rafmacalaba/datause-encoder-data huggingface dataset:
# Policy Research Working Paper Series

|**Title**|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1 072 Costs of Alternative Treatments for<br>Incomplete Abordlon|Brooke R. Johnson<br>Janie Benson<br>Janet Bradley<br>Aurora Rabago Ordofhez<br>Catia Zambrano<br>Leonard Okoko<br>Leticia Vazquez Chavez<br>Paulina Quiroz<br>Khama Rogo|January 1993<br>|0.Nadora<br>31091|
|WPS1 073 Fiscal Aspects of Developing<br>Country Debt Problems and Debt-Se<br>Reduction Operations: A Conceptua<br>Framework|Peter J. Montiel<br>rvice<br>l|January 1993|R. Vo<br>31047|
|WPS1074 How Moving to World Prices Affects<br>the Terms of Trade in 15 Countries<br>of the Former Soviet Union|David G. Tarr|January 1993|N. Artis<br>38010|
|WPS1 075 Many Paths to Skilled Employment:<br>A Reverse Tracer Study of Eight<br>Occupations in Colombia|Adrian Ziderman<br>Robin Horn|January 1993|PHREE<br>33680|
|WPS1 076 A Presumptive Pigouvian Tax on<br>Gasoline: Analysis of An Air Pollution<br>Control Program for Mexico City|Gunnar S. Eskeland<br>|January 1993|C. Jones<br>37699|
|WPS1 077 Risk Management Prospects for<br>Egyptian Cotton|Panos Varangis<br>Elton Thigpen<br>Takamasa Akiyama|January 1993|D. Gustafson<br>33714|
|WPS1 078 The Evolution of Welfare and Poverty<br>Under Structural Change and Econom<br>Recession in Cote d'lvoire, 1985-88|Christiaan Grootaert<br>ic|January 1993|E. Vitanov<br>38400|
|WPS1 079 How Useful Are Integrated Househol<br>Survey Data for Policy-Oriented<br>Analyses of Poverty? Lessons from<br>the C6te d'lvoire Living Standards Su|d Christiaan Grootaert<br>rvey|January 1993|E. Vitanov<br>38400|
|WPS1080 A New Regional Price Index for C6te<br>d'lvoire Using Data from the<br>Intemational Comparisons Project|Christiaan Grootaert<br>Ravi Kanbur|January 1993|E. Vitanov<br>38400|
|WPS1081 Correcting for Sampling Bias in the<br>Measurement of Welfare and<br>Poverty: The Case of the Cote d'lvoire<br>Uving Standards Survey|Lionel Demery<br>Chrstiaan Grootaert<br>|January 1993|E. Vitanov<br>38400|
experience an additional 1.4 percentage points decline in total employment from the credit crunch, when compared to industries with lower financial dependence.<sup>6</sup> As this estimate is based on data from representative labor force surveys, it captures employment in the entire universe of firms.

We then analyze the heterogeneity in the impacts of the financial crisis, and find that temporary, unskilled, young and migrant workers, as well as those in smaller firms, experienced disproportionately larger declines in employment during the credit crunch. The disproportionate impact on those employed in smaller firms is consistent with prior evidence from firm-level studies (Fort et al., 2013; Greenstone at al., 2014; Chodorow-Reich, 2014). The greater vulnerability of unskilled workers could be because firms “hoard” skilled workers, since the costs of training and finding skilled workers are likely to be higher (Halac and Schmukler, 2003; Anderton et al., 2012). At the same time, the larger impact on temporary workers suggests that labor market regulation too could have mattered.

In theory, stronger Employment Protection Law (EPL) is expected to reduce the extent to which firms are able to adjust employment in response to economic shocks.<sup>7</sup> But there is a duality in European EPL, with high mandated dismissal costs for permanent workers coexisting with relatively lax regulations on temporary contracts (Boeri and Garibaldi, 2007). The stronger the protection for permanent workers relative to that of temporary workers, the higher the incentive for firms to adjust employment on the other margin; that is, through increased use of temporary workers (Bentolia et al., 2012; Chaurey, 2015; Kahn, 2007). This increased reliance on temporary workers could actually make employment more responsive to shocks. Indeed, it has been argued that the deeper fall in employment after the Great Recession in Spain as compared to France is due to the bigger gap in the stringency of EPL for permanent and temporary workers in the former (Bentolila et al., 2012).

