rafmacalaba/datause-encoder-data huggingface dataset:
impact of income per adult on its own, they compute the impact of changing both the labor and nonlabor income per adult. This is done because, in principle, the sum of labor and nonlabor income should be equivalent to changing total income per adult. The results of these two simulations are different, however, and the simulation of labor income is not done explicitly, but rather ends up being a “residual” in step 8 to ensure that the cumulative effect adds up to the total distributional change.

We modify the procedure in three ways: (1) we focus on consumption as a measure of welfare; (2) we compute a cumulative counterfactual distribution by adding one variable at a time; and (3) we compute Shapley-Shorrocks estimates of each component. First, the focus on consumption is because most developing countries use a consumption aggregate to measure poverty. Second, in contrast to the Barros et al. (2006) approach, this method does not separately identify the contribution of the interaction between variables in the observed distributional changes; doing so is partial at best, given that changing any variable can potentially affect all other variables. Instead, the impact of changes in each variable and its interactions with all other variables is calculated as the difference between the cumulative counterfactuals. Box 3 shows an example for one possible path, taking into account the fact that nonlabor income is made up of pensions, transfers, capital income, and other income.

The third methodological change is to address the fact that this methodology suffers from pathdependence, as much of the micro-decomposition literature does. In other words, the order in which the cumulative effects are calculated matters.<sup>6</sup> One of the major contributions of this paper is that we apply the best known remedy for path-dependence, which is to calculate the decomposition across all possible paths, and then take the average between them following the method proposed by Azevedo, Nguyen, and Sanfelice (2012).<sup>7</sup> This involves calculating the cumulative decomposition in every possible order, and then averaging the results for each component. Because we have eight variables, this adds up to 40,320 potential decomposition paths (the result of 8!). The average effect for each variable is also known as the ShapleyShorrocks estimate of each component.<sup>8</sup> There is one remaining caveat to this approach: The counterfactual income distributions on which these decompositions rely suffer from equilibrium-inconsistency. Because we are modifying only one element at a time, the counterfactuals are not the result of an economic equilibrium, but rather a fictitious exercise in which we assume that we can in fact modify one factor at a time and keep everything else constant.

> 6 Path-dependence is common in the micro-decomposition literature. See Essama-Nssah 2012, Fortin et al. 2011, and Ferreira 2010 for recent reviews of the literature.

> 7 A Stata ado file by Azevedo, Sanfelice, and Nguyen implements this approach. To download it, within Stata type: “ssc install adecomp”

> 8 See Shapley 1953 and Shorrocks 1999.

11
Figure 3

# COLOMBIA: SECTORAL SURPLUSES

<!-- Start of picture text --> Government and F'reign<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>3<br>2<br>0--<br>It<br>-2<br>70 721 6 7 0 8 48<br>-3<br>70 72 74 78 78 so 8384 8<br>a Govemrnment Deficit F roreign Surplus<br><!-- End of picture text --> Figure 4 INDIA: SECTOR FLOWS AND GROWTH <!-- Start of picture text --> Business and Foreign<br>10-<br>9<br>a<br>7<br>4<br>3-<br>kt 2<br>0<br>-2<br>-3-<br>-.4<br>70 72 74 78 78 Go 82<br>g luulnuas (0) + Foreign (S) 0 GNP Growth<br>45<br><!-- End of picture text -->
71

# TABLE 12: Proportlon of Cropland under Irrigation by Region and Tenure Category, 1970 & 1981

# Percentages

||Ejld|o|Privat|e|
|---|---|---|---|---|
||<br>1970|<br>1981|1970|1981|
|All Mexico|13.8|17.7|15.5|186.|
|North Pacific|59.4|45.5|68.8|59.7|
|North|20.1|17.9|26.7|18.3|
|South Pacific|3.6|4.0|4.1|4.5|
|Centre|13.0|21.2|15.6|13.3|
|Gulf|1.7|2.5|1.1|2.3|

