rafmacalaba/datause-encoder-data huggingface dataset:
35 may be naive, however, since these data bases were not typically designed to focus on incometesting -- rather they served as police files. And, where household surveys have been undertaken, substantial flaws appear to exist in these data bases - in Poland several million people seem to be "off the books"; Hungary and Slovakia have important low-income ethnic groups that are similarly elusive; consumption and income data diverge significantly. Recent household expenditure and income surveys show how flawed some of the data are, and how much improvement is needed. In the interim, imperfect targeting methods -- whether based on imperfectly measured income -- or proxies for income, may have to be used.

# _General wage or pension increases_

Increasing wages, pensions, unemployment benefits or other fixed incomes across-the-board will benefit all energy consumers, not just the poor, and is likely to have inflationary and budgetary costs. Judged from the perspective of targeting, the leakages to the non-poor are disproportionately - large. However, for major price increases, such a compensation scheme or perhaps a series of - lump sum payments may be politically necessary to compensate households for the impact on incomes. On the positive side, this approach allows the price mechanism to work, and does not interfere with the finances of the utility. In Albania, increases in wages, pensions, unemployment benefits, and welfare payments were used to compensate households for 75 % of the increase in power tariffs in 1994. The main issue associated with wage increases would be the possible, and in the transition economies probable, wage-price spirals that may be generated by wage compensation for price increases. "Political economy" aspects make such restitution particularly dangerous in the transition economies. Given the socialist legacy of entitlement, this approach could establish a perceived entitlement to _general_ compensation for price increases (as distinct from _targeted_ compensation to at risk groups), and may set unwise precedents. Any compensation should therefore take a one-time-only form and be targeted to at-risk groups.

# _The preferred approaches_

Cash payments to at risk groups are probably the preferred compensation scheme. They are
<!-- Start of picture text --> Eo<br>a<br>aoO<br>oO<br>ae<br>5<br>oa<br>ia<br>UW) yp<br>a7<br>o<br>ao<br>wi<br>Cc<br>en<br>3<br>a<br>a<br>o<br>Male Female Male Female Male Female<br>Ghana Rwanda Uganda<br>HEE Household Enterprise MEE Micro Enterprise<br>HE Small Enterprise ME Medium Enterprise<br>(ON Large Enterprise<br><!-- End of picture text -->
<!-- Start of picture text --> .008 Correlation: .995-1<br>.006<br>.004<br>.002<br>0<br>300 400 500 600<br>HLO<br>Mean Ratio<br>Regression Linear<br>SUPPLEMENT FIGURE IV<br>Comparison of Scores Across Linking Methods<br><!-- End of picture text --> _Notes:_ We compute Harmonized Learning Outcomes scores using multiple methods including regression, linear, mean and ratio linking. This figure compares scores using a density plot.

Supplement Figure IV shows how scores compare across methods. Overall, we find a correlation of .995 and above, indicating high levels of robustness. Supplement Figure V below breaks down scores distributions by test and method. This reveals similar patterns, with testing regimes with more overlapping countries showing more consistent scores across method. Taken together, these results reveal overall robustness. A caveat is that scores from regional assessments from PASEC and SACMEQ in particular should be interpreted carefully and focus less on precise scores and more on relative ranks and country groupings.

Over time, as more countries participate in more assessments, we anticipate the linking functions used to produce harmonized scores will become increasingly robust. The approach outlined here produces a first set of global comparisons, demonstrates aggregate reliability, quantifies uncertainty to bound estimates, and provides a foundation for continuingly more robust data and comparisons as more countries partake in regional and international assessments.

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to submit several disbursement requests up to the total amount requested in the business plan. Cash beneficiaries were supported to open accounts with accredited banks and microfinance institutions and the grant was then paid into that account up front. The disbursement procedure then differed in whether it was the first request or a follow-up request. To start the disbursement with an amount less than 15 percent of the grant, the beneficiary withdrew the amount directly from the financial account without approval from MEBF. To withdraw an amount exceeding 15 percent of the grant, the beneficiary had to request approval from MEBF. Starting from the second disbursement, four tranches of disbursement rates were defined. Within each tranche, the beneficiary could disburse many times, but when disbursing in the next tranche, the beneficiary had to again request approval from MEBF to ensure sufficient control. Consultants hired by MEBF also supported these beneficiaries to prepare their disbursement requests, and MEBF and the beneficiaries’ financial institutions coordinated to check whether the disbursements were done according to the rules.

