rafmacalaba/datause-encoder-data huggingface dataset:
Q. Zhang, and Yin (2021) and G. Li et al. (2019).<sup>6</sup> In the Islamic Republic of Iran, Ainin, Naqshbandi, and Dezdar (2016) similarly document that firms adopting green information technology practices tend to be higher performing. Positive associations between green investments and firm performance paint a promising picture of profit-maximizing measures with climate co-benefits.

While positive associations between green investments and firm performance are encouraging, designing policies to foster such investments and innovation is difficult. Münch and Scheifele (2022) show that Indian auctions for solar that required local content versus those that did not did not lead to any differences in patenting, highlighting the difficulties of creating policies to spur green innovation. Given the prevalence of small firms in many LMICs, there are also concerns about which firms will find investments in climate mitigation to be profit-maximizing. G. Li et al. (2019) show that large Chinese enterprises realize greater gains in competitiveness when they invest in green technologies relative to smaller firms (see W. Przychodzen, Gómez-Bezares, and J. Przychodzen (2018) and Leoncini et al. (2019) for similar results from HICs). If green technologies involve economies of scale or if small firms do not have access to the types of technologies that would make lowering emissions privately optimal, private incentives will be insufficient for mitigation efforts.<sup>7</sup> Even if switching to less carbon-intensive production does not lower costs, it may be a lucrative investment if it allows firms to better compete in global markets. Earnhart, Khanna, and Lyon (2014) review the empirical evidence on the extent to which international markets transmit environmental pressure from HICs to suppliers in LMICs, although their review is not explicitly focused on climate mitigation measures. They find evidence from multiple studies consistent with multinational companies transmitting environmental pressure from

> 6Amore and Bennedsen (2016), W. Przychodzen, Gómez-Bezares, and J. Przychodzen (2018), Woo et al. (2014), and Gagliardi, Marin, and Miriello (2016) document similar results in HICs.

> 7Dechezleprêtre, Glachant, and Ménière (2013) document that lax intellectual property regulations and restrictions on trade and foreign direct investment can limit transfers of climate-friendly technologies between countries. Barriers to technology transfer may further exacerbate distributional differences in the returns to green investment.

25
**<u>Table 3: Manufacturing Productivity and Firm Characteristics (Dependent variable: Ln TFP).</u>**

|<br>**Variables**|<br>**(1)**|<br>**(2)**|
|---|---|---|
|Small Firm|0.13 ***<br>(0.001)|0.13 ***<br>(0.001)|
|Medium Firm|0.21 ***<br>(0.003)|0.21 ***<br>(0.003)|
|Large Firm|0.30 ***<br>(0.004)|0.30 ***<br>(0.004)|
|Moscow|0.12 ***<br>(0.003)|0.11 ***<br>(0.003)|
|Firm Age|-0.006 ***<br>(0.000)|-0.006 ***<br>(0.000)|
|Age Squared/1000|0.03 ***<br>(0.001)|0.03 ***<br>(0.001)|
|New Entrants (age 0 -1)|-0.02 ***<br>(0.002)|-0.02 ***<br>(0.002)|
|Exiting Firms|-0.065 ***<br>(0.003)|-0.06 ***<br>(0.003)|
|Federal/State-Owned|-0.22 ***<br>(0.011)|-0.22 ***<br>(0.011)|
|Municipal-owned|-0.28 ***<br>(0.008)|-0.25 ***<br>(0.009)|
|Market Share (of NACE level-4 subsector total)|0.98 ***<br>(0.054)|0.98 ***<br>(0.054)|
|Industry Structure Herfindhal Index|-0.003<br>(0.009)|-0.01<br>(0.01)|
|Year Dummies(2004 – 2008)|Yes|Yes|
|IndustryDummies|No|Yes|
|Constant|2.3***<br>(0.002)|2.2 ***<br>(0.000)|
|Number of Observations|170,554|170,554|
|Number of Firms|524,87|52,487|
|Adjusted R sq|0.20|0.23|

Note: Column (1) excludes sector dummies, whereas column (2) includes them. dependent variable calculated is ln (TFP) calculated according to the technique of Levinsohn and Petrin(2003). Standard errors adjusted for clustering on firms are noted in parentheses. The industry dummies are defined at 2-digit NACE level for manufacturing (10 – 35). *** significant at the 1 percent level, ** significant at the 5 percent level, * significant at the 10 percent level.

