rafmacalaba/data-use-mentions-extended huggingface dataset:
worked with the MFI to introduce an individual-liability, _uncollateralized_ loan product of up to US$7,500. This uncollateralized loan product was smaller than the maximum amount accessible with collateral, and was priced at an interest rate above the collateralized product, to dissuade women who had access to collateral from applying. Women entrepreneurs who applied for an uncollateralized loan, passed psychometric screening, and passed a business appraisal were randomized into the offer of an uncollateralized loan or a control group. Our design enables us to estimate the causal impact of creating access to psychometric-appraised loans in lieu of traditional collateral requirements, compared to a control group who are assessed to be creditworthy via psychometrics but cannot access uncollateralized lending.

Using a panel survey of women and their enterprises, as well as MFI administrative data, we compare outcomes across the randomly selected treated and control entrepreneurs. The results show that access to psychometric-appraised loans relaxed women entrepreneurs’ credit constraints. Treatment more than doubled the likelihood that women had accessed a formal loan from any bank or MFI for their business by endline — from 42% of the control group to 89% of the treatment group. Overall, the proportion of women accessing any business loan (formal or informal) increased from 61% of the control group to 90% of the treatment group. Treatment also increased the total amount of borrowing, both on average and conditional on a woman taking any business loans.

Access to psychometric-appraised loans reduced the firm death rate to 17% over three years, compared to a firm death rate of 33% in the control group through the COVID19 pandemic, conflict in Ethiopia (including in the Oromia region where the study took place), and related economic challenges. There is no significant impact on women exiting entrepreneurship altogether, since many control-group respondents who closed their original firm reopened a new firm, typically in a different sector. Almost all firms that remain open report positive profits in the last year, hence there is no evidence of access to psychometricappraised loans keeping “zombie firms” alive.
# <u>Soil carbon pools</u>

In 1982, Post and colleagues (1982) estimated that soil organic carbon contains about two-thirds of the carbon in terrestrial ecosystems. This is a remarkable figure and it is perhaps surprising that the preservation of rich carbon pools have not garnered greater attention. There are several shared characteristics between below-ground carbon pools and above ground forests that are important for policy. Often forests and carbon pools are situated on lands where use rights are unclear and access to the resources are not easily limited by governments or private groups. In addition, while both land restoration and reforestation were included as features of early climate change treaties, including the Kyoto Protocol, the preservation of forests and carbon pools were not. To date, while the so-called REDD (Reducing Emissions from Deforestation in Developing Countries) agenda is expected to be a key component of post-2012 climate change treaties, the preservation of carbon in soils has been given less political urgency.<sup>6</sup> A geographic dataset of organic carbon pooled in the topsoil constructed by FAO-UNESCO (2007) shows that immense stores of carbon are held in ECA‘s soils (Map 1). The Russian Federation has vast areas of relatively high-carbon pools most notably in the boreal forests of Siberia and nearby environments. In addition, Central Asia has a few carbon hotspots surrounded by relatively lower concentrations of soil carbon. Eastern Europe is mixed with all categories of terrain represented.

FAO (2010) also produced a dataset on land use, based on data from 1998-2008 (Map 2). A closer look at land use map reveals large forest area in the north and large area of crops and agriculture in mid-latitudes and the Eastern Europe. Central Asia has a few hotspots of agricultural and crop use.

By combining the two datasets, it is possible to say something about how the land containing carbon pools is used and managed. Aggregated data for the region as a whole is reported in Table 7.<sup>7</sup
Policy Research Working Paper 10867

# **Abstract**

Accurate poverty measurement relies on household consumption data, but such data are often inadequate, outdated, or display inconsistencies over time in poorer countries. To address these data challenges, this paper employs survey-to-survey imputation to produce estimates for several poverty indicators, including headcount poverty, extreme poverty, poverty gap, near-poverty rates, as well as mean consumption levels and the entire consumption distribution. Analysis of 22 multi-topic household surveys conducted over the past decade in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam yields encouraging results.

Adding household utility expenditures or food expenditures to basic imputation models with household-level demographic, employment, and asset variables could improve the probability of imputation accuracy by 0.1 to 0.4. Adding predictors from geospatial data could further increase imputation accuracy. The analysis also shows that a larger time - interval between surveys is associated with a lower probabil ity of predicting some poverty indicators, and that a better imputation model goodness-of-fit (R<sup>2</sup> ) does not necessarily help. The results offer cost-saving inputs for future survey design.

This paper is a product of the Development Data 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 hdang@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
unobservable effects. If both modeling approaches lead to the same substantive conclusions, we can draw robust conclusions regarding our hypotheses.

