rafmacalaba/datause-encoder-data huggingface dataset:
accounted for only 19.6% of export growth; of that, 18% came from the export of existing products to new markets.

Another, time-honored strand of the trade literature, going back to the work of Hanson (1996), has emphasized the formation of regional production networks by multinational firms. According to this literature, a country’s exports may be determined by the outsourcing decisions of multinationals based in other countries. Anecdotal evidence also suggests that it is retailers who decide which foreign suppliers (and hence countries) are included in cross-border supply chains. Thus, for producers located in developing countries, export opportunities are, at least partly, driven by the policies of large buyers in OECD countries. If those buyers decide to concentrate on a few suppliers in order, say, to simplify logistics or quality-control processes, opportunities will be fewer for entrants at every level of productivity and trade costs. Put differently, given the continued importance of OECD markets for developing-country exports, it seems difficult to understand how developing-country exports evolve without looking at how OECD imports evolve. This is what we set out to do in this paper.

Using a very large database of OECD imports at the SITC4 level since 1963, we find that, overall, OECD markets have been diversifying their sources of supplies (geographically) at the product level. This is reflected in decreasing concentration indices and a rising number of export sources. However, the trend in concentration has reversed itself in recent years. We show that this trend reversal is entirely explained by the rising share of Chinese products in OECD imports, as concentration indices decrease monotonically when China is excluded. We also find that the pattern of import diversification at the product level is broadly consistent with a simple model where buyers screen suppliers for quality and toss them out when they under-perform. We test indirectly this conjecture by looking at how concentration indices vary across types of products. We find that they are more volatile, over time, for Rauch’s differentiated products (where quality can be expected to be more 4
# **Table 2: Robustness checks on OLS specifications in Table 1**

||Base<br>estimation<br>(Column 2,<br>Table 1)|Excluding<br>exports<br>from Brazil|Excluding<br>exports<br>from China|Excluding<br>electrical<br>machinery|Using a<br>2%/98%<br>winsorized<br>sample|<br>Using ISIC<br>2-digit level<br>industry<br>groups|<br> <br>Kerr (2008)<br>sample<br>using Imp-<br>ISIC2-Year<br>fixed|<br> <br>Kerr (2008)<br>sample<br>using Imp-<br>ISIC3-Year<br>fixed|<br> <br>Using<br>exporter-<br>level<br>clustering|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||Pa<br>The d|nel A: Weigh<br>ependent vari|ting bilateral<br>able is Δ log|routes by th<br>bilateral exp|e interaction<br>orts on the in|of exporter a<br>tensive marg|nd importer t<br>in by exporte|rade in indus<br>r-importer-in|try<br>dustry|
|ΔLog country-industry labor|0.573***|0.573***|0.472***|0.390***|0.493***|0.266**|0.287**|0.281**|0.573***|
|productivity|(0.185)|(0.185)|(0.097)|(0.121)|(0.133)|(0.112)|(0.113)|(0.138)|(0.086)|
|Observations|103,839|103,010|101,221|97,326|103,839|51,483|23,345|23,345|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|||||Panel B: E|xcluding sam|ple weights||||
|ΔLog country-industry labor|0.210***|0.210***|0.221***|0.154***|0.264***|0.097***|0.248***|0.184***|0.210***|
|productivity|(0.041)|(0.041)|(0.041)|(0.039)|(0.043)|(0.037)|(0.066)|(0.069)|(0.048)|
|Observations|103,839|103,010|101,221|97,326|103,839|51,483|23,345|23,345|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 1.
# PRE Working Paper Series