- Fourth, **state and municipal ownership** shows strong negative and statistically significant relationships with productivity, perhaps reflecting the weak commercial incentives in state and municipal firms compared with private firms (see Figure 7).

13
The fourth lesson is that the IPF can take into account both efficiency and efficacy considerations. This is shaped by the construction of variables. For example, one could use the absolute number of beneficiaries as an input to the SEI to consider policy effectiveness when service expansion is a priority. On the other hand, ‘beneficiaries per dollar spent’ may be more appropriate if the goal is fiscal efficiency. In the case of Panama, where development of water services in rural areas was an important policy goal, the decision was made _not_ to control indicators by project size in order to protect against the possibility of privileging urban projects with greater economies of scale (Marcelo, Mandri‐Perrott, & House, 2015).

The final lesson is that IPF can be used as an opportunity to strengthen data weaknesses. The process of discussing relevant indicators by which to select projects is a catalyst for improving information levels to move towards more complex economic analysis. In Panama, the discussions prompted by the IPF have resulted in improved data project data collection. The IPF, as a process, can be a valuable starting point for improving appraisal by inciting aspirational discussions about the kinds of data that would be most helpful to deciding among projects within a sector.

# **Implementing IPF**

The sequence of implementing IPF, as described in the technical description and pilot applications above, is summarized in Figure 10. With respect to this sequence, the pilot in Panama also gave rise to an important discussion of requirements for implementing IPF and factors of particular interest in further piloting to improve its applicability. These issues are discussed in the following section. Some are process‐oriented and relate to overall infrastructure planning; some are organizational and deal with issues of resources, authority, and capacity; and some are technical, relating to the informational inputs required.

Figure 10. Sequence of the IPF <!-- Start of picture text --> I<br>Criteria • Deliberation and consultation with decision<br>Identification makers and experts<br>• SEI and FEI based on existing appraisal data<br>II<br>• PCA when objectivity in criteria weighting is preferred<br>SEI and FEI<br>• CBA elements incorporated when available / calculable<br>calculation<br>• Sensitivity analysis<br>• (SEI, FEI) coordinates<br>III<br>Visual interface • Quadrants defined by budget<br>constraint<br>• Based on informed political<br>IV and technical debate<br>Selection • Quadrant A projects high<br>priority to invest<br><!-- End of picture text --> 23
Policy Research Working Paper 10658

# **Abstract**

The European Commission’s “NextGenerationEU” COVID-19 recovery package has underscored interest in the size of regional fiscal multipliers in Europe. While the objective of these funds is the long-term transformation toward more sustainable green growth and digitalization in EU economies, several recent papers have also focused on their short-term stimulatory effects and have estimated large short-term regional multipliers on historical EU structural and investment fund spending. This has contributed to a view that EU funds can boost growth substantially not only in the long term, but also in the short term in countries receiving large flows, particularly in Central and Eastern Europe. This paper reevaluates the evidence by estimating regional short-term multipliers using recent data on EU fund spending and a leave-one-out predicted disbursement schedule instrument. In contrast with much of the recent literature, there is little evidence of large relative GDP multipliers at either the national or subnational level in the short term. This is despite a strong response of regional investment to EU funds, which often increases euro for euro. The results suggest that expectations should be tempered on using EU structural and investment funds as a tool for short-term regional fiscal stimulus, and instead policy makers may want to focus on the long-term benefits of EU funds, in line with their original purpose.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ffiuratti@worldbank.org, dnikolova@worldbank.org, spennings@worldbank.org, and mschiffbauer@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
12 Capital stock series were constructed by using the perpetual inventory method. A complete description of the sources of data and the methodology is offered in K. Siddique (1989).13 Sector specific input price deflators were computed by using input-output coefficients and output price deflators (For details of the methodology and data requirement see Siddique, op. cit).

Data on total value of imports and exports also classified according to ISIC were obtained from the UN Geneva data base. Foreign trade data, expressed originally in US dollars were converted into Turkish lira by using mid-year average exchange rates. Trade figures in local currency wer. then used to derive import penetration and export share series.

The next step involved the correction of the data for inflation distortion. This correction was necessary to make stock and flow variables comparable within a year by expressing all stocks and flows in mid-year current prices. Once the data were corrected for the inflation bias, they were expressed in real terms by deflating outputs and inputs by industry-specific output and input price deflators.

