rafmacalaba/data-use-mentions-extended huggingface dataset:
50% bandwidth|˗0.262|˗0.224|0.654|˗2.153|˗2.022|0.038|
|100% bandwidth|˗0.206|˗0.221*|0.708|˗0.372|˗0.502|˗0.013|
|200% bandwidth|˗0.238**|˗0.183*|0.708|˗0.479|˗0.854|˗0.029|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level.

26
.......................................... 44**|
|Appendix 1 Household Income and Expenditure Survey ............................................................. 48|
|Appendix 2 Methodology of PIT Estimation ................................................................................. 49|

# List of Figures

|Figure 1 Shadow Economy and Electronic Transactions ............................................................... 1|
|---|
|Figure 2 Two Types of Shadow Economy and Their Structures ..................................................... 7|
|Figure 3 Korea's Policy Shifts to Reduce the Shadow Economy ................
73|0.37|9.45|9.51|9.23|0.020**|
|Tea|20,190|23,825|14,507|0.82|0.98|0.58|9.90|9.93|9.85|0.019**|
|Rubber|20,702|20,757|20,539|1.36|1.37|1.36|9.43|9.47|9.28|0.119|
|Coconut|13,747|12,829|16,324|1.48|1.46|1.52|9.01|9.01|9.00|0.832|
|Coffee, pepper, betel|18,814|21,501|11,236|0.73|0.8|0.52|9.81|9.82|9.77|0.856|
|Banana/fruits|18,230|19,177|14,751|0.98|1.09|0.5|9.42|9.47|9.21|0.194|
|Total<sup>a</sup>|15,487|16,970|11,086|1.7238|1.9452|1.0438|9.14|9.09|9.30|0.000***|

Source: Authors’ calculation using HIES 2016. Notes:<sup>a</sup> Sri Lankan farmers cultivated 1.32 crops on average. b Productivity is measured by yield, i.e., gross real monthly output divided by the area cultivated (Rs./acre).

*, **, and *** indicate that the male-female difference is significant at the 10%, 5%, and
|Cashin P.,|The paper addresses two|Real annual data of the|Following Watson|No evidence of a break in the|
|---|---|---|---|---|
|McDermott C.J.<br>(2001)|research questions: what is the<br>empirical behavior of<br>commodity prices? Are there<br>any changes in the variability<br>of commodity prices and in<br>the trend growth of prices over<br>time?|nominal industrial<br>commodities price index<br>(dollar based), deflated<br>by the GDP deflator of<br>the United States over<br>the period 1862-1999.|(1994) the paper<br>applies two<br>econometric tests on a<br>“peak-trough”<br>analysis to determine<br>the length of a cycle<br>and its statistical<br>significance.<br>Descriptive statistics<br>complement the<br>analysis.|long-run trend decline in<br>commodity prices. Evidence<br>of a ratcheting up in the<br>variability of price<br>movements. The amplitude<br>of price movements increased<br>in the early 1900s, while the<br>frequency of large price<br>movements increased after<br>the collapse of the Bretton<br>Woods regime of fixed<br>exchange rates.|
|Cuddington J.T.,<br>Liang H. (2003)|The study investigates<br>differences in real primary<br>commodity price volatility<br>across fixed and flexible<br>exchange rate regimes.|The paper combines<br>three datasets: Grilli and<br>Yang (1988), Boughton<br>(1991) and IMF’s_IFS_.<br>
(2011), highlights structural change, defined by the movement of resources (particularly labor) across sectors. It highlights the observed contrast between the rise in productivity growth during the ISI years (which reflected the massive movement of labor from the lower productivity agricultural sector to the higher productivity industrial sector), with the subsequent stagnation and decline in aggregate Gauging quality requires therefore appropriate proxies, whether at the macro level, as in the case of Hausmann et al (2007), or at the micro level, based on enterprise and market surveys or granular price data. Thus, as recently shown by Bernard et al. (2020) for enterprises in advanced economies, differentiating products based on the product mix and prices, as well as on the skills used in production, uncovers quality differences that are associated with very different growth performances across firms. Along similar lines, Comin et al. (2020) find that demand shifts toward expenditure-elastic sectors (rather than technical change) explain a large share of the labor market polarization in advanced economies.

10 See Baer (1972) for an early assessment of the impact of ISI policies on the LA region and Irwin (2020) for a comprehensive review of the evolution of the academic discussion on this topic.

9
over domestic savings, which could reflect the catching-up process of less developed economies (Ghosh and Ramakrishnan, 2020; Carranza, 2002). In 2019, all six MENA countries had a negative trade balance (Figure 1). Imports represented more than 30% of GDP of these countries (with the exception of Egypt), which is above the average of UMI (upper middle income) and LMI (lower middle income) benchmark economies, which are around 20% of GDP. Exports accounted for about 18% of GDP, in line with the average of LMI and UMI countries.

