rafmacalaba/datause-encoder-data huggingface dataset:
when the countries’ major objective of the reform is export promotion and there are balance-of-payments concerns.

Finally, in considering the specific nature of capacity building, it is critical go to the country detail. The panels of Figure 10 show, for example, that Guatemala has a great potential for trade gains from its domestic reform in service sector infrastructure. In contrast, for Indonesia, the gains from regulatory reform dominate those associated with the enhancement of service sector infrastructure. Finally, in Nigeria, the reform in its customs system could have the most valuable outcome. Across all the countries considered, domestic reform will have much larger impact on total trade (imports plus exports).

# **6. Conclusions and Approach to Capacity Building Design**

The analysis in this paper builds on the method developed in Wilson, Mann and Otsuki (2003). Four indicators of trade facilitation are developed: port efficiency, customs environment, regulatory environment, and e-commerce use by business (as a proxy for service sector infrastructure). These indicators are implemented in a gravity model of trade. Simulations are designed that take account of the differential character of trade facilitation in each country as measured by each of the four categories. Using this set of indicators, modeling approach, and simulation design offers policymakers more information about what type of trade facilitation efforts might provide the largest gains in terms of increasing trade flow.

The improvements to this paper include broadening the country set to 75 countries. In addition, a better measure of regulatory environment was constructed that is less ambiguous in interpretation of its impact on trade.

A particularly crucial improvement in this paper is to consider the effect on bilateral trade flow of trade facilitation reform both from the standpoint of the reforming country’s exports and its imports. In the earlier paper, a country gained in exports on account of the improvements to its trading partner’s trade facilitation efforts. In this new specification, a country can increase its exports unilaterally through trade facilitation efforts. This will provide information that is useful if a country looks to trade facilitation reforms as a strategy of export promotion.

The total gain in trade flow in manufacturing goods from trade facilitation improvements in all four areas is estimated to be $377 billion; all regions gain in imports and exports. Most regions gain more in terms of exports than imports in large part through increasing exports to the OECD market. The most important ingredient in getting these gains, particularly to the OECD market, is the country’s own trade facilitation efforts. In terms of regional analysis, South Asia has the greatest potential for both export and import growth, with export gains greater than import gains. In contrast countries in Africa and the Middle East have relatively small export gains compared to import gains because they are less integrated into the global trade in manufactures, and 21
**Figure 5. Death rates by per capita GDP**

**Figure 6. Comparison of labor productivity at baseline for firms that closed vs. survivors**

Includes data from BENINFORM, GHANAFLYP, IFLS, KENYAGETAHEAD, MALAWIFORM, MXFLS, NGLSMS-ISA, NGYOUWIN, SLKFEMBUSTRAINING, SLKINFORMALITY, SLLSE, SLMS, TOGOINF, TTHAI.

31
85

# **D Estimation Details**

## **D.A Preference Parameters**

**Engel Elasticity.** To estimate the Engel elasticity ( _η_ ), we start from model equation (13). Rearranging, taking logs, and adding household ( _h_ ) subscripts, we obtain the following equation:

We then collect relevant terms into a region fixed effect and add a household size control variable and an error term, obtaining equation (31).<sup>112</sup> As explained in Section VI.A.1, equation (31) is estimated using Two-Stage Least Squares (2SLS) with two IVs: income obtained from lottery (and other _ad hoc_ gains) and income obtained from disasters and other relief payments. We use Weighted 2SLS estimation, weighing observations with the sampling weights available in the HIES data, and with standard errors clustered at the Primary Sampling Unit level. Complete estimation results can be seen in Column (1) of Appendix Table A7, including the estimated coefficients, standard errors, and first-stage results.