Most of the data series cover the period 1973-1985, although in the process of data transformation a number of years are lost. As a result our regression analyses in the following sections cover the years 1976 to 1985.

The list of industries in our sample is contained in Table 1. In order to study more closely the effect of trade liberalization on 13.See "Data Requirement and Data Availability", chap. 4 of her PhD dissertation.
# **7 Tables**

Table 1: Descriptive statistics

||Mean|SD|Min|P25|Median|P75|P95|Max|N|
|---|---|---|---|---|---|---|---|---|---|
|_Study level_||||||||||
|Sample Size|3,374|3,800|418|1,038|1,913|4,079|13,987|13,987|18|
|Clusters|296|413|14|40|206|314|1,637|1,637|15|
|Number of Outcomes|43|46|3|9|37|54|189|189|18|
|Multiple treatments|0.56|0.51|0.00|0.00|1.00|1.00|1.00|1.00|18|
|_Outcome × treatment level_||||||||||
|Inputted Controls|405|814|6|39|182|331|3,340|3,340|780|
|Inputted Controls to Sample Size|0.17|0.19|0.00|0.06|0.16|0.19|0.69|1.73|780|
|Inputted Controls to Clusters|2.27|4.40|0.04|0.20|0.86|2.00|16.06|17.58|694|
|Overall Attrition|0.14|0.16|0.00|0.04|0.10|0.23|0.34|0.93|780|
|Treatment Attrition|0.14|0.17|0.00|0.03|0.09|0.24|0.39|0.94|780|
|Control Attrition|0.16|0.16|0.00|0.04|0.11|0.24|0.35|0.92|780|
|Diferential Attrition|0.05|0.08|0.00|0.00|0.02|0.07|0.17|0.56|780|

_Notes._ Based on 780 outcome by treatment combinations from 18 studies.

28
Table 3. Parental Consanguinity and Children’s Ravens Score, Age 5-13

|||OrdinaryL|east Square||LIML E|stimator|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Parents are related|-0.144**|||-0.104|-0.832**|-1.09**|
||(0.072)|||(0.075)|(0.419)|(0.545)|
|F-coefficient of inbreeding||-0.816|||||
|||(0.576)|||||
|Parents are 1st cousins|||-0.105||||
||||(0.072)||||
|Parents are 2nd cousins|||-0.108||||
||||(0.086)||||
|Age|0.086***|0.086***|0.086***|0.087***|0.093***|0.095***|
||(0.009)|(0.010)|(0.010)|(0.009)|(0.011)|(0.012)|
|Male|0.059|0.059|0.059|0.052|0.069|0.067|
||(0.045)|(0.045)|(0.045)|(0.043)|(0.046)|(0.048)|
|Wealth index|0.156***|0.160***|0.159***|0.129***|0.104**|0.129***|
||(0.049)|(0.051)|(0.050)|(0.045)|(0.050)|(0.046)|
|Dummy for Sindh Province|0.266*|0.284**|0.283**||0.146||
||(0.136)|(0.139)|(0.139)||(0.163)||
|Dummy for KPK Province|-0.498**|-0.497**|-0.499**||-0.628***||
||(0.232)|(0.242)|(0.242)||(0.225)||
|Father's Years of Education|0.017**|0.017**|0.018**|0.018**|0.021**|0.025**|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.009)|(0.010)|
|Father's Years of|-0.016|-0.016|-0.017|-0.014|-0.026|-0.033*|
|Education*Sindh|(0.015)|(0.015)|(0.015)|(0.014)|(0.017)|(0.018)|
|Father's Years of Education*KPK|0.050*|0.052*|0.051*|0.031|0.044*|0.014|
||(0.028)|(0.029)|(0.029)|(0.027)|(0.025)|(0.032)|
|District Fixed Effects|No|No|No|Yes|No|Yes|
|**Test Statistics**|||||||
|Hansen J statistic|||||0.930|0.001|
|p-value|||||0.305|0.972|
|F-statistic of Instruments|||||8.27|5.30|
|F-probability|||||0.001|0.007|
|CLR Test|||||5.01|4.87|
|p-value|||||0.036|0.04|
|95% Confidence Interval, based|||||[-2.12,|[-3.93,|
|on CLR Test|||||-0.30]|-0.30]|
|Number of observations|1411|1411|1411|1411|1411|1411|

Notes: Village-level cluster robust standard errors are reported in parenthesis. Columns (4) and (6) control for factors related to village location with district fixed effects. The Hansen J statistic tests for over-identification. We report cluster-corrected F statistics to test for weak instruments and then implement the conditional likelihood ratio (CLR) test developed by Moreira (2003) to test for weak instrument bias. The CLR test tests the hypothesis that the instruments and the coefficient on the endogenous variable are jointly zero. We also show the 95% confidence interval suggested by the CLR test. The CLR test is generalized for clustered dependence in error terms using the minimum distance approach by Finlay and Magnusson (2009).