||ima|Ainho|_DA&for_|Contact<br>p?age|
|---|---|---|---|---|
|WPS346|India's Growing Conflict<br>between Trade and Transport:<br>Issues and Options|Hans Jurgen Peters|January 1990|T. Lim<br>31078|
|WPS347|Housing Finance in Developing<br>Countries: A Transaction Cost Appr|Robert M. Buckley<br>oach|December 1989|WDR Office<br>31393|
|WPS348|Recent Trends and Prospects<br>for Agricultural Commodity Exports<br>in Sub-Saharan Africa|TakamasaAkiyama<br>Donald F. Larson|December 1989|D. Gustafson<br>33714|
|WPS349|How Indonesia's Monetary Policy<br>Affects Key Variables|Sadiq Ahmed<br>Basant K. Kapur|February 1990|J. Rompas<br>73723|
|WPS350|Legal Process and Economic<br>Development: A Case Study of<br>Indonesia|Cheryl W. Gray|December 1989|B. Dhomun<br>33765|
|WPS351|The Savings and Loan Problem in<br>the United States|Stanley C. Silverberg|March 1990|WDR Office<br>31393|
|WPS352|Voluntary Export Restraints and<br>Resource Allocation in Exporting<br>Countries|Jaime de Melo<br>L Alan Winters|||
|WPS353|How Should Tariffs be Structured?|Arvind Panagariya|February 1990|K. Cabana<br>37946|
|WPS354|How Commodity Prices Respond<br>to Macroeconomic News|Dhaneshwar Ghura|February 1990|S. Lipscomb<br>33718|
|WPS355|The Evolution of Credit Terms: An<br>Empirical Study of Commercial Bank<br>Lending to Developing Countries|Sole Ozler<br>|February 1990|S. King-Watson<br>31047|
|WPS356|A Framework for Analyzing Financial<br>Performance of the Transport<br>Sector|Ian G. Heggie<br>Michael Quick|February 1990|W. Wright<br>33744|
|WPS357|Application of Flexible Functional<br>Forms to Substitutability among<br>Metals in U.S. Industries|Ying Olan|February 1990|S. Lipscomb<br>33718|
|WPS358|The Long-Term Behavior of<br>Commodity Prices|Pier Giorgio Ardeni<br>Brian Wright|March 1990|A. Kitson-Walters<br>33712|
|WPS359|A Survey of Recent Estimates of<br>Price Elasticities of Demand for<br>Transport|Tae H. Oum<br>W. G. Waters, ll<br>Jong Say Yong|January 1990|W. Wright<br>33744|
# _Privacy_

Privacy will be a key consideration in any implementation. As demonstrated in this paper, the scoring model can be estimated with anonymous data, by anonymizing the identifier that links phone and lending data. However, to generate a prediction for a lending decision, the model must be run on that potential borrower’s data. An implementation can be designed to mitigate privacy risks. It can be opt-in, so that only consumers who consent are scored with the system.<sup>20</sup> It can reveal to lenders only a single number summarizing default risk, rather than the underlying features describing behavior. Additionally, it can be restricted to use features that are less sensitive, such as top up behavior rather than the network structure of an individual’s contacts.

# _Manipulation_

Some indicators are ‘gameable’ in the sense that a subscriber may be able to manipulate their score if they knew the algorithm. The feasibility of manipulation depends on the complexity of the final model and the susceptibility of individual indicators to manipulation. Both dimensions of the model can be tailored to reduce the probability of manipulation. For example, it is preferable to use indicators that are less susceptible (e.g., manipulating spending or travel can be costly).

# _Heterogeneity in performance by subgroup_

An extension to this paper evaluates performance by different subgroups (Björkegren & Grissen, 2018), replicated in Figure S3. The model is trained on all individuals except the omitted fold, and performance is reported for the given subgroup (women, men, and residents in or outside the capital) within the omitted fold. Although our error bars can be wide, performance is not widely heterogeneous across groups, suggesting the method may be able to score different types of individuals.

> cannot be used to score our population of interest, and our method scores this population at a higher level of performance within this range.

> 20 Potential borrowers who opt in may be differentially selected from the broader population, in which case a model estimated on anonymous data from the broader population may not be optimal for use in practice. After the system is operational, it can be periodically refit on outcomes from borrowers who opt in. (Thanks to an anonymous referee for this point).