> 6 This estimate is based on a 20 percentage points decline in the credit to GDP ratio, the average level of decline in our sample.

> 7 See, for example, Oi (1962); Nickell (1986) and Hamermesh (1993).

4
|Urban<br>Husband’s ideal number of sons:<br>_>_ actual number _≤_actual number<br>(5)<br>(6)<br>-0.490*<br>-0.033<br>(0.277)<br>(0.180)<br>3,740<br>9,330<br>0.333<br>0.573<br>Urban<br>Husband’s ideal number of sons:<br>_>_ wife’s ideal<br>_≤_wife’s ideal<br>(5)<br>(6)<br>-0.206<br>-0.132<br>(0.269)<br>(0.174)<br>3,023<br>9,992<br>0.492<br>0.509<br>x<br>x<br>x<br>x<br>x<br>x<br>ates as specifed by equations 2 and 1. We<br> indicator for whether the woman is using<br>ently has. Panel B examines heterogeneity<br>ol for the share of seats in the district that<br>les for living in a rural area, religion, and<br>SC/ST population in the district, and the<br>cance at the 1, 5, and 10 percent levels.|
|---|
|**Panel A: Heterogeneity by husband’s desire to have more sons**<br>Full Sample<br>Rural<br>Husband’s ideal number of sons:<br>Husband’s ideal number of sons:<br>_>_ actual number _≤_actual number _>_ actual number _≤_actual number <br>(1)<br>(2)<br>(3)<br>(4)<br>_Using modern contraception_<br>Fraction of female leaders<br>0.159<br>0.252*<br>0.354***<br>0.375**<br>(0.117)<br>(0.137)<br>(0.134)<br>(0.161)<br>Observations<br>13,111<br>31,011<br>9,371<br>21,681<br>Outcome mean<br>0.299<br>0.582<br>0.286<br>0.585<br>**Panel B: Heterogeneity by diference in husband’s and wife’s ideal number of sons**<br>Full Sample<br>Rural<br>Husband’s ideal number of sons:<br>Husband’s ideal number of sons:<br>_>_ wife’s ideal<br>_≤_wife’s ideal<br>_>_ wife’s ideal<br>_≤_wife’s ideal<br>(1)<br>(2)<br>(3)<br>(4)<br>_Using modern contraception_<br>Fraction of female leaders<br>0.305<br>0.198*<br>0.482**<br>0.335**<br>(0.187)<br>(0.110)<br>(0.210)<br>(0.133)<br>Observations<br>9,887<br>34,005<br>6,864<br>24,013<br>Outcome mean<br>0.486<br>0.502<br>0.483<br>0.499<br>State fxed efects<br>x<br>x<br>x<br>x<br>District characteristics<br>x<br>x<br>x<br>x<br>Individual characteristics<br>x<br>x<br>x<br>x<br>_Notes_: Data are from the 2015-16 National Family Health Survey (NFHS) of India. All columns present 2SLS estim<br>defne close elections as those in which the victory margin is less that 3 percent of votes. The dependent variable is an<br>modern contraception. Panel A shows heterogeneity by husband’s desire to have more sons than what the couple curr<br>by the discordance in husband’s and wife’s desired number of sons. All regressions include state fxed efects and contr<br>had close elections between women and men. Individual controls include age, years of schooling, and indicator variab<br>caste. District-level controls include the share of female population, the share of urban population, and the share of <br>female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote signif|

38
capacity building services to its members. As an apex body, UNACAFEN is an umbrella organization for 17 smaller associations. UNACAFEN is governed by a general assembly of 10 delegates from every member association, and three representatives from UNACAFEN are represented in CODOCAFE. Member associations can sell or export coffee on their own or sell to UNACAFEN for export. UNACAFEN typically pays a slightly higher price than other exporters but because the price is not paid in full upon delivery, its popularity as an export agent is limited.

