rafmacalaba/data-use-mentions-extended huggingface dataset:
**|**85**|**8.7**|**12.5**|**119.3**|
|Population (million)|192|1,324|1,140|277|2,934|
|Outward FDI(USD billion)|11.5|68|14.6|2.7|96.8|

Source: ILO, Penn World Tables and UNCTAD, 2011 The employment of labor intensive jobs in low wage countries currently is also large but pales in comparison to the numbers in either China or the emerging market countries (Brazil, China and India) collectively. As an example, in 2009, all developing countries in Sub-Saharan Africa collectively had a labor force of 338 million. Most of it was employed in subsistence agriculture that fetches below poverty level wages. Assuming that about 3 percent are in manufacturing suggests that the sector employs about 10 million African workers. To achieve employment in manufacturing equivalent to about 20 percent of total employment implies that Sub-Saharan Africa needs a total of about 70 million jobs. In most industrial countries, the share of 47
# **VII. Conclusions**

For the analysis of firms’ investment strategies, researchers have predominantly observed and described the role of various determinants, including information barriers and financial constraints in investment strategies in developed countries (Thollander et al, 2007; Fleitera et al, 2012; Kalantzis and Revoltella, 2019). Building upon previous research, this paper develops a new framework of firms’ mitigation measures. Using unique data from the joint EBRD-EIB-WBG Enterprise Surveys, it empirically examines the determinants of the mitigation scope (i.e. the total number of mitigation measures) and the number of measures that firms take to follow certain strategic preferences of firms’ investment areas. We specifically analyze factors that other researchers had previously suggested as possible determinants for firms’ investment strategies (De Haas et al, 2022; Kalantzis and Revoltella, 2019). Methodologically, we apply an IV logistic regression model in addition to the logistic regression model in our econometric analysis.

In line with our priors, we find that better green management practices positively impact the transition strategy of firms to a sustainable future. Moreover, the magnitude of this impact increases with the number of measures implemented. This result implies that more aware firms about climate change impacts and the various existing measures that tackle them are the ones to engage the most in mitigation measures. This relationship is exponential and positive and suggests that firms with the best management practices overcome information barriers and adopt more mitigation measures.

Our econometric analysis also shows that firms’ access to finance is an important determinant for adopting measures, especially for capital-investment measures, such as those in production processes. Specifically, the more financially constrained firms are, the less likely they pursue many mitigation measures to reduce energy costs and their carbon footprint. Once again, the exponential relationship is evident, implying that the impact of financial constraints on the investment strategy of firms is higher for the lower and upper range of measures that could be implemented.

Based on the above, the key findings are that financial constraints and information
_ 0.10. OLS estimates at the target child-level are presented using school outcomes reported by the caregiver at endline. Standard errors clustered at the community level are shown in parentheses. All regressions include district fixed effects. Westfall and Young (1993) _p_ -values are shown in square brackets, where 10,000 replications were used. The primary school enrollment index is constructed over columns (2) to (4) following Kling et al. (2007). Estimates are weighted to give each community an equal weight in the analysis. Covariates are not included in the regressions.

# **6 Main Results**

## **6.1 First Stage: Preschool Enrollment**

The DICIPE reduced the cost of access to preschool education through the construction of preschools and the provision of early childhood services in beneficiary communities. Thus, the first stage of our impact evaluation investigates whether DICIPE caused an increase in preschool enrollment.

Participation in the program was high in the treated communities. As shown in column (1) of Table 2, treated communities saw a 73.4 percentage point increase in the enrollment of the target children at preschool compared to the control group, which had a preschool enrollment rate of only 1.9%. This preschool enrollment rate in the control group is not due to proximity to preschools in treated communities, since none of the ECD centers is located within a reasonable walking distance from the households in the control group.<sup>14</sup> Instead, enrollment in the control group is mainly driven by short-term

> 14On average, households in the control group are located 8.4 kilometers from the nearest ECD center in

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the government to formulate and implement sound policies and regulations that permit and promote private sector development”. And lastly, Voice and Accountability summarizes the “extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media”. Countries that score high in those indices are perceived to perform better in each of those dimensions of institutional quality.