22
<mark>remarks, we discuss several hypotheses that emerge from these facts regarding systematic differences between the labor markets of developing and advanced economies.</mark>

# Data

The International Income Distribution Database (I2D2) is a harmonized database of households and individuals drawn from nationally‐representative surveys, built to compare income equality across countries. Depending on the country, the I2D2 draws observations from different types of surveys, usually conducted by national statistical agencies, including Household Budget Surveys, Household Income and Consumption Surveys, Labor Force Surveys, and multi‐topic surveys (such as Living Standards Measurement Study Surveys). It includes major economies such as Bangladesh, China, Vietnam, Mexico and Chile. Crucially, along with hourly wages, the data include the size of the worker’s firm, along with indicators of worker demographics, occupation, industry and wage.

We restrict attention to the 69 surveys (25 countries) in which data on the size of the employer firm are provided, which cover 1988‐2015. Where data are available, I2D2 includes a categorical variable for the size of the workers’ firm (for instance, <100 employees, between 100‐500 employees, or more than 500). The breakpoint used varies across countries however, and in the lowest income countries very few or zero individuals report working at firms with more than 500 employees, or even 100. For this reason, to maximize the number of countries in the sample, we define the LFWP as the (log) difference in wages between those workers in firms with 100 or more employees, and those workers in firms with fewer employees. The share of workers in our sample in large firms ranges from 31 percent in China and 26 percent in Chile, to 0.5 percent in Mali and 0.3 percent in the Comoros.

# Results

The LFPW is estimated using the following regression specification, for worker i, in a survey in country k and year t: �,� where wage� is the hourly wage (in real dollars, at current exchange rates); Large� indicates whether worker i’s employer has more than 100 employees; α<sup>�,�</sup> is a country year fixed effect accounting for productivity differences across countries and years; Xi is a vector of Mincerian worker characteristics (i.e. age, age squared, and dummies for completion of primary, secondary and tertiary education); 3
the data set.

The MNL regression includes a set of climate variables, a set of control variables, and a set of soil variables. The climate variables measure annual temperature and precipitation. Both a linear and a quadratic term are introduced to capture the expected non-linear effect of these variables. The control variables include water flow, farmland, a dummy for electricity, household size, and elevation. The soil variables include slope, texture and soil type. Soiltype1 reflects soils that have a fine texture and are in hilly to steep slopes. Soiltype2 incorporates soil types such as eutric gleysols or solodic planosols. Soiltype3 reflects lithosols or soils with medium texture in steep areas. Soiltype 5 includes orthic ferralsols and chromic luvisols.

The control and soil variables affect crop choice. Higher elevation encourages cowpea, sorghum, cowpea-sorghum, maize-groundnut and maize-millet and discourages only millet-groundnut. Lower flow is associated with farmers choosing maize-beans, cowpea-sorghum, maizegroundnut, maize-millet, millet-groundnut and ‘other crops’. Lower flow probably implies that farmers cannot irrigate. Choosing crop combinations is one way for farmers to adapt to dryland farming in Africa. Farms that have electricity are more likely to choose maize and maize-beans but less likely to choose every other crop. Electricity may help in the production of maize or it may simply signal access to urban markets which often accompanies access to electricity. Farmers whose farms have steep slopes and fine texture soils are more likely to pick milletgroundnut but less likely to pick cowpea, sorghum, cowpea-sorghum, maize-beans, and ‘other crops’. Those whose farms have eutric gleysols and solodic planosols are less likely to pick every crop except cowpea-sorghum and maize. Those whose farms have lithosols or medium texture soils in steep areas are more likely to pick cowpea, maize-beans, and ‘other crops’, but less likely to pick sorghum. Finally, those whose farms have orthic ferrasols and chromic luvisols are more likely to pick millet-groundnut but less likely to pick sorghum and maize-millet.