**Cross-crop Elasticity of Substitution.** To estimate _σA_ , we first take logs of equation (17) and add household ( _h_ ) subscripts, obtaining:

We then collect relevant terms into crop- and household-specific fixed effects.<sup>113</sup> Finally, we add an error term to obtain equation (32), which is then estimated using Weighted 2SLS with standard errors clustered at the household level, with crop price ln( _Pnhk_ ) instrumented by a dummy variable _Dnk_<sup>_GAEZ_</sup> that indicates whether region _n_ has any growing potential for crop _k_ (according to the FAO-GAEZ data). The crops included in the estimation sample are: rice, maize, groundnut, onion, potato, coconut, and tea. See Column (2) of Appendix Table A7 for the complete estimation results. **Cross-origin Elasticity of Substitution.** As explained in the text, _σK_ is set so that the average increase in agricultural producer prices generated by the model-driven counterfactual exercise of Section VII.A matches the corresponding increase that is observed in the data. Formally, for each candidate value _sK_ for parameter _σK_ , we compute the corresponding counterfactual average price increase _p_ ̄<sup>ˆ</sup><sup>_cf_</sup> ( _sK_ ) as follows: 112 Region-specific fixed effects are defined as: _ωn ≡_ ln( _ν_ ) + _ηφ_ ln( _P An_<sup>) + (1</sup><sup>_−η_)</sup><sup>_φ_ln(</sup><sup>_P M_</sup> _n_<sup>).</sup> 113 Crop- and household-specific fixed effects are defined respectively as: _ωk ≡_ ln( _bk_ ) and _ωh ≡_ ( _σA −_ 1) ln( _P Anh_<sup>).</sup>
21 The second intuition building example is the comparison of the Eastern European countries (EE) with their West and South European counterparts. Even prior to the beginning of the recent structural reforms the EE had very high levels of education and yet massively lower levels of output (the large falls in per capita income at roughly given levels of education since the reforms of course only reenforce this point)<sup>34</sup> . Average years of schooling were almost as high in the three EE countries for which we have comparable data and yet output per worker was only a third as high. Educational attainment was much higher in EE than in the Southern European countries, yet again GDP per worker was only about half.

Table 6: Comparisons of years of schooling and GDP per worker, some Eastern European countries versus averages or Western and Southern Europe

||Years of<br>Schooling,<br>1985|Real PPP GDP per Worker,<br>1985<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|
|---|---|---|
|Poland|8.4|7,388|
|Hungary|10.7|10,565|
|Yugoslavia|7.2|9,892|
|Western Europe|9.3|28,471|
|Southern Europe|6.7|18,772|

The third example is that of the countries and regions that are often cited educational overperformers, such as Sri Lanka, Costa Rica, Jamaica, and Kerala state in India. For instance, years of schooling in Sri Lanka, at 5.37 B-L (or 6.07 N-S-D) are more than a full year of schooling higher than would have been predicted on the basis of per capita output. The positive spin to put on these countries performance is that they have had persistently high educational levels for their level of income. Yet the flip side is that these countries have had persistently very low levels of output for their level of education,

**34** These high levels of education appear to reflect real levels of achievement in EE countries. In an internationally comparable test of reading ability of 14 year old Hungarian. Slovens and E. German students all scored above Denrnark or W. Germany (Elley 1994). In internationally comparable science exams Hungary had the highest score (Posthiewaite and Wiley 1992).
-24 -

as the maximum that a family should pay for its minimum water requirements (about 25 liters per capita per day). Over 90% of the SARs reviewed specified an affordability measure. The basic needs water requirement averaged 2.7% of income for the poorest households in the project areas. Because water projects tend to benefit entire communities, and because the consumption of poorer households is a small percentage of total consumption, distributional concerns can often be accommodated wtihin the framework of efficiency pricing.

92. The housing sector faces a different set of circumstances. Its projects are often designed specifically to address **a** distributional problem and thus are exclusively targeted to low income populations. This is especially true of slum upgrading projects, although it is also true of many **sites** and service projects. Families that have the ability to pay for adequate shelter can generally find it in the private housing market. Thus the scope for intrasectoral cross-subsidy is more limited than it is in water.