<u>Financial markets. We use 3 measures of financial market development. First, we gathered data on the</u> total domestic credit to the private sector as a percentage of GDP, collected by the World Bank. The data span 182 countries again for the period of 1996 to 2016. As seen in Table 1, the average in our sample is 49% (median = 35%) and there is considerable variation across time and countries. Highermiddle and high income countries have an average of credit to the private sector as percentage of GDP almost 3 times higher than low and lower-middle income countries (67% and 23%, respectively, as seen in OA Table 1). Similarly to insurance penetration, a few countries have a very high share of domestic credit to GDP.

The other two measures of financial market development are more limited in terms of time and geographical coverage. The second set of data reflects the level of a country’s financial liberalization. The data were put together by Abiad, Detragiache and Tressel (2010) and capture seven dimensions of financial market development: credit controls and reserve requirements, interest rate controls, entry barriers, state ownership, policies on securities markets, banking regulations and restriction on the financial account. In our analysis we use the standardized index (Financial Reform Index) also produced by the authors that summarizes all the seven dimensions. Higher values of the index correspond to higher levels of financial liberalization. Finally, we have data from the Global Findex database, produced by the World Bank (Demirgüç-Kunt et al. 2018
To implement our identification strategy, we need both the amount of aid received by each service sector in a country as well as information on how intensively a manufacturing sector uses upstream services sectors. Information on service intensity can be computed from inputoutput tables which provide information on the inter-industry relations of an economy. We use the total input requirements which are estimates of the inputs—including services—for each industry that are directly and indirectly required to deliver a dollar of the industry output to final users. Thus, exports are determined by: where: _Xijt_ is exports of sector _j_ in country _i_ in year _t_ ; a _idikt_ , is the amount of aid addressed to the service sector _k_ of recipient country _i_ in year _t_ ; _k_intensityij_ is the intensity with which the manufacturing sector _j_ uses upstream service sector _k_ in country _i._ which can be roughly interpreted as the cents of service k used in the making of a dollar of product j. We also include country-sector fixed effects, , to control for invariable characteristics specific to a manufacturing sector in a given country, such as constant taxes and subsidies. Country-year effects, , control for shocks specific to a country in a given year, such as inflation, exchange rates, political or economic shocks, and climate shocks such as natural disasters. Sector-year effects, , control for shocks specific to a sector worldwide in a given year, such as any supply or demand shock having an impact on world market prices.

# **4. Data Description**

Aid flow data were compiled from the OECD’s Creditor Reporting System (CRS); exports, specifically mirrored imports, were taken from UNCTAD’s COMTRADE database; output was taken from UNIDO’s INDSTAT 4; and input-output tables were compiled from 10
OECD and EC-DG Regio to define global Functional Urban Areas, scaling down the population counts to be relevant in a subnational context. Our approach identifies 219 areas with urban clusters. To establish a link between urban air pollution and the INDO-DAPOER database, we summarized the PM2 _._ 5 annual grids to the district-level using the mean value for pollution grids sensed over urban patches in each district. This captures output directly from urban activity, and reduces the outside influence of fires and agricultural activity.

> Since we are particularly interested in pollution, we drop any areas that at one or more

> 9https://data.worldbank.org/data-catalog/indonesia-database-for-policy-and-economic-research. Some district names and boarders have changed over time. To construct the time series, we used the database’s “District Proliferation Crosswalk” file to match observations in the data to the district definition provided by the Global Administrative Areas repository (GADM) available at http://www.gadm.org/. Indonesia’s latest district configuration covers 497 districts of which 427 were successfully matched.