Note: For list of states comprising each region see Table 2. SOURCE: Agricultural Census, 1970 & 1981
Table 5. Implication 1: Price and Catch Across Cooperatives

||Log price|||Log catch|||
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Abreojos|-1.095***|-1.112***|-1.124***|0.984***|1.037***|1.002***|
||(0.029)|(0.023)|(0.016)|(0.183)|(0.148)|(0.125)|
|Constant|3.180***|3.185***|3.189***|4.059***|4.039***|4.052***|
||(0.009)|(0.007)|(0.005)|(0.070)|(0.057)|(0.048)|
|Obs|4310|4310|4310|5015|5015|5015|
|Num. groups|626|1379|3505|703|1572|4081|
|Within-R2|0.790|0.840|0.916|0.035|0.047|0.081|
|Fixed effects|Sp-qtr|Sp-month|Sp-week|Sp-qtr|Sp-month|Sp-week|

Note: Sample includes observations at the weekly level from Abreojos, Pichilingue, and Sargento. The omitted category is Pichilingue/Sargento. All specifications use linear fixed effects at the species-quarter (sp-qtr), species-month (sp-month), or species-week (sp-week) level. Standard errors are clustered at the level of the fixed effect.

4
country pairs covered by the firm’s exports; the number of products the firm exports; and the number of countries to which the firm exports.<sup>23</sup> These are calculated for the year 2000.

Table 7 splits the sample by initial exporter productivity. We use two alternative cut-offs: the mean and the median. The estimated coefficient in the odd columns (high-productivity exporters) is always higher than that in the even columns (low-productivity exporters).<sup>24</sup> The product-level spillovers related to how well a product conforms to the local production structure are thus especially important for high-productivity exporters.<sup>25</sup> Table 8 reproduces Table 7 for domestic firms only, where the respective cut-offs now also refer only to domestic firms. The results are unchanged, with the estimated coefficient being roughly four times higher for high-productivity firms. In unreported results, we considered heterogeneity for foreign-owned firms.<sup>26</sup> These findings confirm the insignificance of product density for foreign firms regardless of the productivity level. The conditional effect of product density by initial exporter productivity is thus particular to domestic firms. Our findings therefore suggest that spillover diffusion can indeed be hindered by insufficient absorptive capacity.

# **V Conclusion**

We propose the first evaluation using micro-level data on the gains from the consistency of activities with local comparative advantage. Using firm-level data from Chinese customs for 2000–6, we investigate the relationship between the export performance of firms and how their products relate to local comparative advantage. Our key indicator measures the density of the links between a product and the local product space. It hence combines 26
Table. 5a. Likelihood of Choosing Job A with alternate retirement income coverage offers/ earnings, total and by Income, Retirement Pension Coverage status

|||Income||Other Job|s Currently|
|---|---|---|---|---|---|
||Low Income|Med Income|High Income|No|Yes|
|No retirement savings Fund, 5% higher income|0.756***|0.795***|0.702***|0.712***|0.780***|
||[0.0477]|[0.0446]|[0.0474]|[0.0419]|[0.0350]|
|No retirement savings Fund, 10% higher income|0.771***|0.796***|0.658***|0.735***|0.756***|
||[0.0464]|[0.0399]|[0.0547]|[0.0439]|[0.0341]|
|Monthly pension upon retirement based on<br>contribution, 5% lower income|0.674***|0.639***|0.609***|0.583***|0.693***|
||[0.0492]|[0.0530]|[0.0512]|[0.0439]|[0.0391]|
|Monthly pension upon retirement based on<br>contribution, 10% lower income|0.663***|0.641***|0.581***|0.664***|0.595***|
||[0.0513]|[0.0546]|[0.0535]|[0.0435]|[0.0430]|
|N|343|347|348|466|572|