93. For housing projects, the design phase typically includes collecting information on income levels of the target population. Project costs are estimated, sales prices and loan terma are worked out, and the latter are compared with incomes, using a rule of thunb that poor families cannot spend'more than 20-25% of their income on shelter. If the comparison excludes too large a portion of the target population, then plots are downsized or standards are reduced. The overriding concern is that the project be "replicable," in the sense that cost recovery should be high enough that financial costs are covered over the long term. This pattern was followed, and quantification was provided, in all of the SARs reviewed in this sector.

94. While this approach has the advantage of being easily applied and easily explained to government policy makers, it suffers from several drawbacks. The overriding attention to distributional objectives has meant that efficiency prices are not explicitly estimated. Costs are calculated in financial terms, often with both land and interest rates below economic levels. The objective of replicability focuses attention on aggregate levels of cost recovery rather than on the structure of marginal costs for the different project components; e.g., land, location, water, electricity, communal service facilities, house construction, etc. Follow-up studies on existing projects have shown that there is often a relatively broad mix of income levels represented among beneficiaries, so the scope for efficiency pricing may be larger than supposed.

# Differential Prices

95. If low income consumers are to be assisted through price subsidies, then it would be cost-effective to limit those subsidies to the poor through a differential price system. In many sectors this is infeasible because the poor cannot be readily identified. However, in the power, water and education sectors, techniques have been devised to achieve this.
# **Table 10: : Receiving SMS vs. Reporting Fewer Visits**

|||Fewer V|isits=1; D|o not=0||||Fewer V|isits=1; Do|not=0||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|source|0.274***|0.263***|0.255***|0.023***|0.023***|week|0.193***|0.214***|0.154***|0.032|0.034|
||(0.007)|(0.007)|(0.007)|(0.006)|(0.006)||(0.019)|(0.020)|(0.022)|(0.021)|(0.021)|
|Urban||-0.040***|-0.040***|||Urban||-0.035***|-0.036***|||
|||(0.011)|(0.010)|||||(0.011)|(0.011)|||
|Ln(income pc)||0.034***|0.035***||0.003*|Ln(income pc)||0.038***|0.038***||0.003*|
|||(0.002)|(0.002)||(0.002)|||(0.002)|(0.002)||(0.002)|
|Constant|0.688***|0.596***|0.551***|0.745***|0.736***|Constant|0.743***|0.637***|0.575***|0.750***|0.740***|
||(0.006)|(0.009)|(0.034)|(0.001)|(0.005)||(0.005)|(0.009)|(0.034)|(0.000)|(0.005)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.065|0.097|0.158|0.001|0.001|R-squared|0.003|0.041|0.104|0.000|0.001|
|Unique individuals|2,153|2,153|2,153|2,153|2,153|Unique individuals|2,153|2,153|2,153|2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 16
**Figure 1a: Currency Cyclicality Index (CCI), for Nominal Effective Exchange Rate**

<!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4 Procyclical Acyclical Countercyclical<br>-0.6<br>CCI<br>EstoniaSwitzerlandIrelandNetherlandsLithuania LatviaChina HKBulgariaLuxembourgJapanGermanyCyprusFranceUnited StatesBelize Austria Costa RicaBoliviaMacedoniaBelgiumChina Mainland NorwayMoroccoCanadaItalyMaltaSloveniaIsrael CroatiaDenmark GeorgiaSouth AfricaUnited KingdomRussia SpainPoland HungaryAustraliaIndia Peru GreecePortugalSlovakiaColombiaChileBrazilCzech RepubFinland Iran RomaniaSweden ArgentinaIcelandPhilippinesNew Zealand MexicoThailandMalaysiaTurkeyUkraine UruguayIndonesia Korea Rep<br><!-- End of picture text -->

