rafmacalaba/data-use-mentions-extended huggingface dataset:
developing and transition countries; the data are reported in World Bank (2004) (though I supplemented this source with data from _PovcalNet_ ).<sup>10</sup> The poverty rate is the most recent estimate provided it is no earlier than 1995 (the median survey year is 1999), though the poverty data predate the death rate data. Using the BR method the implied mean CDR for the poor is 22.6 per 1,000 people with a (heteroscedasicity-corrected) standard error of 2.5; the CDR for the non-poor is 8.1 (standard error of 0.5).<sup>11</sup> The implied value of _DRD_ is 2.2.

One region that looks notably different in the relationship between death rates and poverty incidence is Sub-Saharan Africa (SSA). Indeed, if one just focuses on the 18 observations in this data set for SSA then one finds no correlation between death rates and the incidence of poverty. In other words, one cannot reject the null hypothesis that the CDR is the same for the poor as the non-poor. (The estimates implied by the BR method are 19.3 for the poor, with a standard error of 7.1, and 17.1 for the non-poor, with a standard error of 1.56; the difference is not significant; t-statistic=0.6.) South Africa is an outlier, however, with a high CDR relative to its poverty rate ( _DR_ =20; _H_ =7.1%). The correlation between the CDR and the poverty rate is stronger if one drops South Africa, but it is still not statistically significant. Dropping South Africa the estimates CDR for the poor in Africa is 20.3 (standard error of 2.7) while that for the non-poor is 16.2 (standard error of 1.7); the implied _DRD_ is slightly over 1.1.

Since Africa will be prominent in later results in this paper
of nonfarm household enterprise work among Nigerian women, while more men than women work on the farm, which mitigates the impact of the 19<sup>th</sup> ICLS standards on female (relative to male) employment. Conversely, in Malawi, women are less likely than men to work in the non-farm household enterprise and wage sectors, which is one of the factors explaining a disproportionate decline in female employment (Palacios-Lopez and Lopez 2015).

Besides these changes in employment-to-population ratios, there is a large drop in the measured share of agricultural employment in total rural employment, which declines by about 50 percent in Nigeria and by about 10 percent in Malawi.<sup>33</sup> This is a considerable change, especially for Nigeria, which, if taken at face value, would paint the picture of a rural population that is much less reliant on agricultural activities

> 32 We do not report data from the Ghana and Malawi agricultural survey experiments in this section, because neither data set is nationally representative, and the samples include only rural households.

> 33 The change in Malawi is most likely underestimated, since a substantial share of workers in Malawi is primarily engaged in casual or ganyu labor. We assigned these workers to the agricultural sector, since their sector of work is not recorded in the data. This increases the share of agricultural employment in total employment under both definitions, but also affects the difference between the two. Of the roughly 10,000 sample individuals who mainly farm for own use (and are therefore in agriculture by the previous standards), about 40 percent are no longer employed by the new standards, while almost 10 percent are reclassified as casual/ganyu workers. Some of these would likely be reclassified as non-agricultural workers if their sector of work had been recorded in the data.

20
, specification. For monetary poverty and income inequality, the linear specification is preferred across all components (as a quadratic fit no longer estimates a statistically significant correlation). In the case of per capita GDP, the quadratic form (first decreasing, then increasing) is preferred for resilience and empowerment (but not for inclusion and all three aggregated components). Table 5 below summarizes the results (see online Annex C for the actual results).

**Table 5: Preferred specification of the association between social sustainability and incomes**

||**Log per capita GDP**|**Monetary**<br>**poverty**|**Income**<br>**inequality**|
|---|---|---|---|
|P1: Social inclusion|Linear|Linear|Linear|
|P2:Resilience|Quadratic (U)|Linear|Linear|
|P3: Social cohesion|Quadratic (U)|Linear|Linear|
|P4:Processlegitimacy|Linear|Linear|Linear|

Source: Social Sustainability Global Database 2022.

19
|
||Private informal<br>Out of labor force|9<br>1<br>1<br>5<br>Private formal|<br>Public|**84**<br>formal<br>U|n-employed<br>O|73% (73%)<br>ut of labor force|

_Notes_ . The upper panel is simulated by Monte Carlo Method described in Section 4 Methodology. 100,000 simulations of transitions after six years were performed for each initial state, where the transition probability matrix per period is taken from the empirical annual transition matrix in Figure 8. The annual transition matrix relies on panel data from the ENOE in 2005 Q1 and 2006 Q1. We restrict our analysis to women aged 20 to 53 who were interviewed in both rounds. We use transition matrices to examine individuals’ transition of their work status/sector between the two dates. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2005 Q1, with the shares in 2006 Q1 reported in brackets. Panel weights are used. The lower panel relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to individuals who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices by sex to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets. Panel weights are used.

