rafmacalaba/data-use-mentions-extended huggingface dataset:
Another strand of FDI literature is on the relationship between FDI and employment in the host country, where evidence suggests that inward FDI creates jobs, particularly through Greenfield investments (Javorcik 2015). A recent study by Coniglio, Prota and Seric (2015) uses UNIDOs’ Africa Investor Survey (which is representative at the firm level) to show that FDI, especially multinational enterprises (MNEs) from the South, have on average contributed 14.2 and 10.6 percent more employment than domestic firms and North MNEs, respectively. More importantly, Chinese firms were found to have the largest labor demand followed by those from South Africa and the Middle East and North Africa (MENA) region. Further, the study showed that Chinese and South African firms tended to require less-skill intensive workers; while local firms that were in joint partnership MNEs were associated with a higher skilled workforce.

Notwithstanding the positive employment effects of FDI at the individual firm level, indications from the literature are that FDI could lead to crowding-out effects on less competitive domestic firms, eventually resulting in a net reduction in employment for the economy. Attracting FDI that is capital-intensive could also impact employment negatively in labor surplus economies such as those in Africa. A reduction in employment could also occur depending on whether FDI takes the form of acquisitions or mergers, even if this leads to greater efficiency (through streamlining the workforce) in the long-run.

# **3. Model specification**

In our quest to examine the labor productivity growth effects of emerging economies’ (BRICS’) versus advanced (G7) countries’ direct investment in Africa, we adopt a neoclassical growth model (Solow 1956; 1957) as specified in equation 1 below: 8
A testable implication is that the negative relationship between aid shares and aid tying should strengthen when the most corrupt recipient countries are dropped from the sample. Accordingly, in equation 1 of table 4 we retain only the less corrupt 50% of observations, namely those scoring above the median value on the “Control of Corruption” variable from the Worldwide Governance Indicators project (higher scores indicate lower perceived corruption). The aid share coefficient increases in absolute value from -.4 (from the base specification of table 3, equation 1) to -.53 (in equation 5) and remains highly significant.<sup>10</sup> For the more corrupt half of the sample (equation 2), the aid share coefficient remains negative, but drops to -.25 and is not significantly different from zero<sup>11</sup> . This result is consistent with the view that theory is ambiguous regarding the effect of aid share on aid tying when losses from aid tying must be balanced against losses from embezzling aid funds where corruption is high.

In equation 3, we return to using the full sample but add Control of Corruption as a regressor. Where recipient-country procurement systems are believed to be more corrupt, donors are more likely to bypass them and use their own rules and procedures, including aid tying. This reasoning implies the coefficient on Control of Corruption should be negative. It turns out to be positive, but very small and not significant. Results for the key aid

> 10Dividing the sample by the median value of the corruption perceptions indicator is arbitrary, but results are very similar if many alternative cutpoints are used. Even if we retain only the least corrupt 30% of observations, aid share remains significant at the .04 level with a coefficient of -0.6.

> 11The .34 difference between these two coefficients is not statistically significant at conventional levels, however.

11
targeted policies and interventions and the evidence-based approaches required to address the deprivations faced by men and women and boys and girls living in forcibly displaced households.

The comparison with monetary poverty shows the differences and complementarity of the measures, can provide a first step to better understand the different conditions and challenges faced by forcibly displaced communities, and suggests potential policy responses for addressing them. The findings demonstrate the value added of the multidimensional monetary poverty measure to analyze the overlapping deprivations, particularly in contexts of forced displacement. The MPI’s indicators allow policy makers to identify the arenas for intervention, and further analysis decomposing each country’s MPI in greater detail and disaggregating by geographic and administrative areas, as well as age groups, would benefit targeting measures.

In general, the results offer compelling evidence in support of a more disaggregated multidimensional poverty analysis. Our findings indicate that differentiation by gender of the household head, displacement status and subgroups of headship have implications for policy and targeting. This is important with the existing limitation of data (particularly in situations of displacement) to capture gender differences in multidimensional poverty. Due to data limitations, our analysis of differences is limited to the gender of the household head and type of headship. Although the analysis at the household level remains relevant given the high prevalence of female-headed households in displacement situations, individual level analysis is invited as individual level data becomes available.