20
# **Table 7: Meta-Analysis of Results in Table 6**

|(1)|(2)|(3)|
|---|---|---|
|No. ofobservations<br>-0.001|0.001|0.004|
|(0.000)***|(0.000)|(0.002)**|
|DummyAustria<br>-1.265|||
|(0.156)***|||
|DummyBelgium<br>7.751|||
|(0.041)***|||
|Dummy Chile<br>16.049|||
|(0.378)***|||
|Dummy China<br>54.978|||
|(11.182)***|||
|Dummy Colombia<br>10.635|||
|(0.610)***|||
|DummyDenmark<br>157.154|||
|(0.016)***|||
|DummyEast Germany<br>3.174|||
|(0.554)***|||
|DummyFrance<br>6.040|||
|(0.580)***|||
|DummyIreland<br>5.770|||
|(0.052)***|||
|DummyItaly<br>17.648|||
|(0.530)***<br> <br>|||
|Dummy Spain<br>17.307|||
|(0.032)***|||
|Dummy Sweden<br>2.668|||
|(0.932)**|||
|Dummy UK<br>10.290|||
|(0.149)***|||
|Dummy West Germany<br>8.219|||
|(1.866)***|||
|Share export starters|6.584|10.911|
||(5.768)|(5.644)*|
|Dummy establishmentleveldata|14.094|16.908|
||(25.715)|(15.951)|
|Average year|1.159|5.555|
||(2.167)|(5.036)|
|GDP||-0.000|
|||(0.000)*|
|GDPpercapita||0.001|
|||(0.001)|
|Openness||-0.827|
|||(0.416)*|
|Ease ofdoing business||-273.886|
|||(123.165)**|
|Government effectiveness||-73.798|
|||(62.936)|
|Regulatory quality||-19.140|
|||(22.208)|
|Observations<br>104|104|104|
|R-squared<br>0.17|0.16|0.34|
|Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** significa<br>Regression includes constant term|nt at 1%||

50
distributing across the cells of the ‘USE’ matrix<sup>3</sup> the total value of domestically produced goods and services consumed by the productive sectors (intermediate consumption), using technology coefficients from the GTAP<sup>4</sup> 2007 SAM for Bangladesh. The use of 2007 GTAP coefficients means ignoring possible structural change between 2007 and 2012 in the Bangladesh economy.

All elasticity parameters are taken from Timilsina and Shrestha (2007). As it is difficult to empirically estimate the elasticity of substitution across commodities for individual countries, using values from the literature is the norm. Data on government transfers to BPDB were obtained from the Medium Term Macroeconomic Policy Statement of the Ministry of Finance, Government of Bangladesh, while quantity forecasts for natural gas and LNG were obtained from Petrobangla (2012), with natural gas price data for 2015 for Bangladesh coming from Petrobangla and LNG price data for India from Sen (2015).

# **4. Results and Discussion**

In this section, we first discuss results from the CGE model simulations of electricity subsidy removal followed by results from simulations in which the price of natural gas is increased to its opportunity cost. Under each set of results, aggregate level results, such as impacts on GDP, total sectoral outputs, total household demand, total exports, total imports, total investment, disposable income, the trade balance, and government income are discussed first; this is followed by a discussion of sectoral and commodity level impacts.

# **4.1 Impacts of electricity subsidy removal**

## **4.1.1 Aggregate economic impacts**

Elimination of the electricity subsidy and recycling the associated budgetary savings to the economy would be beneficial because this would end distortions caused by the electricity subsidy,

> 3 The ‘USE’ matrix in a SAM refers to inputs of different goods and services for the production of various goods and services.

> 4 GTAP is a global network of researchers who conduct quantitative analysis of international economic policy issues, especially trade policy.  A key output of the network is a global economic database, covering many sectors and all parts of the world.  It is coordinated by the Center for Global Trade Analysis,  Department of Agricultural Economics, Purdue University, West Lafayette, IN.

15
online experiment. No other demographic characteristic was consistently associated with awareness of a COVID-19 vaccine being available across both surveys.

**Figure 5 – Awareness of the existence of a COVID-19 vaccine in PNG**

<!-- Start of picture text --> a) Phone survey (late May/early June 2021) b) Online experiment (late June/July 2021)<br>18.0%<br>27.3%<br>72.7%<br>82.0%<br>Had not heard of vaccine Heard of vaccine Had not heard of vaccine Heard of vaccine<br><!-- End of picture text --> Note: These charts are based on answers to question 1 and 32 (control group only) in Appendix B.