_Standard errors in brackets * p<0.05, ** p<0.01, *** p<0.001_ Table 5b. Likelihood of Choosing Job A with alternate retirement income coverage offers/ earnings by current access to EPF and private retirement savings

||EPF Cu<br>|rrently<br>|Retireme<br>|nt Savings<br>|
|---|---|---|---|---|
||No|Yes|No|Yes|
|No retirement savings Fund, 5% higher income|0.753***|0.740***|0.752***|0.746***|
||[0.0317]|[0.0517]|[0.0360]|[0.0410]|
|No retirement savings Fund, 10% higher income|0.731***|0.797***|0.759***|0.736***|
||[0.0320]|[0.0488]|[0.0362]|[0.0403]|
|Monthly pension upon retirement based on contribution,<br>5% lower income|0.646***|0.627***|0.614***|0.675***|
||[0.0346]|[0.0563]|[0.0395]|[0.0440]|
|Monthly pension upon retirement based on contribution,<br>10% lower income|0.623***|0.644***|0.638***|0.616***|
||[0.0352]|[0.0628]|[0.0411]|[0.0462]|
|N|762|276|577|461|

_Standard errors in brackets * p<0.05, ** p<0.01, *** p<0.001_ 15
**Table S5: Lag structure of first-stage and reduced-form estimations**

||Fir|st-stage estimati|on|Red|uced-form estima|tion|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Panel A: Wei<br>DV: Δ Log cou|ghting bilateral r<br>ntry-industry lab|outes by the intera<br>or productivity|ction of exporter<br>DV:|and importer tra<br>Δ Log bilateral e|de in industry<br>xports|
|Δ Log estimator for technology|0.589**||0.524**|0.938***||0.886***|
|flows from the United States,|(0.272)||(0.241)|(0.298)||(0.272)|
|lagged five years|||||||
|Δ Log estimator for technology||0.376*|0.275||0.393|0.223|
|flows from the United States,||(0.212)|(0.169)||(0.253)|(0.256)|
|contemporaneous|||||||
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|
|||<br>|Panel B: Excludin<br>|g sample weight<br>|s<br>||
||DV: Δ Log cou|ntry-industry lab|or productivity|DV:|Δ Log bilateral e|xports|
|Δ Log estimator for technology|0.267***||0.265***|0.648***||0.636***|
|flows from the United States,|(0.078)||(0.078)|(0.112)||(0.107)|
|lagged five years|||||||
|Δ Log estimator for technology||0.064|0.059||0.473***|0.459***|
|flows from the United States,||(0.071)|(0.069)||(0.111)|(0.105)|
|contemporaneous|||||||
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3. The first-stage and reduced-form performance of the lagged technology flows, the preferred instrument, is contrasted with contemporaneous flows.
3 the time of the research), and 40% in developing countries assuming that a better business and enabling environment is put in place<sup>6</sup> .

The trend observed in the capital markets of more industrialized countries, such as the United States, Western Europe and Singapore, is continued growth in the use of online trading. In emerging economies, South Korea represents the high end. Since e-brokering was launched in Korea in 1997, the proportion of its total trading value steadily climbed from 19 percent in 1999, to 43 percent in 2003. In Russia, approximately 40 percent of stocks traded on the Moscow Interbank Currency Exchange (MICEX) occurs via the Internet. On the low end, in Singapore and Taiwan, e-brokering accounts for approximately 10 percent of total trading volume. In Chile, market players estimate that 5-10 percent of securities trading occurs online. The table below depicts a snapshot of the growth of internet based trading in certain markets.

**Table I:  Penetration of Online Trading**

||**1999**|**2003**|
|---|---|---|
|EU Nations|2 %|10 %|
|South Korea|19%|43%|
|Singapore|5%|10%|
|Hungary|6%||
|India|11%||
|Brazil|6%||
|Mexico|3%||

E-Finance promises many benefits to users and adopters. First, e-finance lowers the costs of intermediation and increases competition in financial services. It also expands the reach and scope of access to financial services by formerly unreachable clients (Claessens et-all 2001) ~~.~~ This increased penetration of e-finance in developed as well as developing countries is are also erasing the boundaries between finance and technology, transactions and information processing, and finally financial service providers and technology providers. The distinction between “efinance” and “finance” will become obsolete, as financial delivery assumes the use of electronic means.

