rafmacalaba/data-use-mentions-extended huggingface dataset:
the sample and replaced by new dwellings from the same PSU or the next PSU on the list.

PALMS is well suited for this paper’s analysis. It includes occupational information up to the four-digit level and uses the South Africa Standard Classification of Occupations (SASCO) from 2003 (Statistics South Africa 2003). SASCO 2003 is based on the International Standard Classification of Occupations of 1988 (ISCO-88).<sup>14</sup> To ensure comparability, DataFirst has gone to great lengths to harmonize the PALMS dataset in terms of variable names over time. Additionally, the data set comes with re-calibrated weights using a cross entropy (CE) approach (Branson and Wittenberg 2014) to ensure continuity and comparability between surveys over time.

For this study, we restrict our analysis to the period 2012-2019. Data are available for more recent years but to avoid contaminating the results with the effects of the COVID-19 pandemic, we exclude data collected in 2020 or after. We pool data from the four quarters of each year resulting in 32 quarters (waves) of data.<sup>15</sup> The pooling of the data over the 8 years gives a sample size that will allow analysis at the 4-digit SASCO level. Using these data, we profile the labor force working in green occupations by age, gender, location, skill, and education level.

# 2.1 Methodology to Generate the List of Green Occupations

We adopt the occupational task approach and use Dierdorff et al. (2009) to adapt our definition of green jobs to reference occupational tasks. Specifically, we define strictly green jobs as occupations that require tasks that have environmental impacts. We define broadly green jobs as occupations that are strictly green or occupations with tasks that could have environmental impacts if green technology is used. Some of these jobs benefit from the green revolution, such as miners of lithium for electric car batteries. Others are negatively affected, such as coal miners and those working in coal-fired plants. We include both in our
|(0.0529)|(0.0543)|(0.0404)|
|Momase region|0.00354|-0.0807|-0.0731|-0.0622|
||(0.0460)|(0.0473)|(0.0490)|(0.0365)|
|Secondary school or less|-<br>0.0959***|-0.0566*|-0.0611*|-0.0539*|
||(0.0274)|(0.0280)|(0.0288)|(0.0220)|
|Constant|0.167**|0.258***|0.249***|0.236***|
||(0.0525)|(0.0533)|(0.0555)|(0.0431)|
|Observations|917|878|896|1521|

Note: This chart is based on answers to question 34 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

44
Policy Research Working Paper 9388

# **Abstract**

Measuring and comparing the levels of household welfare and poverty in a country require cost-of-living differences across regions to be properly adjusted. In measuring the spatial cost of living, the recent literature underscores the importance of detailed product quality information in the price data. Taking advantage of the price data availability in Ghana, this case study explores the Consumer Price Index price data as a source for spatial price measurement.

It applies the country product dummy method to the Consumer Price Index price data and compares the results with other methods based on different price data. The empirical analysis indicates a potential bias in estimating spatial prices stemming from the lack of product quality information and, therefore, suggests the potential usefulness of the Consumer Price Index price data for spatial price adjustment in poverty analysis in low- and middle-income countries.

This paper is a product of the Poverty and Equity 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 snakamura2@worldbank.org.

_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
municipal effect|-0.102|0.222|4.047|1.011|
|Kurtosis of municipal effect|4.840|6.168|36.395|5.679|
|Estimated variance of municipal effect|0.004|0.002|0.0013|0.0008|
|Estimated household residual|0.017|0.017|0.012|0.017|
|Ratio of estimated municipal effect variance to<br>total variance|0.186|0.107|0.098|0.046|

# **Municipality - Full Enumeration of Selected AGEBs**

|Number of (non-state) variables selected<br>|33|26|28|39|
|---|---|---|---|---|
|Marginal R<sup>2</sup>|0.277|0.224|0.164|0.158|
|Of which due to state dummies|0.113|0.060|0.082|0.100|
|Conditional R<sup>2</sup>|0.597|0.451|0.467|0.525|
|Skewness of municipal effect|-0.610|-0.394|2.460|1.613|
|Kurtosis of municipal effect|6.128|4.779|24.136|12.472|
|Estimated variance of municipal effect|0.004|0.001|0.0011|0.0015|
|Estimated variance of household residual|0.005|0.003|0.0020|0.0020|
|Ratio of estimated municipal effect variance to<br>total variance|0.442|0.292|0.363|0.436|
|Number of AGEBs|7,733|7,733|
). In particular, a currency crisis in 2018 rapidly increased both food and fuel prices by nearly 50 percent in three months and significantly worsened the food security situation in the country (e.g., Favari et al. 2021). Aside from rapid increases in prices, there also has been a gradual increase in both food and fuel prices that continues to erode household purchasing power at a time when a large share of households struggles to find stable employment (e.g., ACAPS 2020; Favari et al. 2021).

