rafmacalaba/datause-encoder-data huggingface dataset:
10 for the months in between these observed postings for which we assume the establishment was operational, but not hiring. In total, 23.5% of all green technology-related postings in our sample countries in this period are matched to a firm with both CDP energy consumption and Factset sales data.

# **3. Differential impact of the energy crisis**

## **3.1 Background on the energy crisis**

The 2022 global energy crisis began in the aftermath of the COVID-19 pandemic, when natural gas prices in the EU and Asia started to increase in response to a supply-demand gap and a tight liquefied natural gas (LNG) market. Rising prices subsequently escalated into a widespread global energy crisis following the Russian invasion of Ukraine in February 2022. The invasion, and the international sanctions that followed, resulted in a reduction in natural gas imports from Russia, the suspension of the Nord Stream 2 pipeline’s certification and the halting of gas deliveries through the Nord Stream 1 pipeline. In the first half of 2022, Russia’s gas exports via pipeline to the European Union and the United Kingdom fell by nearly 50 percent compared with the previous five-year average (EIA, 2022). These supply shortages were exacerbated by uncertainty and concerns of further reductions as well as reduced hydroelectric and nuclear electricity output that pushed gas consumption for electricity (Tertre, 2023).

This led to soaring natural gas prices in parts of the world dependent on Russian imports and in other countries that competed in the global LNG market. In Europe the benchmark natural gas price peaked in August 2022 at a record high of $69.98 USD per MMBtu, a $42.09 increase from the January 2022 price, while the benchmark LNG price in Asia increased by $27.33 over the same period. Natural gas prices were comparatively stable in countries not reliant on imports, with the Australian benchmark price increasing by $4.79, the US benchmark by $4.55, and the Canadian benchmark falling by $0.99 over this period (see Supplementary Appendix Figure A3(a)).

In the EU, the increase in natural gas prices translated into an increase in wholesale electricity prices in all countries, although there was also sizable variation in the extent to which countries
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Table B12: Mechanism at play, destination-by-sector, controlling for industry specific trend

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>HHI|(6)<br>log(# MNEs)|(7)<br>log(K)|
|---|---|---|---|---|---|---|---|
|Uncertainty (standardized)|0.005|-0.007|0.018***|0.018***|0.103***|-0.134***|-0.049*|
||(0.003)|(0.004)|(0.005)|(0.005)|(0.021)|(0.024)|(0.029)|
|Uncertainty _×_ Developing||0.025***<br>(0.006)||||||
|Observations|9,132|9,132|3,935|3,922|3,922|3,922|3,922|
|R-squared|0.745|0.746|0.713|0.713|0.657|0.803|0.689|
|Controls|No|No|No|Yes|Yes|Yes|Yes|
|Data Coverage|All c|ountries|||Developing|||

_Note:_ Each observation corresponds to a destination-industry-year, represented as _ist_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Country-industries averaging fewer than 5 foreign investors are excluded from these calculations. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

37
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models and to limit sample selection bias,<sup>7</sup> we divide greenfield FDI into FDI by geographically diversified firms and FDI by other firms. Whereas we restrict the coefficients of our control variables to be fixed at the country-year level, the effect of political conflict and the constant are allowed to vary over the values of the diversification dummy. In the section containing our robustness analysis, we also present the results of a firm-level model.

The data on our control variables come primarily from the World Bank’s World Development Indicators Database. We control for _GDP_ given in millions of US$ in 2013 prices; the size of the _population_ ; and _inflation_ measured as the annual growth rate of the GDP implicit deflator. In addition, we add three variables that control for continuous political risk: the _level of democracy_ ; _regulatory quality_ ; and _control of corruption_ . Democracy is measured by the Polity Index developed by Marshall ‐ et al. (2013), which ranges from 10 to +10, where low negative numbers indicate autocracies and high positive numbers correspond to democracies. The quality of regulations indicator, part of Kaufman’s World Governance Index (WGI), measures perceptions of a government’s ability to formulate and implement sound policies and regulations that permit and promote private sector development. The control of corruption measure also comes from the WGI and measures the extent to which public officials use power for private gain. Both WGI variables are measured as a z-score varying from approximately -2.5 to 2.5, with higher values corresponding to better governance. Furthermore, we control for nominal _exchange rates_ (level and standard deviation<sup>8</sup> ) using data collected through OANDA. Appendix 1 provides descriptive statistics and the correlation matrix. Appendix 2 shows a list of all countries included in the sample.