# **References**

Agbodji, A. E., Batana, Y., M., and Ouedraogo, D. (2015). Gender Inequality in Multidimensional Welfare Deprivation in West Africa. _International Journal of Social Economics_ , 42(11): 980-1004.

Alkire, S. and Foster, J. (2011). Counting and Multidimensional Poverty Measurement. _Journal of Public Economics_ , 95(7): 476-487.

<mark>Alkire, S., Foster, J. E., Seth, S., Santos, M. E., Roche, J. M., and Ballon,
> <sup>31</sup>

on transfers to narrow constituencies (swing districts), which would also reduce corruption; presidential systems might encourage low corruption, but also low public good spending (see Persson and Tabellini 2000). Using information from the _DPI_ , countries are coded as _majoritarian_ if they have plurality electoral rules and the median district magnitude is less than three, following Persson, Tabellini and Trebbi (2003). The political systems of countries – whether they are parliamentary or presidential – is determined based on the _system_ variable from the _DPI_ . Countries with more years of continuous elections are approximately 50 percent more likely to be parliamentary democracies with proportional representation electoral rules. Majoritarian rules by themselves are not, however, correlated with the continuous years of competitive elections.
, Mahapatra, P.S., Adhikari, S., Shrestha, S., Sharma, P., Shrestha, K.L., Pradhan, B.B., Puppala, S.P., 2019. A Comparative Study of Stack Emissions from Straight-Line and Zigzag Brick Kilns in Nepal. Atmosphere 10, 19.

- NSO, 2023. National Population and Housing Census 2021. National Report. National Statistics Office (NSO), Kathmandu.

- NTIP, 2023. Import Report By Commodity Countries [Dataset]. Nepal Trade Information Portal (NTIP), Ministry of Industry, Commerce and Supplies (MOICS), Kathmandu.

- Poudel, K., 2023. BEEN: Promoting Hollow Brick, New Spotlight Fortnightly. Keshab Poudel, Kathmandu.

- Prajapati, S., Malla, A., Adhikari, N.P., Shrestha, S.L., 2019. Technical and Economic Assessment for the Promotion of Non Carbonized Pellets in MSME Sector in Nepal. Journal of the Institute of Engineering 15, 261-266.

- Prasain, K., 2021. Coal shortage hits domestic brick and cement factories, The Kathmandu Post, Kathmandu.

39
low-income countries. Thus income elasticities estimated using income at market exchange rates tend to be smaller than those using income at PPP exchange rates. For a discussion of purchasing power parity exchange rates, see Robert Summers and Alan Heston, "The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988," _Quarterly Journal of Economics, vol._ 56 (May 1991), pp. 327-368.
The analysis suggests that, purely on account of demography, the share of HICs could reach 31 percent, which is almost three times lower than the present estimate. Conversely, the share of UMICs could more than double to 39 percent, whereas that of LMICs and LICs could rise, respectively, to 27 percent (by a factor of 9) and 4 percent (by a factor of 22). These estimates are subject to the influence of other factors, but these in our view do not offer a convincing explanation of the stark discrepancies between rich and poor countries. In fact, collectively, they may well serve the raise the share of the developing world relative to the baseline.

The third part of the paper speculates on the likely path going forward. It concludes that the observed disconnect between the reported and estimated data entails a grim prediction for the future mortality toll of COVID-19 in the developing world. It entertains the possibility that the current skew towards HICs may be partly the result of inequality in data quality but deems that the first-order reason for the present dichotomy between rich and poor countries is because the pandemic has not yet run its full course through the age distributions of the different parts of the world. Whether this grim prediction will be realized will depend importantly on measures governments and individuals will undertake to mitigate and slow the pace of infection.