# <u>5.1.3 Levels of vaccine hesitancy (based on both the phone survey and online experiment)</u>

The phone survey and control group in the online experiment revealed very high levels of vaccine hesitancy in PNG. Only a small minority of Papua New Guineans were planning on getting vaccinated. Most respondents stated they were unwilling to get or not sure about getting a COVID-19 vaccine (see Figure 6). There were no large differences in willingness to get vaccinated between respondents with different demographic characteristics, except that men were slightly more likely to be willing to get vaccinated (see Figures A1–A4 in Appendix A). This finding is consistent with existing work showing that vaccine hesitancy does not tend to be primarily associated with socioeconomic status or education levels (Larson et al, 2014.). Respondents were asked “Have you been vaccinated for COVID-19?” and “Are you planning to be vaccinated?” Because the focus of this paper is on vaccine hesitancy, we restrict our analysis to respondents who were aware of the vaccine but were yet to get vaccinated. Among this subset of respondents, a very similar share in the phone survey and control group in the online experiment stated they would get vaccinated (only 18.2 and 18.4 percent). Respondents to the phone survey were much more likely to state that they would not get vaccinated (53.3 percent), whereas participants from the control group in the online experiment were more likely to state they were unsure (55.5 percent).

**Figure 6 – Levels of vaccine hesitancy in PNG**

16
Prior to the marketing campaign, we conducted a baseline survey of all households in the area where PRISMA was planning to expand. A total of 6,718 families were included in the participation study in three rounds of expansion: the first with 1,804 households in the district of Castilla (March 2010) and the last two rounds in the district of Piura with 2,173 and 2,744 households, respectively (June 2010 and August 2010). Eighty-one percent of the households, or 5,436 families, were present at the time of surveying. Households were given one of two surveys: a short survey of questions to provide basic information for analysis (such as phone numbers, questions related to their economic situation, and opinions and interest in receiving information about recycling) or a long survey intended to provide detailed information from a representative sample of households in the area. Of the full sample, 523 families received a long survey. Supplemental table S1.2 provides summary demographic statistics and verifies that assignment to treatment was uncorrelated with the demographic information collected at the baseline.

After being surveyed, all households were randomly assigned to treatment groups for messages, detailed below, aimed at increasing participation. One week before PRISMA began marketing, a flier with the assigned message was delivered to the household. We received valid phone numbers for approximately 35 percent of the sample and sent those people SMS messages that reiterated the flier’s message on the night before the marketer’s visit and once a week through the end of the study. If people were present when the flier was delivered, a short verbal summary of the message accompanied the flier; if no one was home, the flier was left on the doorstep. Sixty-five percent of fliers were delivered in person, 32 percent were left on the doorstep, and three percent failed to be delivered. An orthogonality check on these two groups did not raise 9
property, because the index between any two countries is affected by prices and weights in third countries (precisely because it combines the indirect Fisher indices).

The GK index compares domestic prices with world prices. The problem is that in the computation of these world prices, the weight attached to a particular country depends on its physical volume of consumption. Thus in practice, the rich countries dominate these composite world prices. Since goods that are relatively expensive in rich countries (say, services) tend to be consumed in relatively large quantities in poor countries, precisely because they are cheaper, the use of a GK index tends to overestimate the value of consumption in poor countries. This is simply a manifestation of the well-known Gerschenkron (1947) effect (or substitution bias) which says that a country’s consumption is overvalued when evaluated at the prices of another country, and the further the two price vectors, the greater the overvaluation. The EKS index does not suffer from this bias because it averages the consumption weights from both countries, making “a compromise that is arguably the best that can be done in the circumstances” (Deaton and Heston, 2010, p.11).<sup>14</sup> As a result of the Gerschenkron effect, the GK index understates global inequality (and global poverty, see Ackland et al. (2013)). Deaton and Heston (2010) compute population-weighted between-country inequality of GDP per capita using different indices. They obtain a Gini index of 53.3% for EKS and a value of 52.7% for GK.

In summary, we use the EKS index as suggested by Anand and Segal (2008), Deaton and Heston (2010), Ravallion (2010), and Ackland et al. (2013). For other purposes, the GK index which satisfies additivity might be more relevant.<sup>15</sup> We use a single PPP exchange rate per country (differentiating only between rural/urban China, India and Indonesia), thus ignoring any consumption and price differences along the income distribution.<sup>16</sup>

> 14 For example, consider alcohol in Bangladesh, where it has a small share, but a relatively high price (see Deaton and Heston, 2010). The Fisher index strikes a compromise between two extreme positions: Bangladeshi budget shares would understate Bangladeshi prices, whereas OECD shares would overstates them.