# 4. ESTIMATION AND EMPIRICAL RESULTS

Using a fixed effects estimator to estimate the dynamic model (1) presents a problem. In panels with a large number of countries but a small number of periods, the standard fixed effects estimates are inconsistent because the transformation process creates a correlation between the regressor and the error (Nickell, 1981). We therefore use the least-squares dependent variable estimator, also known as the LSDVC model, developed by Bun and Kiviet (2003) to correct for this bias. The bias correction is initialized by a system GMM estimator. In a simulation, Flannery and Hankins (2013) compared the 20
Autor, D. and D. Dorn. 2013. “The Growth of Low‐Skill Service Jobs and the Polarization of the US Labor Market”, _The American Economic Review_ , 103(5), 1553–97.

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Autor, D., F. Levy, and R. J. Murnane. 2003. ‘‘The Skill Content of Recent Technological Change: An Empirical Exploration,’’ _Quarterly Journal of Economics_ , 118, 1279–1333.

Bender, S., Bloom, N., Card, D., Van Reenen, J. and Wolter, S., 2016. “Management practices, workforce selection and productivity” (No. w22101). _National Bureau of Economic Research_ .

Bloom, Nicholas, and John Van Reenen. 2007. “Measuring and Explaining Management Practices Across Firms and Countries.” _Quarterly Journal of Economics_ , 122(4): 1341–1408.

Bloom, N., and J. Van Reenen. 2010. “Why Do Management Practices Differ acrossFirms and countries?” _Journal of Economic Perspectives_ , 24: 203–24.

Bloom, N., R. Sadun and J. Van Reenen. 2012. "Americans Do IT Better: US Multinationals and the Productivity Miracle." _American Economic Review_ , 102(1): 167‐201.

Bresnahan, T.F., E. Brynjolfsson, and L.M. Hitt. 2002. “Information Technology, Workplace Organization and the Demand for Skilled Labor: Firm‐level Evidence.” _Quarterly Journal of Economics_ , 117: 339–76.

Brynjolfsson, E. and L. M. Hitt. 2003. “Computing Productivity: Firm‐Level Evidence.” _Review of Economics and Statistics_ 85(4): 793–808.

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Castillo, V., Maffioli, A., Rojo, S. and Stucchi, R., 2011. Innovation Policy and Employment. Evidence from an impact evaluation in Argentina. IDB Technical Notes, No. IDB‐TN‐341. Washington, DC: IDB.

Crespi, G. and E. Tacsir, 2012. “Effects of innovation on employment in Latin America.” Washington, DC: Inter‐American Development Bank.

Crespi, G. and P. Zuñiga. 2012. “Innovation and Productivity: Evidence from Six Latin American Countries.” World Development 40(2): 273–290.

Crespi, G., E. Tacsir and F. Vargas. 2016. Innovation Dynamics and Productivity: Evidence for Latin America”, in M.Grazzi and C.Pietrobelli. Firm Innovation and Productivity in Latin American and Caribbean: The Engine of Economic Development. New York: Palgrave Macmillan.

De Elejalde, R., D. Giuliodori, and R. Stucchi. 2011. “Employment Generation, Firm Size and Innovation Microeconometric Evidence from Argentina.” IDB Technical Notes, No. IDB‐TN‐313. Washington, DC: IDB.

22
these data are collected for only a very small number of observations, the degrees of freedom are relatively few. Column 5 and 6 of Table 9 show all of the sectoral dummies to be insignficant. However, the standard deviations are significant for self-employment with no employees and civil employment. The risk aversion variable reduces these in only the slightest degree suggesting that, for our small sample, it is not the source of heterogeneity.<sup>30</sup> Thus, while our sample is very small and our risk-aversion measure crude, we tentatively conclude that differences in risk aversion are not driving differences in job satisfaction. However, since our estimates are imprecise further research is necessary to validate this inference.

# 7 Conclusion

This paper employs mixed (stochastic parameter) ordered probit estimators to characterize the distribution of subjective wellbeing across employment sectors for Ghana, which is broadly representative of sub-Saharan African countries. The extraordinarily detailed Ghana Urban Household Panel Survey offers unusual insight into developing country labor markets. The self-reported job and life satisfaction measures it collects are useful proxies for wellbeing as they are comprehensive and rely on individuals own weighting of the importance of various job attributes, thereby mitigating the limitations of objective proxies such as earnings. The random parameter methodology permits quantification of sectoral satisfaction premia as well as how conditional satisfaction varies across agents within sectors, thereby allowing us to document heterogeneity in conditional subjective wellbeing that fixed parameter models cannot detect. They are thus a useful tool in characterizing different employment sectors, and offer important insight into the debate over the functioning of developing country labor markets in general and, the role of the informal sector particular. Analogous to the debate over self-employment in the advanced countries, understanding whether (and to what extent) the latter is, indeed, the inferior part of a segmented labor market, or offers superior employment opportunities for those employed in it requires quantification of the

> 30In an attempt to regain some power, we also ran static specifications (fixed parameter-not shown to conserve space but available upon request). To test the hypothesis that risk-averse individuals will be less happy in self-employment where earnings are arguably more volatile, and happier in wage employment, which is arguably less risky, we interact sector dummies with our experimentally elicited measure of risk aversion. The resulting interactions were neither individually nor jointly significant, yet the pattern of satisfaction premia was robust to their inclusion.