Combined, these price shocks and the challenge of finding stable employment have resulted in a persistent food emergency. In 2015, only three months after the conflict 6These statistics on household satisfaction with electricity are based on authors’ calculations using questions added to a monthly WFP survey. Also, from the same survey, access to electricity grids is rare in the country.

7
# **Table 11. The Effect of Titling Firms’ Ownership on preferential treatments**

The sample consists of all firms in the CSMAR database that has Allocated land before the land titling program starting from 2012. This table presents parameter estimates from OLS regression of title firms’ ownership on the preferential treatment received by the firm. In Column (1), the dependent variable is a dummy variable that is 1 if the firm has disputes either with the government or with other firms and 0 if otherwise. In Column (2), the dependent variable is a dummy variable that is 1 if the firm has disputes with the government and 0 if otherwise. In Column (3), the dependent variable is a dummy variable that is 1 if the firm has disputes with other firms and 0 otherwise. All variables are winsorized at the upper and lower one percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. *,**,*** indicate significance at the 1%, 5% and 10% level respectively.

||All Disputes|Disputes with government|Disputes with private firms|
|---|---|---|---|
||(1)|(2)|(3)|
|SOE dummy|-0.367**|-0.432**|-0.388**|
||(-2.087)|(-2.542)|(-2.205)|
|Log(Total Asset)|0.002***|-0.000|-0.001|
||(3.293)|(-1.000)|
South Africa has long been Sub-Saharan Africa's financial hub; however, since 2004, PE funds have expanded activity in West African markets such as Nigeria and Ghana. While Kenya joins Nigeria as what are considered the most sought-after PE markets in the Sub-Saharan, according to EMPEA, West Africa, in particular, has seen remarkable growth in recent years. These countries have witnessed a surge in PE activity from firms such as Helios Investment Partners, Adlevo Capital, Actis, and Emerging Capital Partners (ECP). Funds raised in West Africa’s PE market increased exponentially from 2013 to 2014, from US$40 million to US$271 million. Meanwhile, PE/VC investments in the West African market represented US$848 million, up from US$392 million in 2013.

Ghana is a major player in West Africa's PE growth, although investment in Ghana is often made through regional or pan-African funds, as discussed later in this report. According to EMPEA data, fund managers invested US$178.9 million and completed 15 deals in Ghana in 2014 (see Annex 2 for list of deals). When it comes to funds raising capital in Ghana-specific vehicles, there is a dearth of recent data. But this is unsurprising because the majority of investors access PE/VC investment opportunities in Ghana via regional funds. Because of the paucity of Ghana-specific vehicles, it is difficult to discern a trend within Ghana-specific fundraising. While a few country-dedicated funds have reached closes (one close of US$4 million in 2010 and one of US$1 million in 2012), the dearth of Ghana-specific fundraising speaks more to investor preference for regional vehicles rather than a decline or increase in fundraising for Ghana.

# **HISTORY OF DOMESTIC PRIVATE EQUITY/ VENTURE CAPITAL IN GHANA**

# **Ghana Venture Capital Fund, 1991**<sup>**12**</sup>

Private equity has been prevalent in Ghana since the early 1990s. In late 1991, USAID<sup
line with a more competitive and mobile labor market.

# 6. Conclusion

This paper provides evidence that exposure to negative economic shocks and health risks associated with the COVID-19 pandemic is greater among immigrant workers than among nativeborn people in high-income Western European countries. Using data from the European Union Labour Force Survey for 2011-2018, we show that this greater risk exposure stems from systematic differences in occupations of immigrant and native workers. Immigrant workers have significantly lower presence in occupations that can be performed from home (telework), and higher presence 30
surveys are the way to go and do not require more than a couple of months to collect field data.

While data matters, the amount needed depends often on how much details are really needed from a regulatory viewpoint The devil can often be in the details but the cost of a survey is directly related to the level of detail that is needed. Of course, this depends on the questions at stake . If the concerns of the regulator is the potential payoff from the addition of service requirements such as a toilet or a business center in a train station, there are not too many alternatives to targetted field survey. But in many more general issues regarding prospects, fairly aggregate data can be sufficient as seen in the case of the port of Antwerp. The fact is that often the specific concerns are not well defined and this can results in wastes of time and resources. Targetting the issues also allow a better targetting of the data requirements and hence the design of demand studies and the choice of a methodolohy that meet their purpose in the most cost effective way.

