rafmacalaba/data-use-mentions-extended huggingface dataset:
)1(1)_|
|**Low group (16 countries)**|489|800<br>24|3%|
|**Middle group**(9**countries)**|606|**1,268**<br>145|11%|
|**High group (7 countries)**|**606**|**1,560**<br>**403**|**26%**|
|**iTotal**|**548**|**1**<br>**1098**<br>**141**|**13%**|

**_(*)_** Five year total for each country has been taken, then tfhe simple average for each group was calculated for the five year period.

Source: OECD/DAC _(i) Poverty indicators and export earnings._ Neither absolute poverty (in terms of GNP per capita) nor lack of access to foreign exchange (through exports) seem to have been a criteria in allocating ODA debt relief and pure grants in recent years. In fact, the countries in the low group, are the poorest among all three groups, indicating that poverty did not serve as the basis for the allocation of grants or forgiveness (Table 2).9 In addition, countries in the high group are also significantly better off in terms of their capacity to generate foreign exchange through exports; that is, despite receiving more grants than the low group, relative to exports the grants received by the high group are about one-third that received by the low group. Therefore it can be argued that if one criteria to allocate grants is the size of the export industry in the recipient countrygiving more grants and forgiveness to countries where foreign exchange is more scarcethen grants seem to have been poorly allocated in recent years.

> The countries in the
the pandemic has been strongly associated with increased uncertainty among firms (Altig et al., 2020). Data collected by the World Bank from over 100,000 firms in 51 primarily lowand middle-income countries using short COVID-19 Business Pulse Surveys (COV-BPS), reveal that firms focused post-COVID-19 labor adjustments along the intensive margin, by increasing furloughs and reducing working hours, with only a small fraction laying off workers (Apedo-Amah et al., 2020). This study further underscores that the shock has exacerbated financial constraints most among small firms. While the long-run effects of the shock have yet to be measured, Barrero et al. (2020) use survey evidence to argue that the pandemic is in 4
Similarly, remittances may well be private receipts for the households receiving them, but if the size and coverage (in terms of the number of households receiving them) of these transfers are large, they could fundamentally alter the economic landscape of the village. The increased economic activity may lead to a greater extent of the market, increased demand for local nontradeable services, and an increase in land rents. All of these changes could potentially further draw labor out of agriculture and cause a complete dislocation of the sector and this would in turn have important implications for land use changes and local natural resources. In addition, the income growth that comes with greater openness may have an independent effect on natural resource extraction.

In this paper, we study the effect of labor migration and remittances on one important natural resource: forests. We build a general equilibrium model of an isolated village economy and theoretically investigate the mechanisms through which the relationship between an outflow of labor, a concurrent inflow of capital and local forest resources would be mediated. We derive predictions from this model and test these predictions using a newly assembled village-panel dataset from Nepal. This dataset combines remote sensing data on land use and forest cover change together with data from multiple rounds of living standards surveys. What makes Nepal an ideal setting for this enquiry is that the country has experienced a sharp increase in rural emigration and this has been accompanied by an unprecedented increase in remittance receipts. On the one hand, the country is overwhelmingly rural with agriculture accounting for roughly two-fifths of the economy. On the other hand, livelihoods of rural Nepalis are linked intricately to forests which are the primary sources of firewood used extensively for cooking and heating as well as fodder (used as feed for livestock). In addition, the widespread perception in Nepal is that forests are declining. These perceptions are somewhat validated in government statistics which show that between 1995 and 2005 land area covered with forests declined from 41 percent to 32 percent.

The effect of out-migration and remittances on agriculture has been studied,
Pensions and direct transfers are the most progressive component of the tax-benefit system in Moldova with the Kakwani index of 0.81 and 0.8 respectively (Table 13). They are followed by in-kind health and education benefits. Direct taxes and pension contributions are also slightly progressive, while indirect taxes (mainly VAT) are the only regressive component of tax-benefit system in Moldova. Pensions also have the highest marginal contribution to poverty and inequality reduction followed by other direct transfers.<sup>10</sup> Obviously, all benefits positively contribute to poverty reduction. In the case of inequality, the results are similar - direct taxes, pension contributions and inkind benefits reduce inequality, while indirect taxes increase inequality.

