rafmacalaba/data-use-mentions-extended huggingface dataset:
costs could be due to several reasons including improvements in transportation technologies, reduction in fuel costs, and/or improvements in road infrastructure. When focusing on infrastructure improvements, it is unrealistic to design a counterfactual in which transportation costs are reduced in a uniform way across all locations. Most governments have limited fiscal space and implementation capacity for country-wide infrastructure programs. Therefore, we design a counterfactual that quantifies the dynamic gains from reductions in trade frictions due to _optimal_ investments in road infrastructure in the LAC countries and projects the economic adjustments to these changes over the subsequent 100 years.

We obtain the geolocations where bilateral trade costs are reduced in an optimal way and the size of these reductions from the work of Gorton and Ianchovichina (2021). They use data from multiple sources and the general equilibrium spatial framework of Fajgelbaum and Schaal (2020) to identify the endogenous change in trade costs due to optimal expansions of the domestic road networks in most Latin American countries. Gorton and Ianchovichina (2021) demonstrate that optimal investments are more likely along road segments linking more productive and populous locations, which in LAC tend to have relatively high bilateral costs, as shown in Section 4. Since these costs ζ<sup>nn</sup> (s, r) vary across goods n, we calculate the change in average trade costs from optimal investments along each link ( _s_ , _r_ ) as follows: where _N_ is the number of goods transported in the economy and is good _n_ ’s share of all goods _N_ traded along route ( _s_ , _r_ ). ππ<sup>nn</sup> (ss, rr) In Figure 7, we compare the performance of LAC relative to the global economy in terms of utility and GDP and the evolution of these metrics over the next century in the baseline (shown in red) and the counterfactual of optimally reduced trade costs (shown in blue). The results suggest that optimal reductions in domestic trade costs are likely to increase LAC’s share in the world
is available.|
|**Innovation dummy**|It takes a value of 1 if the innovation score isgreater than 0,0 otherwise.|
|**Adoption score**|The adoption score uses the questions (1) to (3) used for computing the innovation<br>score. For each question, a value of 1 is assigned to Yes answers, 0 otherwise. The<br>score is then rescaled using the total number of questions for which an answer is<br>available.|
|**Adoption dummy**|It takes a value of 1 if the adoption score isgreater than 0,0 otherwise.|
|**Invention**|The invention dummy considers the question (4) only. It takes the value of 1 if a<br>firm has recorded expenses in research and development,0 otherwise.|
||**_Main independent variables_**|
|**% Fixed assets funded by banks**|Proportion of fixed assets financed bybanks.|
|**% Fixed assets funded by NBFI**|Proportion of fixed assets financed by microfinance institutions, credit<br>cooperatives,credit unions,and finance companies.|
|**% Fixed assets funded by other**|Proportion of fixed assets financed by family and friends, money lenders, trade<br>credit,and advances from customers.|
||**_Other controls_**|
|**Log (employees)**|Natural logarithm of the firm’spermanent full-time employees.|
|**log (laborproductivity)**|Natural logarithm of annual real sales divided byfull-time employees|
|**Age**|Difference between the year the firm was formally registered and the year the firm<br>was surveyed.|
|**Percentage of domestic ownership **|Percentage of ownershipassociated to domestic entities/individuals.|

Note: The source of data is World Bank Enterprise Surveys.

The
# <mark>Bibliography</mark>

Adamopoulos, T., Restuccia, D., 2020. Land Reform and Productivity: A Quantitative Analysis with Micro Data. Am. Econ. J. Macroecon. 12, 1–39. https://doi.org/10.1257/mac.20150222

- Azevedo, J.P., 2011. WBOPENDATA: Stata module to access World Bank databases. Bentze, T., 2024. Github repository: LSMS-ISA-harmonised-dataset-on-agricultural-productivity-andwelfare: https://github.com/lsms-worldbank/LSMS-ISA-harmonised-dataset-on-agriculturalproductivity-and-welfare.