It is true that the generally younger age distributions of the developing world will offer a measure of protection against the virus as younger people are exponentially less vulnerable keeping all else constant. But we should not forget that the developing world, as a whole, counts in absolute terms many more people in the vulnerable age cohorts than the HICs combined: its 60+ population is 2.4 times larger and the 70+ population 1.8 times. The fact that these vulnerable groups in developing countries represent a smaller share in their own populations compared to HICs should not diminish our focus on mitigating the impact of pandemic in the developing world.

# **2. Context
months. We further disaggregate modification within 24 months by the providing the percentage share of economic sectors as well as the policy initiator for all laws and regulations that were modified within 24 months.

# 4. LEGISLATIVE PREDICTABILITY IN AN INTERNATIONAL CONTEXT

Uncertainty in policy making is a global issue and legislative predictability concern should not be seen in isolation. Figure 3 provides a set of global comparators with data drawn from six diverse countries, covering both low- and high-income countries, as well different political systems.<sup>4</sup> The purpose of this section is to situate the overall pattern of legislative predictability in the Kingdom of Jordan in a

> 4 The figures are calculated based on the LEGDAT dataset (mid-2023 version). More information on the data and country scope can be found www.globalcorruptionpbservatory.com.

10
trade, I use the European agreement to harmonize product standards to international norms as a natural experiment. This agreement was achieved thanks to the cooperation between CENELEC —the European Committee for Electrotechnical Standardization— and the IEC —the International Electrotechnical Commission— laid down in the Lugano and Dresden agreements in 1991 and 1996. I combine this information with U.S. linked/longitudinal firm trade data and the U.S. Censuses of Manufactures to empirically confront the results of my theoretical model for the Electronic sector.

An important contribution of my analysis is the creation of a new set of industry level measures of EU product standards for the Electronic sector. Since there is currently no official mapping between the classification of standards and any industry or product classification, I develop a method to obtain a concordance between five-digit ICS codes and the four-digit standard industrial The empirical findings largely confirm the theoretical results. First, I confirm that industries with relatively high harmonization and high reduction in tariff rates exhibit relatively high U.S. export value to the EU. Furthermore, I show that product standards harmonization increases the 18
of respective sectoral value added obtained from the WDI. These variables reflect the dependence on imports and the lack of production capacities in domestic economies, countries’ competitiveness abroad, and the importance of each sector in the total value added. In addition, the study uses the capital and labor<sup>10</sup> from the PWT 9 in constant 2011 U.S. dollars but converts them to constant 2010 U.S. dollars using the U.S. price levels from the PWT 9 to ensure consistency between the PWT 9 and WDI data. The capital and labor help control for demand effects from both the industry and household sides.

Figure 1. Electricity Constraints, Self-generation, and Average Electricity Consumption.

<!-- Start of picture text --> Electricity Constraints and Self-generation Average Electricity Consumption (GWh)<br>0.8 80000<br>70000<br>0.6 60000<br>50000<br>0.4 40000<br>30000<br>0.2 20000<br>10000<br>0 0<br>High Income Upper Lower Low Income High Upper Lower Low Income<br>Middle Middle Income Middle Middle<br>Income Income Income Income<br>Average share of self-generated electricity across<br>firms<br>Agriculture Manufacturing Services<br>Average share of firms that say electricity is an<br>obstacle<br>% of Electricity Constrained Firms<br><!-- End of picture text --> Source: Authors’ computations using WB Enterprise Surveys Data.

Interesting stylized facts emerge when comparing electricity constraints, self-generation, and consumption across country income groups and sectors: low-income countries lag behind advanced economies in terms of electricity consumption across all sectors, they have a higher share of firms

> 10 Capital stock estimates are derived from past investments and deprecation by asset class using the perpetual inventory method (see online appendix C in (Feenstra, Inklaar, & Timmer, 2015))
additional variables to capture the government’s ability to enforce and facilitate tax collection as well as local economic and political conditions that could influence tax outcomes. We then use matching to isolate the remaining variation to better estimate our treatments’ impact on tax outcomes after accounting for local tax facilitation and enforcement capabilities, economic conditions, and other confounding variables. All dependent variables are in constant, 2010 Brazilian _reais_ .