**Figure 1b: Currency Cyclicality Index (CCI), for Real Effective Exchange Rate**

<!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>Procyclical Acyclical Countercyclical<br>-0.6<br>CCI<br>JapanSwitzerland Lithuania Netherlands France Costa Rica CyprusEstonia NorwayGermanyBelize Latvia Ireland LuxembourgAustria BelgiumUnited States PortugalMalta ItalyPhilippinesCroatia Slovenia Morocco Canada GeorgiaBolivia China Mainland United KingdomPeru South Africa Denmark HungaryAustralia Poland Greece Iran Romania India Israel BulgariaMacedonia Russia Finland SpainColombia Brazil Iceland Sweden New Zealand Thailand MalaysiaTurkeyUkraine Indonesia Mexico Chile ArgentinaUruguayKorea RepSlovakia Czech Repub<br><!-- End of picture text -->

Note: Correlations between exchange rates and GDP are calculated using quarterly data and HP trend filtered series. Two-tail tests with significance at 10 percent level are used to divide the countries into procyclical (positive and significant correlation), countercyclical (negative and significant correlation) and acyclical (non-significant).

6
On average, we expect to find a positive correlation between foreign banks’ subsidiaries and parent banks’ distance to default, after controlling for country level averages in the distance to default of all companies in the home and host countries and for global factors such as the VIX index. In other words, we expect that the financial health of the parent will be associated with that of the subsidiary. How large this correlation will be is an empirical question that we hope to address.

A number of theoretical papers emphasize the role of capital, profitability, asset liquidity and funding structure as potential buffers in absorbing liquidity and economic shocks (Allen and Gale, 2000; Repullo, 2004; Von Thadden, 2004; Diamond and Rajan, 2005; Cifuentes et al., 2004). We expect that the correlation between the distance to default of the subsidiary and the parent will be higher for more fragile foreign subsidiaries (i.e., those lacking the means to absorb shocks at the beginning of the crisis). At the same time, we expect the correlation between the subsidiary and the parent to be lower for subsidiaries operating in countries where the regulatory authorities impose tighter regulatory regimes, which _de jure_ or _de facto_ help to ring fence the foreign subsidiary from a parent in distress.

We also explore the importance of two measures of distance/proximity between the subsidiary and the parent: _geographical distance_ (log of distance, measured in kilometers, between the parent/home country and the host country) and _cultural proximity_ (as measured by whether the subsidiary and the parent have a common official language)<sup>12</sup> . A priori, we expect geographical distance to reduce the correlation between the parent and subsidiary distance to default, since geographically distant subsidiaries may be less integrated into the parent group and the parent bank might find it difficult to exercise control over the local management of the

> 12 Both variables, geographical distance and cultural proximity, were taken from Mayer and Zignago (2011). See http://www.cepii.fr/anglaisgraph/bdd/distances.htm.

13
# **Figure 6: Average Constraint Imposed on Labor Productivity, by Province**

<!-- Start of picture text -->
1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Central North Central North Western Sabaragamuwa Southern Uva Western<br>Government Ownership Export participation<br>Frequency of worker training Competition with foreign firms<br>Interest rate of loans Share of investment financed externally<br>Losses from crime, theft & disorder (% sales) Percent of electricity from generator<br>Source: Authors' calculation.<br>Note: "Average constraint" calculated as the product of estimated coefficient from reduced-form regression and the average constraint incidence in the<br>reference segment.<br><!-- End of picture text -->