- Carletto, C., 2021. Better data, higher impact: improving agricultural data systems for societal change. Eur. Rev. Agric. Econ. 48, 719–740. https://doi.org/10.1093/erae/jbab030

- Carletto, C., Savastano, S., Zezza, A., 2013. Fact or artifact: The impact of measurement errors on the farm size–productivity relationship. J. Dev. Econ. 103, 254–261. https://doi.org/10.1016/j.jdeveco.2013.03.004

- Central Statistical Agency, Living Standards Measurement Study (LSMS), World Bank, 2021. Ethiopia Socioeconomic Survey (ESS), ESS Panel II, 2018/2019.

- Dercon, S., Gollin, D., 2014. Agriculture in African Development: Theories and Strategies. Annu. Rev. Resour. Econ. 6, 471–492. https://doi.org/10.1146/annurev-resource-100913-012706

- Food and Agriculture Organization of the United Nations, 2015. World programme for the census of agriculture 2020.

- Gollin, D., 2010. Chapter 73 Agricultural Productivity and Economic Growth, in: Handbook of Agricultural Economics. Elsevier, pp. 3825–3866. https://doi.org/10.1016/S1574-0072(09)04073-0

- Gollin, D., Lagakos,
find positive peer effects from social ties. In line with the results by McKenzie and Puerto (2021) and contrasting those in Cai and Szeidl (2022) and Bakhtiar, Bastian, and Goldstein (2022), our results suggest that successful women entrepreneurs generate positive economic spillovers.

Combined, our results show that building skills can be an effective tool for improving economic inclusion among ultra-poor, rural women. We find that skills increase the likelihood that women in our sample independently generate and control income, and improve their resilience to shocks. Although the results we observe are far from transformative for poverty alleviation or private sector development, they provide key evidence on the importance of skills in larger efforts to promote women’s economic inclusion.

# **2 Background and Context**

Based on the 2018 Living Standards and Measurement Survey (LSMS) in Uganda, 74% of rural women are employed in some form of productive activity (including paid work, self-employment, and unpaid work in family businesses). Thirteen percent of rural women engage in self-employment. In central Uganda, the region where our study is based, average monthly household expenditures are UGX 678,876 (USD 172.52 with a median of UGX 504,966) per household, and the median household size is four.

Although our partner implements its programs throughout Uganda, the women in our sample reside in five communities in central Uganda. Our implementing partner selected all study locations based on conversations with community leaders, their evaluation of the economic needs of the communities, and their estimate of the population of women who might be interested in participating.<sup>9</sup> Of the five communities where we worked, four are rural and

> man and Magruder (2012) show that women are less likely to get job referrals than equally qualified men.

> 9Allowing our partner to select the study locations precludes random site selection; however, we argue that it yields representative study sites given that the program we study and others like it are unlikely
birth cohorts, attempt to compute death tolls using the death probabilities calculated in my analysis and baseline data provided in Heuveline (2001 (b)). This provides an idea of the extent of the underestimation caused by the sample approach. As the appendix shows a very substantial degree of underestimation, it is possible to conclude that most differences obtained using the sample approach probably represents underestimates since the families in which deaths were most likely to occur between 1975 and 1979 are underrepresented in the sample. I have also chosen not to include the respondent in the counts, but only her siblings, because otherwise mortality would be further underestimated since, by definition, respondents are all survivors. Another issue is international migration (widespread as many Cambodians took refuge in neighboring Thailand or Vietnam): to the extent that families migrated and did not come back to Cambodia and if those families experienced a different mortality rate<sup>4</sup> , this might induce some bias.

Despite these shortcomings, the mortality estimates derived from the siblings’ data from a large representative sample represent a useful contribution. First, even if they underestimate the number of deaths, they provide an accurate measure of the impact of mortality on the current population structure in Cambodia. Such data also allow the analysis to address questions about the timing and the socio-economic distribution of excess mortality that cannot be answered with the reconstruction approach.

