rafmacalaba/data-use-mentions-extended huggingface dataset:
## V. **Principal lessons learned**

51. A number of important lessons can be derived from the survey of experience in leasing and MSB finance undertaken for this report. These principal lessons deal with the affinities that leasing and MSBs have for each other; financial and technical skills needed for leasing; the macroeconomic, legal and regulatory environments under which leasing contributes to broadening access to financial services; the default experience which appears to be in line with general loan recovery experience in microfinance; and the role of direct and indirect subsidies in programs designed to promote lease financing, MSBs and alternative energy systems.

# **_Leasing and MSBs_** _have affinities_ **_for each other._**

52. As shown by the Bank's experience in the Pakistan Microenterprise Project leasing strengthens the financing and operating linkages between small-scale industry and microenterprises which relate to each other's business operations through sub-contracting arrangements. Sub-contracting arrangements promote the growth of MSBs and their expansion and integration into the organized fornal business sector as small- and medium-sized enterprises. In the case of Pakistan, data from the Surveys of Small and Household-based Manufacturing Industries (SHMI) for 1976-77 and 1983-84 clearly show (a) an over 100% increase in value added contributions from the small and microenterprise sector between the two survey periods, (b) an 80% increase in the microenterprise sector's employment-absorption rate, particularly for women in household-based production units, and (c) extensive linkages between microenterprises vis-a-vis (i) larger enterprises in urban areas where subcontracting accounted for about a fifth of the subsector's total value added, and (ii) the agricultural sector in rural villages where subcontracting accounted for about a third of total value added of the sector. 22 53. This observation is also supported in the Bangladesh experience,<sup>23</sup> which highlights the diversity in financing arrangements across industries. It also provides a clear picture of the use of credit by informal operators to finance those who purchase their goods
We address at least two further challenges with the structure of the primary dataset for creating our independent variables. First, we find several discrepancies between the same policy's yes/no and years variables. Appendix Table A.2 describes the eight types of discrepancies in the dataset and our solution to address each type of discrepancy.

Second, there are occasional continuous variables in the dataset and variables that we cannot transform into panel data. These occasional continuous variables disrupt our efforts to address collinearity and reduce the dimensionality of the dataset, as discussed in the following section. Please refer to Appendix Table A.3 for more details.

The final dataset has 76 policies over a panel of 133 countries between 1980 and 2018, as most policies were not implemented before then (see Appendix Table A.4).

# **Reducing dimensionality**

We must reduce the highly dimensional policy instrument data appropriately for regression analysis and interpretation. ESMAP indicators include 76 renewable energy policies grouped into seven high-level energy policy packages, or “Headings” (Table 2). Each energy policy package contains a different number of policies and sometimes contains clusters (or groups) of policies. The dendrogram in Appendix B shows that similar policy instruments usually reside within the same Heading. We, therefore, use these Headings, or energy policy packages, as our independent variables.

Table 2: Headings in the RISE Renewable Energy Pillar

||**Heading / Energy policy package **|**Acronyms**|
|---|---|---|
|1|Legal framework for renewable energy|LF|
|2|Planningfor renewable energyexpansion|PE|
|3|Incentives and regulatorysupport for renewable energy|IR|
|4|Attributes of financial and regulatoryincentives|AI|
|5|Network connection and use|NC|
|6|Counterpartyrisk|CR|
|7|Carbonpricingand monitoring|CP|

_Source: RISE website_ , <u>https://rise.worldbank.org.
Policy Research Working Paper 6021

# **Abstract**

A broad consensus has emerged among practitioners and researchers that failure to build accountable and legitimate institutions is a critical risk factor associated with vicious circles of repeated violence. Despite this consensus, very few studies have tested the extent to which local government performance and decentralized service provision shape citizens’ beliefs toward political authorities. This paper contributes to fill this gap by examining the antecedents of trustworthy local government authorities in a post-conflict and fragile setting, Sierra Leone. Taking advantage of a unique longitudinal survey, the National Public Services, it examines the impact of sub-national variation in local government performance on citizens’ beliefs about the trustworthiness of local government authorities. To test the hypothesis, it uses multilevel models to exploit variation over time and within and across subnational units in Sierra Leone. The results suggest that improvements in the quality of decentralized service delivery, as well as perceptions of local councillors’ honesty, are positively associated with perceptions of local government officials as trustworthy political authorities. These findings speak to the possibility that local service provision can play a role in shaping the relationships between citizens and the state and in overcoming the root causes of fragility and conflict.