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**A9 China**

**Table A9.1: Regional disparity trends in China**

|Year|Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Deviation<br>(Rw)|Unweighte<br>gini index<br>**(Gu)**|d Weighted<br>gini index<br>**(Gw)**|Theil<br>index (T)|
|---|---|---|---|---|---|---|---|
|1978|14.311|0.994|1.673|0.814|0.412|0.246|0.167|
|1979|12.499|0.938|1.501|0.765|0.375|0.235|0.152|
|1980|12.432|0.923|1.471|0.759|0.394|0.233|0.146|
|1981|_12.442_|0.891|1.403|0.741|0.362|0.226|0.138|
|1982|10.254|0.844|1.298|0.710|0.350|0.219|0.127|
|1983|9.739|0.809|1.197|0.667|0.346|0.218|0.121|
|1984|8.739|0.778|1.135|0.677|0.342|0.219|0.117|
|1985|9.131|0.757|1.086|0.606|0.337|0.214|0.104|
|1986|8.527|0.729|1.018|0.586|0.334|0.218|0.102|
|1987|7.991|0.698|0.950|0.566|0.329|0.219|0.098|
|1988|7.630|0.675|0.905|0.570|0.324|0.222|0.097|
|1989|7.321|0.652|0.882|0.580|0.317|0.222|0.094|
|1990|7.309|0.644|0.882|0.581|0.311|0.213|0.089|
|1991|**7.483**|**0.683**|0.976|**0.662**|0.334|0.236|0.106|
|1992|8.563|0.699|0.972|0.653|0.339|0.239|0.109|
|1993|9.352|0.682|0.960|0.681|0.342|0.251|0.115|
|1994|9.648|0.635|0.874|0.663|0.332|0.248|0.108|
|1995|9.690|0.618|0.822|0.626|0.326|0.244|0.103|

# **Table A9.2: Regional disparity trends in China (new)**

|Year|Max/Min<br>**(MMR)**|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Deviation<br>**(Rw)**|Unweighte<br>gini index<br>(Gu)|d Weighted<br>gini index<br>(Gw)|Theil<br>index (T)|
|---|---|---|---|---|---|---|---|
|1993|9.323|0.664|0.931|0.680|0.339|0.252|0.113|
|1994|9.790|0.662|0.920|0.687|0.343|0.255|0.114|
|1995|10.223|0.669|0.900|0.664|0.345|0.253|0.112|
|1996|11.022|0.672|0.894|0.653|0.343|0.247|0.108|
|1997|11.625|0.692|0.924|0.666|0.351|0.250|0.111|
|1998|12.183|0.709|0.952|0.679|0.357|0.254|0.115|
|1999|12.507|0.730|0.987|0.694|0.365|0.264|0.125|

43
Forum Global Competitiveness report as well as the World Competitiveness Yearbook from IMD Lausanne and the Maritime Transport Costs and Port Efficiency from the World Bank Group. This data is not always available for all countries. In addition it can only be used with caution, while resulting mostly from qualitative analysis.

Our study differs from the paper of Wilson et al. (2003) as we do not try to measure trade facilitation as such. Instead we set up to isolate and evaluate the causal effect of one program assumed here to affect trade and then interpret its effect given the knowledge we have about the program specific characteristics. In this case the robustness of the method of estimation is more important.

We set out to explore the effect of private inspection on trade volumes in this paper, using the most common vehicle to test the effect of policy changes on trade volumes: the gravity equation. We estimate a standard version of the gravity equation with importer and exporter fixed effects on a large panel of countries tracked over twenty-six years.

Estimating the effect of private inspection on trade flows is a standard case of “treatment effects”. We use a difference-in-differences (DID) estimator which consists, intuitively, of comparing the difference in an outcome variable (here the volume of trade) between before and after the treatment for a “treatment group” (here the countries that had private inspection programs at some point) and for a “control group” (here the countries that never had private inspection programs). The average effect of the treatment on the treated is then the estimate of the coefficient on a dummy variable equal to one during the treatment period (private inspection program) for the treatment group (private inspection-using countries).

DID estimation raises two specific issues. First, as discussed in Bertrand, Duflo and Mullainathan (2004), possible serial correlation in the outcome variable (here trade) is exacerbated by very strong autocorrelation in the treatment variable (a binary variable that changes value only once or twice in the sample period). The result, they show, is a high probability of type-I error (rejecting the null hypothesis of no effect when it is true, i.e. when there is no effect).<sup>7</sup> We control for this using the two-step method suggested in their paper. In the first stage, we run a standard panel gravity equation for all countries and years and retrieve the residuals. In the second stage, we keep those residuals only for the treatment group (countries that had a private inspection program at some point), take their without- and

> 7 Performing repeated estimations on “manufactured data” with non-existent (placebo) treatments, they showed that the null was rejected in up to half the cases when it should be no more than 5% if standard errors had been correctly estimated. Page | 6