E-finance however exacts numerous costs that have not been appropriately examined. For example, the dependence of online backend operations has increased operational risk significantly. Although transactions appear to be completed in less time, the underlying complexity required to handle such transactions in an automated fashion has increased exponentially. As a result what we appear to achieve in terms of transactional efficiency on the one hand may be lost through transactional complexity on the other.

Over time, it is now clear that online fraud rates for e-commerce are much higher than for those transactions completed via more traditional modes<sup>7</sup> . The anonymity and speed perpetuated by the internet allows for greater rates of fraud. At present the most valid number to come forth

> 6 Claessens, Glaessner & Klingebiel. 2001. _E-finance In Emerging Markets:  Is  Leapfrogging Possible?._ World Bank.

> 7 Glaessner, Kellermann & McNevin. October  2003.  Electronic Safety and Soundness: Securing Finance in a New <u>Age. World Bank.</u>
made from fabric originating anywhere in the world. The ‘rule of origin’ provision has been relatively more liberal to this group of countries. For the other<sup>11</sup> SSA countries, under ‘rules of origin’ requirements, the sum of the cost or value of the materials produced in one or more AGOA beneficiary countries plus the direct cost of processing operations may not be less than 35 percent of the appraised value when the product is imported to the US. The impact of ‘rules of origin’ is not clear in terms of its effect on exports and subsequent gains in trade and investment. When it is a binding constraint, it may restrict export opportunities. It could also benefit countries in encouraging domestic manufacturing by encouraging sourcing of apparel from domestic production and processing. The subsequent impact on the local economy of having either a more liberal or restrictive ‘rule of origin’ requirement is still an open question. In addition to the rules of origin, preferential treatment for textile and apparel requires that all beneficiary countries adopt an effective visa system and related procedures that assist in complying with the ‘rules of origin’ requirements.

The most recent AGOA Extension and Enhancement Act of 2015 calls for greater reciprocity in the elimination of barriers to trade and investment in SSA. It put forward an out-of-cycle review mechanism, that ‘ _at any time..._ ’ the Office of the U.S. Trade Representative (USTR) ‘ _may initiate an out-of-cycle review of whether a beneficiary country is making continual progress in meeting the requirements_ ’ for eligibility. This allows entities from the private sector or ‘ _any interested person, at any time_ ’ to file a petition with respect to the failure of compliance of a country ‘ _with eligibility requirement_ ’.<sup>12</sup> These changes might adversely affect future export opportunities by raising uncertainty.

> 1998 as measured by the World Bank.

> 11See a full list of these countries in Table 1.

> 12In July 2017, USTR announced an initiation of an out-of-cycle review of the eligibility of Rwanda, Tanzania, and Uganda in response to a petition filed by a trade group that represents secondhand clothing exporters - the Secondary Materials and Recycled Textiles Association (SMART).

9
period.<sup>11</sup> In addition, (ii) we only focus on the top 15 source countries in terms of total import value reported in Tunisia or total export value declared in source countries<sup>12</sup> and (iii) HS6-source countryyear combinations for which both reported imports in Tunisia and reported exports by partner countries are positive.<sup>13</sup> The resulting sample comprises 1,386 products and 16 countries,<sup>14</sup> which cover 69.75% of all exports to and 61.03% of all imports declared in Tunisia. We also add productlevel information on non-tariff measures from the World Bank (Malouche et al., 2013).

11This reduces the sample from 5449 to 1493 products which together account for 91.05% of exports to Tunisia delcared by partners and for 92.05% of imports declared in Tunisia.

12 These countries account for 84.26% of import value declared in Tunisia over the period and for 78.92% of all exports to Tunisia reported by partners. Focusing only on products which account for more than 0.01 percentage points of cumulative imports reported in Tunisia or 0.01 percentage point of cumulative exports to Tunisia reduces these numbers to respectively 76.97% and 73,32%, 13Such source-product combinations account for 81.44% of all exports to Tunisia reported by partners and 73.65% of imports reported in Tunisia. We also examined whether the likelihood of imports being “orphaned”, i.e. existing in Tunisian customs declarations without having a corresponding matching declaration in the alleged source country, was related to the importance of Ben Ali firms but could not reject the null hypothesis that this was not the case. Results are omitted to conserve space but available upon request.