According to Enterprise Surveys, most firms in these regions engage in trade. But the breakdown of firms’ trading profiles outlines the import dependence of most of the countries, in particular in Jordan, Lebanon and the West Bank and Gaza (Figure 1).<sup>5</sup> This may reflect the relatively small size of the economy in these countries. This may also indicate that firms are unable to find inputs in the domestic market or reflect policies overvaluing currencies for example, due to pegged exchange rate to hard currencies in Lebanon and Morocco (Jaud and Freund, 2015). The share of non-traders is particularly large in Egypt and Morocco. In these two economies, this needs to be interpreted in light of the size of the economy, as they are significantly larger than the other countries in the MENA region.

Most firms that export their goods or services also import at the same time, indicating that they participate in GVC by importing, transforming and adding value before re-exporting. The share of

> 3 Figure A1 in the Appendix highlights that real GDP growth and GVC integration are correlated. For the positive association between real GDP and both service and goods trade in MENA, see Karam and Zaki (2015). 4 A current account deficit is considered unsustainable when it may trigger a drastic policy shift or when it leads to a crisis, for example, an exchange rate collapse that prevents the country from servicing
Figure 2. Arrival Data of Afghan Refugees in Tajikistan in 2023 <!-- Start of picture text --> 1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>Afghans Inflows to Tajikistan<br>2017m1 2017m7 2018m1 2018m7 2019m1 2019m7 2020m1 2020m7 U.S Withdrawal 2021m1 2021m7 2022m1 2022m7 2023m1<br><!-- End of picture text --> Date _Source:_ Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021.
# **3.1 LAND-COVER DATA**

To analyze land cover change, we used the Hansen et al. global analysis data (2013), version 1.2 (accessed in 2016).<sup>9</sup> The data set provides forest cover in 2000 and loss of forest cover from 20002014. The Hansen data are the only available data source with annual variation in land-cover during our period of study. The data set is based on a combination of Landsat (30m resolution) and MODIS (250m resolution) satellite images. We use the reported annual changes from Hansen’s data, summing reported deforestation for each polygon after application to the program. This means that for those polygons that enter in 2011, we are summing deforestation from 2011 to 2014, while for those entering in 2012, we sum from 2012 to 2014, etc. To calculate percent changes, we take the total forest cover loss divided by the area of forest in the data set baseline year of 2000. Given that our land cover data are measured across different time periods for each cohort, they are not comparable to the government official rates. Furthermore, they are not easily comparable across cohorts, since for the earliest cohorts we sum deforestation across four years and for the later ones only across two.

The Hansen et al. data set has important limitations. It was intended for global/regional change analysis, not change analysis at the small parcel-level, as is necessary to evaluate the PES program. It has been shown that the accuracy of the classification algorithm varies across different countries and ecosystem types (Hansen et al., 2014; Bellot et al., 2014; Burivalova et al., 2015). For example, assessments by the CONAFOR remote sensing team suggest that the Hansen product offers better results in Mexico when the percentages of forest cover are below 30 or above 60 percent. The data are likely to understate loss of natural forest because they may classify plantations and agroforestry crops as forested areas
147*|
|Log population density|0.041|-0.002|-0.010|
|Age profile|0.096*|0.109*|-0.005|
|Share of population in scheduled caste/tribe|0.047|-0.028|0.045|
|Female labor force participation rate|-0.031|-0.034|0.004|
|Educated worker share|0.170*|0.050|-0.094*|
|Literacy rate|0.105*|0.073|0.032|
|Infrastructure: electricity access|0.126*|0.106*|0.027|
|Infrastructure: paved roads|-0.030|0.037|0.109*|
|Travel time to nearest of India's ten largest cities|-0.070|-0.142*|0.051|
|Strength of household banking sector|0.123*|0.130*|0.081|

Notes: Table documents correlations between district traits and unorganized sector employment changes. The top panel considers the change in the unorganized sector's share of district total employment. The bottom panel considers the log growth in unorganized sector employment across the period as measured by log (unorganized employment end / unorganized employment start). District traits in Column 1 are from the 1991 Population Census; district traits in Columns 2 and 3 are from the 2001 Population Census. District traits are expressed in log values or percentage point values as indicated. In both panels, a negative correlation indicates that the district trait is associated with a decline in relative unorganized activity across the period. An asterisk denotes a correlation is statistically significant at the 10% level.
# **Infrastructure Performance and Reform in Developing and Transition Economies: Evidence from a Survey of Productivity Measures**

