rafmacalaba/data-use-mentions-extended huggingface dataset:
, do governments prioritize the payment of some government functions at the expense of others? Payment delays impose several costs on suppliers and might affect the provision of public goods (Flynn and Pessoa, 2014). The importance of this issue is reflected in several recent regulations and initiatives that governments implemented in an attempt to shorten payment terms.<sup>1</sup> Yet, the scarcity of granular data on the stages of the budget execution renders the answers to these questions elusive.<sup>2</sup> In this paper, we introduce a new dataset on municipal public finance in Brazil (MiDES, _Microdados de Despesas de Entes Subnacionais_ ) that allows users to answer several of these questions.<sup>3</sup> We collect, clean, and harmonize microdata on public procurement and budget

> 1Examples include the QuickPay initiative, launched in 2011 in the United States; Regulation 113 of the Public Contracts Regulations, passed in 2015 in the United Kingdom; and the introduction of the Centralized Payment Platform (PPC) in 2020 in Chile.

> 2According to Potter et al. (1999): “For the fiscal economist seeking to monitor budget execution, choosing which stage(s) of the expenditure management procedure to monitor is often constrained by information availability. In principle, the data given at the verification stage may be particularly relevant because they measure the actual liability of the public entity and thus the accrued account liability. For example, if bills are verified promptly when they arrive, it allows a good measure of the potential arrears, where strict cash limits constrain the amounts available to make payments. But such information is rarely available.”

> 3The English translation of _Microdados de Despesas de Entes Subnacionais_ is Subnational Entities’ Expenditure Microdata.

2
in developing countries where financial

> 1 See Levine (2005) for an overview of the literature on finance and growth and Beck (2009) for a detailed discussion of the different techniques used to analyze how finance affects aggregate, industry, or firm level growth.

> 2 See Oi (1962, 1983), Hamermesh (1989), and Benmelech, Bergman, and Seru (2011).

> 3 For example, see International Labor Organization’s (ILO) Global Employment Trends reports (2009-2014)

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affected and this is likely to pose challenges for policy makers to protect the social gains achieved. In addition, to the extent that the rapid pace of social gains seen in previous years may have been internalized by the population at large and created expectations for future gains, the new normal may result in societies facing a mismatch between expectations and actual social gains and mobility.

# **2. How is the slowdown affecting poverty and inequality?**

As economic growth slows in the region, a particular concern is that it may translate into a slowdown—or even a reversal—of the social gains achieved during the recent period of sustained high growth. Indeed, we find evidence that the slowdown has already translated into a slower pace of poverty reduction and a stagnation of the growth of the middle class (Figure 1).<sup>7</sup>

> 7 The numbers presented in this brief are based on comparable household and labor force surveys in the SEDLAC and LABLAC databases. See Annex 1 for more details about the data and the definitions of the different economic groups. All poverty rates are calculated based on international poverty lines, in particular, $4 per day adjusted for 2005 purchasing power parity (PPP).

2
Levine (2004) examine a broad cross-country sample of 58 developing countries and find that financial development (as measured by the ratio of private sector financial intermediation to GDP) reduces income inequality by disproportionately raising the incomes of the poor. Moreover, Singh and Huang (2011) find that poverty is inversely related to financial deepening. In addition, financial deepening reduces absolute levels of poverty, but does not affect income inequality to a significant degree in African countries.

# **3. Stylized Facts on Housing Finance in SSA**

## **3.1. Data**

To develop our stylized facts and econometric analysis, we examined a sample of 54 African countries with data from African Development Indicators (ADI), the Financial Development and Structure Database (FDSD), and the Housing Finance Databases of the World Bank. The database summary and description is presented in the appendix. The analysis is limited to 2000-2012 to ensure more up-to-date results. Housing market and finance policy data are drawn from the newest housing finance databases of the World Bank.

## **3.2. Benchmarking the SSA housing market and finance policy: Typology and characteristics**

Benchmarking the SSA housing market and finance policy along with a comparative approach is essential to have a clear picture of what has been done to date and what remains to be done. The following subsection presents a general perspective applicable to all SSA countries and is followed by a presentation of countryspecific views, which vary based on fundamental characteristics such as wealth, legal origins, political stability, regional context, and oil resources.

## **_SSA housing market and finance policy: A general point of view_**

Overall, the banking sector in Africa has been growing since liberalization two decades ago when African governments adopted new legislation for financial institutions and institutionalised private banking systems, in some cases ending state monopolies in this sector. SSA countries’ financial systems are growing rapidly and becoming increasingly integrated into the global financial system. At the core of the systems
4|--|-30.4|-11.2|-5.1|35.5|-7.0|32.2|-1.4|0.3|
|Pension||||-14.7|--|20.0|-2.3|-8.9|188.3|5.0|33.7|11.7|-0.3|
|Transfers|20.0|19.9|24.7|53.5|--|66.0|88.2|54.4|166.9|68.3|-78.7|50.4|23.7|
|Other nonlabor income|-55.7|||1.2|--|33.2|19.5|11.4|-230.8|12.5|22.2|-7.7|7.0|
|Total change|100.0|100.0|100.0|100.0|--|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

_Source:_ Authors' calculations with data from RIGA for Ghana and Nepal, from household surveys for Bangladesh, Moldova, Romania, Peru, and Thailand, and from SEDLAC (CEDLAS and the World Bank) for countries with income-based measures of welfare.