The third challenge to drawing generalizable and robust conclusions regarding our hypotheses lies in potential selection bias; that is the sample used for the analysis may not accurately represent the entire population of interest. To address this, we use data from the whole population of published government contracts in Bulgaria from 2011 to 2019. We carefully identify green procurement contracts within this dataset through both market CPVs and tender text searches. Both green procurement contracts and the remaining Bulgarian public procurement contracts are expected to adequately represent the total population of contracts during this period, with the exception of small value contracts below mandatory reporting thresholds.

_The relation between GPP and firm productivity_ As with other types of public procurement contracts, the relation between GPP and firm productivity can be subject to reversed causality. For example, a positive relation can either be driven by more competitive procedures applied to green contracts, or by attracting more productive firms with more sophisticated technology or management practices to bid since they are able to better comply with the green criteria and technology requirements. Irrespective of the direction of causality, however, a positive relation implies that public procurement allocates resources to more productive firms improving the allocative efficiency of resources among firms in the economy which contributes to private sector productivity growth.

Arguments against the allocative efficiency result would thus need to be based on an omitted variable bias in that unobservable factors need to exist that are correlated with more productive firms being awarded GPP contracts for reasons other than those firms’ productivity drivers such as their technology or management. One such factor may be the application of more or less competitive procurement practices. In contrast to most previous studies, however, the combined firm-public procurement dataset allows to observe the degree of competition and corruption risk in the applied (green) public procurement procedures. As it will be shown later, we find that GPP is associated not only with higher firm productivity but also with the application of more competitive procurement
ternative estimates of caloric intake using NSS data, reported in column 5, provide a sense of the magnitudes of this source of measurement error. The details of the procedure are discussed in Appendix C, but the main departure from the standard NSS practice is imputation of calories for most expenditures in the beverage and processed food categories (which includes purchased “meals”). These expenditures rise from about 5% to 9% of average household food expenditure, and as they lack quantities or report units like “number of cooked meals” or “number of cold beverages: bottled/canned,” untested assumptions about how to impute their caloric value have become more important. The procedure used here yields very similar levels of estimated intake for 1983 through 1994, but a substantially smaller decline between 1994 and 2005, such that the total decline between 1983-2005 is only 66% as large as estimated by (NSSO 2013) or (Deaton and Dr`eze 2009). – The estimates begin to diverge even more after 2005 while there is a similar pattern of decline from 2005-2010 and recovery from 2010-2012, overall the decline between 2005 and 2012 is about 40 calories per day as opposed to an increase of 47 per day according to official estimates. The alternative assumptions are not necessarily better than the official procedure, as there is no “ground truth” against which to assess either measure. Rather, the point is that measurement assumptions can have a large effect even using the _same data_ , particularly in recent years when the importance of cereals and other unprocessed agricultural staples has fallen. These imputation assumptions have the potential to generate bias of a similar magnitude as the recall errors that potentially bias down both the level and decline in caloric intake.<sup>7</sup> Returning to the other side of the calorie equation, columns 6 and 7 again report estimates of population TEE and average TEE for adults. Relative to the official estimates using the 30-day recall period, the estimated population – TEE is almost perfectly negatively correlated over
are considered as unskilled. In receiving countries where no information about skill is available, we transpose the skill distribution observed in the rest of the OECD area. Note that the latter skill distribution is speci...c to each country of birth. For example, if we have no information about the skill structure of immigrants to Italy, Algerian emigrants to Italy are assumed to be distributed in the same way as Algerian emigrants to the rest of the OECD area.

Formally, the stocks of emigrants of skill s from country j at time t (Mt;s<sup>:j)are</sup> obtained as follows: 6For example, Australian data mix information about the highest degree and the number of years of schooling.

7
expenditure are measured in logarithmic form. In panel A, the current month is taken from **the** first of the 4 waves of the data. The first wave of the Russian data was collected in November/December of 1994 and the first wave of **the** Polish data was collected between January and December of 1993. Correlations between persistent income/expenditure can only be calculated for waves 2 and 3 of the data. In both countries, wave 2 was collected 1 year after the first wave, and wave 3 was collected 2 years after the first wave. Standard error for these correlations are generally around 0.02 to 0.03.