# _6.2. After the study_

Once the study is done, the job is not over. First, it makes sense to ensure that the validity of the results of the study can be assessed. A first benchmark consist in a comparison with th results experienced in similar situations elsewhere. It is of course difficult to find exact comparability but the "distance" between the situations can be evaluated and provide an idea of the "distance" between the results. It is often useful to request from the consulting team doing the analysis to provide a survey of their own studies or similar studies. This information should include a review of the elasticities of traffic to prices and cross-price elasticities when substitutes or complements are involved. In this context, conventional wisdom can also help and surveys can be consulted to provide first order estimates.<sup>2 5</sup> Last but not least, the study should provide a point estimate of course,
.g., Jiang et al. 2015, Hong et al. 2013, and Jiang and Tan, 2013) assess the impacts of energy subsidy removal using input-output models. Their findings are, therefore, limited to the impacts on energy prices due to subsidy removal. As expected, they find that removal of energy subsidies would result in an increase of energy prices. These results are not only intuitive but also do not provide any insights on the impact of energy subsidy removal or reforms on the economy. Al Iriani and Trabelsi (2015) examine the effects of energy subsidy removal on the economies of seven members of the Gulf Cooperation Council (GCC), which have historically heavily subsidized energy, using an econometric cum algebraic approach. The study first establishes a causal relationship between energy consumption and GDP growth using data for 4
exposure with a newly constructed database of water quality that combines in situ monitoring station data with a geospatial statistical model for stream networks developed by ver Hoef and Peterson (2010). We carefully integrate early‐life exposure to nitrogen pollution between the critical years of development – from the period of birth up until year three – with women’s health outcomes, climatic factors, correlated pollutants, household inputs and other socioeconomic demographics for our analysis.

Our research design exploits the direction of river flow and the upstream‐downstream geographic relationship used in past literature (Do et al. 2018; Garg et al. 2016) to estimate a pollution‐health relationship. Because the costs imposed by water pollution are largely felt in downstream regions, the analysis focuses on the impact of upstream pollution on health outcomes in downstream regions. To isolate the average pollution spillover at downstream locations, the analysis uses a rich set of controls. These are meant to control for time‐invariant, location‐specific characteristics such as local soil quality and natural resource endowments, as well as factors that vary by year and month, such as weather, and national trends in economic

> 4 A number of biomedical and epidemiological studies in the United States and other countries have documented a relationship between agrichemical exposure and birth defects such as Down’s syndrome and Spina Bifida, especially for children conceived during the crop-sowing months, and among children of agrichemical applicators who are consistently exposed to toxins.

3
Of course, the two shadow rate equations are not incompatible. Some or all of the other variables shown may be endogenous. If both UIP and domestic monetary equilibrium (with the money demand function adopted) prevail, then the two shadow interest rates will be equal. Thus, for example, in a fixed exchange rate regime, the money stock may adjust to ensure that the money market equilibrium condition (2) prevails, with i,° = _i,+._

# _Previous literature: majcor contributions to measuring liberalization_

Two main approaches in the literature draw on this equilibrium framework to assess the degree of capital account openness. The first, due to Edwards (1985) and Edwards and Khan (1985), defines an open capital accounts as one which satistfies UIP. This approach nests the UIP in a broader maintained hypothesis encompassing both the domestic monetary equilibrium and UIP conditions as special cases. For example, we can estimate<sup>36</sup> the parameter (p in a regression defined by: (4 ) The value _(o_ = 1 is interpreted as complete capital mobility, q = 0 as a closed financial system.

The Edwards-Khan approach requires the use of data on interest rates. However, such data may not be available, at least for some of the relevant domestic financial markets. For example, published official interest rates may be applicable only to a fraction of borrowings and allocate(d by administrative directive. If there is a parallel market, then the unobserved interest rate on that market may give a better indication of the degree to which the financial system is integrated in the World economy. In order to deal with the unobserved interest rate issue, Haque and Montiel (1991) proposed a way of solving for the unobserved shadow interest rate that would prevail in the absence of capital mobility.

