rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 1. Net imports of virtual-C (1,000 tons of CO2)</u>**

|Exports<br>from:|Imports int|o:||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||BRA|CAN|CHN|E15|eit|IND|JPN|liy|MEX|RUS|USA|xhy|Xmy|xx1|ZAF|
|BRA||40||10,318|11||1,707||1,873||5,840|331||822||
|CAN||||11,831|||3,379||||6,516|548||964||
|CHN|10,630|17,841||294,101|28,580|27,377|157,990|38,143|29,189||261,587|162,077|96,499|37,115|5,651|
|E15||||||||||||||||
|eit||1,364||127,128|||1,974||158||8,661|10,506||9,589||
|IND|559|1,885||38,109|323||2,973|7,783|198||21,269|6,026|3,768|2,075||
|JPN||||9,452|||||74|||9,148||||
|liy|91|741||35,219|514||2,651||412||13,609|||3,041||
|MEX||434||6,726||||||||||868||
|RUS|1,890|2,768|6,464|109,970|39,493|4,903|12,861|8,221|3,342||33,866|18,991|38,181|23,300||
|USA||||120,230|||15,684||12,834|||11,081||14,688||
|xhy||||33,637||||3,542|1,193|||||1,472||
|xmy|4,560|11,400||212,120|1,565||55,141|22,796|6,637||109,281|59,734||28,189||
|xx1||||26,627|||5,601|||||||||
|ZAF|583|1,127||27,638|452|597|8,604|8,907|848|283|5,024|6,566|5,848|3,920||

Note: figures indicate that the column-country is a net importer of virtual-C from the row-country. Virtual carbon trade flows have been estimated using the BTIO approach. Source: authors 26
- The trading segment is more concentrated and asymmetric. We suppose that 5 traders have 9 percent of market share each, and the remaining 34 traders have 1.62 percent of market share each.

- The trading segment is more concentrated and symmetric. We suppose that concentration is similar to 12 traders with 8.33 percent of market share each.

- The last two cases are built on the basis that the market data summarized in Table 3, together with the possible prices that may arise in equilibrium, are consistent with a maximum market share of traders of approximately 9 percent.

For each case, we calibrate the model to the new assumptions and simulate the effect of the introduction of the FUNPI contribution (see Table B7).

Results are as expected. As the model assumes “more constant” marginal costs, pre-tax exporters’ and traders’ profit are lower (in the exporters case, from 74 USD/ton to 28 USD/ton; in the traders case from 41 USD/ton to 24 USD/ton) because marginal costs tend to equate average costs; hence strategic bids and offers, based on marginal costs, tend to align with average costs, and average margins decrease. Consequently, taking international prices and farmer supply as given, farmer prices should be higher (435.95 USD/ton in the base case vs. 450 USD/ton in the almost-constantmarginal-cost case).

On the other hand, as concentration in the traders segment is higher, traders earn higher profits (from 41 USD/ton to 60 or 84 USD/ton), partly because of lower farm-gate prices (from 436 USD/ton to 429-433 USD/ton) and higher intermediate prices (from 520 USD/ton to 522-536 USD/ton). Moreover, exporter costs must be lower in order to be compatible with a more concentrated segment at the traders’ link.

Once the new calibrated case is set, however, the simulation results do not differ significantly. The introduction of FUNPI reduces farm-gate prices by 18-22 percent, and quantity goes down by 1012 percent.

38
Table 2. Determinants of Formal Financial Inclusion

||**Formal Accounts**|**Formal Savings**|**Formal Credit**|**Digital Cards**|
|---|---|---|---|---|
|Female|-0.0349***|-0.0355***|-0.1515***|-0.1068***|
||[0.0095]|[0.0128]|[0.0210]|[0.0140]|
|Age 35-54|-0.0279***|0.0028|0.0402*|-0.1979***|
||[0.0105]|[0.0151]|[0.0238]|[0.0169]|
|Age 55-64|-0.0618***|0.0149|0.0051|-0.4244***|
||[0.0151]|[0.0185]|[0.0347]|[0.0223]|
|age 65 & above|-0.1249***|-0.0713**|-0.0536|-0.5221***|
||[0.0201]|[0.0301]|[0.0524]|[0.0257]|
|Secondary education|0.0666***|0.0920***|0.1737***|0.2135***|
||[0.0113]|[0.0176]|[0.0233]|[0.0163]|
|Tertiary education|0.1536***|0.1329***|0.3255***|0.3980***|
||[0.0122]|[0.0244]|[0.0501]|[0.0356]|
|Formal employment|0.0499***|0.0238*|0.1246***|0.1264***|
||[0.0098]|[0.0132]|[0.0223]|[0.0150]|
|Observations|4791|2179|2268|4251|
|Pseudo R2|0.0669|0.0558|0.0646|0.1851|
|Loglikelihood|-1610.4649|-637.62312|-1482.1384|-2374.9875|
|Predicted probability|||||
|(at mean values)|0.8833|0.9054|0.4943|0.5317893|