The convergence seen in the upper panels of Figure 9 and Figure 10
Figure 5: Performance of SOE Relative to POEs by Ownership Type, 2011-2019 <!-- Start of picture text --> (a) Log of ...... (b) Growth of ......<br>5.0 *** 0.08<br>***<br>4.0 0.06 *** ******<br>***<br>3.0 *** *** *** *** *** *** 0.040.02 ** ***<br>2.0<br>*** 0.00<br>1.0 *** *** ***<br>-0.02<br>0.0 *** ** * ***<br>-0.04 ***<br>***<br>-1.0 *** *** -0.06 ***<br>-2.0 -0.08<br>10-100% 10-24.9% 25-49.9% 50% or 10-100% 10-24.9% 25-49.9% 50% or<br>more more<br>Ownership % Ownership %<br>Employment Average wage Employment Average wage<br>Assets per worker Labor productivity Total assets Labor productivity<br><!-- End of picture text --> Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.

Note: The sample includes all firms. Employment is the log of the number of employees. Wage level is the log of average wage bill, where average wage bill is (real) labor cost divided by number of employees. Assets per worker is (real) total assets divided by the number of employees. Labor productivity is (real) revenue per worker. Employment growth
survey data.

**(measured at median income) seen in transport (32% gap), construction (26% gap), and accommodation and food services (18% gap)** . We report the tax (non)compliance across the sectors of the economy in Table 5. Because the EU-SILC sample size imposes a constraint on the analysis and estimates for smaller sectors of the economy may not be reliable, we discuss only outcomes for branches employing at least 5% of employees in the EUSILC data. Our results show that tax compliance is lower than 100% in all sectors of the economy, and the median imputed tax income is lower than the median survey income, indicating underreporting of income. The lowest tax compliance was observed

> 15 The ratio is expressed as: imputed tax income / survey income.

24
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earlier results and examine the trend in welfare gains over the 20-year period to determine whether microcredit participants are trapped in poverty and debt, as speculated in the public domain.

Our analysis was restricted to panel households of microcredit program participants and non-participants. The descriptive analysis found that microcredit participants did better than nonparticipants for some outcomes, while non-participants did better than participants for others. Although simple comparisons do not prove a causal role of microfinance, the simple differences across participants and non-participants show the direction of changes in welfare gains over time. We found that poverty rates in both groups fell substantially over time. While 77.8 percent of participants were extremely poor in 1991/92, only 14 percent were extremely poor in 2010/11; the corresponding figures for non-participants were 77.7 percent in 1991/92 and 22.0 percent in 2010/11.<sup>21</sup> Thus, the key issue concerns the net role of microfinance in reducing poverty. At the same time, program participants experienced a greater increase of debt-asset ratios at the margin than did non-participants. Does this mean that microcredit participants became more over-indebted than their non-participant counterparts?

No doubt, 20 years is a long time. Over that period, many economic changes occurred in Bangladesh beyond the expansion of microcredit, including physical infrastructure and economic policies that may have contributed to the welfare status of both participants and non-participants. In such a setting, even with panel survey data, it may be difficult to isolate the net effects of microcredit expansion on the welfare gains of borrowers over time. It is possible that those who did not participate in microcredit programs may have gained from the spillover effects of microcredit expansion. Thus, the changes in gains over the 20-year period by both groups, which do not differ

> 21 Note that the difference in the reduction in poverty rates is above the attrition rate of 7.4%. This shows that the finding is robust.

23
18 to be efficient, but do government road departments? To the extent that governments invest in roads based on economic approaches (such as cost-benefit analysis) or in response to economic pressures, road provision may be reasonably efficient in economic terms. As shown earlier, road provision has strong relations with economic variables.

A direct measure of the ratio of inputs for a production function approach would be actual traffic volumes (vehicle kilometers traveled) per kilometer of road network. There is surprisingly little information on aggregate traffic volumes at the national or urban level, however, and such data are often based on intermediate variables such as fuel consumption. Most traffic volume data are measured on specific streets or roads because the information is needed to analyze network use. Motor vehicles per kilometer of road has been used as an indirect measure of the ratio of inputs in the work reported here and is a proxy for the more desirable volume measure.

