rafmacalaba/data-use-mentions-extended huggingface dataset:
from GVC participation being higher in auto, electronics and IT/ITeS industries compared to textiles and chemicals. Third, a higher intensity of overall service input use is not necessarily associated with an increase in productivity. The composition of service inputs and the origin of services matter more for productivity. Fourth, an increase in the share of service expenditures on complex services by 1 percent is associated with a 10 percent higher productivity. Complex services are typically high-skill intense. Service inputs like research and development activities, technical, managerial and other sophisticated professional services, and in particular IT/ITeS services have substantially contributed to enhance within-firm productivity. Fifth, the origin of services (domestic vs. foreign) matters for firm performance, with firms importing services having a 10 percent higher productivity compared to firms not importing services. Around three percentage points of this productivity premium can be attributed to imports of complex services. Sixth, firms more deeply integrated in GVCs benefit more from importing services. Firms that are integrated at an intermediate level into GVCs have, on average, a 12 percent higher TFP than purely domestic firms. If these firms also import services, the total productivity premium reaches around 28 percent. Firms importing services and highly integrated into GVCs have on average a 54 percent higher productivity than purely domestic firms.

This paper is organized as follows. Section 2 describes the data and approach to analyze the relationship between firm performance, GVC participation and service input use at the firm level. Section 3 presents the descriptive analysis. The results of our estimation of the productivity premium associated with GVC participation and service input usage are presented in Section 4. Section 5 concludes.

# 2. Data and Empirical Strategy

We use detailed firm-level information from the Prowess database collected by the Centre for the Monitoring of the Indian Economy (CMIE). Prowess contains information primarily from income statements and annual reports of publicly listed companies in India. The data spans the years from 1990 to 2017. The data is a panel of medium and large firms accounting for around
# **Table 11. CLAN Analysis: Baseline characteristics of the bottom and top quartiles of** **<u>predicted impacts on women's employment at midline</u>**

||Mean most<br>affected (Q5)|Mean least<br>affected (Q1)|Difference|
|---|---|---|---|
|Age of Woman Beneficiary|29.0***|34.3***|-4.4***|
|Index of Woman's mobility - factor analysis|0.241**|-0.225***|0.546***|
|Index of Woman's decision making - factor analysis|-0.089|0.12|-0.197|
|Share of adult men who have worked in last 7 days|0.887***|0.602***|0.291***|
|Indicator for self-esteem, beneficiary woman|21.4***|21.2***|0.1|
|Beneficiary women has worked in last 7 days|0|0.453***|-0.458***|

Notes _:_ Column 1 (respectively 2) display the average characteristics of the top (bottom) quintile of the predicted impact on women's employment at midline 50
into the program, recipients can further adjust their behavior by enrolling some of their program-eligible children. While take-up of the food transfers is almost complete, there is thus a margin for increasing the take-up of the schooling component, which can be seen as an intensive margin of program participation.

# _Village Neighborhoods_

In this paper, we use the term ”neighborhood” to describe areas within a given radius around each evaluation village. We borrow this terminology from a literature based mainly on urban data, but, in our context, ”neighborhood” means an area or cluster of villages.

8
estimate consumption of infrastructure services from the survey data. First, for the 25%-35% of households that were able to provide the interviewer with a copy of their utility bill, the level of consumption could be

> 5 The family expenditure survey of 1997 was the last to provide official data for expenditure on utility services. However, even this survey does not present a very detailed breakdown given that expenditures are presented in four broad categories, namely: water and electricity combined, telephone and post combined, fuels and transportation.

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, capital accumulation, and increases in productivity. The volumes of both types of labor grow exogenously at the rate of growth of the working age population (ages 15-64), obtained from World Bank population forecasts. The capital stock in each period is the sum of depreciated capital from the period before and new investment. For all sectors, capital productivity remains fixed throughout the model horizon, while growth in labor productivity in the business-as-usual (BaU) scenario is calibrated to real GDP growth in the World Bank‘s medium- and long-term forecast for Panama.<sup>9</sup> In all other scenarios, labor productivity is fixed in each period at the BaU level, and GDP growth becomes endogenous.<sup>10</sup> Thus, real GDP growth may differ from BaU due to faster/slower accumulation of capital or shocks to the productivity shift parameters, allowing the variations in GDP growth across scenarios to be directly attributed to the simulated policy reforms.