This paper is a product of the Public Sector Governance Group, Poverty Reduction and Economic Management Network; and the Public Sector Reform and Capacity Building Unit, Africa Region. 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://econ.worldbank.org. The authors may be contacted at asacks@worldbank.org and mlarizza@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
for CHL,</sup> GHA, MEX, TUR, ZWE r, = 0.5(ln (1+ itd) + 0.5( ln (1+ it_ld)) - Aln(deflt+i)). _ia_ = nominal interest rate = short term deposit rate, year average - Source : Central Banks Bulletins.

**_jd_** = nominal interest rate = short term deposit rate, December value - Source : Central Banks Bulletins.

flit = index of financial liberalisation - Source: our calculations govst = public sector saving rate (relative to GNDI) - Source: WB "World Savings Database" Rev. 3.00.

38
25</sup> Including both groups of controls (group A and group B) and controlling for 22Although we include year fixed effects in our general specification (equation 3) for reasons explained in section 3, we omit those in our results as they absorb most of the effect of the RSQ on food consumption. The main reason lies in the high spatial correlation in RSQ experienced across departments for each individual year, which causes the inclusion of year fixed-effects to induce an over-controlling problem. We provide maps of the 5-month SPI at the department-level for each year of the studied period (20102014) in appendix (Figure B.5) to illustrate the fact that, for any given year, the rainy season quality is fairly homogeneous across departments. We also use our household-level dataset to regress 5-month SPI values against year fixed-effects and find that the latter explains 82% of the variance in the 5-month SPI in our sample – whereas cross-sectional variation represents only 10%. To confirm that year fixed-effects effectively absorb the effect of the RSQ, we estimate a basic model without interactions and including all controls and year fixed-effects (column (1) of Table C.1 in appendix). We find that year fixed-effects are almost perfectly correlated with a (rural) population-weighted average of department-level 5-month SPI (Pearson coefficient equal to 0.96).

> 23There are only 34 clusters (i.e. departments) so we use a bootstrapping method to calculate our clustered standard errors.

> 24Full results including coefficients associated with all controls are provided in Table C.1 in appendix.

> 25The 5-month precipitation z-score yields the highest goodness-of-fit for this specification although results of estimations using other indices are provided in appendix, more on this below.

21
Grama Niladharis to select beneficiaries. The household head characteristics used in Model 1 are unlikely to match those of the family head in all cases. Further, even the use of household assets across models may be inaccurate to the extent that household and family assets differ, and family size will be systematically lower than household size. It may therefore be worth considering using the household‐level model and adjusting program rules as needed to account for multiple families in a single (eligible) household.

Two further considerations are important. First, the choice of eligibility cutoff is key, since it influences both the number of eligible households and the PMT’s targeting performance. A higher cutoff simultaneously increases coverage among the target group and increases leakage of benefits to those outside the target group. The choice of cutoff is ultimately dependent on policy priorities, the size of the budget, and the desired average benefit. Second, it is recommended that the formula should be updated every 6 years to reflect structural changes in living standards and patterns of poverty. Such an update would be in line with the triennial timetable for the HIES. Finally, the basic household level information used to determine household eligibility should be collected and updated more frequently for existing beneficiaries or new applicants, taking into account budgetary and practical considerations, but ideally every two to three years.

We recommend a few steps for validating and refining the models presented in this paper. A pilot survey to collect additional data on household characteristics, assets and household consumption could be used to validate and refine the PMT formula. The data could also be used to verify the extent of measurement error in the PMT formula due to potential differences in definitions of household and family. This pilot could also be used to validate the PMT model by checking if particular types of households are systematically included or excluded (inclusion and exclusion errors). Additional criteria can be proposed for selecting beneficiaries in these scenarios. An important aspect of this pilot should be to verify the definition of the family that is used as
-0.0509|-0.225|
|||(0.0672)*|(0.0641)|(0.172)||(0.0879)*|(0.0842)|(0.194)**||(0.0711)|(0.0738)|(0.141)|
|IDN exports per worker *||||-0.175||||-0.288||||0.179|
|Share of hhlds with computer||||(0.156)||||(0.192)||||(0.164)|
|Observations|165|165|165|165|165|165|165|165|165|165|165|165|
|R-squared|0.995|0.995|0.995|0.995|0.995|0.995|0.995|0.996|0.967|0.968|0.967|0.971|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. High-educated workers: workers with at least complete secondary education Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively
# **Trade and Innovation in MENA**<sup>1</sup>

Matteo Ficarra (Graduate Institute), Meryem Gökten (CEPS), Péter Harasztosi (EIB), Zsóka Koczán (EBRD), Roberta Lesma (EIB), Rozália Pál (EIB), Christoph Weiss (EIB) JEL code: D22, F14, 014, 031 Keywords: Trade, innovation, firm performance, business environment, COVID-19

