rafmacalaba/data-use-mentions-extended huggingface dataset:
enrollment decision. Specifically, students in the control group may be better motivated, as they decided to study despite the lower financial support from SOFES. To the extent that this motivation effect increases student performance, the treatment effect is biased downwards. Our estimated impact from loans on student performance from the RD approach should therefore be considered as a lower bound of the unbiased impact.

Table 4 shows the results using the survey data. We now can use two proxies for student performance, namely grade point average and fraction of exams that the student had to repeat. The first column shows the results for the student’s grade point average in university. SOFES-recipients have a 0.175 point higher GPA than students without a SOFES loan. This effect is statistically significant at the 1%-level. This is a 2% improvement in academic performance. Also, female students, older students, and students with a higher grade point average at upper secondary school show higher GPAs at university.

|**Table 4: Impact of the SOFE**|**S loan on student performa**<br>GPA|**nce, survey data.**<br>REPEAT|
|---|---|---|
|_Treatment effect_|||
|**DCREDIT**|**0.175**<sup>*******</sup>|**-0.005**|
||**(0.037)**|**(0.004)**|
|_Socio-economic controls_|||
|TUITION (×1000 pesos)|0.006<sup>***</sup>|0.000|
||(0.002)|(0.000)|
|INCOME (×1000 pesos)|0.001|-0.000|
||(0.001)|(0.000)|
_and James R. Tybout_ . Published for the World Bank [by] Oxford University Press, Oxford ; New York :.

Stokey, N. L. (2008). _The Economics of Inaction: Stochastic Control Models with Fixed Costs_ . Princeton University Press.

Veracierto, M. (2001). Employment flows, capital mobility, and policy analysis. _International Economic Review_ , 42(3):571–95.

# **A Calibrating the Distribution of Product Attributes**

This appendix shows how to use the data about the distribution of output shares in multi-product firms to calibrate the shape parameter of the Pareto distribution of product attributes. We restrict attention to the case were firms are in the interior of the product space.

In the model, the output of a firm with productivity _e_<sup>_ω_</sup> producing a good with product attribute _e_<sup>_λ_</sup> is given by where the factor of proportionality is a function of prices and other parameters in the model.

Under Pareto distribution of product attributes, the total output of the firm is: Therefore, the output share of a product _e_<sup>_ω_+</sup><sup>_λ_</sup> is The determine the shape of the distribution of this share, its cumulative distribu36
term outcome of interest (e.g., empowerment, labor market participation, consumption per capita, etc.). A recent paper by Athey et al. (2019) shows a way to construct these surrogate indices for the long-term outcome of interest using experimental or observational data, so that interventions can be evaluated in the shorter-run to form a good idea about their long-term effectiveness.

For such an approach to be useful for our colleagues and client countries at the World Bank, constructing a “library of surrogate indices” has been proposed (see McKenzie, 2020). Such a library would be extremely useful, because if, over time, we have estimates of the relationship between a number of short- and medium-term outcomes and the long-term outcome(s) of interest obtained from different settings – from experiments and from observational longitudinal data – then we can collect the components of the surrogate index when evaluating different/competing interventions and compare their expected long-term cost-effectiveness. This is potentially a much more satisfactory way to assess the effectiveness of interventions than focusing on one or two intermediate outcomes. Such a process of coordinated evaluations would further build the ‘library’ of knowledge, improving our confidence in predicting the longerterm impacts of interventions during adolescence using short-to-medium-term outcome measures.