**Table 13. Progressivity and redistributive effect by types of interventions.**

||Progressivity<br>Kakwani index t|o Gini index to national poverty <br>|to international poverty (3.2) to international poverty (5.5)<br>Marginal contributions, p.p.|
|---|---|---|---|
|SSC|0.132|3.2<br>-1.8|-0.3<br>-2.9|
|Direct Taxes|0.135|1.0<br>-0.6|0.0<br>-1.1|
|Pensions|0.812|12.6<br>18.3|20.2<br>20.7|
|Direct Transfers|0.803|2.9<br>6.8|3.6<br>8.2|
|Indirect Taxes|-0.372|-1.2<br>-9.7|-1.7<br>-8.7|
|In-kind Health|0.464|0.1<br>3.6|0.8
3 Block #3: participation and size of procurement**

There are four parameters driving the size and composition of procurement: _Yg_ , _mg_ , _η_ 0, and _η_ 1. The parameters _Yg_ and _mg_ are policy parameters governing the relative size of procurement in the economy and the fraction of goods bought by the government. To capture the size of procurement in the aggregate economy, we set _Yg_ to match the share of procurement in GDP equal to 12.1%, which is the value we measure in the Spanish national accounts in the year 2006. To capture the number of firms that sell to the government, we proceed as follows. In our micro data, when looking across all sectors, only 0.5% percent of firms participate in procurement in a given year. However, whereas our balance sheet and income statement data covers the quasi-universe of Spanish companies, our procurement data only captures around 13% of the total procurement value measured in national

> 22We note that both _φp_ and _φg_ satisfy Assumption 1, which means that capital cannot be self-financed through the earnings-based constraints, see Lemma 2 in Appendix D.

26
warning" systems for disruptive imports with studies by Watkins and Karlick (1981) and Tuong and Yeats (1975) being representative of the earlier work. Indices of comparative advantage could play a major role in the development of such systems if it were established that these measures are accurate predictors of future trade patterns. This study shows that the NBER labor intensive indices are generally accurate predictors of changes in South-North trade flows and that they may provide a lead time of as much as one or two decades.
.247|3.626|0.030|273.537|0.240|3.642|

Notes: (1) The table shows the regression results across districts in Africa during 2002–2007. (2) The results show the local economic impacts of China’s WTO accession in the presence of mineral deposits in Africa. (3) The standard errors, clustered at the country level, are in brackets; * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01. (4) Growth, Spatial Gini, and Sen Index are measured using lights data as described in the data section. (5) Population density is measured as population per square kilometer. (6) Prices are reported as average US dollars. (7) Fixed effects include time-fixed effects, district-fixed effects, and country-year-fixed effects. (8) Climate is measured using the standard deviation of rainfall distribution at the district level. (9) Analysis based on World Bank GEM data has fewer observations because the GEM prices data covers fewer mineral products than in the USGS data. Source: Authors’ estimations Table 6b: Robustness checks – the local economic impacts of active mining.

||<br>|_USGS avera_<br>|_ge prices_<br>||_Worl_<br>|_d Bank GE_<br>|_M realpr _<br>|_ices_<br> <br>|
|---|---|---|---|---|---|---|---|---|
|_Dependent variables_|_Per_<br>_capita_<br>_Growth_|_Pop._<br>_density_|_Spatial_<br>_Gini_|_Sen_<br>_Index_|_Per_<br>_capita_<br>_Growth_|_Pop._<br>_density_|_Spatial_<br>_Gini_|<br>_Sen_<br
**Figure 12. Average markup differences by county relative to Bucharest**