## **Table III.10: Major Coffee Exporters 1993-1999**

|**Type of Exporter**|1993/94|1994/95|1995/96|1996/97|1997/98|1998/99|
|---|---|---|---|---|---|---|
|**1. Traditional Exporters**<br>|||||||
|**1.1. Induban Group**|**51,905.31**|**123,640.06**|**115,408.71**|**89,943.55**|**71,064.77**|**36,845.51**|
|% National Exports|14%|23%|17%|23%|13%|17%|
|**1.2. Cibao Groups**|||||||
|Total of the Cibao Group|**150,439.17**|**248,981.47**|**271,228.17**|**164,109.66**|**292,501.70**|**68,559.30**|
|% National Exports|42%|46%|39%|42%|53%|32%|
|**1.3. Barahona Group**|||||||
|Total Barahona Group|**52,052.26**|**56,886.93**|**140,209.26**|**34,720.79**|**120,114.88**|**53,411.40**|
|% National Exports|0.14|0.11|0.20|<br>0.09|<br>0.22|<br>0.25|
|**1.4 Total Traditional Exporters**|**254,396.74**|**429,508.46**|**526,846.14**|**288,774.00**|**483,681.35**|**158,816.21**|
|% National Exports|70%|80%|76%|73%|87%|74%|
|**2. Other Exporters**|67,542.00|72,821.76|133,580.57|94,370.38|54,698.15|39,977.31|
|% National Exports|19%|14%|19%|24%|10%|19%|
|**3. Exporter Producers**|6,134.31|5,373.71|5,677.95|1,615.41|2,480.18|7,440.53|
|% National Exports|1.7%|1.0%|0.8%|0.4%|0.4%|3.5%|
|**4. Producer Associations**|||||||
|Fedecares and Asoc.|11,326.13|8,267.25|5,787.08|3,306.90|6,198.78|826.73|
|Asoc. De Villa Trina|10,747.43|13,640.96|11,160.79|1,240.09|793.66|77.71|
|Various Other Associative Groups|11,988.87|6,674.98|7,961.38|4,563.53|5,181.91|6,271.79|
|Total Producer Associations|34,062.43|28,583.19|24,909.25|9,110.52|12,174.35|7,176.23|
|% National Exports|9.4%|5.3%|3.6%|2.3%|2.2%|3.4%|
|**GENERAL TOTAL**|**362,135.48**|**536,287.12**|**691,013.91**|**393,870.31**|**553,034.03**|**213,410.28**|

Source:SEA; Statistics section, Division of Special Studies,''CODOCAFE".

# **E.4 Domestic Coffee Market**

The domestic coffee market traditionally commands an important share of national production. Roasters buy green coffee, roast, grind and sell it on local markets with different brands and forms of presentation. The domestic processing industry is more highly concentrated than the export market; one company – INDUBAN – accounts for over 95% of domestic coffee processing.

A major question is the extent to which INDUBAN’s commanding market share affects market efficiency. Scale economies may favor large-scale domestic coffee marketing. However, important questions need to be addressed about how INDUBAN’s domination in the coffee sector affects producers’ choices of production and post-harvest technologies. For example, since 45
*-

# Morley and Alvarez (1992) use the real wage as a poverty indicator.

Sometimes a proxy is chosen which is one step removed in the line of reasoning - for example, real social expenditure per capita. But for such a measure to be satisfactory it must be further demonstrated that the poor were benefitting from these services and that it is the services from which they benefit that have been cut. Indeed, data from Latin America have shown social indicators to be resilient in the face of declining social expendituree - either the social expenditures did not matter for the welfare of the poor, or they are now better targeted, or the relationship has a lag so that declines are to be expected in the near future. Other objective indicators are more narrowly defined - often by measuring some of the above measures by a particular target group or area.

Since such a range of measures is available care should be exercised in the choice, construction and interpretation of these measures. For example, Cornia and Stewart (1987: 115) say that education expenditure per head of population aged 0-14 in Jamaica fell by 14 per cent in the early 1980s. Behrman and Deolaikar (1990: 13) question this figure, suggesting one instead of only 0.1 per cent (calculated as per head of population aged 5-14). The reasons the latter authors give for this difference are: (i) Cornia and Stewart use the GDP deflator, whereas real wages in education have dropped sharply; (ii) difference in period of comparison; Qiii) different denominator, but with that used by themselves being the more appropriate.