23
i.e., when a single firm can produce a<br>product at a lower cost than the combined cost of two or more firms<br>producing identical products), thus making them more efficient for just<br>one producer to operate. Some examples include water utilities, energy<br>transmission, and postalactivities withuniversalobligation.|

Source : (Dall'Olio, et al., 2022b) We implement the WB sector taxonomy to classify the economic activities using the NACE 4- digit code for the 16 European countries studied. In total, 95% of the firms in our sample operate in competitive sectors. Table 4 shows the distribution by sector type between BOSs and POEs by sector type. On average, 67% BOSs are found in competitive sectors for which the economic rationale for state ownership is less clear as can be often served by the private sector. The remaining 17% of BOSs are found in partially contestable sectors such as passenger transportation, and 16% in natural monopolies such as energy transmission.

_Table 4. Share of POEs and BOSs by sector type and year_

||2011|2012|2013|2014|2015|2016|2017|2018|2019|**2020**|
|---|---|---|---|---|---|---|---|---|---|---|
||||**PO**<br>|**Es**<br>|||||||
|**Competitive**|95.8%|95.7%|95.6%|95.6%|95.5%|95.7%|95.6%|95.7%|95.7%|95.7%|
|**Natural Monopoly**|0.8%|0.8
.161<br>(0.101)|0.221<sup>_∗_</sup><br>(0.124)|_−_0.041<br>(0.091)|
|log _Tari f fpcdt_|0.012|0.035|_−_0.019|0.012|0.036|_−_0.019|0.012|0.036|_−_0.019|
|Observations<br>|101,379|39,502|61,265|101,379|39,502|61,265|101,379|39,502|61,265|
|Adjusted _R_<sup>2</sup>|0.632|0.542|0.646|0.631|0.542|0.646|0.632|0.542|0.646|

Notes: “All” denotes all sectors, “Agric.” denotes the agriculture and food sectors, while “Manuf.” indicates the Manufacturing sector. Robust standard errors clustered at the origin-destination-product level are in parentheses. ***, ** and * denote significance at 1%, 5%, and 10%. All models include controls for firm-origin-product, firm-product-year and destination-year fixed effects, which are excluded from the table for brevity.

Intra-African borders remain ‘thick’, with many barriers including NTMs inhibiting regional trade (Brenton et al., 2014; Cadot and Gourdon, 2014). For instance, Sanjuán López et al. (2021) found that both technical and non-technical NTMs reduce intra-Africa trade. We thus assess whether the trade-reducing effect of NTMs on the intensive margin is less or more pronounced for intra-Africa exports compared to exports to destinations outside the continent. To do this, we interact an indicator variable for Africa destinations ( _AFRd_ ) with our main variable of interest. The results are reported in columns 4 to 6 of
Estrades, Maliszewska, Osorio-Rodarte, and Seara E Pereira. (2021) Brunei, Cambodia, Malaysia, Myanmar, New Zealand, Singapore, and Thailand, have just one tariff schedule that is on offer for all other members, while others have variations by partners.<sup>6</sup> The level of protection applied by RCEP members in intra-RCEP trade differs, due to the different previous agreements in place. For some countries, such as Korea, China, Lao PDR, and Japan, the average tariff applied to RCEP partners is around the same as the tariff applied to countries outside RCEP (Figure 3). In a few cases, the average tariff applied to RCEP countries is even higher than the tariff applied to non-members. This is the case of Australia, New Zealand, and Indonesia. Cambodia, Thailand, Philippines and Malaysia apply a significantly lower protection to RCEP imports than to non-members. Implementation of chapter 2 (trade in goods) in the RCEP agreement is expected to level out these differences.

Figure 3. Average tariff applied by RCEP countries. Trade weighted average <!-- Start of picture text --> 5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>Korea China Laos Australia- Vietnam Cambodia Indonesia Thailand Japan Philippines Malaysia<br>New<br>Zealand<br>RCEP ROW<br><!-- End of picture text --> Source: Own elaboration with data from MacMap and Comtrade.