The estimated marginal effects are reported. Standard errors are in parentheses. ⁎⁎⁎ Denotes significance at the 1% level. ⁎⁎ Denotes significance at the 5% level. ⁎ Denotes significance at the 10% level.

Table 2 shows how different characteristics of individuals influence the probability of them using formal financial products. Specifically, our results show that women are less likely to have a formal account, access formal credit, have formal savings or use digital cards. While this is similar to observed behavior among women in Pakistan (Zulfiqar et.al., 2016), it is somewhat different from the observations in most other studies such as the ones covering Africa<sup>21</sup> (where formal credit was not sensitive to gender), China<sup>22</sup> (where formal savings was not significant to gender), Argentina<sup>23</sup> and Peru<sup>24</sup> (where there was no gender disparity among all variables). Considering the level of

> 21 Zins and Weill (2016).

> 22 Fungacova and Weill (2015). 23 Tuesta et.al (2015).

> 24 Cámara et al., (2014).

15
missionaries might have targeted more populated areas or cities. Regressions therefore control for a fourth-order polynomial in average population density in the 18th century obtained from the History Database of the Global Environment (HYDE). Furthermore, the control set also includes a dummy for the presence of cities at any time before 1800.

According to Robinson (1915), competition with Islam was a deterring factor, because spreading the gospel in predominantly Muslim areas was complicated. Muslim populations may receive less development aid for political and religious reasons, so the (log) distance from the closest Arab medieval trade route (which Michalopoulos, Naghavi, and Prarolo [2018] showed had a strong impact on adherence to Islam) is controlled for.<sup>17</sup> Table 2 presents summary statistics of the controls for cells with and without missions. More details about the data sources are given in appendix A.

# **2.4 Results**

Equation (1) is estimated by least squares, and the results are reported in table 3. The dependent variable is an indicator that equals 1 if the cell ever received a World Bank project between 1995 and 2014.<sup>18</sup> All regressions include the full set of historical and geographical controls described above. The first set of regressions employs different data sources and definitions of the dummy Mission _ik_ . In the first four columns of table 3, the mission data are from Roome (1924). The regression includes only Catholic missions in column 1, only Protestant missions in column 2, and both kinds of mission in column 3; in column 4 the two types of mission are collapsed into a single

> 17Some missions were set up for the purpose of ending the slave trade (Johnson, 1967), a practice that was especially prevalent along the coast of west Africa and had long-lasting detrimental effects on development and social capital (Nunn, 2008; Nunn & Wantchekon, 2011). The authors are not aware of precisely georeferenced measures of slave trade; however, the regressions in this study already control for many of its correlates, such as distance to the coast, terrain ruggedness, and distance to Arab trade routes, as well as ethnic-level dummies.

18Here and in the rest of the article the analysis always relies on linear probability models (LPMs) when the dependent variable is binary, rather than using nonlinear models. As the focus is on estimating differences in averages between groups, rather than predicting outcomes, the LPM performs well compared to nonlinear models, and the coefficients are more straightforward to interpret, especially in the presence of interaction terms. Furthermore, the controls often introduce large sets of dummies in the regressions, which regularly cause failure of convergence of the likelihood maximization algorithm for logit or probit models.