# **5.1.2. Reporting on Cost and Cost Structure of Policies and Interventions**

The reporting on the cost of policies and the cost structure of policies affecting adolescents is limited. However, knowing the cost and cost structure are vital for comparing the overall effectiveness of different policies and for understanding whether a policy is likely scalable. Fortunately, strides have been made in reporting and comparing costs of economic inclusion policies via the World Bank’s Partnership for <u>Economic Inclusion’s Costing Dashboard. Expanding this tool to more policies, in</u> particular the policies that affect adolescents, would be extremely beneficial for policy makers to determine the best policies for improving adolescent well-being. Once more costing data is available, we
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the vastly different experiences of SMEs and large firms with obtaining finance, it is important to analyze SMEs separately from large firms. Third, empirical studies that have examined the corruption-finance nexus indicate that corruption increases SME financial constraints (Qi & Ongena, 2019; Wellalage et al., 2019; Wellalage et al., 2020; Ullah, 2020). However, these studies typically use firms’ perception of corruption and access to finance problems rather than objective measures based on actual experience of firms with paying bribes and obtaining finance.<sup>3</sup> The present paper contributes to the above literature in several ways. First, we use objective indicators of corruption derived from firms’ actual experience with bribery in obtaining a specified list of government services. These objective measures do not suffer from perception bias potentially affecting macro-level indicators of corruption used in the literature (discussed in detail below). Objective measures are also used for the access to finance indicator. Second, taking advantage of firm-level surveys, we explore several heterogeneities in the corruption-finance nexus. Some of the heterogeneities derive from the specific ways or mechanisms by which corruption impacts access to finance.

As Rajan and Zingales (1998) note, testing for the theoretical mechanisms that drive the relationship between variables could be the “smoking gun” on the issue of causality. For instance, part of the credit market imperfections arises due to asymmetric information between borrowers and lenders – borrowers have better information about the profitability of investment than the lenders. The clandestine nature of bribe payments implies that the problem of asymmetric information is exacerbated by corruption. Thus,

> 3 I n addition to perception-based measures, Ullah (2020) uses objective measures such as whether the firm has overdraft facility or not, whether the firm bought fixed assets last year or not, etc. However, our measure of access to finance is more in-depth based on the sorts of problems firms faced in obtaining a loan (high interest rates, lack of collateral, etc.) and the actual outcome of
|6.9|18.8|21.5|17.5|
|Risk/poverty ratio|83.9|83.2|37.9|45.8|
|_Census predictions: com_|_mune average, %_||||
|Poverty-induced|9.5|32.6|63.0|51.8|
|Risk -induced|7.3|24.4|20.5|20.2|
|Risk/poverty ratio|77.6|75.0|32.5|39.0|
|_Estimated population,_|_2018_||||
|Total, million|3.3|3.6|8.7|15.5|
|Share of total,%|21.3|23.2|56.1|100|

_Sources:_ Calculations using commune vulnerability rates from the poverty mapping exercise; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, https://www.ansd.sn/enquete-et-etude/recensement-generalde-la-population-et-de-lhabitat-de-lagriculture-et-de-lelevage.

To evaluate the effect of vulnerability rates on the allocation of eligibility quotas to urban and peri-urban communes, we computed the ratio of risk-induced vulnerability to poverty-induced vulnerability. A higher ratio indicates that risk-induced vulnerability is more important in the total quota. This ratio is highest in urban Dakar and other urban areas (see table 4). For instance, urban Dakar shows poverty- and riskinduced vulnerability rates of 8.2 and 6.9 percent. This produces a ratio of risk-induced vulnerability rate 19
of the weaknesses in the use of exchange rates is one based on purchasing power parities (PPPs). A PPP is the number of units of a national currency needed to purchase the same amount of goods and services as a reference currency unit (for example, the US dollar) would buy in the reference country. PPPs allow more meaningful comparisons to be made across countries for many indicators—but they are not appropriate for some purposes. For example, international trade, capital flows, and the values of foreign debt must be measured at market exchange rates. Still, it seems clear that PPPs are the appropriate conversion factor for country classification purposes, compared with market exchange rates.

The major constraint to the use of PPPs has been the availability of regular and reliable time-series estimates for all countries of interest. This issue has been repeatedly discussed by World Bank Executive Directors (for example, World Bank, 1989, 1994 and 2000) but the consistent conclusion has been that the coverage and quality of annual PPP data (rather than the PPP estimates produced at each benchmark year) are not sufficiently robust for operational use. The international statistical community has undertaken substantial work in recent years to improve the quality of PPPs, especially in the 2005 and the more recent 2011 benchmark rounds of the International Comparison Program (ICP). The number of countries participating in the ICP has increased over time, and price collection procedures, valuation, and computation methods have all improved substantially.