_Source:_ World Bank staff calculations based on SBS data set. _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for county fixed effects—relative to Bucharest, the omitted category—in a fixed-effect regression model controlling for firm covariates plus year and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**A variance decomposition analysis suggests that ownership is the most relevant characteristic to explain total variance of markup performance across firms in Romania, followed by capital intensity and export status.** Drawing from estimations of equation (11), it is possible to evaluate the relative importance of each variable to explain the variance of the markup. Letting _d_<sup>_Exp_</sup> represent any of the explanatory variables of equation (11), the contribution to the variance of the markup of that variable can be defined as E = 3<sup>2</sup> <u>� �</u> 3 <u>(</u> 3 <u>)</u> ,                                         (12) δδ VVaarr dd<sup>EEE</sup> +2δδ MMC dd<sup>EEE</sup> ,μμ−δδ dd<sup>EEE</sup> CCTTnnaadd EE VVaarr(μμ)

or as the variance of the variable plus 2 times the covariance of the variable, with the part of the markup not explained by that variable. A similar expression can be derived for any of the remaining explanatory variables. The relative contribution of each variable is computed after discounting the residuals, sector, and year contributions. Results are displayed in figure 13. Ownership structure is shown to be the most relevant feature to explain the variance of markup performance across firms: it accounts for 34.7 percent of the explained
|
|3. In what type of contexts can CDD approaches<br>enhance social cohesion? Which types of social<br>cohesion (linking, bridging, and bonding)?<br>What core design adjustments are necessary<br>in CDD approaches to create or improve<br>impacts on social cohesion?|•<br>Qualitative (KIIs, FGDs, ethnography) data collection<br>across multiple projects/countries before and during<br>CDD implementation.<sup>47</sup>|
|4. What approaches to hiring, training, and<br>supervising facilitators are adequate/effective<br>i) to achieve project outcomes, and ii) to<br>create spillovers on other activities that<br>promote local development? How do<br>contextual factors affect successful<br>implementation of this facilitation? How can<br>small changes to design and implementation<br>contribute to consistent facilitation?|•<br>Systematic review of current and past CDD project<br>implementation<br>documents,<br>including<br>Implementation Status and Results Reports (ISR), Mid<br>Term Reviews (MTR), and Implementation and<br>Completion Results Reports (ICR).<br>•<br>Use of phone surveys or other digital tools to monitor<br>and assess facilitation at high frequencies over time.<br>•<br>Anthropological process studies.<br>•<br>HR records for basic demographics<br>•<br>RCTs|

Importantly, the learning agenda requires not only impact evaluations but in-depth studies to understand processes, contexts, and the _mechanisms_ that link project inputs to changes in outcomes. Ideally, such work on understanding the contexts in which CDD projects will work will be conducted _before_ CDD projects are implemented (i.e., as part of the project preparation phase). Knowledge of how local
<br>0<br>Exports to SADC Exports to ROW<br>Zimbabwe Zambia Tanzania Seychelles SA Mozambique Mauritius Malawi Madagascar Namibia<br><!-- End of picture text --> Notes: Own calculations using HS 6-digit 2008 data obtained from UNComtrade.

Looking at the actual products exported in Table 3, the top 10 products to the Rest of World and SADC are comprised mainly of primary products, although Mauritius and Malawi are also exporters of clothing and textile products.<sup>4</sup> A further observation is

> 4 Not shown in this table is the relatively high level of apparel exports from Swaziland and Lesotho associated with preferences under the African Growth and Opportunity Act (Portugal-Perez 2008;