Panel A of Table 3 shows the correlation between current log income or log expenditure, and its value in future periods. The correlation between a family's current economic situation and that 12 months from now is slightly less than 50% in Russia and somewhat more than 50% in Poland. This would suggest a lot of churning. However, these correlations fall only very little if we move out one, two or three extra years. Does this mean that the churning has largely ceased after the first year? The explanation for this pattern of correlations is that the correlation between any two years is less than unity for two reasons: (i) the transitory shocks that occur in each of the two years, and (ii) the underlying amount of churning - the persistent shocks between the two years. Whether 24
# Table 4. Contributions of global shocks to alternative measures of global inflation

||||Struc<br>|tural Shocks<br>||
|---|---|---|---|---|---|
|Sensitivity|Proxy variables|Oil<br>Price|Global<br>Supply|Global<br>Demand|Global<br>Interest rate|
|Baseline (Headline CPI)||38.4<br>[12.9 66.9]|17.6<br>[2.4 33.3]|27.8<br>[4.5 59.5]|16.2<br>[1.2 34.4]|
|Ali Ifli M|Core CPI|6.9<br>[0.5 14.5]|41.1<br>[8.3 77.3]|25.7<br>[1.9 55.6]|26.4<br>[2.5 54.3]|
|ternatve naton easures|Producer price index|38.0<br>[12.4 68.9]|22.5<br>[3.0 42.2]|22.9<br>[3.4 48.5]|16.5<br>[1.5 35.7]|

Note: This table reports variance decompositions of global inflation (in percent) based on median from 1000 successful Bayesian draws. The numbers in the brackets are 16-84 percentiles draws. “Baseline” indicates the variance decompositions of global inflation based on the global FAVAR model that consists of oil price growth, and the global factors extracted of inflation, output growth, and interest rates from the dataset of G7 countries. “Alternative inflation measures” indicates that global inflation factor is replaced by either global core
_j_ and  _m_ .

However, a panel fixed-effects model may not suffice to account for time-varying unobserved factors that influence how households respond to stimulus and its associated impacts in the local economy. To account for time-varying unobserved heterogeneity, our approach in this paper is to estimate a dynamic panel model (Arellano and Bond 1991; Jalan and Ravallion, 1998, 2002) using the multiple waves of monthly data. As described below, we also account for a range of 20
|0.219***<br>(0.0532)|||0.195***<br>(0.0586)|
|Firm size|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm age|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector2d|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|10605699|10605699|10605476|9685870|9685870|9685705|
|R-squared|0.163|0.163|0.163|0.157|0.157|0.157|

_Note:_ The table presents the results of firm-level regressions of the average wage on ownership status, controlling for firm size, age, 2-digit industry and year fixed effects. Data from RAIS, from 2016 to 2020. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

23
*

Our empirical analysis primarily relies on panel household phone surveys that have been regularly collected since the outbreak of the COVID-19 pandemic in Ethiopia and Kinshasa. In addition, we use Facebook movement data to construct an index to capture mobility trends.

_Ethiopia and Kinshasa high-frequency phone surveys_ In Ethiopia, the World Bank designed and conducted an HFPS of households to monitor the effects of COVID-19 on Ethiopia’s economy and people and to inform interventions and policy responses (Wieser et al. 2020). The HFPS builds on the national longitudinal Ethiopia Socioeconomic Survey (ESS) that the Central Statistical Agency (CSA), Ethiopia’s national statistical office, carried out in 2019 in collaboration with the World Bank. The HFPS drew a subsample of the ESS sample that was representative of households with access to a working phone. The HFPS is collected every month for 12 survey rounds, starting in April 2020. The 15-minute questionnaire covers topics such as knowledge of COVID and mitigation measures, access to educational activities during school closures, employment dynamics, household income and livelihood, income loss and coping strategies, and assistance received.

To monitor the impacts of COVID-19, the National Institute of Statistics ( _Institut National de la Statistique_ , INS), the national statistical office of the DRC, and the World Bank have collected HFPS data every month since June 2020 (INS 2020). The HFPS builds on the baseline Kinshasa Household Survey, which was collected from about 2,600 households in Kinshasa by the INS and the World Bank in December 2018. The Kinshasa HFPS drew a subsample of the Kinshasa Household Survey sample that was representative of households with recorded phone numbers. Respondents of the surveys were not 7
sources including the World Development Indicators (WDI, World Bank) and La Porta et al. (1999).