Beneficiaries were also encouraged to formalize, but they were not forced. Yet, if they did not formalize, they had to name an underwriter who had to confirm to MEBF that they would guarantee for the beneficiary.

**Figure 1. Timeline of the program’s main activities**

<!-- Start of picture text --> Scored<br>business<br>Collected Trained and Final Signature Follow-<br>plans and Baseline Public<br>business interviewed selection of grant up<br>short- survey lotteries<br>plans firms applications agreements survey<br>listed<br>applicants<br>Jan–Mar Apr–May Jun–Aug Sep Nov–Dec Apr–May Aug–Sept Oct–Nov<br>2018 2018 2018 2018 2018 2019 2019 2020<br><!-- End of picture text --> _Source_ : Own representation.

# _3.2 Pathways to impact_

The overall goal of both interventions was to identify dynamic, growth-oriented, capital-constrained MSMEs to supply these firms with capacity-building services or alternatively capital to address shortages in skills and finance that paralyze their productivity and inhibit their growth. Hence, both interventions were intended to enhance productivity and profits. The ultimate goal of the interventions was an increase in employment in the intervention area and lasting effects on investment. The intended pathway leading to these goals is illustrated in Figure 2. Yet, BDSs and investments in capital could of course also lead to reduction of labor demand if production processes become more efficient and if labor is substituted by capital. The latter could especially happen if firms previously had to be labor intensive due to imperfect capital markets.

The rationale behind the matching grants intervention is to support recipient firms in selecting trainers or providers of BDSs that can address their needs. Firms may want to improve their business skills, optimize 8
trends in trade and labor market outcomes. These include the share of rural population, the literacy rate (share of population aged 15 and older who is literate), total population, and the shares of workers in the primary, secondary and tertiary sectors of economic activity as well as the median income per capita (all variables for the year 2000). The summary statistics are shown in Appendix 5.

# 3. Trade and local labor market effects: Baseline results

## 3.1 Exports, imports and the employment channel

Table 1 shows the impact of exports and imports on labor force participation and employment rates, using both OLS and the IV approach. The OLS estimates indicate that higher exports per worker tend to increase labor force participation rates (panel a). In particular, a 10 percent increase in exports per worker raised the share of working-age individuals working or looking for a job by 0.05 percentage points (column 6). The economic significance of this impact is rather small, considering that the labor force participation rate in the average municipality in 2000 was 45.8 percentage points. In line with these findings, the OLS results suggest that exports per worker and employment rates are strongly associated, although the economic significance is small (panel b). Specifically, a 10 percent increase in exports per worker is associated with an increase of 0.06 percentage points in the local employment rate, which represents only 0.13 percent of the employment rate of the average municipality in 2005 (see also Rodríguez-Castelán et al. 2020).

Because OLS estimates may be biased if unobserved shocks at the local level are correlated with both exports and income levels, the analysis adopts an IV approach. The results of the first-stage regressions indicate that the instrument is highly correlated with the endogenous variable, as the F-statistic exceeds the standard significance threshold (see Appendix 6 and Figure 7). Applying the IV approach, higher exports per worker raise labor force participation rates significantly and the magnitude increases by a factor of three relative to OLS estimates. In contrast, the IV estimates are less significant than OLS estimates for employment rate. The only statistically significant result is the specification excluding control variables, suggesting that a 10 percent increase in exports raised employment rates by 0.24 percentage points on average.

Similarly, focusing on imports per worker (Table 1, bottom panels), the IV estimates only find a significant effect on both labor force participation and employment rates when no control variables are added. On the positive side, the absence of negative effects points to a more nuanced role of imports, as the latter could include not only final imports that may compete with domestic production, but also imported inputs used for domestic or export production. Overall, the results so far find more significant and stronger effects for exports than for imports per worker.