We also present the results of hypothesis tests using various lags between the participatory institutional treatment and tax outcomes. For example, we know when PB programs began, with many emerging during the timeframe of the study. We can therefore assess different arguments surrounding when tax outcomes might be expected to appear, if they are indeed connected to PB. We begin with one-year lags, but also develop models using two and five-year lags of our dependent variables as well as a three-year moving average of tax outcomes. Unfortunately, we do not know when municipalities adopted policy councils and therefore estimate their relationship with tax outcomes based on the councils’ presence, with no lag in terms of potential impact.

# **Dependent Variables**

_Local Tax Share of Municipal Revenue_ We collect data on the local tax share of municipal revenue as our first indicator of tax compliance. The local tax share of revenue reflects the community’s willingness to fund the government as well as the municipal government’s ability to collect tax revenue. The tax share of municipal revenue is distinct from federal transfers, which comprise a large percentage of local budgets in Brazil. The measure has a mean of 5% and a standard deviation of 5.

# _Per Capita Local Tax Revenue_

This indicator provides a broad view of the local tax environment. Annual, per capita, local tax revenue also reflects the relative willingness of municipal residents and firms to fund the local government and the municipal government’s ability to collect revenue. We use the base 10 logarithm of this indicator, whose mean is 3.80 with a standard deviation of 1.13.

#
# **Introduction**

Data on sectoral investment and capital stocks are essential for empirical research in sectoral productivity, yet cross-country panels are rare for countries outside of the OECD. Crego et al. (1998) introduced a database on the capital stock in the agricultural and manufacturing sectors for 57 developed and developing countries for the years 1967-1992. We have updated the agricultural component of this database to the year 2000 for a subset of 30 countries. We construct three capital sub-components series: treestock, livestock, and fixed capital in agriculture. We modify a commonly used methodology for integrating investment to obtain the fixed capital stock. This methodology can also be used to compute comparable fixed capital stocks for other sectors and for the economy as a whole, in order to facilitate comparative analyses.<sup>1</sup> After describing selective characteristics of the updated agricultural capital data set, including the evolution of aggregate capital stocks over time and the changing composition of agricultural capital, we revisit an earlier analysis of agricultural productivity that utilizes our data set on agricultural capital. We then show how the frequently used practice of employing farm machinery as a proxy for agricultural fixed capital leads to substantially different results.

# **Background**

Measures of agricultural capital are fundamental to two important and related fields of empirical study. The first concerns the determinants of agricultural productivity and growth; the second concerns the structural transformation of developed and developing countries. Both areas of study are important for understanding economic development, since agriculture remains the largest source of employment in many poor countries today.<sup>2</sup> In general, as economies grow, labor flows from agriculture to other sectors. As a consequence, the shares of labor and GDP in the agricultural sector decline as part of this process; however in economies that perform well, the sector continues to grow through a build-up of physical and human capital and through the adoption of more productive technologies.<sup>3</sup>

> 1 This avenue of research
-|---|---|
|Bilateral|22|Australia, Austria, Belgium, Canada, Denmark, Finland, France,<br>Germany, Ireland, Italy, Japan, Korea, Luxembourg, Netherlands,<br>New Zealand, Norway<br>Portugal, Spain, Sweden, Switzerland, United Kingdom, United<br>States|
|Multilateral Development Banks<br>(MDBs)|4|African Dev. Bank, Asian Dev. Bank, Inter-American Dev. Bank,<br>WorldBank|
|Other Multilateral Organizations<br>(Other MOs)|4|EU, IFAD, IMF, United Nations|
|Vertical funds|2|GAVI Alliance, Global Fund|
|**Total**|**32 **||

_Islamic Development Bank is not included in the list, as there is no data of the bank in FCS Source: Author_

> 9 The selection of key development partners is based on an ODA threshold of more than US$100 million and/or their intention to publish data (OECD (2011, p.141))

6
research (Azoulay et al 2015). In a similar vein, Ganguli (forthcoming) studies a large-scale grant program for scientists in the former Soviet Union, funded by financier George Soros, and finds that the grants more than doubled researcher publications.