14
Table 3: Fragmentation indexes: model vs. data

|Index|D||D*||N||N*||M sha|re|
|---|---|---|---|---|---|---|---|---|---|---|
||Data|Model|Data|Model|Data|Model|Data|Model|Data|Model|
|USA|2.252|2.680|0.208|0.121|2.422|2.804|0.316|0.089|0.657|0.387|
|SGP|2.558|4.254|1.062|0.234|2.656|4.228|0.535|0.143|0.672|0.231|
|JPN|2.370|2.683|0.278|0.132|2.364|2.894|0.182|0.133|0.558|0.317|
|TWN|2.567|3.778|0.699|0.227|2.542|3.832|0.423|0.173|0.644|0.258|
|KOR|2.537|3.256|0.503|0.240|2.536|3.329|0.358|0.174|0.632|0.310|
|MYS|2.755|3.471|0.912|0.264|2.515|3.449|0.501|0.155|0.704|0.272|
|THA|2.373|2.951|0.591|0.252|2.443|3.001|0.400|0.164|0.593|0.265|
|IDN|2.509|2.893|0.521|0.524|2.109|2.234|0.244|0.104|0.823|0.523|
|CHN|2.707|2.785|0.278|0.101|2.620|3.160|0.183|0.189|0.472|0.197|
|PHL|2.006|2.859|0.483|0.329|2.216|2.692|0.307|0.137|0.677|0.315|
|Correl.|with data|0.443||0.321||0.860||0.159||0.726|

# **5.4 Counterfactual simulations**

East-Asian economies have been the setting for tremendous changes in recent decades. Arguably the most significant changes are the increased fragmentation of production, China’s opening to international trade and its subsequent rapid economic growth. In our theory these phenomena can certainly be related, as China’s opening to trade could have facilitated fragmentation along chains in which it is now involved. Rapid economic growth may be associated with trade-related increases in productivity, but multi-factor productivity growth not specifically related to trade might also have been important. With a calibrated model at hand, we can now examine various counterfactual simulations to study how structural changes would affect economic outcomes such as output, trade, welfare and the fragmentation of production.

We see at least four experiments that would provide interesting insight into the reorganization of supply chains in Asia:

- Counterfactual 1): Trade costs have fallen significantly over the past decades and their reduction is cited as the most likely source of the increased fragmentation of production in Asia. Trade costs may decline even further in the near future, as there is still room to improve trade agreements, especially on a multi-lateral basis (Baldwin, 2008). We model this structural change with a 10% reduction in cross-border trade costs.

- Counterfactual 2): Arguably the most dramatic change in the Asian economy over the past two decades has been the very high rates of growth in the Chinese economy, along with its opening to trade. With GDP growth rates reaching 10%, the Chinese economy is inducing very large changes in how production in Asia is organized. To examine the

46
reforms in Germany. China could benefit from assessment of international lessons and expansion of pilots domestically in these areas.<sup>35</sup> While this study has focused primarily on the relationship between pension eligibility, mandatory retirement and work activity, the positive relationship in urban China between educational attainment and continued employment at the high end of the education distribution (for those with more than high school education) reflects the possibility that workers with more skills, or ability to learn new skills, may find it easier to work at older ages. In a review of policies followed in Europe, the OECD has recognized this phenomenon and noted that support for skills upgrading at mid-career can be attractive for employers and employees alike, and may help to enhance the skills and employability of workers later in their careers as well.

# **References**

- Appleton S, Knight J, Song L and Xia Q (2002) Labor retrenchment in China: Determinants and Consequences.” _China Economic Review_ . 13:252-275.

- Benjamin, D, Brandt, L and Fan, J (2003) Health and labor supply of the elderly in rural China, unpublished manuscript, Department of Economics, University of Toronto.

Blau DM (1994) Labor force dynamics of older men. Econometrica 62: 117-156.

- Brodjonegoro B, Simanjuntak R (2002) Pension plan in Indonesia: Avenues for reform, Hitotsubashi Journal of Economics 43: 151-161.

- Bound, J (1991) Self-Reported versus objective measures of health in retirement models, Journal of Human Resources 26(1): 106-38.

- Bound J, Stinebrickner T, Waidmann T (2010) Health, economic resources and the work decisions of older men, Journal of Econometrics 156(1): 106-129.

- Bound J, (1999) The dynamic effect of health on labor force transition of older workers, Labor Economics 6: 179-202.

- Cai F, Park, A and Zhao, Y (2008) The Chinese Labor Market in the Reform Era in China’s Great Economic Transformation, Brandt L and Rawski T (eds) Cambridge: Cambridge University Press.

> 35OECD (2006) provides a useful review of policies and approaches to reducing barriers and disincentives to continue working.