> 7 Although other assumptions about closing the government balance, such as adjustments in indirect taxes, increased borrowing, or reduced spending, are also plausible, choosing the direct tax rate as an instrument is a fairly neutral way (in an allocative sense) of restoring  fiscal balance in case of a shock. It also simplifies welfare measurements since the incidence of making up the budgetary shortfall falls squarely on consumers (in contrast to indirect taxes, which, for example, may motivate producers to allocate a larger share of production toward exports which may be taxed at a lower rate).

> 8 We use the deflator of GDP at factor cost as a measure of movements in the real exchange rate.

> 9 Labor productivity growth in the canal sector is always exogenous.

> 10 Thus, in the absence of any shocks, the BaU GDP growth rate is reproduced exactly.

8
Disasters (climate change)<br>Education and health improvements Baseline<br>Fill investment needs<br>Baseline 6<br>6<br>4<br>4<br>2<br>2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>impact impact<br>EMDE EAP<br>EMDE EAP<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 USA JPN DEU MYS THA CHN VNM<br><!-- End of picture text --> Sources: International Monetary Fund; Penn World Tables; UN Population Prospects; World Bank.

Note: Shaded areas indicate forecast. GDP-weighted averages (using average real U.S. dollar GDP at average 201019 prices and market exchange rates). Period averages.

A. Potential growth estimates based on production function approach. “Other factors” include trend improvements in human capital and investment growth relative to its long-term average. Sample includes 53 EMDEs (6 from EAP).

B. East Asia includes 10 EMDEs in EAP; Island economies includes 13 EMDEs in EAP. Disaster frequency is calculated based on the annual average number of natural disaster incidents from 1980-2021 per 10,000 square kilometers of land area.

C. The working age population is defined as those aged 15 to 64.

D. Per capita income in the year that working-age population share peaked (years shown above the bars). Red bars are EAP economies and include only those whose working-age population shares are expected to have peaked before 2020.

E.F. Potential growth estimates based on production function approach. Sample includes 53 EMDEs (6 from EAP: China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand).
on their actual monthly consumption. The analysis of the utility’s billing data set suggests recurring cases of “average billing”, in which the household’s bill amount was imputed based on its average half-yearly consumption rather than the cotemporaneous consumption. There could be several reasons for why these bills had to be generated, including the possibility of faulty meters or meters being inaccessible to the meter reader or, in the worst case, a potential collusion between the meter reader and the consumer to deflate the cost of electricity consumption. Designing incentives to prevent the issuance of such imputed bills and modeling the impact of their strategic position on household level demand is outside the scope of this paper. However, an increase in the incidence of average billing cases will result in greater fixed cost burden per household. As a result, households that pay their bills based on their actual contemporaneous consumption face significant welfare losses.

26
lover annual earnings of APP (compared with VOC) Is on the margin of slgnificance only, at the 5 poecont levol; In **general the** overall **findings** (and policy eonclusions) of Borus are confirmed. All of the other variables are insignificant for most of the years **except** for COMP. Earlier course completion leads to higher earnings at the beginning of the followup horizon (no douibt **due** to greater post-training market **experience),** though this earnings advantage gradually washes out. The earnings data for the years 1975 and 1976 were Incomplete (many firms had as yet failed to report their employees' earnings) when recwived from the NII in the late 1970. **Because** of the small nuaoer of observations for 1976, the regression results for that year **are** not reported here, but they do follow the goneral pattern.

11
their household budgets and expenditure patterns, or (ii) their household size and per capita consumption patterns. The most straightforward way to address this conceptual deficit is to calibrate the EIR affordability threshold by income segment.

As such, the use of a _progressive_ EIR is recommended (e.g., a 10 percent threshold for the first income quintile, 20 percent for the second, and 30 percent for the remaining quintiles). Depending on data availability, these EIR thresholds could be refined by using _equivalized and non-equivalized_ income. This would need to be developed on a country-by-country basis, with the possibility of preparing a crosscountry analysis once there is a sufficient number of country-level progressive EIRs available.

This paper also posits that for the purposes of calculating the EIR for households in developing countries with incomes below the 40th percentile, it is reasonable to assume that household expenditure could be used as a close proxy for household income (i.e., households do not or have very limited accumulate savings and do not have access to financing for consumable goods). As such, the ratio of ‘housing expenditure’ to ‘total income’ could be viewed as equivalent to ‘housing expenditure’ to ‘total expenditure.’ A _progressive_ EIR methodology has been applied as part of World Bank technical assistance to the National Affordable Housing Program in Indonesia. Expenditure data available by decile from the National Bureau of Statistics of Indonesia (Susenas) was used as a proxy for income and the progressive EIR calculated for

> 15 There is currently no standard approached used by the World Bank. However, the most common methodology used is the EIR ranging from 20 to 40 percent.