Branstetter and Sakakibara (2002) and Sakakibara and Branstetter (2003) measure the impact of government-sponsored company-to-company cooperative R&D projects in Japan and the US using matched control groups of firms. They find that participating in these consortia leads to a higher number of patents. Other papers have used matching techniques or structural models to estimate the effect of EU sponsored research joint ventures, some of which include collaborations between firms and universities. These studies tend to find a positive association between participating in a research joint venture and firm productivity and profitability (see for example, Benfratello and Sembenelli 2002 and Barajas, Huergo, and Moreno 2012), but they do not distinguish between joint ventures that include universities and those that did not.

Our findings are thus in line with previous findings regarding the positive impact of R&D subsidies on publications, patents and commercialization. To our knowledge, ours is the first paper to analyze data from a survey that was specifically conducted to measure the impact of the subsidy program.<sup>4</sup> These data allow us to consider a broader range of outcomes than previous studies, and, in particular, to document the positive effect of Poland’s In-Tech program on science-industry collaboration, thus adding a new finding to the literature.

The rest of this paper is organized as follows. Section 2 describes the In-Tech program and its application process and scoring. Section 3 discusses the RD design and follow-up survey. Section 4 presents the results and Section 5 concludes.

# **2. The In-Tech Program**

Poland’s National Center for Research and Development (NCBiR) is a government entity whose programs account for around 40 percent of the national research and development budget. One of the programs it offers is Innotech, which is
savings using financial institutions, but not compared to countries with similar coverage. Compared to all 145 countries in the FINDEX database, the dissimilarity index is high at 33.7 (Figure 30). However, countries with similar coverage also have very high inequity in formal savings, and many at rates higher than in Turkey. The decomposition of the dissimilarity in 2011 shows that level of income and gender are the two main reasons of not being able to save at financial institutions in Turkey. Age stands out as a larger source of dissimilarity among the benchmark countries.

Between 2011 and 2014, Turkey’s coverage rate in formal savings increased from 4.2 percent to 9.2 percent. However, this increase is not coupled with an improvement in the d-index as Turkey’s dissimilarity rises slightly during the same period. The Shapley decomposition of the d-index shows that the impact of gender decreased while the contribution of education increased. However Turkey’s gender portion of dissimilarity in 2014 was still higher than the benchmark countries with the exception of Algeria. Going forward, making formal savings more abundant amongst individuals with a lower level of income and education could help Turkey in lowering the d-index and boost equitable financial inclusion.

13
.|
|Total Assets>935000LKR|0.06|0.04|0.740|0.07|0.13|0.574|
|Monthly Profits|15167|15036|0.969|17197|19921|0.618|
|Profit Data Missing|0.02|0.04|0.450|0.00|0.00|.|
|Monthly Profits<2000LKR|0.02|0.00|0.552|0.04|0.00|0.552|
|Monthly Profits>30000LKR|0.06|0.04|0.740|0.04|0.00|0.552|
|Business Practices Score|9.81|8.17|0.315|8.22|10.88|0.227|
|From booster sample|0.59|0.65|0.627|0.53|0.50|0.865|
|Retail Sector|0.36|0.17|0.101|0.27|0.63|0.046|
|Colombo|0.31|0.57|0.032|0.49|0.63|0.487|
|Kandy|0.58|0.30|0.024|0.44|0.38|0.721|
|Any paid worker at baseline|0.16|0.13|0.769|0.22|0.25|0.866|
|Monthly Sales|56787|57263|0.981|49618|61307|0.552|
|Owner's Age|35.41|34.30|0.509|35.60|38.38|0.250|
|Business is Registered for Taxes|0.31|0.22|0.393|0.29|0