In contrast to Edwards and Khan, who use the shadow rate _i°_ defined as the rate that clears the money market for the observed money stock, Haque and Mcntiel use
Policy Research Working Paper 8951

# **Abstract**

This paper presents the results of an impact evaluation of a performance-based financing pilot in rural areas of two regions of Tajikistan. Primary care facilities were given financial incentives conditional on general quality and the quantity provided of selected services related to reproductive, maternal and child health, and hypertension-related services. The study relies on a difference-in-difference design and large-scale household and facility-based surveys conducted before the launch of the pilot in 2015 and after three years of implementation. The performance-based financing pilot had positive impacts on quality of care. Significant impacts are measured on facility infrastructure, infection prevention and control standards, availability of equipment and medical supplies, provider competency, provider satisfaction, and even some elements of the content of care, measured through direct observations of provider-patient interactions. While the communities in the performance-based financing districts reported higher satisfaction with the local primary care facilities, and despite the improvements in quality, the findings suggest moderate effects on utilization: among the incentivized utilization indicators, only timely postnatal care and blood pressure measurements for adults were significantly impacted.

This paper is a product of the Development Research Group, Development Economics and the Health, Nutrition and Population 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 gshapira@worldbank.org.

_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
%|
|% urbanization|<br>27|1%|2%|135|3%|9%|223|2%|6%|225|2%|8%|230|0%|1%|902|4%|15%|182|<br>1%|4%|

Note: density in Brazil and Ecuador is at the state level. The statistics are computed before imputations. Source: authors' calculations based on ThinkHazard! EM-DAT, and SEDLAC database.

11
|**Table OA4. Characteristics of eligible households, b**|**aseline vs. addition**<br>Data<br>source|**al sample**<br>Total|Baseline<br>sample|Additional<br>sample|<br>P-value (3)<br>= (4)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Head of household is male (0/1)|Follow-up|0.520|0.553|0.491|_0.061_|
|Age of household head|Follow-up|51.2|50.9|51.4|_0.615_|
|Head of household is married (0/1)|Follow-up|0.637|0.651|0.625|_0.408_|
|Head of household can read and write (0/1)|Follow-up|0.728|0.747|0.712|_0.233_|
|House has concrete floor (0/1)|Follow-up|0.474|0.471|0.477|_0.857_|
|Number of Observations||920|460|460||
|P-value ofjoint F-test that all coefficients are 0|||||_0.469_|

Note: This table shows descriptive statistics using individual level data from the household survey. Column 1 describes when the data were collected, either at baseline or follow-up. Column 2 reports averages for the full sample. Column 3 reports the averages for the baseline sample while column 4 reports the averages for the additional sample interviewed at endline. In Column 5 we report the p-values of the mean comparison between the baseline and additional sample. P-value of joint F-test corresponds to the p-value of a
||
|DEINDU (%) (1980-2020)|-0.04|-0.70|-0.88|
||[0.343]|[0.480]|[0.628]|
|MFG (%) (2020)||1.57***|1.06***|
|||[0.279]|[0.395]|
|SERV (%) (2020)||0.41*||
|||[0.244]||
|FIRE (%) (2020)|||0.69**|
||||[0.327]|
|SERV (non-FIRE) (%) (2020)|||0.57*|
||||[0.303]|
|Beta Coef. MFGSERV|0.57|||
|Beta Coef. MFG||0.62|0.22|
|Beta Coef. SERV||0.16||
|Beta Coef. FIRE|||0.14|
|Beta Coef. SERV (non-FIRE)|||0.16|
|Controls|Y|Y|Y|

_Notes:_ Obs. = 115. We control for initial conditions c. 1960 – i.e., the urban share and the value of the variables c. 1960 (except for FIRE and SERV (non-FIRE) since there is no existing data for the FIRE sector in the 1960s – and add the controls for area, population, small islands, and urban definitions. Robust SE in parentheses.
52. Jones, R. L., Guha-Sapir, D. & Tubeuf, S. Human and economic impacts of natural disasters: can we trust the global data? _Sci. Data_ **9** , 572 (2022).

53. World Bank. World Development Indicators (database). (2022).

54. Conforti, P., Markova, M. & Tochkov, D. _FAO’s methodology for damage and loss assessment in agriculture_ . http://www.fao.org/documents/card/en/c/ca6990en (2020) doi:10.4060/ca6990en.

55. Markhof, Y. V., Ponzini, G. & Wollburg, P. R. _Measuring Disaster Crop Production Losses using Survey Microdata: Evidence from Sub-Saharan Africa_ .

- http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-9968 (2022) doi:10.1596/1813-94509968.

56. Gelman, A. _Bayesian data analysis_ . (CRC Press, 2014).

57. Heeringa, S., West, B. T. & Berglund, P. A. _Applied survey data analysis_ . (Chapman & Hall/CRC, 2010).

58. Haziza, D. & Beaumont, J.-F. Construction of Weights in Surveys: A Review. _Stat. Sci._ **32** , (2017).

59. Himelein, K. Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking. _Stat. Public Policy_ **1** , 40–45 (2014).

21