How good a proxy is the indirect measure (motor vehicles per kilometer of road) for the desired traffic volume measure? There is a strong relation between speed (or congestion) and traffic volume that is typically summarized in a speed-volume diagram that shows that speed falls (and congestion rises) as traffic volumes increase. At the urban level, the ratio of vehicles per kilometer of road has a relation with average speed similar to that speed-volume curves for segments of streets or roads (figure 4).

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distribution across Malawi, while the 2015 estimates only provide information about aid distribution in the FIAS districts, which are primarily in the Southern Region.

Despite the increase in aid distributed, household receipt of aid is more stable across time than might be presumed given the aid distribution data. The imperfect overlap between the receipt of aid and climate shocks is shown by the lower aid distribution rates and the lower incidence of household aid receipt when interacting with shock dummies. Since our TA-level measure of aid distribution is continuous and the household variable is dichotomous, it is helpful to consider the magnitude of the difference in terms of standard deviations. The mean household interaction is 5.7 SD lower than mean household aid in 2015, and 12.9 SD lower in 2016, while the aid interaction is 13.7 SD lower in 2015 and 19.5 SD lower in 2016.

Explicit household targeting demographics are somewhat more stable across time. The trend is upward on the number of mobile phones, which is consistent with increasing mobile penetration.

19
of funding, compiling, and publishing academic technology transfer data, particularly in North America. The AUTM STATT database is a statistical resource for policy makers to understand the extent and impact of licensing programs so far. In the USA in 2010, AUTM member organizations executed 4,284 licenses, resulting in a total of 38,528 active licenses and options (AUTM 2010). 651 start-ups were formed, and 3,657 continued to operate in total. 20,642 inventions were disclosed, 12,281 new US patent applications were filed, and over $2.4 billion in total licensing income was generated (AUTM 2010). This type of impact assessment is vital in order to understand the role of TTOs and other organizations in patent monetization. AUTM has developed a universal licensing survey in order to support technology transfer globally through accurate impact measurement. The Association of European Science and Technology Transfer Professionals (ASTP) adopted this framework in 2009. Policymakers in developing countries should help domestic actors access these resources. Professional technology transfer associations are a valuable resource for TTOs in the developing world. International bodies such as AUTM, ASTP, the South African Research and Innovation Management Association (SARIMA) and the International Federation of Technology Transfer Organizations (IFTTO) should be leveraged to increase the efficacy of TTOs in the developing world. The Society for Technology Management (STEM), a non-profit organization recently created in Hyderabad, India to promote best practices in technology transfer and foster commercialization ecosystems is one of the initiatives that has been jointly funded by SARIMA, AUTM and IFTTO. The global network of IFTTO members is a particularly useful starting point for policy makers in developing nations to familiarize themselves with the practice of technology transfer and patent monetization.

TTOs play an important role in supporting the monetization of IP while simultaneously fostering knowledge spillovers. They are particularly valuable in nations that have thinner technology 20
**Table A.21:** Alignment with Citizens’ Preferences, by Social Distance to Village Government

|||**Bureauc**|**rat survey**||**Villag**|**e head survey**|**Citize**|**n survey**|
|---|---|---|---|---|---|---|---|---|
||**Offcials/**|**citizens agree on:**|**Complain**|**ts received about:**|**Village he**|**ad takes action on:**|**Citizen**|**Perceptions**|
|**Village Services:**|Priority|Worst-quality|Priority|Worst-quality|Priority|Worst-quality|Access|Quality|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||**Panel A: F**|**irst-Degree Connec**|**tion**||||
|New village head|0.113|0.288<sup>_∗∗∗_</sup>|0.192<sup>_∗∗∗_</sup>|0.074|0.098|0.212<sup>_∗∗_</sup>|0.046|0.130|
||(0.110)|(0.114)|(0.063)|(0.084)|(0.071)|(0.112)|(0.040)|(0.109)|
|Observations|1067|1067|1067|1067
# **Findings**

Evidence on growth patterns showed that firms in conflict areas grow slower than in other countries. This is especially true for urban areas: in countries with severe conflicts were not the growth poles as in other countries of South Asia Region. Furthermore, there is evidence that larger companies grow faster. However, unconditional and even conditional tests of the quality or difference of means do not provide the full picture that can be outlined using the Interim Enterprise survey data.