Note: Brunei, Myanmar and Singapore are aggregated into the Rest of Southeast Asia region.

The highest level of protection in RCEP is found in the agriculture and food sectors, considering tariffs applied to both RCEP countries and to the rest of the world (Figure 4). The trade-weighted average tariff applied to imports of agricultural and food products from the rest of the world is 11
head was, while 13.6 percent were willing when the household head was not). This pattern is explored more rigorously in the following regression analysis whereby other background characteristics are also controlled for.

The regression analysis also shows that willingness of a household head to get vaccinated had a very strong association with the proportion of household members who would be willing to get a vaccine. After controlling for background characteristics, the proportion of household members who were willing to get vaccinated was around 35 percentage points higher when the household head was also willing to get vaccinated (see Figure 5 and Table A2.2 in the Appendix). The only other characteristic that had a very strong association was the proportion of household members who think that a COVID-19 vaccine would be effective (around 39 percentage points). All other factors had a negligible correlation.

# **3.3 Summary of Findings from the In-person Survey**

Three key findings about intra-household dynamics and attitudes towards COVID-19 vaccines emerge from this nationally representative, in-person survey that was conducted in Zambia prior to widespread access to COVID-19 vaccines. Firstly, there is considerable variation across households in terms of willingness to get vaccinated against COVID-19, with around half of individuals being willing. Secondly, the main reasons people were unwilling to get vaccinated seemed to somewhat stem from a lack of understanding about the benefits from getting a COVID-19 vaccine. Thirdly, consistent with Hypothesis 1, we illustrate that there is a strong correlation between the attitudes of individuals within households towards COVID-19 vaccines. Collectively, these results suggest that intra-household dynamics may play an important role in shaping people’s attitudes towards COVID-19 vaccines and that there could be greater acceptance of vaccines if people were made aware of the benefits.

17
Justice and the Law: Sudan. New York: UNDP.

- UN OCHA (2019). Humanitarian Needs Overview Sudan. Sudan: UN OCHA. UN OCHA (2020). Humanitarian Needs Overview Sudan. Sudan: UN OCHA.

- UN OCHA (2021). Humanitarian Needs Overview Sudan. Sudan: UN OCHA.

- UNICEF (2017). Child Marriage in the Middle East and North Africa – Sudan Country Brief. Sudan: UNICEF

- UNICEF (2019). Education: Annual Report. Sudan: UNICEF

- Valentine, G. (2007). Theorizing and researching intersectionality: A challenge for feminist geography. The professional geographer, 59(1), 10-21.

- Verwimp, P., P. Justino and T. Brück (2019). The Microeconomics of Violent Conflict. Journal of Development Economics, 141, 102297.

- World Bank (2017). Forcibly displaced: Toward a development approach supporting refugees, the internally displaced, and their hosts. Washington, D.C.: World Bank Group.

- World Bank (2019a). Sudan Risk and Resilience Assessment. Unpublished report.

- World Bank (2019b). Profiting from Parity: Unlocking the Potential of Women's Business in Africa. Washington, D.C.: World Bank Group.

- YL (2018). How poverty and gender combine to affect youth and adolescence. Young Lives Impact Case Study. Oxford, United Kingdom.

- Young, H., & Jacobsen, K. (2013). No way back? Adaptation and urbanization of IDP livelihoods in the Darfur Region of Sudan. Development and Change, 44(1), 125-145.

- Young, H., & Ismail, M. A. (2019). Complexity, continuity and change: livelihood resilience in the Darfur region of Sudan. Disasters, 43, 318-344.