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**<u>Table 3. Average time spent on different activities by method and country (in minutes)</u>**

|||Banglad|esh||Ugand|a|
|---|---|---|---|---|---|---|
|Activity|24-hour(TD)|7-day (SQ)|Difference(TD - SQ)|24-hour(TD)|7-day (SQ)|Difference(TD - SQ)|
|Work as employed|35.49|44.25|-8.76|12.53|14.09|-1.55|
|Own business work|39.96|30.96|9.00*|33.57|48.03|-14.46**|
|Farming/livestock/fishing|152.86|143.26|9.60|174.33|257.27|-82.94***|
|Shopping/getting service|13.35|23.65|-10.31***|11.66|31.12|-19.46***|
|Weaving/sewing/textile care|20.00|15.01|4.99|3.70|6.56|-2.85*|
|Cooking|74.25|72.50|1.75|81.63|88.25|-6.62|
|Domestic work|111.84|102.00|9.84|42.12|106.65|-64.53***|
|Care for children/adults/elderly|31.95|40.72|-8.76***|23.09|68.94|-45.84***|
|Travelling/commuting|43.76|31.25|12.51***|63.80|51.79|12.00**|

**Source:** Authors’ calculations based on WEAI 2.0 pilot data. **Notes:** N=399 in Bangladesh; N=328 in Uganda. Paired _t_ -tests performed comparing the mean time spent on each activity between methods. TD = time diary. SQ = stylized question. ***, **, and * denote statistical significance levels at 1%, 5%, and 10%, respectively.

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**Figure 3: Migration classification, 2004-05 and 2011-12**

Source: Authors’ calculation based on the IHDS While stock and flow estimates of migrants vary based on the data source and definition used, the broad patterns based on the IHDS are largely consistent with others. This can be seen with regard to (a) migration within rural areas, within urban areas, and between rural and urban areas; (b) migration within and across state boundaries; and (c) less affluent states see more people migrating out while the most affluent states are the largest recipients of migrants.

The share of those moving within states was higher than those moving across states, but the latter has increased in importance over time. In 2011-12, the share of internal migration flows within and between Indian states was, respectively, 55 and 45 percent. The share of interstate migration increased from 40 percent in 2004-05 to 45 percent in 2011-12 (Figure 4). This conforms to trends based on Census data, which show that interstate migrants as a proportion of total migrants increased from 11.9 percent in 1991 to 13.31 percent in 2001.

In terms of spatial movements, rural-urban migration expectedly dominates migration for economic reasons. Migration from rural to urban areas accounted for more than half of internal migration flows in both 2004-05 and 2011-12 (Figure 5). This trend fits in with the pattern of urbanization and structural transformation expected in a developing economy. It is also in line with the 2001 Census numbers, where among employment-related migrants, rural-urban dominated the other streams, i.e., rural-rural, urban-urban and urban-rural (Srivastava, 2012). Rural-rural migration flows also remain important and even increased from 27 percent in 2004-05 to 29 percent in 2011-12 (Figure 6). The precise destination of the migrant is not covered in the IHDS, but the data allow us to surmise that 20 percent of migrants in 2011-12 traveled to a metro city.

7
**The Emergence and Diffusion of Green Technologies: Firm-level Evidence from Textual Analysis of Patents and Earnings Calls**<sup>∗</sup>

Paulo Bastos<sup>†</sup> Lucio Castro<sup>‡</sup>

**Keywords** : Green technologies, earnings calls, patents, carbon emissions, firm-to-firm linkages. **JEL Classification** : F6, O3, Q5.

> ∗We are grateful to Juan Tapia Montenegro, Samuel Asuquo Edet and Matias Garibotti for excellent research assistance. We are also grateful to Juan Pablo Rud and seminar participants for very useful comments. The authors gratefully acknowledge financial support from the Umbrella Facility for Trade, which is funded by the governments of The Netherlands, Norway, Switzerland, Sweden and the United Kingdom, and the Research Support Budget of the World Bank.

> †Development Research Group, World Bank; CEPR; and REM/UECE. E-mail: pbastos@worldbank.org.

> ‡World Bank. Email: lcastro@worldbank.org
The results of Table A1 suggest that the survey is under-representing _Jefes_ participation.

It is unclear what the source of the discrepancy might be. It could be due to respondent ignorance of _Jefes_ participation, or a conscious desire to hide participation because of formal ineligibility. Alternatively it might reflect over-counting in the administrative data. This could arise if there is some expropriation of the _Jefes_ transfers for other purposes. However, once one allows for the residence-based assignment of participants it does not appear that the discrepancy is so large as to warrant serious concern about sampling bias in the EPH.