From the perspective of this study, the most important coefficients in Table 3 concern annual climate. Judging by the significance of the coefficients on both the linear and squared terms, annual temperature and precipitation are both quite important to crop choice. In order to show how temperature affects farmers’ choices, in Figure 2 we present the probability a farmer will choose each crop combination at each temperature. Figure 2a reflects crops that prefer relatively 10
**Table 7: Farm Size and Incidence of fertilizer use**

||**Maize**|**Sorghum **|**Teff**|**Wheat**|**H. Beans**|**Sesame**|**Coffee**|
|---|---|---|---|---|---|---|---|
||||**Panel**<br>|**A: All obser**<br>|**vations**<br>|||
|Cropped area (α1)|0.027*|-0.006|0.054**|0.022|-0.037*|0.012|-0.000|
||(1.798)|(-0.370)|(2.378)|(0.911)|(-1.716)|(0.328)|(-0.011)|
|Cropped area squared(α2)|-0.004|-0.000|0.001|-0.008*|-0.008***|-0.001|-0.001|
||(-1.582)|(-0.151)|(0.094)|(-1.706)|(-2.867)|(-0.214)|(-0.406)|
|Commercial farm dummy|0.152***|0.082|0.053|0.042|-0.076|-0.166*|0.159**|
||(3.404)|(1.379)|(0.914)|(0.439)|(-0.787)|(-1.790)|(2.116)|
|Cropped area * commercial (β1)|0.044*|-0.045|-0.025|-0.021|-0.030|0.083*|-0.196***|
||(1.818)|(-1.480)|(-0.612)|(-0.530)|(-0.659)|(1.667)|(-5.637)|
|Cropped area sq. * commercial (β2)|-0.000|0.021***|-0.003|0.010|0.027***|0.004|0.039***|
||(-0.014)|(4.144)|(-0.203)|(1.548)|(3.541)|(0.522)|(9.355)|
|Constant|0.416***|0.155***|0.646***|0.690***|0.201***|0.231***|0.048*|
||(18.726)|(6.088)|(31.806)|(24.819)|(4.366)|(3.682)|(1.802)|
|Number +of observations<br>|2,276|2,620|1,425|998|674|1,724|1,147|
|R<sup>2</sup>|0.106|0.048|0.024|0.037|0.041|0.109|0.184|

_Note:_ Woreda fixed effects included throughout. Cropped area is in logs, denominated in hectares.

21
<!-- Start of picture text --> 60 1800<br>Reform<br>50 1500<br>40 1200<br>30 900<br>20 600<br>10 300<br>0 0<br>Production Producer Price<br>Production ('000 mt) Price (Real TSh/kg)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

**Figure 2: Tobacco Production and Producer Prices in Tanzania**

In these two cases the reforms were successful despite internal and external shocks and other structural breaks. The sectors were able to find ways to reestablish a new equilibrium and continue the growth process. Output in both cases increased substantially despite the fact that producer price did not increase significantly. Significant quality increases were also achieved in both cases due to better extension by the multinational companies. These companies, in both cases, were permitted by the state to experiment with alternative contractual arrangements for the provision of “support” while exercising their monopsony powers. Both experienced negative shocks and output collapses, once in the start of the privatization program in Tanzania due to side-selling, and twice in Zambia -once due to side-selling and once due to a price collapse caused by the appreciation of the currency.<sup>18</sup> At the same time, there are differences between the two cases. First, the Tanzanian tobacco producers were able to establish a tobacco board from the beginning and were allowed to set up a company that was an effective monopsony. In Zambia, there were attempts to set up a cotton board but it never became effective and the firms were left to their own devices.

Second, throughout the period of 1990-2008, the volatility of cotton producer prices in Zambia was more than twice as high as the volatility of tobacco prices in Tanzania. So the higher price volatility in Zambia cotton could have contributed to the two collapses.

Third, significant productivity increase occurred in Tanzania tobacco after the reforms, while there was almost no productivity increase in Zambia cotton. While there might be alternative explanations for the difference -higher volatility, shocks in the middle of the reform period, etcone possible explanation might be the selection of the farmers. In Zambia, the expansion was carried out essentially by increasing the number of farmers, thus area expansion. Most of the

> 18 Recent policy reversals in Tanzania and elimination of the longer-term contracts might create another collapse if not managed carefully.

12