- Young, H., Osman, A. M. K., Aklilu, Y., Dale, R., Badri, B
<!-- Start of picture text --> 50<br>45<br>40<br>>E 35<br>><br>® = 30<br>Pyz5 24.3\<br>B 20<br>E<br>2 45<br>2<br>10<br>5<br>0<br>Jan19 Apr19 Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22<br>— Male Female Total<br>‘Source: CMIE People of India (15 yrs above)<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>Ss<br>2£ 45<br>= 235<br>g3s2©8 « ° > . : . 40.3<br>.<br>2<br>Re}<br>5 35 1<br>4<br>30<br>Jan19 Apri9 Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22<br>—= Urban Rural Total<br>‘Source: CMIE People of India (15 yrs above)<br><!-- End of picture text -->
results show that direct transfers are more efficient in reducing poverty among the poorest. However, there is still space for improving their effectiveness.

**Table 5.** Effectiveness of direct transfers. Uruguay, 2017

|Indicator|Officialpovertyline|
|---|---|
|Vertical Expenditure Efficiency|0,465|
|Spillover Index|0,213|
|Poverty Reduction Efficiency|0,366|
|PovertyGapEfficiency|0,307|

Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology

**C. Incidence and progressivity of taxes and public benefits** This section presents several indicators that help deepen our understanding of the effects on inequality observed in the previous tables. To analyze the incidence of social spending we calculate the ratio of the amount of benefits to Pre-fiscal income by deciles of Pre-fiscal income. We also estimate the concentration coefficient, which assesses the distribution of the benefits when people are ordered by Pre-fiscal income. A benefit is considered progressive when the concentration coefficient is lower than the Gini of Pre-fiscal income. The difference between the concentration index of transfers and the Gini index of Pre-fiscal income is the Kakwani index. Therefore, a negative Kakwani index indicates that the benefit program is progressive. The Kakwani index may be negative even when the concentration coefficient is positive. A negative concentration index suggests that the transfer is more concentrated among the poorest. All of these instruments may be also applied to the study of taxes.

**Table 6** presents the incidence analysis and Kakwani index of the taxes and contributions included in the analysis. The incidence of direct taxes increases with income (or with the household’s relative position in the income distribution): they represent 0.1% of Pre-fiscal income of the poorest 10% of the population and 9,9% of the richest 10%. Besides, since the Kakwani index is positive, direct taxes are progressive. This result follows the analyses from Bucheli et al. (2013
gets and for the common case of multi-programme assessment highlighted by Tohari et al. [2019].

In view of the huge logistical scale of many targeting surveys, sample population surveys are perhaps the most closely related information collection exercise. Looking at their economic aspects, Groves and Heeringa [2006] proposed a responsive survey design to reduce survey cost while maintaining accuracy. Focusing on settings with cost and operational constraints on data collection, Pape and Mistiaen [2018] proposed an effective imputation approach to estimate the population distribution of consumption rather than a full LSMS-style survey. A similar focus on a reduced length survey is Christiaensen et al. [2021], who investigated the use of components of consumption to estimate household values but find both theoretical and empirical problems with this approach.

# **2.2 PMT design**

Three essential design criteria, echoed in Grosh et al. [2022], shape PMT:

- _Accuracy._ Identifying the intended beneficiaries accurately is the essential paradigm of a targeting mechanism.

- _Cost._ The multiannual nation-wide PMT survey sweeps common in low- and lower-middle income countries require major fiscal outlays. The resource needs of on-demand registration, which countries with sufficient administrative capacity tend to invest in, are also high and imply the need for concise surveys that economize enumerator time.

- _Verifiability._ Survey responses determine PMT outcomes, creating a monetary incentive for misreporting personal and household characteristics towards responses that are associated with poverty<sup>2</sup> . As a result, targeting surveys are often restricted to variables that are observable by enumerators, such as physical assets, or that are verifiable via documentary evidence.

In comparison to other commonly used targeting methods such as geographic targeting, community-based targeting, means testing or hybrid means testing, PMT uses easy-to-verify characteristics. It is most appropriate when informality is high and when some form of household specific ranking is desired. At lower levels of informality in an economy, means or hybrid means tests would be more appropriate and yield higher accuracy than