9
281|
|Urban|0.65|0.02|0.60|0.70|113.99|5.59|102.94|125.05|567|
|Warrap|0.86|0.05|0.77|0.95|63.98|7.13|49.88|78.08|135|
|Northern Bahr El||||||||||
|Ghazal|0.90|0.03|0.84|0.95|62.63|5.64|51.49|73.77|299|
|Western Bahr El||||||||||
|Ghazal|0.90|0.02|0.87|0.94|60.17|6.33|47.66|72.68|310|
|Lakes|0.84|0.02|0.80|0.88|71.22|3.46|64.38|78.06|232|
|Western Equatoria|0.53|0.04|0.46|0.61|130.51|7.45|115.79|145.23|300|
|Central Equatoria|0.80|0.05|0.70|0.90|86.53|8.27|70.18|102.88|311|
|Eastern Equatoria|0.95|0.01|0.93|0.98|43.88|3.58|36.80|50.96|261|

_Note: Standard errors estimated through linear regressions; all estimates weighted using population weights._

# **Source: Authors’ own calculations based on HFS 2016‐2017 data.**

The incidence of poverty is much more widespread in rural areas compared to urban areas. Rural poverty was
in enhancing the understanding of the specific risks facing individual capital assets so that the technical and engineering design of adaptation investments can be tailored to these risks. The first step in strengthening resilience of SIDS to climate change should be the preparation of rigorous and carefully-costed adaptation strategies, based on a comprehensive analysis of the risks to which they are exposed, and the potential costs if these risks materialize. The governments of Grenada and Dominica have already prepared Disaster Resilience Strategies which set out long term programs of capital investments for adaptation alongside other measures such as accumulating insurance funds; these are examples which other SIDS should consider implementing.

The public debt burden of SIDS is heterogeneous but, with two exceptions, Antigua and Barbuda and Suriname, is not assessed as being unsustainable in the most recent DSA undertaken by the IMF/World Bank. Two other SIDS are assessed as being in debt distress, but this is for legal and technical reasons rather than lack of fiscal resources—because they have unresolved bilateral arrears.

More than a third of SIDS which are independent UN member states have relatively high public debt ratios. Seven SIDS for which PV data are available in the DSAs breached the applicable threshold or benchmark for the PV of PPG debt to GDP in 2022, although only two of these countries also breached the threshold for external PPG debt to GDP. Fourteen SIDS were assessed in the DSAs as being at high risk of sovereign debt distress and three more are assessed as having substantial, elevated or significant risks. Although high levels of public debt which are projected to be above the applicable thresholds for prolonged periods is a major source of risk for three of the SIDS at high risk of debt distress, a more common reason is their 27
Figure C10: Distributions of poor populations across urban versus rural areas by country, $2.15 poverty line <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>AGOBFABGDCIVCOLEGYETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGAVNM AGOBFABGDCIVCOLEGYETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGAVNM AGOBFABGDCIVCOLEGYETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGAVNM<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used.

Figure C11: Distributions of poor populations across urban versus rural areas by country, $3.65 poverty line <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>AGOBFABGDCIVCOLEGYETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGAVNM AGOBFABGDCIVCOLEGYETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGAVNM AGOBFABGDCIVCOLEGYETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGAVNM<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used.

76
2018). In particular, we use the LASSO procedure (Tbishirani, 1995) to choose, among all District characteristics and interviewees’ selection traits, which variables are most predictive of DLT scores. LASSO is considered to be particularly suited, among other machine learning techniques, when estimates for several of the potential explanatory variables are believed be approximately zero (Abadie and Kasy, 2017). In practice, the LASSO minimizes an objective function that includes a goodness-of-fit term (sum of squared errors) and a penalization for the sum of covariates’ coefficients. The weight of the penalization, the lambda parameter, is chosen by cross-validation, i.e. by estimating the model in test subsamples and evaluating its performance in the remainder of the sample (Athey, 2018).

Following Best, Hjort and Szakonyi (2017), we proceed in two steps. We first estimate a LASSO model<sup>30</sup> with District-level health score as the dependent variable and a set of 40 covariates. We then select the variables with non-zero coefficient in the LASSO model and report the results of both bivariate and multivariate regressions.