25
|**Table 4d: Expectations and Perceptions**||Mean (Control|||
|---|---|---|---|---|
|Dependent variables|Obs.|group at<br>baseline)|ITT Difference|ITT ANCOVA|
|Expected earnings from working at garment|825|1,320|-99.8***|-82.8**|
|factory (in birr)||[526]|(38.0)|(37.4)|
||||{0.01}**|{0.028}**|
|Subjective factory job quality (scale 1-10)|827|8.19|-1.48***|-1.40***|
|||[2.03]|(.174)<br>{0.001}***|(.176)<br>{0.001}***|
|Perceives garment factory jobs as healthy|827|.623|-.189***|-.174***|
|(yes=1)||[.486]|(.035)|(.036)|
||||{0.001}***|{0.001}***|
|Perceives factory jobs provide a steady<br>|827|.814|-.011|.002|
|income (yes=1)||[.390]|(.037)|(.038)|
||||{0.181}|{0.235}|
|Perceives factory jobs as permanent (yes=1)|827|.839|-.053**|-.041*|
|||[.368]|(.024)|(.025)|
||||{0.016}**|{0.05}**|

Note. The table reports the intent-to-treat (ITT) estimates of the effect of the job facilitation intervention on individual’s expectation and perceptions of factory jobs. 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.

As indicated by Table 4e, there are no significant impacts on several indicators or proxies of women’s (economic) empowerment.<sup>19</sup> Unlike cases from other countries, where female entry into garment employment is associated with reduced empowerment and intensified domestic violence at least in the short-term (e.g., Ahmed 2011; Heath 2014), we find no such effect in Ethiopia. In contrast to income, where shocks and windfalls gains can affect welfare instantly, we admit that the short time horizon of the follow-up survey might be too early to document empowerment related effects. Shift in empowerment constitute not only a change in one’s own and other household members’ entrenched behavior and attitude towards gender norms but also a change in intra-household bargaining and resource allocation in favor of the female member in the

> 19 Table A2 employs a slightly modified specification that considers only married women in the sample. Save for the mobility score, the results on empowerment indicators are largely similar to Table 4e.

27
> - 50 -

technologies cost the same. This latter assumption fails to distinguish between - ex ante and ex post substitution the former requires completely new plant whichever fuel le chosen, while the latter may require no new plant (multifirlng) or require expenditure on conversion if the fuel mix is to be changed. The assumption of continuous substitution is particularly restrictive since no one plant is likely to use a mixture of fuels in the way that it uses a mixture of labor, capital and energy. Rather there is a preferred fuel/technology which will be switched at a certain value of the price ratio. Only if there are many plants, each with slLghtly different values for the switch point (caused by differences in local fuel prLces, age of capital etc.), will the aggregate behave as if there were a single plant with continuous fuel substitutability. In order for there to be a decreasing rate of substitution, as implled by the traditional cost function, the distribution of switch points would need to follow an extremely limiting and unlikely pattern. However, if plants have idle capacity, then there ie a possibility of continuous substitution until full capacity is reached as pointed out by Johansen [1972]. For industries with many small plants the econometric model may be a reasonable approximation to the actual possibilitiec but it is very unlikely for it to be close for power systems. In addition the published data for power systems in developing countries suggest that often only one or perhaps two fuels are presently used, while policy may be to encourage the choice of another fuel with better pollution characteriatics. Econometric models for such a country could give no guide as to the price at which this might happen, aince the fuel in question could not be included in historical substitution analysis.

By the nature of the aggregate data used econometric models must use average emission rates for the various fuels - they cannot be technology specific.

Engineoring studies attempt to evaluate specific aituations in which one fuel may be substituted for another and by taking all costs and technical information into account to reach an accurate picture of the aensitivity of fuel choice to the price of that fuel, as well as to other variables. In power
# **<u>Table 8:</u> Determinants of inclusion in revisit sample**

Ordinary least-squares regressions <u>Dependent variable: Indicator for inclusion in revisit sample</u>

|<br> <br>**Preferences under**|<br>**commitment**||
|---|---|---|
||Fraction Present Biased, Non-Implemented Interest Rates|0.002<br>(0.035)|
||Fraction of all tokens allocated to "sooner"|-0.031|
|||(0.097)|
||Indicator: days to first disbursement (targeted) <=6|-0.108***|
|||(0.025)|
|**Financial sophistica**|**tion**||
||Fraction of Decisions Consistent with Law of Demand|0.001<br>(0.076)|
||Words recalled|-0.015*|
|||(0.008)|
||Raven's Tests Correct|0.019|
|||(0.013)|
||Financial Literacy Questions Correct|0.005|
|||(0.014)|
|**Socialpressure**|||
||Spouse minus own allocation to sooner (MK)|-0.000|
|||(0.000)|
||Number of relatives in the village|0.001<br>(0.002)|
|**Rate of return to w**|**aiting**||
||Implemented interest rate {.1,.25,.5,.75,1}|0.057<br>(0.036)|
|**Baseline characteri**|**stics**||
||Male|0.036|
|||(0.030)|
||Age 35 or under|0.080*|
|||(0.044)|
||36-56 yrs old|-0.037|
|||(0.031)|
||Some primary school|-0.034|
|||(0.029)|
||Primary school|-0.055|
|||(0.045)|
||More than primary school|-0.056|
|||(0.045)|
||Have adequate maize|0.007|
|||(0.027)|
||Total HH Wealth|-0.000***|
|||(0.000)|
|Controls for:|||
||Spousal characteristics|Y|
||R-squared (adj.)|0.05|
||<br>N|<br>722|