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# **Appendix A. Pilots combining the output approach and the process approach in developing countries**

The ILO carried out two interesting pilot surveys to test the output and process approaches in developing countries. These pilots were deployed in Albania and Mongolia.<sup>64</sup> The instrument differed to those of the BLS in three ways. First, instead of a standalone survey the pilots were thought of as modules that could be attached to already established surveys. Second, while the U.S. examples are both enterprise surveys, the ILO piloted an enterprise module and a household module to be attached to already established surveys—the enterprise survey (ES) in Albania and the national labor force survey (LFS) in Mongolia. The reason for testing a household module was that in developing countries the informal sector, the self-employed, and the agriculture sector are a large part of the economy and are usually not captured in enterprise surveys. Third, the pilots included in both modules a combination of questions from both the GGS and the GTP surveys, with some adaptations to developing countries. For example, the instrument includes sustainable and organic agriculture in a separate category to the GTP conserving natural resources. Despite modules parallelism, results are not comparable due to differences in sampling frames and the underlying differences in each of the surveys. For example, the Albanian Enterprise Survey (ES) did not include the agriculture sector while the labor force survey (LFS) did, and the Mongolian ES did not include the banking and financial sector, private health sector, and government agencies while the LFS did.

Although the pilots have limitations natural to the approaches, they also present some advantages. First, by including in a single firm survey instrument questions on outputs and processes, jobs can be added up to get a single number of jobs (those involved in the production of an EGS and/or those involved in environmentally friendly processes), without the issue of double counting. Second, since the household module is attached to the LFS, it also provides useful information for occupational profiling of such jobs, including occupation titles, gender, age, educational achievements, and wages. The disadvantage of this combined approach is that it still might be including jobs which do not have any green tasks by counting all jobs at a firm producing a green output and all jobs using green technologies or carrying out green practices (but not necessarily green tasks).

> 64 Stoevska, Elezi, & Muraku (2014) and Oyunbileg & Stoevska (2017).

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<!-- Start of picture text --> 0.2100 0.0080 0.0072<br>0.0069<br>0.2024 0.0060<br>0.2000 0.1975<br>0.0040<br>0.0020<br>0.1900<br>0.0000<br>-0.0020 -0.0010-0.0010<br>0.1800<br>-0.0040<br>-0.0060<br>0.1700<br>-0.0080<br>-0.0087-0.0087<br>0.1600 -0.0100<br>Redistributive Poverty Poverty<br>Effect reduction effect reduction effect<br>0.1500 ( $ 2.5 PPP ) ( $ 4 PPP )<br>PIT original PIT simulation<br>PIT original PIT simulation<br>Source: Own estimates using the Albania HBS 2015. Source: Own estimates using the Albania HBS 2015.<br><!-- End of picture text -->

**The CEQ model for Albania could be useful for additional policy simulations** . To the extent that the policy change refers to the elements incorporated in the analysis, further exploration could be carried out.

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58 Angenendt, S. , Kipp, D., Meier A. “Mixed Migration” Betlesmann Stiftung, 2017 Avle, S. (2014). Articulating and enacting development: Skilled returnees in Ghana’s ICT industry. _Infor‐ mation Technologies & International Development, 10_ (4), 1–13.

Borjas, G., & Bratsberg, B. (1996). Who Leaves? The Outmigration of the Foreign‐Born. _The Review of Economics and Statistics, 78_ (1), 165‐176. doi:10.2307/2109856 Borjas, G. J. (2003). The labor demand curve is downward sloping: Reexamining the impact of immigration on the labor market. _The quarterly journal of economics_ , _118_ (4), 1335‐1374.

Borjas, G. J. (2017). The labor supply of undocumented immigrants. _Labour Economics_ , _46_ , 1‐ 13.

Black, Richard and Gent, Saskia (2006) Sustainable return in post‐conflict contexts. International Migration, 44 (3). pp. 15‐38. ISSN 0020‐7985 Black, R., King, R., & Litchaeld, J., with Ammassari, S., & Tiemoko, R. (2003). _Transnational migration, return and development in West Africa_ (Final Research Report). Sussex, UK: Sussex Center for Migration Re‐ search, University of Sussex.