> 1 Ficarra: PhD student, Graduate Institute. Email: <u>m.ficarra@eib.org. Gökten: Researcher, CEPS. Email: meryem.goekcegoekten@ext.eib.org. Harasztosi: Economist, European Investment Bank. Email: p.harasztosi@eib.org.</u> Koczán: Associate Director, Senior Economist, European Bank for Reconstruction and Development. Email: <u>koczanz@ebrd.com. Lesma: Trainee, European Investment Bank. Email: r.lesma@eib.org. Pál: Economist, European</u> Investment Bank. Email: <u>r.pal@eib.org. Weiss: Senior Economist, European Investment Bank. Email: c.weiss@eib.org.</u> Matteo Ficarra and Meryem Gökten were trainee and consultant at the European Investment Bank while working on this paper. This paper was commissioned by the World Bank Middle East and North Africa Chief Economist office as part of the activity “Assessing the MENA formal Private Sector.” The activity is task managed by Asif M. Islam. The study is a background paper for the Chapeau report titled “Unlocking Sustainable Private Sector Growth in the Middle East and North Africa: Evidence from the Enterprise Survey.” The opinions expressed in this paper do not represent the views of the World Bank Group, its Board of Directors or the Governments they represent. The opinions also do not reflect the views of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB
benefits and<br>material aid to the unemployed, mln rubles|37590|37590|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on childcare allowances up to 1.5<br>years, mln rubles|157361|157361|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on child benefits up to 16(18) years,<br>mln rubles|51709|51709|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on cash payments to some categories<br>of citizens, mln rubles|931251|931251|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on housing subsidies and privileges,<br>mln rubles|359001|359001|Source: Russian Statistical Annual Digest 2017.|
|Expenditures on scholarships,mln rubles|82400|82400|Source: Russian Statistical Annual Digest 2017.|

Source: RUSMOD for 2017 policy year and 2016 RLMS-HSE data

# **Annex 2. Main assumptions and model validation**

Validating simulation output against official statistics is a necessary step in assessing the robustness of a microsimulation model. To ensure comparability with external sources, such as national accounts or administrative data, some assumptions were used in the model. Three main assumptions/adjustments are discussed below. In general, although our modeling reflects as close as possible the existing statutory rules, in the baseline scenario we tried computing economic rather than statutory incidence of taxes and benefits.

First, RUSMOD excludes informal sector workers from simulations of PIT and SIC. The RLMS attempts to capture informal sector jobs, which appear to be quite widespread, especially for additional jobs. According to RLMS data, as of 2016, the share of informally employed amounted to 19.8 percent at the main job and 44.2 percent at the second job, among employees. Among the selfemployed, 63 percent were classified as informal workers at the main job.<sup>36</sup> Among those who reported occasional self-employment, 89 percent were informal.
high-frequency data collected from the International Organization for Migration in 17 West and Central African countries over the period 2018-2019, and estimate that droughts occurring during the growing season reduce international migration. As they find these effects only in middle-income areas (and not in rich nor poor ones), they infer that liquidity constraints and income losses are the key channels. Mueller et al. (2020) use data from LSMS-supported longitudinal surveys conducted in Ethiopia, Malawi, Tanzania, and Uganda over the period 20092014 and find that climate variability and weather anomalies cause a reduction in temporary urban 3
indicators are comprehensive and rely on data from many important sources, and tend to be _de facto_ measures, but the methodologies underlying their indicators are not uniform, and their sample of firms is not representative of a country’s universe of firms.

Given the pros and cons of each data source, how robust are the business environment effects and what additional insights can be gained from using all three instead of only one? Are related indicators from different sources closely correlated? How can we explain why and how the estimated effects of the same business environment theme on firm performance differ for specific indicators, depending on whether those indicators are from de facto and de jure sources? Do performances of firms with different bargaining power (such as domestic vs foreign firms, and small versus large firms) depend on the business environment differently? While important, these questions have not been addressed by the existing literature. They are the focus of this paper.

This paper makes three contributions to the literature.

First, given that each series of the business environment measures has its own measurement errors, using all three data sources serves as robustness and soundness checks. We find that they offer independent sources of variations in explaining firm performance. The _Enterprise Survey_ indicators seem to explain the largest share of variations in firm performances. Their qualitative results on the performance-BE relationship tend to be the most expected. For instance, they suggest that corruption depresses firm growth, while physical safety, access to finance, and ease in access to land enhance firm performance. These results, however, are either insignificant or have the opposite signs, when examined with the other two data sources.