# **_II.1 Extent and timing of excess mortality_**

As argued above, this section of the paper does not attempt to calculate “the number”, i.e. an estimate of the excess number of deaths during the Khmer Rouge period.<sup>5</sup> It rather describes the impact of the excess mortality during the period on the current population structure in Cambodia.

4 Information about past international migrations is scant in the Demographic and Health Survey. It is known whether a respondent always resided at the same place or not. If not, the length of the stay in the current
are left unchanged. Their model highlights that the poor care about prices more than product quality or other factors. Urzúa (2013) discovers welfare losses because of the exercise of market power in Mexico. His results suggest that the welfare losses deriving from firms with market power are larger among the poor, in the rural sector, and in the southern (poorest) states. Meanwhile, Brummund (2013) analyzes the effects on poverty reduction that arise because firms are behaving in a monopsonistic way, that is, paying lower wages and hiring fewer workers. Using data on Indonesia, the author finds a substantial increase in wages and employment and a corresponding lower poverty rate if the labor market is perfectly competitive. Ghani and Reed (2015) study the economic relationship between a monopoly ice manufacturer and retailers in Sierra Leone, highlighting the importance of market entry in promoting growth and welfare. According to the paper, growing competition disrupts collusive arrangements, thereby leading to a reduction in prices and other positive effects such as the establishment of trade credit services.

To clarify the conceptual relationship between market concentration and poverty, this paper develops a simple model to study the welfare effects of market power. The model relies on equivalent income functions and the class of poverty measures defined by Foster, Greer, and Thorbecke (1984) to estimate the welfare effects. The analysis is based on specific functional forms so that the relevant poverty measure can be calculated explicitly. In particular, equivalent income functions associated with an oligopolistic equilibrium are computed directly to simplify the welfare analysis. Then, comparative statics are developed in terms of exogenous variations in the number of firms in a market for a homogeneous good to determine the effects of greater market concentration on poverty.

The first set of results that emerge from this model shows that in an economy in which consumers have homogeneous skills (productivity) and there are different degrees of firm ownership, the negative price effect of market concentration is greater than the positive profit effect of firm ownership so that greater market concentration increases the
# Labelled Loans, Credit Constraints and Sanitation Investments

Britta Augsburg, Bet Caeyers, Sara Giunti, Bansi Malde, Susanna Smets<sup>_∗_</sup> JEL codes: O16, D14, G41, I12 Keywords: Microfinance, Labelling, Credit Constraints, Sanitation

> _∗_ Affiliations: Augsburg - Institute for Fiscal Studies (IFS), UK (britt ~~a a~~ @ifs.org.uk); Caeyers - IFS, UK and FAIR, NHH (be ~~t~~ c@ifs.org.uk); Giunti - IFS, UK (sara.giunti@ifs.org.uk); Malde - University of Kent and IFS (b.k.malde@kent.ac.uk); Smets - World Bank (ssmets@worldbank.org). Funding from the World Bank Strategic Impact Evaluation Fund and the ESRC Centre for the Microeconomic Analysis of Public Policy at the Institute for Fiscal Studies is gratefully acknowledged. Ethics approval was received from the IRBs of University College London, UK, Project ID: 2168/008; and The Institute of Sustainable Development, Chennai, India. The evaluation is registered in the AEA Trial Registry, AEARCTR-0001955. We thank Orazio Attanasio, Sonia Bhalotra and participants at the EDePo seminar, Kent, UNU-WIDER conference, Oslo Microfinance Workshop, ISI Delhi Conference and CERMi for useful discussions and suggestions. Felipe Brugues and Borja Perez-Viana provided excellent research assistance. We thank the implementing agency for their cooperation and endless patience through the project. All errors are our own.
the objective function by assigning weights to the data elements depending on quality. High, medium and low quality data are assigned the weights of 5, 3, and 1, respectively.

This setup implies that a given adjustment in data carry different penalties depending on what data element is adjusted. For example, a given adjustment in imports carries a five times higher penalty in the objective function than an identical adjustment in intermediate demand. Obviously, this results in more and larger adjustments in data with low weights (quality) than in data with high weights (quality). This is exactly the purpose of the design.