Black, R., Koser, K., Munk, K., Atfield, G., D'Onofrio, L., & Tiemoko, R. (2004). Understanding voluntary return.

Black, R., & Gent, S. (2006). Sustainable Return in Post‐conflict Contexts. _International Migration_ , _44_ (3), 15‐38.

Bloch, A. and Atfield, G. (2002) _The Professional Capacity of Nationals from the Somali Regions in Britain_ . Report for Refugee Action and IOM.

Brücker, H., & Jahn, E. J. (2011). Migration and Wage‐setting: Reassessing the Labor Market Effects of Migration. _The Scandinavian Journal of Economics_ , _113_ (2), 286‐317.

Butcher, K. F., & Card, D. (1991). Immigration and Wages: Evidence from the 1980's. _The American Economic Review_ , _81_ (2), 292‐296.

Card, D. (1990). The Impact of the Mariel Boatlift on the Miami Labor Market. _Industrial and Labor Relations Review, 43_ (2), 245‐257. doi:10.2307/2523702 Carrington, W.J. and de Lima, R. 1996. “The Impact of 1970s Repatriates from Africa on the Portuguese Labor Market.” Industrial and Labor Relations Review.

Clemens, M. A., & Hunt, J. (2017). _The Labor Market Effects of Refugee Waves: Reconciling Conflicting Results_ (No. w23433). National Bureau of Economic Research.
**Table 10: Factor Analysis on Banks and Equity Markets (2000-2006) High Low**

|**Variable**|**Banking/Size**|**Stocks/Liquidity**|**Banking/Efficiency**|**Stocks/Size**|**Uniqueness**|
|---|---|---|---|---|---|
|**Log stock market capitalization**|**0.33**|**0.14**|**-0.04**|**0.91**|**0.04**|
|**Log value traded**|**0.15**|**0.78**|**-0.09**|**0.59**|**0.01**|
|**Log number of listed firms**|**0.25**|**0.38**|**-0.01**|**0.10**|**0.78**|
|**Log turnover**|**-0.04**|**0.98**|**-0.12**|**0.01**|**0.03**|
|**Log private credit via deposit money banks**<br>**to GDP**|**0.89**|**0.10**|**-0.22**|**0.15**|**0.12**|
|**Log deposits to GDP**|**0.86**|**-0.08**|**-0.21**|**0.23**|**0.17**|
|**Log total assets of deposit money banks to**<br>**GDP**|**0.94**|**0.05**|**-0.10**|**0.20**|**0.07**|
|**Log net interest margin**|**-0.37**|**-0.13**|**0.71**|**0.00**|**0.34**|
|**Log interest spread**|**-0.07**|**-0.11**|**0.59**|**-0.09**|**0.63**|
|**Log overhead to total assets**|**-0.37**|**-0.20**|**0.70**|**-0.09**|**0.32**|
||**Number of obs**|**ervations: 87. Num**|**ber of parameters: 34**|**.**||

# **f. Core indicators**

Table 11 provides a synopsis of relevant criteria (described in the previous section) for narrowing our core set of indicators. Main steps followed in the selection process were as follows:

- As regards bank size indicators, they rank very similarly as regards all criteria except that of linking up with final welfare objectives (where credit variables are better) and coverage (total claims on the private sector has a much lower coverage). Hence, _narrow credit_ is a clear winner on account of both coverage and welfare linkages.

- In addition to narrow credit, however, we also retained _deposits_ in our core list on account of the fact that attracting and placing funds can have different dynamics, particularly in incipient systems or in systems that are undergoing systemic stress.

- As regards bank efficiency, the net interest margin dominates operating costs on account of its goodness of fit and the spread on account of its higher factor loading and lower year-to-year variability. The fact that its definition (and construction) is clearer than that of bank spreads (where there are some questions as regards the consistency with which rates are being picked across countries and time periods) is an important further argument in favor of _net interest margins_ .

- As regards bank reach, the number of deposit accounts dominates the other two indicators as regards factor loadings and its more limited dependence on idiosyncratic and perhaps rapidly evolving technological factors. It would naturally be the indicator of choice, except for the fact that its coverage is severely more limited (its goodness of fit is also a bit lower, which might suggest

29