13
br>.460<br>.436<br>Aggr.<br>Yearly<br>Nom<br>All<br>.265<br>.323<br>.201<br>Aggr.<br>Yearly<br>Trend<br>All<br>.281<br>.339<br>.218<br>Aggr.<br>Yearly<br>Nom<br>All<br>.324<br>.370<br>.274<br>Aggr.<br>Yearly<br>Trend<br>All<br>.319<br>.361<br>.273<br>1: Average of the adjusted _R_2 from regressing the fows shown<br>ral advanced countries and another factor estimated from 12 em<br>orresponds to advanced countries (characterized by nonbig=0 o<br>hile "Small" correspond to 63 non-large countries (characterized<br> "CCR" uses the factors provided in Cerutti et al. (2017c); "PC" sta<br>ipal components estimator; "Aggr." indicates that all fow types a<br>quarterly factors are aggregated to the annual level (rather than b<br>rend" indicates that fows are normalized by trend GDP (compute<br> at yearly frequency), as opposed to current GDP, indicated as "<br>rom advanced and 2 factors from emerging countries are used. <br>of freedom are excluded. Values in the frst row correspond to<br>17c). When using disaggregated fow data, the column "All ty<br>17c), the average the adjusted_R_2over FDI, portfolio equity, portf<br>(portfolio debt plus portfolio equity).|
|Factors<br>CCR<br>PC<br>PC<br>PC<br>PC<br>CCR<br>PC
versus T1 localities as the added effect of including messages based on behavioral sciences principles, reminders and encouragement through proactive text messaging, and the checklist. The differences between T2 versus T0 can be interpreted as the effects of all additions combined. While our main outcome is the average number of applications submitted per locality and the average number of women per locality in those applications, which would suggest manifested interest in participating in NRM programs, we also explored how many were successful in getting their programs funded (actual participation).

The standard outreach was implemented by the agency in charge of the World Bank project and call for proposals, while the additional activities of T1 and T2 were implemented by a local research center.<sup>6</sup>

# _2.3 Data sources_

First, we used administrative data from the Instituto Nacional de Estadística, Geografía e Informática (INEGI) and the National Forestry Commision (CONAFOR) to extract the sociodemographic variables of localities (randomization unit and level of outcome variables). This data is primarily from the latest Population and Household Census of 2010, INEGI Land Use and Vegetation Series VI 2016, National Population Commission (CONAPO) marginalization index by locality 2010, and a private dataset of cell phone and internet coverage provided by the local research center. Outcome data was primarily extracted from the application forms received by the implementation agency of the call for proposals and aggregated at the locality level. Finally, we performed a representative survey of women living in the RCT localities to gather individual-level data of intermediate output indicators (see Section 4.2).

# _2.4 Sampling_

The call for proposals for the World Bank project was promoted and implemented in many localities across five Mexican states; however, because of budgetary reasons, we restricted our sample to two of the five states: Yucatán and Oaxaca. Morover, we only ran the RCT and collected data in a subset of the available localities in these states, choosing a sample of 113 localities<sup>7</sup> with
differ widely from the effects of the same regulations in richer countries. At the same time, there is considerably less research in lower-income settings, most likely due to a lack of reliable data. Therefore, we conclude that the availability of consistent indicators for a wide set of countries and years is particularly important to evaluate the stringency of various regulations and heterogeneous effects across prevailing market conditions. The data that the OECD provides on its member countries are an important baseline and reference.

Our study holds important implications for policy makers. In general, regulatory agencies would benefit from developing the capacity to understand how new regulations could affect workers in their countries, monitor the effects of _de facto_ policy changes and key trends in real time, and adapt regulatory frameworks to changes in prevailing product and labor market conditions. Developing this capacity is as important, if not more so, than crafting the regulations themselves, yet far more effort has been expended in attempts to design and legislate an ideal regulatory framework. This capacity can provide information for governments to flexibly adapt regulations when the current constellation is not yielding the desired outcomes in the local context.

78
Finally, the formula to construct the measure for the human capital stock combines the estimated attainment data with the information on the duration of each schooling level. It is given as: where _ht_ stands for the average years of schooling, _hrj_ is the estimated attainment ratio of the labor force and _dj_ is the average number of years of education received in the respective schooling level j. Average education values have been calculated from the _Encuesta Nacional Socio-Demográfica_ (ENS) from 1989 and are assumed to have remained constant over time. This may result in a slight overestimate of the human capital stock for the period prior to 1989 and underestimate the average years of schooling for later periods. However, data from more recent household surveys suggest that this assumption may not be a large source of error.