## **Descriptive statistics suggest a non-linear relationship between growth intensities and explanatory**

**factors like size or age.** If we want to study firm behavior at both ends of the distribution we should use a different econometric method than those methods usually employed (e.g. ordinary least squares, panel or robust regression) when studying firm growth. Traditional methods aim to identify average firm behavior, which assumes an average effect of explanatory variables to hold across the entire distribution of growth rates.

The results show a non-linear relationship, and largely confirm the insights from the descriptive statistics:

- **First, investment in physical assets is a key driver for employment growth** – the faster firms grow the more important investment is.

- **Second, size in the base year gains importance with increasing expansion; age does not matter.** The size of a firm in the base year is positively associated with higher growth for fast growing firms; yet, it has no significant effect on companies with reducing or stagnant staff figures. These results are sensitive to the choice of growth indicator: the relative growth indicator, which favors small firms, finds significant results for fast growing firms, yet, the coefficient is negligibly small and negative.<sup>14</sup> This is an interesting finding, since one would expect the relative growth indicator to be strongly negative for high levels of growth, due to its computational method (see for instance Dunn et al, 1989 on growth patterns and
The approach of this paper is based on ex-ante macro-micro simulation methodologies developed in the recent literature: Bourguignon, Bussolo and Pereira da Silva (2008) describe its advantages and drawbacks. Variants of this methodology have been used in various case studies, including Bourguignon and Pereira da Silva (2003), Ferreira and Leite (2003), Chen and Ravallion (2004), and Bussolo, Lay, and van der Mensbrugghe (2008). ehe present paper belongs to the long literature on welfare effects of large infrastructure projects. Exploiting the fact that river gradient affects a district‘s suitability of dam construction, Duflo and Pande (2007) find that such infrastructure projects in India increase agricultural productivity in villages located downstream from the dam. Using a difference-in-difference approach, Lokshin and Yemtsov (2005) find that improvements in school and road infrastructure increase welfare among the poor in Georgia. Using a variety of GMM estimators on a panel data including over 100 countries covering the period 1960-2000, Serven and Calderon (2004) show that infrastructure development can be highly effective for poverty alleviation. Our approach differs from existing papers in two important ways: (1) the CGE-microsimulation model developed here allows capturing the economy-wide effects of the Canal expansion without losing the heterogeneous impacts on different households; (2) based on stylized facts, we assume that the Canal is a separate sector with only marginal linkages with the rest of the Panamanian economy, hence having marginal, if any, effects on total factor productivity.

# **_3.1. Macro framework_**

The CGE model used in this paper is the World Bank's prototype single-country model.<sup>4</sup> Production takes place under perfect competition and constant returns to scale, and is modeled in a nested constant elasticity of substitution (CES) fashion to reflect various substitution possibilities across inputs (see Figure 2). While the production nesting for the canal sector is similar to other activities, we assume that the canal uses a Leontief technology and employs a canal-specific
of return was "impossible to estimate" since the main effect of the project was the transfer of additional resources from the private to the public sector. Jt also noted-that additional collections were likely to be forthcoming from identified non-filers, even after the projectfs useful life was over.

_Evaluation:_ Taken on its own terms, the assumed real growth rate of GDP during 1994-2001 (7% per year) was overly optimistic given the 4.5% rate actually achieved during 1992-97, according to World bank data). The assumption that tax evasion would fall to 40% of its initial level appears similarly optimistic, especially since the project design does not explicitly address corruption of tax officials, 22
between income Gini coefficient of a given income definition (e.g. market income, disposable income) and the concentration coefficient of benefits (the reverse in the case of taxes). A positive Kakwani index means the tax (benefit) is progressive (regressive).

**Table 5: Redistributive and Poverty effect of taxes and transfers in Mongolia (2016)**

||Market Income|Net Market Income|Disposable Income|Consumable<br>Income|Final<br>Income|
|---|---|---|---|---|---|
||||||(5) = (4) +|
||(1)|(2) = (1) - Direct<br>Taxes - SSC (EE)|(3) = (2) + direct<br>transfers|(4) = (3) -<br>indirect taxes|<br>inkind<br>transfers|
|**Inequality**||||||
|Gini index|**0.4183**|**0.4096**|**0.3626**|**0.3752**|**0.3507**|
|Theil index|0.3220|0.3118|0.2434|0.2581|0.2257|
|90/10|7.8513|6.8426|5.1933|5.7006|4.9468|
|**Poverty headcount**|**47.31%**|**54.20%**|**37.84%**|**38.88%**|**31.96%**|
|**Poverty gap**|25.34%|28.25%|13.22%|14.42%|