> 15 For example, this might be important when studying the composition of GDP because components converted at GK conversion factors are additive which is not the case with the EKS index. This is a reason why the Penn World Tables continue to use the GK approach.

> 16 Reddy and Pogge (2010) argue that consumption PPPs should not be used in the measurement of global poverty because the consumption baskets of the poor are systematically different from the rest of the population. For example, the poor might face different prices because of where they buy (Frankel and Gould, 2001) or how much they buy (Rao, 2000). However, Deaton and Dupriez (2009) find that re-weighting commodity baskets to account for

10
varies across AGEBs, and normalizing the weights appropriately within each municipality. Second, area-level models cannot produce estimates at lower levels of geography, such as subareas. In addition, area-level and sub-area models of the type implemented here model poverty rates rather than welfare, therefore discarding information on whether non-poor households in a sub-area are near or far from the poverty line. Household-level models predict welfare instead of poverty rates, conditional on the sample, and therefore better preserve this information.

Currently available software for estimating area-level also models cannot easily accommodate different weights for different areas. This can become a constraint when the analyst wishes to give more weight to more populous areas when estimating the model, which may be preferred in some policy settings. Finally, both the standard sub-area and area-level models considered here impose the assumption that poverty rates are a linear function of the predictors, which can also reduce prediction accuracy.

Corral et al. (2021) note that the nested error regression model we use in the household-level model is subject to “omitted variable” model bias, when household-level auxiliary variables are replaced by their respective area-level population averages and when the true data generating process is based on a household-level model. This bias occurs when the population averages used as predictor variables on the right-hand side are correlated with the discrepancy between the area-level means of the population and the particular sample used. As a result, they recommend using area-level models when feasible rather than household-level models. However, as detailed in Appendix F, the exact same omitted variable bias also appears in area-level models, under the same assumptions that the data generating process occurs at the household level and that the predictors are drawn from the population.<sup>9</sup> Thus, because the same omitted variable bias appears in both household and area-level models, this bias is not a sufficient justification for preferring area-level models over unit-context models.

There are two other reasons why the omitted variable bias identified in Corral et al (2021) may not be important in practice. First, if sampling error is exogenous, as one would expect with a random sample, both the household and area level models are consistent, implying that this bias will tend towards zero as the number of AGEBs included in each area becomes large. Furthermore, in both

> 9 Auxiliary data in area-level models is typically drawn from administrative data or other sources that represent the population rather than the sample.

10
data. Section 5 discusses the empirical method, namely double differences combined with propensity score matching.

The empirical results are summarized in Section 6. Double differencing with propensity score matching shows that SBM increases the national achievement test score by 4.2 points over three years. The mathematics score increased by 5.7 points. This is larger than the estimate previously reported from the Philippines, probably due to the fact that our sample schools had learned from SBM experiences accumulated in those schools that introduced SBM earlier. Our empirical results also show that schools endowed with more experienced principals and teachers are likely to be better prepared for introducing SBM. Conclusions are mentioned in the last section.

# **2. Literature Review**

Recently there has been a growing number of studies that evaluate the impact of SBM (see Bruns, Filmer and Patrinos, 2011, Table 3.4). Here I collect studies that used either randomization or a combination of panel data and matching methods. The first group used randomization approaches to identify SBM impacts (Duflo, Dupas and Kremer, 2012; Chaudhury and Parajuli, 2010; Das, 2008l; Pradhan et al., 2010; Gertler, Patrinos and Rodrigues-Oreggia, 2012). With an ideal introduction of randomized trials, the analysis is free from endogenous program placement that could bias the treatment effect. Duflo, Dupas and Kremer (2012) introduced randomized experiments to Kenyan schools to compare school resource effects and teachers’ incentive effects on learning outcomes. They find that improving school resources, measured by decreasing the pupil-teacher ratio, leads to reduced teacher effort in the absence of any other changes, but students who were assigned to locally-hired contract teachers (with the presence of parents’ involvement in school management) showed significant improvements in test scores.

Chaudhury and Parajuli (2010) and Gertler, Patrinos and Rodrigues-Oreggia (2012), using school-level randomized experiments in Nepal and Mexico, respectively, showed that devolution of decision making 4