The 40 variables used in the LASSO model include the average for 12 selection traits already explored, separately for bureaucrats and politicians: PSM, Integrity, Big Five, NonCognitive, Altruism, Indicator for mistake in risk game, Cognitive, Risk Aversion, Assets and indicators for 3 education levels. We also include regional dummies and the following socio-economic and political District level characteristics: vote share of the presidential candidate of the NRM, Number of radio stations, Poverty rates, log Population, Share of urban population, Herfindahl index for Councillors vote share and the UCE score as a measure of education in the district population (lower values are asdociated with better education outcomes).

The LASSO model selects eight variables with non-zero coefficients: four socio-economicpolitical characteristics of Districts (Vote share of the presidential candidate of the NRM, Poverty Rate, Number of Radios and UCE score), three bureaucrats’ traits (Altruism
Figure 4. Information sources for innovative firms <!-- Start of picture text --> Most important source of information for innovation<br>70% 63%<br>60% 52%<br>49% 49%<br>50% 42%<br>40%<br>27% 26% 26%<br>30% 23%<br>19%<br>20% 11%<br>6%<br>10% 0%0.1% 2.5%3% 1.9%0.1% 0%0.6%<br>0%<br>Bangladesh India Nepal Pakistan<br>In‐house Private Sector Government Academia Customers<br><!-- End of picture text --> Source: Author’s elaboration from Enterprise Survey (2014)

# 5.7 Innovation Patterns across Regions

It is widely recognized that innovation activity is spatially concentrated, and that R&D investments and innovations are more spatially concentrated than employment (see Carlino and Kerr, 2014 for an excellent empirical and theoretical review). In terms of outputs, the creation of new products is more spatially concentrated than the introduction of new patents (Acs et. al., 1994); while the origination of the latter tends to be concentrated in few and large cities (Forhnahl and Brenner, 2009; Bairoch, 1988, respectively). Theories explaining concentration of R&D and innovation activities allude to agglomeration advantages in three dimensions: (i) sharing of common specialized inputs and factors of production, (ii) improvement of the quality of the matching process between specialized inputs or factors of production and innovating firms, and (iii) presence of externalities such as knowledge spillovers that facilitate the flow of tacit knowledge and new ideas.

Why is innovation spatially concentrated? “Sharing” theories highlight the importance of thick local factor or input markets that allow innovating firms which are spatially concentrated in
The paper then shows that the educational composition of emigrants also changes as countries develop. Analysis using data on immigration to the United States, where the most consistent data are available, shows that the education level of migrants increases steadily with development. This pattern is driven by an increase in the proportion of highly educated in the population of the origin county, as well as by an increased propensity of the highly educated to migrate.

The stylized patterns highlighted in this paper illustrate that the aggregate migration response is a result of multiple transformations happening in low- and middle-income countries as they develop. Increasing incomes and the increasing level of education and skills of their populations are key, but increased integration with the global economy, urbanization, and a demographic transition can also play important roles. Another key insight from this analysis is that destination countries are heterogeneous in terms of the costs of migration, returns to migration, and skill premiums. The interplay between the transformations and the heterogeneity of destinations shapes the aggregate migration response, as well as the destination and skill compositions of the migrants.

For instance, development increases incomes, which can make migration more affordable but less desirable. The key insight from the evidence presented in this paper is that the destinations at the margins of affordability and desirability can be different, which elicits a varied aggregate response. Increased incomes may make migration affordable to some destinations (such as those with high costs of emigrating, but high returns in the destination country) and make migration to other destinations less desirable (such as those with low costs, but low returns). Likewise, development increases the educational profile of the population, which increases returns in the origin country as well as abroad. Here as well, the increase in returns may be larger for some destinations (such as high-income countries) than others. Additionally, higher education may also reduce policy barriers to many destinations due to the higher demand in destination countries.

In summary, this paper shows that, unlike the intentions of ‘root cause’ policies