A second example is provided from two studies using what may be thought to be similar series - Pinstrup-Andersen (1989) shows a figure of child mortality in Brazil over the period 197784, with the gently declining trend giving way to a sharp upward lurch in 1982-84. Zuckerman (1991) reports the infant mortality rate for Brazil over the period 1970-1987 - the number declines steadily throughout the period with no interruption. The two series are not necessarily inconsistent, but if both are correct then the story they tell about changes in welfare is not the unambiguous one caimed by either author.

# _Medwods of analysis_

The original impetus for considering the "social impacts of adjustment' - meaning the impact upon the poor, and which may not be very social at all - came from work done for UNICEF, culminating in the seminal _Adjutment i4th a Hum Face._ The World Bank responded with reasonable speed to these concerns, although the initial reaction was to stress the ways in 94
Table 2: **Macroeconomic Volatility By Country Groups**

||||**By Ec**|**onomic **|**Region**|**By **|**KC**|**By **|**EA**|**By **|**FXI**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Partial**|**Full**|**U.S.**|**AE**|**EMDE**|**Low**|**High**|**Fixers**|**Floaters**|**Low**|**High**|
|_σ_∆S|7.96|2.82|/|2.36|3.41|2.19|3.33|2.40|3.19|3.47|2.35|
||[3.68]|[2.61]|/|[2.33]|[3.05]|[2.16]|[3.06]|[2.29]|[2.93]|[3.20]|[2.21]|
|_σ_R|15.65|10.41|1.56|3.12|14.46|3.49|13.39|6.01|13.1|13.6|8.32|
||[7.34]|[5.21]|[1.56]|[2.42]|[9.63]|[2.62]|[7.80]|[3.63]|[6.79]|[8.04]|[4.24]|
|_σ_∆IP|2.96|2.96|0.65|2.79|3.21|3.07|2.84|3.09|2.82|2.58|3.33|
||[2.57]|[2.57]|[0.65]|[2.32]|[2.98]|[2.58]|[2.56]|[2.75]|[2.39]|[2.28]|[3.01]|
|_σ_∆P|10.17|0.85|0.27|0.29|1.26|0.33|1.13|0.48|1.10|1.09|0.78|
||[1.99]|[0.53]|[0.27]|[0.27]|[0.95]|[0.28]|[0.78]|[0.37]|[0.69]|[0.72]|[0.53]|
|_N_|78|44|1|27|17|22|22|22|22|17|17|

Notes: (1) The table reports the standard deviations among all countries in each group indicated by each column. The bracket below each cell is the average standard deviation for each country’s time series. (2) **Partial** stands for all 78 “Partial analysis” countries and **Full** stands for the 44 “Full analysis” countries. All the other columns except **US** report statistics for some subsets of the “Full analysis” sample. (3) **Low (High) KC** refers to countries with average KC index below (above) the median. **Fixers (Floaters)** refers to countries with average EA below (above) the median. **Low (High) FXI** refers to countries with FXI below (above) the median. (4) ∆S is monthly change in the log of exchange rate per USD, and ∆IP (P) is monthly change in log of IP (CPI). _N_ counts the number of countries in each group.

# **3 Baseline SVAR Results**

## **3.1 U.S. Model with the Global Factor**

We first estimate an “aggregate open economy” VAR including U.S. variables and the global factor constructed by Miranda-Agrippino & Rey (2020). The global factor is a sort of summary statistic of the fortitude of global asset prices. Movements in the factor thus provide an aggregate view of U.S. monetary policy transmission. As shown in Figure 3, the impulse responses of U.S. variables following a U.S. monetary policy shock are conventional, and they suggest an information component in both the NS and forward guidance shocks as expected. Furthermore, we observe an immediate drop in the global factor following a U.S. monetary tightening. This is in line with Miranda-Agrippino & Rey (2020)’s finding that U.S. monetary policy is an important driver of the Global Financial Cycle. Hereafter, we suppress domestic responses to save space.

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with information on inter-firm relationships and firm-level information for the period 2012 to 2021. These multi-country firm-level data cover buyer-supplier relationships, thereby allowing us to inspect for evidence of dissemination of green technologies across global value chains. Furthermore, they contain information on innovation partnerships between firms.