29
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

# **Total Factor Productivity (TFP)**

**We present here both deterministic and stochastic estimates of TFP.** First we discuss the results of a  “deterministic” or “accounting” approach to TFP. Then we present results of a productivity regression which estimates these parameters. Considerable detail is relegated to the Annex 1.

# _Deterministic Accounting Estimate of TFP_

**One conventional approach, first followed here, is to use national income data and other information with assumed parameter values, and simply compute estimates of TFP in a deterministic fashion.**<sup>**14**</sup> Our TFP accounting estimate is presented in Figure 13<sup>15</sup> . The rate of growth of capital stock per worker is derived from the estimates of capital stock, and the growth in human capital from the increase in average schooling discussed in the earlier section.

**Figure 13: Estimate of Total Productivity Per Worker, 1960-2002**

<!-- Start of picture text --> Cote d'Ivoire: Estimated Total Factor Productivity Per Worker<br>1.3<br>1.2<br>1.1<br>1<br>0.9<br>0.8<br>0.7<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000<br>Years 1960-2002<br>TFP normalized, 1960=1<br><!-- End of picture text -->

**Total Factor Productivity (TFP) grew until about 1978, then declined substantially thereafter.** This estimate shows TFP per worker in Cote d’Ivoire to have risen from 1960 until 1979, then to have turned around and declined thereafter. By 2002 TFP per worker

> 14 See Mahajan (2003).  We provide a comparison between out estimates and these other estimates in the Annex 1.

> 15 The approach described in detail in the Annex 1 is used to compute the contributions of the growth rate of physical capital per worker and changes in average schooling to the growth rate of output per worker. In the base year, TFP is normalized by assuming that it equals unity.  Then, one computes the levels by computing the cross products from t = 0 to time period t of one plus the rates of growth for time periods zero to t.

15
# **Moral Incentives: Experimental Evidence from Repayments of an Islamic Credit Card**<sup>_∗_</sup>

Leonardo Bursztyn<sup>_†_</sup> Stefano Fiorin<sup>_‡_</sup> Daniel Gottlieb<sup>_§_</sup> Martin Kanz<sup>_¶_</sup>

> _∗_ We would like to thank Michael Callen, Davide Cantoni, Shawn Cole, Ernesto Dal B ́o, Stefano DellaVigna, Ruben Enikolopov, Nicola Gennaioli, Paola Giuliano, Asim Khwaja, Andr ́es Liberman, David McKenzie, Gautam Rao, Andrei Shleifer, Nico Voigtl ̈ander, Noam Yuchtman, and seminar participants for comments and suggestions. We are grateful to the UCLA Anderson Center for Global Management, the UCLA Anderson Price Center, and the World Bank for financial support.

> _†_ UCLA Anderson and NBER, `bursztyn@ucla.edu` .

> _‡_ UCLA Anderson, `stefanofiorin@ucla.edu` .

> _§_ Washington University in St. Louis, `dgottlieb@wustl.edu` .

> _¶_ World Bank, `mkanz@worldbank.org` .
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31
**Table 6:** Difference in terms of _R_<sup>2</sup> of the levels CC regressed on the ML predictions for different combinations of covariates