Antonio Estache World Bank and ECARES, Université Libre de Bruxelles Sergio Perelman CREPP, Université de Liège Lourdes Trujillo DAEA, Universidad de Las Palmas de Gran Canaria World Bank Policy Research Working Paper 3514, February 2005 _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 cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The authors are grateful to Antonio Alvarez, Phil Burns, Tim Coelli, Claude Crampes, Andres Gomez-Lobo, Emili Grifell, Augustin Mpapa Mbangala, Marcelo Resende, Martin RodriguezPardina, Martin Rossi , Christian Ruzzier, German Sember and Ronaldo Seroa da Motta for useful discussions.
Figure 3: Uneven sectoral specialization in GVCs <!-- Start of picture text --> GVC linkages, 2015<br>Low participation<br>Limited commodities<br>High commodities<br>Limited manufacturing<br>Advanced manufacturing<br>and services<br>Innovative activities<br>Data gaps IBRD 44640 | AUGUST 2019<br>Source: WDR 2020 team, based on the GVC taxonomy for 2015 (see box 1.3).<br><!-- End of picture text -->

innovative goods and services. Figures 3 illustrates the extent to which GVC participation has been uneven across countries.

Despite their widespread use in economic research, it is important to emphasize two key limitations of global Input-Output tables. First, because they rely on fairly aggregated Input-Output data, the resulting sectoral disaggregation of GVC flows is pretty coarse, and thus these data sources miss a significant amount of GVC activity occurring within these broadly-defined sectors. For instance, one can compute the origin of “fabricated metal products”in the production of “motor vehicles”in the United States, but where tires, car engines or windshield wipers o ~~riginate fr~~ om cannot be inferred from these datasets. A second ~~key shortcomin~~ g of world Input-Output tables is that, in constructing them, researchers are forced to impose strong assumptions to back out certain bilateral intermediate input trade flows that cannot be readily read from either customs data or national Input-Output tables.

A different, more granular approach to measuring the degree to which production processes are fragmented across countries was first suggested by Yeats (2001) and consists of computing the share of trade flows accounted for by industry categories that can be safely assumed to contain only intermediate inputs (as reflected by the use of the words “Parts of” at the beginning of the product description). Yeats (2001) found that intermediate input categories accounted for about 30% of OECD merchandise exports of machinery and transport equipment in
Uganda Bureau of Statistics and the regional poverty lines. The regional poverty lines reflect differences in the cost of non-food requirements across regions and urban and rural locations in Uganda. Differences in the cost of food items are already incorporated in the consumption aggregate provided. For this analysis, the ratio of the poverty line across regions is used in order to correct for price differences across regions.<sup>4</sup> The consumption aggregate and the income aggregates are divided by this ratio in order to bring all aggregates to one national price. Descriptive statistics on the consumption and income aggregates are presented in the next section.

In addition to documenting changes in consumption and income aggregates, the paper explores the drivers of changes in these aggregates. It relies on data collected in the household survey on household demographic characteristics, distance to market, extension visits, and the type and quantity of inputs used in agricultural production. Table 2 provides descriptive statistics for the key household characteristics used in the analysis.

As Table 2 highlights, on average, there has been surprisingly little change in the use of agricultural inputs in Uganda; which is perhaps unexpected given that this was a time in which crop prices—and thus presumably the returns to using inputs— changed considerably. In general, input use is very low in Uganda in comparison to other countries in the region with data collected using a similar survey instrument (Sheehan and Barrett 2014, Binswanger and Savastano 2014).

> 4 This may be an imperfect measure of regional price differences if regional food price differences are quite different from regional non-food price differences. Ideally the income aggregates would also correct for food price differences across regions, however in the absence of having these data, using non-food price differences appears to be a reasonable approximation.

5
issue in two complementary ways. First, for each domain we consider 4 to 10 different variables, standardize each of them by subtracting the sample mean and dividing by the sample standard deviation, and define Z as the average of these standardized measures. This approach gives each of the possible variables equal weight and allows for easy interpretation on whether the variables in that domain are related to higher or lower impacts. Alternatively, we include all the variables separately in Z, and consider the F-test of the joint significance of all the variables in each domain. The advantage of this approach is that we can identify heterogeneity in cases when only a few variables in the domain might matter. The specific variables included for each of the domains for both approaches are listed in the appendix.

In addition to the three dimensions above, we also explore whether impacts vary along baseline poverty, which we measure using detailed baseline expenditure data. Consumption-based poverty is likely to capture a number of unobservable household characteristics that might not be explained by observed asset endowments. As an alternative proxy for baseline expenditures, we therefore also analyze heterogeneity using the official proxy means formula developed by the Nicaraguan government for poverty targeting. Following standard practice, we define a household as poor if baseline per capita expenditures are below the national poverty line for Nicaragua. When we do this, 64% of the households in the sample are poor when using actual baseline expenditures and 68% when using the proxy means formula. Analyzing heterogeneity by poverty level helps to shed light on the potential costs of pro-poor targeting of productive interventions.