This brings us to our second adaptation of our balancing procedure: the use of auxiliary data. The use of weights implies that most adjustments will take place in intermediate demand<sup>39</sup> . Now, we want to avoid that intermediate demand is adjusted in an unrealistic way. To achieve this, we install lower and upper bounds based on GTAP data on the variables representing intermediate demand<sup>40</sup> .

Specifically, we aggregate version 7 of the GTAP database to the level of aggregation in the Armenian supply-use table. We then compute coefficients of intermediate demand by

> 39 Low weights also apply to several final demands, but they also make up GDP, which is assigned a high weight, and they are therefore less likely to change. Also, adjustments in intermediate demands may in some cases serve two purposes simultaneously: Reduce excess demand in one sector, and reduce excess supply in another sector. The reason is that intermediate demand is part of domestic supply. 40 See Narayanan and Walmsley (2008) for documentation of the GTAP data.

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applying it generally. This is easier said than done, however. In order to validate the methodology, it is best to use a census – or an unusually large survey –as a measure of ground truth. Furthermore, either the survey or ground truth must have either geolocated enumeration areas or, as in this case, highly disaggregated geographic identifiers that can be matched with a shapefile.

A final important issue pertains to the appropriate weighting strategy for the conditional random effect models relied upon here and in other small area estimation applications. We make three points regarding weights. The first relates to the choice of whether to give equal weight to each municipality or to weight by population when evaluating the estimates. We consider only the former, as is common in the literature on small area estimation. However, in some policy contexts, there is a strong argument that it is more important to generate accurate estimates for more populous areas than less populous areas. This is a decision best made based on political rather than technical grounds.

Second, properly weighing the estimates of a sub-area model such as this one is not trivial. In this case, the weights for each AGEB in the sample were normalized by dividing each weight by its municipal average. This ensured that each municipality was given weight proportional to its sample size, while appropriately giving more weight to more populous AGEBs within municipalities. The latter is important because population weights are used to aggregate AGEBlevel estimates to municipalities. Furthermore, in most sub-area models, it would be beneficial to adjust the weights further to correct for heteroscedasticity, to the extent that the number of 33
the coastal provinces clustered together to form the rich club, while the inland provinces declined to the level of periphery. The emergence of the two peaks, rich and poor (consisting of the coastal provinces and the remote inland provinces, respectively), vividly demonstrates the centerperipheral development pattern in China.<sup>13</sup>

> 9 For the sake of simplicity and to smooth out the impact of short-term shocks, we examine the distribution of the provincial average relative GDP per capita of the first and the last seven years of our period of study (1978-1984 and 1992-1999). The evolution of annual relative provincial GDP per capita, 1978-1999, is available from the author upon request.

> 10 See Ben-David (1994; 1997), Quah (1996; 1997), Cozzi (1997).

> 11 Here, the groups (rich, intermediate, and poor) are classified in order to maximize the homogeneity of the relative GDP per capita level among the members in the same group, and the heterogeneity among members in different groups.

> 12 The three municipalities are already separated as the richest group from the other provinces. For the reasons of readability, we do not illustrate these three provinces in graph 1.

> 13 See Quah (1997; 1999), Desdoigts (1999).

7
at an innovation that is new for the market or world in which case the value of this variable takes a 1. If the innovation is only new to the firm, this observation receives a score of 0. The next two variables measure whether the firm has any national or international patents which is also provided in the Vietnamese data set. Finally, the last variable that is included measures whether the firm undertakes a research collaboration in any format. All variables cover the years 2010‐2013, but due to our lagged structure only three years can be included.