The textual analysis of earnings conference calls transcripts points to a rapidly growing importance of green technologies after 2019, as revealed by a rapid increase in the share of ECTs mentioning keywords associated with specific green technologies, as well as in the set of firms that have ECTs mentioning these green technologies. This rise is primarily driven by a few green technologies, including construction material, carbon capture technology, hybrid/electric vehicles, and wind turbines, which tend to be mentioned together in the same ECT. It is concentrated in firms operating in a few industries—notably manufacturing and utilities—and among larger firms within sectors. We also find evidence of an increased geographical spread in the location of these green firms, with a growing presence in large emerging countries in Asia, Latin America, and the Middle East and North Africa, in addition to North America and Europe. Importantly, we find that high-emissions firms that mention green technologies in their ECTs are significantly more likely to observe a subsequent reduction in their carbon emissions, which supports the hypothesis that ECTs capture firm investments in green technologies among these firms.

We then examine the extent to which the diffusion of green technologies between firms is shaped by firm-to-firm linkages, notably buyer-supplier relationships and innovation partnerships. We find robust evidence that suppliers and buyers of firms that mention green technology keywords in their earnings calls are significantly more likely to mention them as well in their own ECTs. These effects are especially relevant when firms are linked to high-emissions firms that mention green technologies in their own ECTs. Our data consists mainly of multinational enterprises (MNEs), but we also find some evidence of diffusion of green technologies through firms-to-firm linkages to (and from) firms that are not MNEs.

In addition to the papers cited above, this paper contributes to several strands of literature. Prior research on low-carbon technologies has often confined its attention to the diffusion of a few technologies in a specific sector or country (see, e.g., Costantini, Crespi, and Palma (2017)). In addition, the literature on low carbon technologies has focused mostly on their invention and production (Popp, 2002, 2022; Aghion, Dechezleprˆetre, H ́emous, Martin, and Van Reenen, 2016; Dechezleprˆetre, Glachant, and M ́eni`ere, 2013) or on the costs of adopting them (Way, Ives, Mealy, and Farmer, 2022; Grubb, Drummond, Poncia, McDowall, Popp, Samadi, Penasco, Gillingham, Smulders, Glachant, Hassall, Mizuno, Rubin, Dechezleprˆetre, and Pavan, 2021), 2
|Computer|2.9|18.5|72.1|70,000|
|---|---|---|---|---|
|Cellphone|85.3|93.6|100.0|10,000|
|Motorcycle|0.0|11.6|48.8|170,000|
|Auto|11.8|24.4|14.0|380,000|
|rickshaw|||||
|Car|0.0|5.8|27.9|189,000|

While living in Colombo city for many years and facing the challenges of urban life, there are a number of factors that cause stress to households. Stress levels are ranked by them on a scale of 0 to 10, from least to the highest stress levels. As seen in Table 5, majority households (more than 60%) have ranked congestion in the area and floods as the major stressors followed by hectic life, monetary issues, poor housing conditions and other factors. There are interesting insights when these factors are examined across different socio-economic categories. For instance, more than 73% of BPL households are stressed due to floods and congestion in the area followed by poor housing conditions and monetary issues. This is important as floods are causing greater stress than even monetary issues that a BPL household is expected to face. In the case of poor households also, floods and congestion are pre-eminent factors causing stress for majority of them. Other factors are monetary issues, hectic life and poor social networks. For the non-poor, hectic life is the most important stressor. However, more than 50% also find floods as an important stressor followed by congestion and air pollution. This clearly shows that for the non-poor households, factors that affect the quality of life are causing greater stress than for other households for whom monetary issues are more important. Thus, differences across three categories reinforce our understanding of their socioeconomic status.