Significance levels: * 10%, ** 5%, *** 1%. Robust standard errors in parentheses. Notes: Unit of observation is individuals targeted for inclusion in revisit sample. Dependent variable has mean of 0.916. Right-hand-side variables are identical to column 6, Table 6, except for omission of shock variables ("death in family" and "shortfall in expected household income"), because shock variables are not available for attriters. See Table 6 for other notes.
# **4.  Conclusions**

In this paper we have reported on the latest update of the Worldwide Governance Indicators for 2006. The WGI are now available biannually since 1996, and annually for the five-year period 2002-2006. We have also continued our practice, introduced last year, of reporting the individual indicators underlying the aggregate indicators. It is our hope that more timely annual reporting as well as access to individual indicators will make the aggregate indicators more useful to users in academic and policymaking circles.

We nevertheless emphasize to all users the limitations of these measures of governance, which are shared by virtually all efforts to measure governance across countries and over time. The aggregate indicators we construct are useful for broad cross-country and over time comparisons of governance, but all such comparisons should take appropriate account of the margins of error associated with the governance estimates. These margins of error are not unique to our perceptions-based measures but are present -- if not explicitly acknowledged -- in any effort to measure governance. They naturally reflect the inherent difficulty in measuring something as complicated and multifaceted as governance. However, we have shown the feasibility of using the aggregate indicators to make comparisons of governance across countries and over time, subject to appropriate consideration of margins of error. In fact, for 2006 we have seen that over 60 percent of all cross-country comparisons result in highly-significant differences, and that nearly 30 percent of countries have experienced substantial changes in at least one dimension of governance between 1998 and 2006.

We also caution users that the aggregate indicators can in some circumstances be a rather blunt tool for policy advice at the country level. We expect that the provision of the underlying data will help users in identifying -- and acting upon -- more specific aspects of governance that may be problematic in a given country. And we also encourage using these aggregate and individual indicators in conjunction with a wealth of possible more detailed and nuanced sources of country-level data on governance in formulating policy advice.

23
**Table 8:  Robustness check on "homogeneous" industries**

||SeparateR<br>|egressions<br>|PooledR<br>|egressions<br>|
|---|---|---|---|---|
||Intermediate|Consumption|Intermediate ve|rsus Consumption|
||A.1|A.2|B.1|B.2|
|qxQ (institutions)|0.137***|0.092***|0.019**|0.022**|
||(6.655)|(2.586)|(1.986)|(2.248)|
|qxQxI|||0.022**|0.018*|
||||(2.413)|(1.828)|
|txT (infrastructure)|0.190***|0.056*|0.025***|0.029***|
||(7.431)|(1.743)|(3.411)|(3.834)|
|txTxI|||0.022***|0.017**|
||||(2.990)|(2.236)|
|kxK (capital)|0.364***|0.491***|0.037***|0.047***|
||(3.498)|(3.409)|(6.763)|(5.170)|
|kxKxI||||-0.012<br>(-1.300)|
|hxH (human capital)|0.055***|-0.091***|0.007*|-0.001|
||(2.594)|(-3.097)|(1.682)|(-0.198)|
|hxHxI||||0.011*<br>(1.692)|
|I (intermediate dummy)|||-0.057***|-0.057***|
||||(-4.817)|(-4.814)|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|R-squared|0.742|0.717|0.728|0.728|
|Number of observations|10,197|4,924|16,889|16,889|
|Number of countries|95|95|95|95|
|Numberof industries|134|62|205|205|

Notes: In separate regressions, the dependent variable is either the natural log of intermediate exports or of consumption exports. In pooled regressions, the dependent variable is the natural log of intermediate or consumption exports. Regressions are run only for homogeneous industries, i.e. industries exporting either only intermediate or only consumption goods. Coefficients are beta standardized and t-values for robust standard errors are reported in brackets. * Significant at 10%; ** at 5%; *** at 1%.

17