Second, we offer novel evidence of the importance of contract enforcement in understanding the effects of de jure business environment indicators. Our analysis suggests that the de jure _Doing Business_ indicators are more effective in improving firm performance in countries with better contract enforcement. In particular, in countries with better contract enforcement, _Doing Business_ Get Electricity has more positive association with firm growth, and _Doing Business_
br>0.2|209|2010|Iran, Islanic<br>Rep. of|169|382|2025|
|Suriname|1|235|2010|Maldives|1|411|2030|
|Trinidad and Tobago|2|182|2010|NepaL|63|377|2035|
|Uruguay|4|142|2000|Pakistan|423|440|2035|
|Virgin<br>Islands (U.S.)|40|231|2005|Sri Lanka|30|189|2005|
|Venezuela|0.2|204|2005|South Asia total|2805|265|2045|
|Other Latin America|0.4|216|2010|||||
|LatinAmerica and the||||Bahrain|1|237|2010|
|Caribbean total|912|229|2030|Cvprus|1|137|2005|
|||||Gaza Strip|3|517|2030|
|Canada|28|109|2030|Iraq|75|471|2025|
|United States (50 states)|279|116|2030|Israel|7|176|2005|
|Puerto Rico|5|148|2000|Jordan|13|357|2020|
|Other Northern America|0.1|130|2030|Kuiait|5|281|2015|
|Northern America total|311|116|2030|Lebanon|5|190|2010|
|||||Oman|5|427|2030|
|Asia|6118|217|2045|Qatar|1|363|2025|
|Brunei|1|246|2010|Saudia Arabia|54|471|2025|
|Burma|102|275|2020|Syrian Arab Rep.|42|401|2020|
|China (excl.<br>Taiwan)|1695|163|2000|Turkey|112|222|2010|
|
broad crosssection of countries at a high level of disaggregation by product.

The problem with using export data seems obvious: a product emerging as a new export may have been produced domestically for some time, and therefore would not represent a true inside-the-frontier innovation. However, exporting a particular good for the first time, even if it was already produced domestically, is itself an entrepreneurial act that requires discovery and can be imitated (Ibeh 2003). In other words, the act of exporting is itself a discovery worth investigating. Furthermore, the increasing importance of trade and foreign markets for developing countries, most of which are pursuing an export-led development strategy, means that discovering new products is primarily a search for new exports.

Worldwide export data are drawn from the United Nations COMTRADE database under the Harmonized Commodity Description and Coding System (HS), available beginning in the early 1990s for most countries at the 6-digit level of disaggregation (approximately 5000 products). These data have been used widely to analyze export dynamism and growth, as well as geographic patterns in export growth, but until now have not been used to study the emergence of new exports.

3
poorer countries, the lack of digital infrastructure might offer a temporary buffer from the risk of imminent automation to some occupations in this category – a trend that likely extends to countries with relatively lower incomes outside Latin America. The plot also re-confirms that such automation-exposed jobs are disproportionately held by women.

The situation is visibly different in the case of occupations with augmentation potential. First, the distribution of such jobs is more equal among women and men. In addition, the shares of jobs that do not use a computer at work are also more evenly distributed across men and women. An intuitive way of thinking about this part of Figure 11 is that the light blue zones (no computer) represent the transformation potential that cannot be attained due to digital infrastructure limitations. If one was to assume that such a transformation could translate into productivity gains in those occupations, the zones without a computer can be seen as an unattainable potential productivity gains.<sup>35</sup> Taking this analysis one step further, we can quantify these effects. For example, if we calculate the unattained augmentation potential as an average value across the LAC countries (weighted by total employment of each country), it corresponds to 6.24 percent of female employment and 6.22 percent of male employment. If we think of this gap as an average share of jobs within all jobs with augmentation potential, it corresponds to a non-negligible 44 percent of such jobs held by women and 50 percent of such jobs held by men. Applying these calculations to the 2023 ILO modelled estimates of total employment in each country, we estimate that there are some 17 million jobs among the 16 LAC countries in our SEDLAC sample that could, in theory, experience additional productivity from the technological transformation with GenAI, but which will not be in position to do so due to the lack of digital infrastructure. Some 7 million such jobs are held by women and nearly 10 million are held by men (see Figure A6 in Appendix for country-level breakdowns).

#
# **Table S1.4: Robustness: different enumerator sample**

||(1)<br>DP|(2)<br>FDC|(3)<br>NRM|(4)<br>UPC|
|---|---|---|---|---|
|Enumerator gender (male)|-0.522***|-0.306**|-0.086***|-0.451***|
||(0.120)|(0.136)|(0.027)|(0.099)|
|Enumerator education|0.150*|0.186*|-0.119***|0.146|
||(0.089)|(0.101)|(0.040)|(0.089)|
|Enumerator age|-0.031*|0.003|0.002|-0.039***|
||(0.016)|(0.019)|(0.003)|(0.012)|
|Enumerator from urban locality|0.444***|0.577***|-0.030|0.443***|
||(0.157)|(0.177)|(0.036)|(0.129)|
|Observations|6,761|6,868|7,175|6,854|
|R-squared|0.475|0.440|0.227|0.464|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an