14 The countries are Algeria, Argentina, China, France, Germany, Italy, Japan, Libya, the Netherlands, the Russian Federation, Spain, Turkey, the United States of America, Ukraine, and the United Kingdom. Note that we have 16 rather than 15 countries because we are including countries that in either the top 15 source countries either based on imports declared in Tunisia or based on exports to Tunisia reported partner countries. While the ranking of different source countries using these different criteria usually line up well, there are a few exceptions where this is not the case. Notably, Argentina is ranked as the 15th largest source country based on Tunisian import data, but the 16th largest source country based on export data reported by partners. By contrast, Japan is considered the 15th largest source country based on import data reported in Tunisia, but the 21st largest importer based on data reported by partners.

16
a healthcard. For instance, it is likely that the distribution of the healthcard was quite different in the more urbanized and densely populated Java and Bali provinces, compared to the Eastern Islands or Sulawesi province. Two region dummies were included to control for this. Within each region, province dummies were included to control for unobserved variation in how the subsidies were allocated from the central government to the next tier of government. District dummies would have been more appropriate, given that the health subsidy program was largely administered through district and sub-district governments. They were not included, however, because the number of treated observations within each district was not large enough to allow for sufficient variation.

Only one of the five BKKBN criteria officially used to allocate healthcards -“have a floor made out of earth” - was measured in IFLS and, therefore, included in the model. Absence of the other four variables from the model does not represent a critical gap in the specification of the propensity score for two reasons. The range of household level predictors included in the model are rich and varied enough to capture the extent of social and economic deprivation that would otherwise have been reflected in the BKKBN indicators. Secondly, it is fairly clear that village leaders did not rely exclusively on the BKKBN indicators to identify eligible households, using instead the criteria they received from their district and sub-district governments, as well as their own discretion and information. I am confident that the household and community level variables included in the model, along with the province and region dummies, are sufficient to capture the 29
use the same hazard model as before, but now instead of pooling across all households, I estimate the coefficient on the transaction costs variable for each household separately. I then use the correlation between holding periods and transaction costs as a proxy for how much each investor pays attention to transaction costs. In order to obtain robust estimates, I require that households make at least 50 round-trip trades over the sample period, and I only keep estimates that are calculated with a 10% confidence level or higher.<sup>23</sup> The summary statistics for the transaction costs coefficient calculated from household level hazard regressions are reported in Table 8. For the majority of households in the dataset (over 60%), the correlation between holding periods and transaction costs is positive. Most investors pay attention to the liquidity level of stocks they trade.

The relationship between holding periods and transaction costs has strong implications for investment performance. I form two groups based on the sign of the coefficient on the transaction costs variable, and calculate 1, 6 and 12 month and holding period returns for each transaction as described in the previous section. I then calculate averages for the two groups. The results are in Table 9. There is a stark difference in the investment performance between the two groups. Households that pay attention to transaction costs earn about 20.5 bps in gross returns and 10.7 bps in characteristicsadjusted excess returns each day, compared to 0.1 bps in gross returns and -6.6 bps in excess returns each day for households that do not. Households that pay attention to transaction costs pay less in amortized spreads and have higher net returns and net characteristics-adjusted excess returns. They earn 7.1 bps per day in net returns, compared to a loss of -10.9 bps per day for households whose holding periods are negatively related to transaction costs. The differences in returns are all statistically significant except for the one month returns. Since the differences are significant for both gross and net returns, the positive relationship between holding periods and transaction costs is consistent with the hypothesis ( _H2b)_ that investors who pay attention to liquidity earn greater _gross_ and _net_ returns.

> 23 I obtain similar results using 20 or 30 trades instead of 50 trades.

24
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