|||||Levels||||
|---|---|---|---|---|---|---|---|
||All crops|Millet|Sorghum|Rice|Maize|Cowpea|Groundnut|
|SR in W, GPS not in W|0.052<sup>_∗∗∗_</sup>|0.047<sup>_∗∗∗_</sup>|0.095<sup>_∗∗∗_</sup>|0.033|0.012|0.072<sup>_∗∗∗_</sup>|0.055<sup>_∗∗∗_</sup>|
||(0.014)|(0.014)|(0.018)|(0.032)|(0.016)|(0.024)|(0.012)|
|SR not in W, GPS in W|0.184<sup>_∗∗∗_</sup>|0.221<sup>_∗∗∗_</sup>|0.234<sup>_∗∗∗_</sup>|0.161<sup>_∗∗∗_</sup>|0.106<sup>_∗∗∗_</sup>|0.103<sup>_∗∗∗_</sup>|0.280<sup>_∗∗∗_</sup>|
||(0.014)|(0.014)|(0.018)|(0.032)|(0.016)|(0.024)|(0.012)|
|SR,GPS in W|0.206<sup>_∗∗∗_</sup>|0.223<sup>_∗∗∗_</sup>|0.248<sup>_∗∗∗_</sup>|0.196<sup>_∗∗∗_</sup>|0.112<sup>_∗∗∗_</sup>|0.167<sup>_∗∗∗_</sup>|0.290<sup>_∗∗∗_</sup>|
||(0.014)|(0.014)|(0.018)|(0.032)|(0.016)|(0.024)|(0.012)|
|Constant|0.277<sup>_∗∗∗_</sup>|0.277<sup>_∗∗∗_</sup>|0.132<sup>_∗∗∗_</sup>|0.342<sup>_∗∗∗_</sup>|0.427<sup>_∗∗∗_</sup>|0.177<sup>_∗∗∗_</sup>|0.309<sup>_∗∗∗_</sup>|
||(0.010)|(0.010)|(0.013)|(0.023)|(0.011)|(0.017)|(0.008)|
|Obs|600|100|100|100|100|100|100|
|Adj. R<sup>2</sup>|0.350|0.797|0.716|0.334|0.448|0.330|0.905|

_Notes:_ The reference group is covariate without SR and GPS extracted. The constant shows the mean average _R_<sup>2</sup> for the reference group Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01

**Table 7:** CC mean yields and differences with ML and SR yields in the test set by crops and by survey year.

||Millet|Sorghum|Rice|Maize|Cowpea|Groundnut|
|---|---|---|---|---|---|---|
|||||2017|||
|CC|936.7|1049.0|2576.1|1685.0|332.5|1841.2|
|ML - CC|-7.1|22.6|-111.6|-0.4|-7.7|185.2|
|SR - CC|-101.0|-284.6<sup>_∗∗∗_</sup>|-1164.8<sup>_∗∗∗_</sup>|-429.6<sup>_∗∗∗_</sup>|203.9<sup>_∗_</sup>|-909.0<sup>_∗∗∗_</sup>|
|N|1072|773|465|775|228|869|
|||||2018|||
|CC|931.0|1182.1|3353.0|1914.1|577.4|1997.2|
|ML - CC|6.1|-61.7<sup>_∗_</sup>|829.2<sup>_∗∗_</sup>|-47.6|-121.0<sup>_∗∗_</sup>|178.0<sup>_∗_</sup>|
|SR - CC|-97.2|-296.6<sup>_∗∗∗_</sup>|-427.4|6.0|-5.8|-928.0<sup>_∗∗∗_</sup>|
|N|346|458|160|428|123|371|

_Notes:_ The training set represent 1/3 of the total sample size. The mean CC yield and differences with ML and SR yields are computed using survey design weights. Statistical significance are assessed using standard errors clustered at the enumeration area level. In the case of the ML, the standard errors used for statistical significance tests account for the between imputations variance computed with 100 simulations. Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 23
# **Land Rental Markets in the Process of Rural Structural Transformation:**

# **Productivity and Equity Impacts in China**

**Klaus Deininger, Songqing Jin**<sup>**1**</sup>

World Bank, Washington DC

> 1 1818 H St. NW, Washington DC, 20433; Tel 202 4736394, fax 202 522 1151, email sjin@worldbank.org.  We thank the rural survey team of NBS for an extraordinary data collection effort and Marc Bellemare, Christopher Barret, Gershon Feder, Tim Hanstad, Guo Li,, Stein Holden, and Yang Yao, for valuable comments. Funding from the DFID-World Bank collaborative program on land policy, and the World Bank’s Knowledge for Change Trust Fund is gratefully acknowledged.
46 use credit, with Egypt being a significant outlier. Specifically, corresponding to its level of Bank Credit to GDP, more than twice as many enterprises should have access to bank credit, as for example in Cape Verde, which has a similar level of Bank Credit to GDP (42.5%).

**Figure 15: Actual and predicted share of firms with credit in Egypt**

<!-- Start of picture text --> 45.000<br>40.000<br>35.000<br>30.000<br>Predicted share of firms<br>25.000<br>with credit<br>20.000<br>Actual share of firms with<br>15.000 credit<br>10.000<br>5.000<br>0.000<br>2006 2008 2011<br><!-- End of picture text --> Source: Global Financial Development Indicators, own calculations 46