_Table 5: Factors causing stress for households_

||**% of**<br>**Total**<br>**HHs**|**BPL**|**Poor**|**Non-**<br>**poor**|
|---|---|---|---|---|
|Hectic life|54.0|38.2|50.3|81.4|
|Housingconditions notgood|46.4|67.6|48.6|20.9|
|Lack of opportunities|24.4|26.5|25.4|18.6|
|Congestion in the area|62.8|75.8|68.8|51.2|
|Transport related issues|17.2|8.8|21.4|30.2|
|Floods|62.0|73.5|67.6|53.5|
|Airpollution|23.2|21.2|22.0|51.2|
|Social networks notgood|42.0|41.2|50.3|32.6|
|Familyissues|35.2|41.2|42.2|25.6|
|Monetaryissues|50.4|58.8|58.4|34.9|

As for the housing characteristics, 24% of respondent families have been living in buildings constructed before 1991 and 19.6% live in buildings that were constructed between 1991 and 2000.

18
Table A.2: Export shares by country, 1994-2004

|Sector|Description|Germany|Russia|Ukraine|Lithuania|Poland|
|---|---|---|---|---|---|---|
|15|Food products, Beverages|3.97|2.58|10.25|11.84|8.28|
|16|Tobacco|0.30|0.09|0.32|0.55|0.17|
|17|Textile|2.42|0.77|0.85|7.18|2.96|
|18|Leather|1.26|0.52|2.82|9.73|6.24|
|19|Footwear|0.50|0.24|1.04|0.99|1.34|
|20|Wood and Products of Wood and Cork|0.62|2.68|1.07|4.92|3.58|
|21|Paper|2.32|2.94|1.30|1.15|2.88|
|22|Printing and publishing|1.05|0.95|0.20|0.43|0.69|
|23|Coke, Refned Petroleum Products and Nuclear Fuel|1.26|22.15|5.81|18.33|2.31|
|24|Chemicals and Chemical Products|13.43|11.74|11.56|9.41|6.91|
|25|Rubber and Plastics Products|3.52|0.84|1.55|2.40|3.90|
|26|Other Non-Metallic Mineral Products|1.49|0.76|1.39|1.50|2.76|
|27|Basic Metals|4.88|33.52|41.64|1.57|8.07|
|28|Fabricated Metal Products|3.37|2.05|2.51|1.95|6.04|
|29|Machinery and Equipment, not elsewhere classifed|16.36|4.11|6.59|4.30|7.22|
|30|Ofce, Accounting and Computing Machinery|2.82|0.16|0.23|0.60|0.28|
|31|Electrical Machinery and Apparatus, not elsewhere classifed|5.42|1.44|2.69|2.78|5.85|
|32|Radio, Television and Communication Equipment|4.92|0.68|0.67|4.50|3.80|
|33|Medical, Precision and Optical Instruments|4.12|1.55|1.22|1.15|0.79|
|34|Motor Vehicles, Trailers and Semi-Trailers|20.14|2.35|1.37|5.93|12.23|
|35|Other Transport Equipment|3.94|5.10|4.42|4.78|5.72|
|36/37|Manufacturing not elsewhere classifed; Recycling|1.89|2.77|0.51|4.03|7.98|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. Table reports export shares by sector over the period 1994-2004 for sectors are classified according to NACE Rev. 1 (2-digit) classification.

Table A.3: Average treatment effects using alternative measures of foreign ownership

|Dep. var.:|(1)<br>Employment (log)||(2)<br>Employment (log)|
|---|---|---|---|
|Post*German ownership (binary)|0.156***<br>(0.041)|Post*German shareholders (num)|0.088***<br>(0.020)|
|Post*French ownership (Binary)|0.155**<br>(0.066)|Post*French shareholders (num)|0.080**<br>(0.037)|
|Post*British ownership (binary)|0.099<br>(0.064)|Post*British shareholders (nun)|0.084<br>(0.055)|
|Year FE|Yes||Yes|
|Firm FE|Yes||Yes|
|Observations|271555||271555|
|Firms|61254||61254|

Notes: Authors’ calculations based on Orbis data. Each cell is obtained from a different specification where the number of employees is regressed on a time-varying indicator of foreign ownership indicated on the row. All regressions include firm and year fixed effects. Coefficients show the average treatment effects of foreign ownership. Results refer to firms that were observed both before and after accession. Data exclude the year 1999 due to anomalies in the data. Foreign ownership in column (1) is measured by a binary indicator taking value one when a firm had a foreign shareholder. Column (2) uses the number of foreign shareholders as an alternative measure of foreign ownership. Standard errors in parentheses are clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

33