# **4. Results**

# _Average impact_

Before turning to impact heterogeneity, Table 1 shows the average program impacts on household employment and returns in nonagricultural activities. Being eligible for the business grant significantly increases participation, gross income and profits from non-agricultural self-employment, as well as per capita expenditures. The business grant has no significant effect on wage employment and related income. Training significantly increases non-agricultural private sector wage income, but on average reduces profits from
4,858|Ministry of Health|

The financial and medical facility data sets contained additional information about the specific type of facility (e.g. hospital, health post, etc.). We took advantage of this to repeat our analysis for important sub‐categories of services contained within.

## Digital Elevation Model (DEM)

DEM data enables accounting for slope when calculating travel speeds and path distances. We used 1 arc‐ second (roughly 30‐meter resolution) Shuttle Radar Topography Mission (SRTM) data for this purpose. Specifically, SRTM tiles N26E084 toN26E088, N27E081 to N27E088, N28E080 to N28E086, N29E080 to N29E084, and N30E080 to N30E082 were extracted, merged and clipped in the shape of Nepal.

## Road network

We employed road network and land cover data to calculate surface speeds. OSM data tagged as a “highway” was merged with Department of Roads (DOR) data collected through the World Bank project on “Measuring Rural Access Using New Technologies” to create a final roads layer. OSM roads not tagged as major roadways (smaller than highway=tertiary) were reclassified as paths. Overlapping roads were ignored as they did not substantially alter reported travel times.
process is progressive. In column (1), we see that vulnerability as defined by TASAF is indeed pro-poor: vulnerable households are, on average, 5.4 percentage points more likely to be categorized as poor using our consumption aggregate and the lower-bound poverty line. Column (2) indicates that the vulnerability criterion identifies a group that is poorly educated rather than one that is poor conditional on education levels.

Columns (3) and (4) compare the actual beneficiaries of TASAF II VG projects to the entire eligible sample, i.e. the population of vulnerable households, and show that those selected to be recipients of TASAF II VG funds are not significantly poorer than the overall eligible sample. Instead, within those eligible to benefit from VG projects, the very same attributes that were shown to be influential at the national-level also determine membership at the local level: those who are more politically active (measured by attendance at village meetings), wellconnected (measured by being a blood relative of village elites) and living closer to the village center are much more likely to be program beneficiaries than others. Finally, in columns (5) and (6), we compare group leaders to the „rank and file‟ membership and find that they are substantially less likely to be poor, and much more likely to have completed primary education and to own a radio or a mobile phone.

These findings confirm the patterns of targeting performance found at the national level and presented above. Earlier, we found that wards with high levels of civic engagement and that party ties to district officials were more likely to receive funds at the national level. Here, we see that households engaged in local politics and have ties to local officials are more likely to be program beneficiaries as well. Hence, while the targeting of TASAF II projects is mildly propoor both at the national and at the local level, these projects (and funds) are also flowing towards households that have high levels of civic engagement and are connected to the
For any given location, these means constitute our point estimates of welfare measure, while the standard deviations are the standard errors of these estimates.

# **Data and Implementation**

# **<u>Data</u>**

The technique combines data from the 2002 Azerbaijan Household Survey (AHBS 2002) and the 1999 Census collected by the State Statistical Committee of Azerbaijan Republic (Goskomstat). The Census covers around 1.7 million households containing 8 million individuals<sup>8</sup> . Administratively Azerbaijan has 9 economic regions, 73 rayon and 4,500 villages. Between village and rayon level there is an additional layer of administrative units, usually combining 3-5 villages together. It has been possible to combine the Census data and AHBS at the village level. The present analysis is limited to the 65 rayon, as it was not possible to include rayons in the occupied zone.

The AHBS 2002 survey covers 8,157 households and 33,000 individuals. The survey provides detailed information on a wide range of topics, including food consumption, non-food consumption, labour activities etc. The survey design incorporates stratification by region (economic zones and urban and rural strata). As has been previously mentioned in the main part of the paper, the HBS in its current format and sample design it is relatively new to the SSC. Since 1999 a group of international technical experts have been working on improving the HBS design in order to produce nationally representative data (Marnie et al. 2001). In designing the new survey, the team has attempted to rectify the faults of the old HBS, while limiting the additional human and financial resources required, since the survey has to be sustainable for the future. For these reasons the new HBS adopts a full quarterly rotation of households. The interviewers, instead of being involved in the collection of data from the same household over a

> 8 We consider the present population as it has been the population considered for the sample design of the Household