**Figure 2. Guatemala: Average Years of Schooling in Labor Force, 1950-2002**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>50 55 60 65 70 75 80 85 90 95 00<br> Barro/Lee Cohen/Soto Nehru/Dareshwar Estimate<br><!-- End of picture text --> _Source:_ Author’s calculations, as well as Barro and Lee (2001), Cohen and Soto (2001), and Nehru et al. (1995) education data.

How do these calculations compare to other sources? The correlation coefficients between the estimated average years of schooling here and those provided by Soto (2002), Barro and Lee (2001), and Nehru et al. (1995), using different techniques and data sources, all exceed 0.95 in the case of Guatemala. Figure 2 compares the results. The time series shown by the solid line harmonizes to a large extent with alternative estimates at different points of time. Unlike the Barro and Lee data set, there is no

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are three common elements to identify an SOE: it is controlled by the government, it is a legally separate entity from its owners, and it engages in market production and commercial activities.

> 16 The 10% threshold is proposed as a proxy of control. Since control cannot be measured ex ante and can be linked to other ways of influence (e.g., golden rights, ability to influence the nomination of the board members), the state participation is used as an indicative variable of control. The threshold of 10% participation was determined after several robustness checks such that it balanced the trade-off between omitting companies with possible ability to influence while excluding companies in which the participation obeys to portfolio or diversification strategies. When testing different levels of participation under 10%, the results indicated that most of the companies were related to mutual funds and international investors (e.g., Blackrock). On the opposite side, when exploring thresholds over 25%, the risk of omitting companies with government participation was higher. After several exercises, the 10% threshold was robust enough to minimize the inclusion of companies with participation under the approach of portfolio investments, while allowing to capture large companies below blocking minority (e.g., Lufthansa). This comprehensive approach allows to do robustness and sensitivity analysis such that if the economic assessment of the policy agenda requires only the subset of companies with minority participation (10%-24%), blocking minority (25% or more), the BOS database offers a systematic variable to do so.

7
in the treatment group. The evaluation found that actual and perceived tenure security increased in the treatment group. The clarification of property rights increased the likelihood of households having purchased land, and a decrease in renting and sharecropping led households to move from merely using the land through rental contracts to ownership through land purchases. Labor use on plots declined and was reallocated to non-farm activities with higher returns. In particular, women were 10 percentage points more likely to be engaged in off-farm work as a result of the intervention (Agyei-Holmes et al., 2020). In this study, we focus on the data collected in the first and second survey waves in 2010 and 2011, one year and two years after the beginning of the intervention in 2009.

Our last dataset is from the phase-in randomization of the national land tenure regularization (LTR) program launched in 2010 by the government of Rwanda. An evaluation of the pilot had already shown a positive impact: improved land access for married women, better records of inheritance rights, a doubling in soil conservation investment and a reduction in land market activity (Ali et al., 2014). Based on these encouraging results, a randomized impact evaluation was designed for the scale-up of the LTR program nationwide. Out of 100 eligible ‘sectors’ spread across the country, 50 were randomly selected to receive the program. The other half of the sectors were to receive the program at the very end of the IE. 3,600 rural households across 300 villages evenly split across the 50 sectors were selected for the evaluation. Whereas the land titling interventions in Benin and Ghana merely formalized existing land rights, the intervention in Rwanda actively increased women’s perceived land rights, as it considered all landholdings by married men as marital property and included wives’ names on the land certificates. Married women in treated areas were also more likely to be able to lease, mortgage, sell or bequeath land (Ali et al., 2015). We use the baseline data collected in early 2011
Policy Research Working Paper 10123

# **Abstract**

This paper uses a 4-year panel (2019–2022) of 10,125 village councils in Ukraine to estimate direct and indirect effects of the war started by Russia on area and expected yield of winter crops. Satellite imagery is used to provide information on direct damage to agricultural fields; classify crop cover using machine learning; and compute the Normalized Difference Vegetation Index (NDVI) for winter cereal fields as a proxy for yield. Without conflict, winter crop area would have been 9.14 rather than 8.38 mn. ha, a 0.75 mn. ha reduction, 86% of which is due to economy-wide effects. The estimated conflict-induced drop in NDVI for winter cereal, which is particularly pronounced for small farms, translates into a 15% yield reduction or an output loss of 4.2 million tons. Taking area and yield reduction together suggests a war-induced loss of winter crop output of 20% if the current winter crop can be harvested fully.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at kdeininger@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 cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World