Despite the improvements, difficulties with using PPP conversion factors for the annual classification of economies remain, because the ICP produces data for benchmark years only. This means that the classification system would have to rely on extrapolated or modeled annual estimates, using proxy measures. While extrapolated estimates are produced and published in _World Development Indicators_ , each benchmark round so far, including the latest results from the 2011 round, has resulted in substantial revisions to this series. In turn, this would have likely resulted in substantial revisions to the classification of countries, which would be
_ESTIMATING FOOD PRICE INFLATION_ _25_

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# **[insert Table C.3; Figure C.3]**

# **_Bottom-up solution: Direct democracy_**

Since the classic Alesina et al. (2008) paper shows that procyclicality (associated with corruption) is a feature of democracies, we explore the role of democratic institutions on procyclicality. Taking advantage of the Varieties of Democracy (V-dem) database, we identify (i) whether the initiative is permitted at the subnational level, and or at the national level; and (ii) whether the popular referendum is permitted at the subnational level, and or at the national level. We focus on the period 2009-2015 (2016 not available yet in data). The correlations (Table C.4 and Figure C.4) show that both the initiative and the referendum are negatively correlated with fiscal procyclicality, but the effect seems stronger for the initiative rights. This finding echoes results of Matsusaka (2014) regarding the effects of initiative on fiscal congruence and fiscal conservatism in U.S. states. Hence, delegating more fiscal legislation to the citizens may be a tool to reduce the procyclical bias.

# **[insert Table C.4; Figure C.4]**

# **_Bottom-up solution: Spending and tax composition_**

The literature shows that different types of spending behave differently along the cycle. Ardanaz and Izquierdo (2017) show that current spending is more procyclical during upswings of the cycle, whereas capital spending is more procyclical during downturns. Hence, for policy making purposes and institutional design, it would be beneficial to limit the growth of current spending in the good times, while in bad times protecting the capital spending would be beneficial for stabilization. In this paper, we show that when spending is more rigid, it will be less procyclical during the good times, but less countercyclical during the downturns. The composition of the tax structure also indicates that VAT rates are less procyclical than the other types of taxes., hence adding a potential
role as an economic center in northern China, contains four natural cities with one located in the city proper and three outside. As the case of Dailan prefecture shows, a focus on the city proper can simultaneously lead to the inclusion of large amounts of rural areas as well as the exclusion of other urban areas inside the prefecture.<sup>13</sup> The natural city data contains both geocoded locations and boundaries of the natural cities, making them ready for matching with WBES firm-level data. Other publicly available city datasets of developing countries such as the one from World Urbanization Prospects only contain centroids of cities.

WBES firms are mapped to their natural cities using geographic information system (GIS) software. To maximize the number of firms that can be matched to our natural cities, 2016 natural city boundaries are used and firms falling outside a natural city but within a 2km band from the city boundary are assigned to that city.<sup>14</sup> Table A1 shows the results of the matching exercise by country. Overall, a total of 21,857 firms are matched to 489 natural cities, an average of 45 firms per city. The share of WBES firms matched to a natural city is 87%, with the matching rates within individual countries ranging from 40% (Mongolia) to 100% (Myanmar and the PRC).<sup>15</sup> The unmatched firms are likely to be those located in rural areas of very small

> 13 Appendix 1 presents further comparison of the two types of city data by an investigation of Zipf’s law for China and India.

> 14 Results do not change if firms falling outside the 2 km band from the analysis are excluded. These are available upon request.

> 15 The initial procedure resulted in a low matching rate for the PRC (59%), which is probably due to the country’s regulations on collecting and using geographic information, and the unique coordinate system used for geographic data in the PRC
or surplus.

As is customary in applied general equilibrium analysis, base-year data together with exogenous elasticities determine the free parameters of the functional forms. Our central case model parameterization deploys the most recent Global Trade Analysis Project (GTAP) data (version 10) for the US economy, which includes detailed balanced accounts of production, consumption, trade, and CO2 emissions together with key elasticities for the base year 2014 (Aguiar et al., 2019). We do not include preexisting taxes to abstract from second-best effects such that the simulation results should adhere closely in qualitative terms to the theoretical predictions outlined above, while capturing the quantitative differences between 23
, 2013). To give an example, the Survey Solutions CAPI/CATI platform automatically provides an extensive paradata file for each survey. This ancillary dataset is a highly disaggregated account of the “life” of a survey and includes time stamped records of all “events” associated with each interview (e.g., interview record creation, interview assignment to an enumerator, answer provision, modification and comment addition in each questionnaire field, interview completion, to name a few).