Figure 9 first provides an overview of the extent to which the IO coefficients and the Innovation Score for the five firm‐level variables are correlated. The Innovation Score for Vietnam is computed in similar way as for Malaysia and the IO coefficients are from the Vietnamese IO tables. An upward sloping fitted values line is plotted indicating that, on the whole, a positive association exists between the two variables. (Note however that for similar reasons as described above the Coke & Petroleum sector is excluded as well as Paper & Printing sector when plotting the fitted values. The two sectors are also excluded in our regressions.) The sector of Chemicals & Pharmaceuticals has a much higher Innovation Score and also a high ICT‐services inputs as part of its total domestic input use. On the other hand, a sector like Food & Beverages reports much lower levels on both indicators. Figure 10 provides an overview of the Vietnamese developments of the data policy index.

Results of the regressions as correlations are reported in Table 6. In almost all columns the results are statistically insignificant with positive coefficient results. The only variable that is negative and significant at the 5 percent level is whether firms target innovations that is new to the market or world in column 2. Interestingly, however, is the fact that also in this case the R&D variable in column 1 is positive as in the case of Malaysia. When applying a 1‐year lag this result becomes significant at
categories and also assuming</u> that the number of employers was zero (if it was missing). This allowed me to calculate the SEBJ for these three countries, and hence the Better Employment Gap and the full GEGI. SEBJ data for Tonga was also updated to 2018 (from ILO). 25 I thank Michael Weber for providing these data (accessed on 25 February 2020). In cases where multiple JOIN surveys are available for the same year, I use the one with the largest total employment.

9
category of consumption (e.g., total expenditure on flour) instead of individual items (e.g., expenditure on corn flour, wheat flour, etc.). However, altering the set of items in the questionnaire can result in a nontrivial change in the reported consumption amount (Olson-Lanjouw and Lanjouw 2001). Both approaches are likely to lead to an underestimation of consumption and overestimation of poverty, as was demonstrated in a study in Tanzania that directly compared various methods of measuring consumption (Beegle et al. 2012).

An alternative approach is to apply methods of cross-survey imputation. In situations where full household expenditure surveys are too costly or impractical to administer, data gaps can be filled using other surveys that have common covariates that are correlated with household expenditure. For example, data from a full consumption survey can be combined with data from shorter and more frequent labor force surveys to generate poverty estimates (Douidich et al. 2013). While such methods may work well even when there is a rapid economic change (Christiaensen et al. 2011), the assumption of a stable structural parameter typically cannot be tested and may not be valid, especially in the context of large and systemic shocks, after implementation of projects, or if a substantial amount of time has passed since the baseline survey was implemented. It is also possible to design a survey such that one sample has a full consumption module and another sample has only the covariates of consumption. Consumption can thus be imputed and poverty estimates can be derived at a reduced cost, even though the magnitude of potential cost reduction may be modest (Fujii and van der Weide 2016). In such a setup, however, the sample for the full consumption module must be chosen randomly to avoid biased estimates of the model parameters. Thus, this approach is only of limited usability in the case of fragile countries as it might not be feasible to administer the full consumption module in particular insecure areas, creating a downward bias in poverty estimates for those
As indicators of this dimension, we use the total spending on social assistance benefits, the duration of unemployment benefits, benefit levels (average unemployment benefit/average wage) and a quality indicator of last resort social assistance and child benefit programs developed by the ECA Social Protection team<sup>18</sup> for the readiness of social benefits to respond to shocks.

**Table 5 - Principal Component Analysis – Social Protection Performance Results**

|**Social Protection Readiness**|**Country**|
|---|---|
|Low|Latvia, Macedonia FYR, Moldova,<br>Russian Federation, Slovenia, Serbia,<br>Turkey|
|Medium|Albania, Azerbaijan, Bulgaria, Croatia,<br>Kazakhstan,Poland,Romania|
|High|Armenia, Czech Republic, Estonia,<br>Georgia, Hungary, Lithuania, Slovak<br>Republic,Ukraine|

Source: Authors’ calculation

> 18 The ECA SP team monitored social benefits responses on a monthly basis using administrative data in 14 countries from 200810 to track the response of benefits to the global crisis. See findings in report by Isik-Dikmelik (forthcoming).

16