results are described further in Appendix S1.

During the matching process, we also identified SUSETI households on the enumeration pre-listings but not registered in the UDB. This additional subgroup allows us to investigate possible enumeration errors that may occur as a result of the addition and removal of households based on community suggestions. Such _misenumeration_ could occur if those added (removed) have on average a higher (lower) socioeconomic status than those that end up being registered in the UDB.

A second important feature of the SUSETI is the availability of information on receipt of the main social protection programs prior to the establishment of the UDB. By comparing the performance of the centralized UDB targeting approach with its more fragmented predecessors, it is then possible to evaluate the change in targeting accuracy for different programs transitioning to using the UDB. At the time of fielding the SUSETI endline in early 2012, the UDB had not yet been used for targeting

> 12 At the same time, many countries also implement programs targeted at other groups such as the elderly and those with disabilities. In a survey of programs across sub-Saharan African countries, Cirillo and Tebaldi (2016) find that nearly 60 percent of programs target households with children, 32 percent with elderly, and 28 percent with disabilities of some sort (with many cross-cutting programs). Meanwhile, according to Honorati et al. (2015), the number of countries implementing conditional cash transfer programs for schooling and health has more than doubled between 2008 and 2014, from 27 to 64 in 2014, and school feeding programs are among the more common programs in developing countries.

12
**Table 6 Past and projected financing and debt relief for Sub-Saharan Africa, 1985-88, 1995, 2000 (average annual percent of GDP unless indicated otherwise)**

||1985-88<br>1995|2000|
|---|---|---|
|Total foreign financing required|||
|US$ billion|14.8<br>26.5|32.0|
|Percent of GDP|10.2<br>14.2|12.7|
|Gross foreign inflows projected|10.1<br>11.9|11.9|
|of which: Loan disbursements|6.5<br>6.2|_5.8_|
|Official transfers|3.1<br>_5.0_|5.3|
|Direct foreign investment|0.4<br>0.7|0.8|
|Net debt relief a/|.. b/<br>1.2|-0.7|
|Residual financing gap|0.1<br>1.1|1.5|
|Memo items:|||
|Debt service indicators after debt relief|||
|Debt service ratio (percent of exports)|31.2<br>22.5|17.7|
|Debt service|6.5<br>6.6|6.1|
|Notes: a/<br>Net of moratorium obligations on consolidate|d amounts.||
|b/<br>Debt relief and accumulation of arrears hav<br>calculating financing requirements because<br>payments, not obligations.|e already been taken int<br>historical debt service|o account in<br>is shown as|

Sources: World Bank data and projections.

Changes in underlying assumptions and targets will affect the projections, mostly because of changes in the trade balance. A few examples illustrate the range of possible effects. If the GDP growth target were one percentage point higher in each year (which implies that the target proposed in Sub-Saharan Africa: From Crisis to Sustainable Growth would be achieved by 1995) -- and there were no improvement in import efficiency, gross financing requirements would rise by 20-25 percent, or by $6-7 billion a year on average. If the assumed export growth were one percentage point higher in each year (bringing it closer in line with that of other developing countries) - - and there were no offsetting increases in imports, gross financing requirements would fall by about 15 percent, or some $4 billion a year on average. If the projected terms of trade index were one percentage point below 41
when the parent has a high level of education (upper secondary or some tertiary), and even in that case the associations are smaller than for women. For 19 to 24 year olds, evidence regarding the importance of socioeconomic context is mixed: for men, the higher the education level of the household head, the lower the probability of being _Nini_ ; while for women, the education of the head of the household is not related to the probability of being _Nini_ (Appendix Table A2).

Figure 9. Proportional Change in the Probability of Being Nini to Changes in Parental Education, Base Group is No Education 1, Youth Aged 15-18 y.o., by Gender <!-- Start of picture text --> (a) Male (b) Female<br>0.05 0.05<br>0.00 0.00<br>-0.05 -0.05<br>-0.10 -0.10<br>-0.15 -0.15<br>-0.20 -0.20<br>Incomplete Complete Complete Media or Incomplete Complete Complete Media or<br>Primary Primary Secondary Tertiary Primary Primary Secondary Tertiary<br><!-- End of picture text --> _Notes_ : The value of the bars represent the marginal effects on the probability of being Nini for the corresponding variables from the underlying estimation of multinomial logit models (one for each gender). The dependent variable in the multinomial models takes three states (Nini, Work Only, and Study Only). Only marginal effect for the outcome of Nini is presented in this graph. The models include controls for history of past decision indices, youth’s own education level, parental education level, household income quintiles, a set of demographic variables, and state economic condition variables. Complete results are presented in Appendix Table A1. _Source_ : Authors’ calculations based on model estimation using data from the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

The education of the household head also matters for youths’ decisions to _study only_ and to _work only_ . For men and women in both age groups, more parental education is associated with an increased probability of _studying only_ , and with a reduced probability of _working only_ . The estimates are large in magnitude, and in many cases larger than those for the outcome of being a _Nini_ (in absolute values). For example, for those in the 15 to 18 age range, we find an increased probability of _studying only_ of 13 p.p. and a reduced probability of _working only_ of 11.6 p.p. for men who come from households where the head has completed secondary school (compared with the base group of no education). Similarly, the higher the level of education of the head of the household, the larger the associations with the probability of _studying only_ and _working only_ (more positive and more negative, respectively). This evidence is consistent with 26
18 fluently and had been able to observe at first hand why Japan had developed so successfully . The influence of US economists on governmen **t** hinking was strong in Taiwan, a good deal less so in Korea. For Hong Kong, observers agree that the colony chose the one course open to it. No one has been privy to Lee Kwan-yu's inner thoughts but from hearsay19 he decided, after listening patiently to the advice of Northern development economists, that the strategy of planning and import substitution which they proposed was wrong, and set his country on its very successful course. Whatever the motivations, the developing world can be grateful that four governments initiated a policy pattern that would over the years revolutionize policy making throughout the South.