> 2 Several indicators for the Sustainable Development Goals (SDGs) require individual-disaggregated survey data, including SDG 1.4.2 (the proportion of total adult population with secure tenure rights to land, with legally recognized documentation and who perceive their rights to land as secure, by sex and by type of tenure), SDG 5.a.1 (a) (the proportion of total agricultural population with ownership or secure rights over agricultural land, by sex), and SDG 5.a.1 (b) (the share of women among owners or rights-bearers of agricultural land, by type of tenure), SDG 5.b.1 (the proportion of individuals who own a mobile telephone, by sex); SDG 8.10.2 (the proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider, by sex). Research has revealed the importance of eliciting self-reported survey data collected in private interviews for the accurate measurement of these and related indicators (Kilic et al., 2021, Kilic et al., 2022, Hasanbasri et al., 2021).

2
sup> fact that _aE, I a Vt = CD(d, )._ With the log-likelihood function in<sup>(6), an iterative optimization routine can be</sup> used to compute the maximum likelihood<sup>estimates. Given starting values for</sup><sup>_,u_anda</sup> and data on equity values _E,_ and<sup>debt D,, equation (3) can be solved to yield a series of</sup> bank asset values V,. Equation (6)<sup>is then used to solve for</sup><sup>_Ai_and v. This process is</sup> iterated to find the maximum likelihood<sup>estimates of</sup><sup>_,A_and</sup><sup>_6,_and their standard errors.</sup> Using the put option formula for<sup>deposit insurance (2), one can solve for the value of the</sup> guarantee per dollar of deposits and<sup>its standard error. Although from a theoretical point</sup> of view, the maximum likelihood method<sup>as developed in Duan (1994) and in Duan and</sup> 8
the case with _g_ = _k_<sup>2</sup> . The priors of Maruyama-George, Forte _et al._ and Zellner-Siow and the case with _g_ = _n_ typically lead to marginal inclusion probabilities under 2.5%.

Tables 2 and 3 also present the median posterior probability of the true model. These tend to be smallest for the hyper- _g_ priors throughout and for the Bottolo-Richardson prior in the case of Model 3. The highest probabilities on the true model are typically found

> 11 The situation with _n_ = 50 and _k_ = 80 means that we need to exclude models with _kj ≥ n −_ 1, as the posterior for such a model would no longer be well-defined. This is simply done by imposing a prior on model space that limits the model size to _n −_ 2. The sampler then rejects any proposed model for which _kj > n −_ 2. In the analysis of these simulated data, the sampler never even gets close to models that large.

> 12 Here and in the subsequent discussion we often focus on the median over the 100 samples we generated. We do not consider the median of the posterior means of _g_ , since the posterior mean of _g_ does not exist for all but the Bottolo-Richardson prior (see the discussion in Subsection 3.3).

19
71|0.42|0.56|0.60|0.41|0.36|0.63|0.60|
|Employed in Industry|0.08|0.14|0.05|0.10|0.07|0.06|0.09|0.11|0.06|0.07|
|Employed in Services|0.26|0.55|0.12|0.36|0.22|0.19|0.36|0.35|0.17|0.20|
|**Access to basic services**|||||||||||
|Improved water|0.69|0.84|0.62|0.74|0.72|0.64|0.75|0.77|0.67|0.64|
|Improved sanitation facility|0.24|0.40|0.17|0.27|0.24|0.23|0.28|0.34|0.20|0.22|
|Access to electricity|0.48|0.82|0.32|0.60|0.45|0.39|0.60|0.56|0.42|0.39|

Source: International Urban Poverty Database. Note: GHSPOP 1km is used for the DOU and DB methods Table B3: Estimation results of regressions on log expenditures with DOU classifications

||||Log of per|capita nominal|consumption exp|enditures|||
|---|---|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>All|(3)<br