The ES consist of a stratified random sample of firms that is representative of the country’s formal (registered) private sector. Sampling weights are provided in the survey to allow for inferences to the targeted universe of firms. The ES do not cover firms in the informal sector, firms with fewer than five employees, the agriculture and mining sectors, as well as some services sectors such as banking and insurance, health care, and education. A common survey methodology and questionnaire is used across surveys. We use the latest round of the ES available in each country. The sample we use consists of over 48,647 private firms located in 66 different countries that are mostly developing countries.<sup>2</sup> The survey year for the ES varies across countries ranging from 2007 to 2016. Summary statistics of the variables used are provided in table 1; correlations between our main explanatory variables are provided in table 2.<sup>3</sup>

# _Dependent variable_

Our dependent variable is the percentage of all permanent full-time workers at the end of the last fiscal year at the firm that are females ( _Female workers_ ). The variable ranges between 0 and 100, with a mean value of 32 (percent), median value of 26 and the standard deviation equals 27.3. The percentage of female workers in a typical firm is highest in Belarus (51%) and lowest in Pakistan (5%). Note that our dependent variable is women’s employment _relative_ to total employment at the firm. Thus, factors such as overall economic development, job availability and labor market conditions that affect men’s and women’s employment equally do not pose any omitted variable

> 2 Of the total 66 countries in our sample, there are 5 high-income countries per the definition of the World Bank. The high-income countries include Chile, Estonia, Poland, Latvia
weights (calculated as the inverse probability of being interview on log of number of students, public status, and population density by state, province, or NUTS 2 region as a measure of location) for the WMS data and _school final weights_ for the PISA data. Samples include both public and private secondary schools for both datasets, with the exception of Colombia where WMS data is only available to public primary schools. Number of WMS/PISA observations are as follow (WMS/PISA): Brazil = 510/561, Canada = 129/770, Colombia = 468/268, Great Britain = 89/422, Germany = 102/158, Italy = 284/926, Mexico = 157/1327, Sweden = 85/179, United States = 263/136.

33
Households: Excessive leverage and exposure to FX borrowing are relevant only for a minor share of households in the region. Debt overhang does not seem to pose a threat to aggregate investment and consumption in the household sector in any particular country.

Banking sector: Substantial levels of non-performing loans suggest that debt overhang may constrain future lending in several countries (e.g. Latvia, Hungary).

The above analysis suggests that the majority of policies taken by authorities in the region hardly address growth constraints arising from debt overhang: Authorities in most countries have focused on **policy measures to curb FX lending** and cushion the effects of exchange rate depreciations on existing FX borrowers, especially in the household sector. Our analysis suggests that these will have little effect on aggregate investment and consumption in the private sector. Indeed it is even questionable whether these measures are well targeted to deal with systemic risk in the banking sector. Recent data from the Hungarian Central Bank, for example, show that the share of non-performing loans is twice as high for local currency loans (12%) than for foreign currency loans (just over 6%). This suggests that the systemic risk to the banking sector in Hungary is currently arising from the real economy rather from foreign currency denominated lending.

Authorities in selected countries (e.g. Latvia and Hungary) have taken measures to **shift the burden of debt from households to banks** . These measures seem to be counterproductive in view of future economic growth: the marginal debt levels of households in the region suggests that over-indebtedness will hardly constrain economic activity in the household sector, while debt overhang in the banking sector seems to already pose a greater threat. From the viewpoint of social policy, the “relief” measures for the household sector also seem misguided as those households with mortgage debt are the more wealthy ones.

Should authorities in the region then take measures instead to shift the debt burden away from the banks instead? Should they follow the example of some industrialized countries in
this hypothesis in an econometric framework. This allows us to estimate the relationship between innovation returns and the context dimensions (which we group into “knowledge” and “market” factors), while also considering firm-specific innovative capacity. In addition, this is one of the first papers that uses data from the second wave of the Employment and Innovation Dynamics National Survey (known as ENDEI, for its Spanish acronym) (ENDEI 2014–2016). By merging ENDEI data with contextual characteristics defined at the sectoral and/or regional level,<sup>5</sup> this paper also improves data availability by creating a novel and original database that could be useful for future studies.

The remaining of this paper is organized as follows. Section 2 describes the Argentinean context and its R&D and productivity performance to motivate the discussion. Section 3 describes the conceptual framework used. Section 4 describes the methodology and data. Section 5 presents results of the estimation of the production function. Section 6 concludes by offering policy recommendations, suggesting lines for future research, and discussing implications for data collection. The appendixes provide supplemental data and analysis.

# **2. The Argentinean Context**

Although Argentina’s expenditure on R&D relative to GDP or in US dollars per capita is much lower than that in developed countries, it is higher than that in regional peers, with the exception

> 4 At the macro level, the potential impact of these complementarities has been explored in, for example, Klenow ‐ and Rodriguez-Clare (2005), Maloney and Rodríguez Clare (2007), and Goñi and Maloney (2017)—but not specifically for Argentina. These studies stress the importance (or the lack) of these complementary factors in explaining differences in the returns of R&D across countries and in the distance to the technological frontier. 5 This database was built using a variety of different data sources, each originally with different levels of disaggregation. Details are presented in appendix A, table A.1