# **7) Half a Century of Historical Experience and** Data

It is data that has driven the progress of development economics, data and the imaginativeness of economists who realized that accepted theory was empirically wrong. Appropriately, the _Handbook_ devotes significant space to the factual knowledge that has accumulated over a half century of development.

The space constraint makes it impossible to detail those chapters. Empirical facts have had such a great impact on development economics that they will sneak their way back over and over again into the argument.

# 7.1) Experiments by Nature

As the founders of modern econometrics at the Cowles Commission emphasized, true experiments are so hard to arrange that economists have to rely on "nature" to design the experiments that verify our theories. We have just seen an example of this truth in our discussion of the policy innovation of the Four Dragons' leaders. The results of those "natural experiments" are the only data that policy makers pay heed to.

Hence the importance of the excellent _Handbook_ chapters 40 and 44 by Anne Krueger on policy lessons from post war experience, and by Corbo and Fischer on structural adjustment.

# 7.2) "Stylized Facts" of Development and the Fashion for Cross-Country Regressions

Development economics is indebted to the economists who, using patience and simple tools, built up and analyzed the quantitative record of development. An excellent chapter 7 by Moshe Syrquin reminds us of the insights that we owe to Kuznets and to Chenery in this area of research, and to the purchasing power data set up by Kravis, Summers, and Heston and later by Summers and Heston; as well as to Paul Bairoch's work on the historical role of agriculture and trade, Angus Madison's work on the historical record of growth, Goldsmith's work on financial data; and many other contributions. Development economics started out with the fact that development implies industrialization; much of the rest we owe to those men.

18 For example Vogel (1991), pp 48-9.

> 19 The source is Helen Hughes, who has extensive and long standing contacts in that island.

_13_
# **Table** **<u>6. Robustness Check for the OLS Estimator</u>**

|Variable|OLS|Huber/White|Huber/White|Huber/White|WLS|WLS|
|---|---|---|---|---|---|---|
|Tariff Rates|-1.155***|-1.155***|-1.155|-1.155**|-1.467***|-0.483**|
||(0.318)|(0.399)|(0.868)|(0.561)|(0.343)|(0.246)|
|Port Efficiencyof Importer|0.307*|0.307*|0.307|0.307|0.246|0.473***|
||(0.163)|(0.161)|(0.414)|(0.204)|(0.157)|(0.119)|
|Port Efficiencyof Exporter|0.924***|0.924***|0.924***|0.924|0.913***|0.537***|
||(0.148)|(0.166)|(0.179)|(0.978)|(0.142)|(0.137)|
|Customs Environment of Importer|0.472**|0.472**|0.472|0.472*|0.472**|1.112***|
||(0.199)|(0.198)|(0.480)|(0.259)|(0.193)|(0.147)|
|RegulatoryEnvornment of Importer|0.281*|0.281**|0.281|0.281*|0.288**|-0.069|
||(0.144)|(0.141)|(0.304)|(0.145)|(0.138)|(0.107)|
|RegulatoryEnvornment of Exporter|0.620***|0.620***|0.620***|0.620|0.594***|<br>0.180|
||(0.132)|(0.144)|(0.163)|(0.867)|(0.127)|(0.118)|
|Service sector infrastructure of Importer|0.729***|0.729***|0.729|0.729***|0.647***|0.494***|
||(0.224)|(0.241)|(0.771)|(0.262)|(0.227)|(0.166)|
|Service sector infrastructure of Exporter|1.943***|1.943***|1.943***|1.943|1.831***|2.336***|
||(0.216)|(0.234)|(0.242)|(1.270)|(0.208)|(0.189)|
|GNP of Importer|0.915***|0.915***|0.915***|0.915***|0.931***|0.892***|
||(0.014)|(0.015)|(0.044)|(0.018)|(0.014)|(0.010)|
|Per capita GNP of Importer|-0.182***|-0.182***|-0.182*|-0.182***|-0.183***|-0.227***|
||(0.037)|(0.038)|(0.099)|(0.056)|(0.037)|(0.028)|
|GNP of Exporter|1.246***|1.246***|1.246***|1.246***|1.239***|1.169***|
||(0.014)|(0.015)|(0.020)|(0.082)|(0.014)|(0.012)|
|Per capita GNP of Exporter|-0.226***|-0.226***|-0.226***|-0.226|-0.231***|-0.153***|
||(0.029)|(0.030)|(0.032)|(0.197)|(0.028)|(0.022)|
|Geographical Distance|-1.258***|-1.258***|-1.258***|-1.258***|-1.238***|-1.143***|
||(0.025)|(0.022)|(0.048)|(0.092)|(0.025)|(0.018)|
|Robust Standard Error|No|Yes|Yes|Yes|||
|Cluster||No|Importer|Exporter|||
|Weighted Least Square|||||Yes|Yes|
|Cluster|||||Importer|Exporter|
|Adjusted R-squared|0.759|0.759|0.759|0.759|||
|Chi-squared against all b beingzero|||||26,755|38,700|

Note: The significance levels at 10%, 5% and 1% are denoted by “*”, “**”, and “***”, recpectively. Source: Authors’ calculation.

35