_a/_ FGT0 refers to the Foster,Greer, and Thorbecke (1984) measure of the headcount index, which measures the proportion of the population that is counted as poor. _b/_ FGT1 refers to the Foster,Greer, and Thorbecke (1984) measure of the poverty gap index, which adds up the extent to which individuals on average fall below the poverty line, and expresses it as a percentage of the
# 3 Empirical Methodology and Identification Strategy

## 3.1 Identification

The first challenge we face in estimating the causal impact of migration on household welfare is the possibility of unobserved characteristics of household heads that affect their decision to migrate also playing a role in determining their consumption level. For example, individuals with higher unobserved ability might migrate in order to have more chances of getting access to vibrant labor markets with more high-paying jobs, and earn higher income which translates into higher consumption. The same unobserved ability that makes them more likely to migrate might also have an effect on their consumption level regardless of whether they migrate or not. If the mechanisms at play were the one just described, a simple comparison of migrant and non-migrant households would overstate the gains in consumption deriving from migration. One could also argue that natural disasters, or negative shocks in general, can induce individuals to migrate and at the same time generate a drop in their consumption level, thus leading to a spurious negative correlation between migration and consumption. Therefore, ex-ante, the direction of any selectivity bias is unclear.

Following a well-established branch of migration literature, starting from Card (2001) to recent studies by Ottaviano and Peri (2005), and Rapoport and McKenzie (2011), we use historical district-level immigration rates as an exclusion restriction for the self-selection equation into migration. We derive the historical internal immigration rate using data from the 1984 Population Census and calculate the share of immigrants for each district of Ghana in 1984 as proportion of the population aged 15 and above that reported to be born in a village (or town) different from the one in which they resided at the time of the Census.

Since the variation of the instrument is limited to the district level, we cluster standard errors at that same level to allow for correlation in the error term of household heads within a district. Ghana is administratively divided into 10 regions, 6 metropolitan assemblies and 55 municipal assemblies which are subdivided into 216 districts, each one with its own district assembly.
# **3. Data**

We use household surveys microdata from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), developed by The Center for Distributional, Labor and Social Studies (CEDLAS) of the University of La Plata, in partnership with World Bank Latin America and the Caribbean Poverty and Gender Group (LCSPP).

Based on the availability of key variables related to infrastructure access and infrastructurerelated assets, we build a five-year interval dataset for 14 countries between 1992 and 2012.<sup>3</sup> The final dataset covers 1.6 million unique household observations and includes the surveys shown in table 1. Table 2 shows the coverage by country and variable.

The main variables of interest are the per capita household income adjusted by purchasing power parity, in 2005 US dollars, and infrastructure. Income here includes labor and non-labor income as well as the imputed rent of owner-occupied housing. Infrastructure is measured through a set of dummies related to access to water, sewerage, electricity, fixed and mobile phones, and internet access. Another set of dummies indicates households ́ ownership of infrastructure-related appliances and assets. We focus on the following variables: refrigerators, washing machine, television, computer, car, and motorcycle.

The use of access / ownership dummies means that we are bound to concentrate on the extensive margin, i.e., on whether households are connected or use a service or not, but are unable to offer insights on the intensive margin due to the absence of specific expenditure data.

Finally, we add a number of standard country-level variables such as GDP per capita, income inequality, population density, and urbanization, all from the World Bank Indicators database. To exploit the panel dimension of the data, we split it into income quantiles, alternatively percentiles or deciles, defined by reference to the regional income distribution.<sup>4</sup> We define quantiles corresponding to the region-wide income distribution at each date. In this way, a household at say
Table 4: Benchmark model parameterization and targets

|Variables / Parameters|Values|Criteria / Targets|
|---|---|---|
|_ρ_, _ωa_, _ωm_|0.96, 0.07, 0.15|Standard in literature|
|_ε_|0.002|Herrendorf et al. (2009)|
|_rt_, _zAm_|0.1131, 1.0085|GDPpc growth, Brazil, 1995-2013|
|_α_, _γ_|0.283, 0.567|Restuccia and Rogerson (2008)|
|_βa_, _βm_, _βs_|0.045, 0.230, 0.108|Own estimates|
|_θa_, _θm_, _θs_|0.023, -0.103, 0.081|Own estimates|
|_μm_, _μs_|0.73, 0.27|Brazilian input-output tables, 2014|
|_ν_|0.18|Sposi (2012)|
|_δk_, _δg_|0.0452, 0.0226|Capital formation investment, Brazil, 1971-2007|
|_Aat_, _Ast_<br>Mean<br>Average growth|Diferent values<br>0.650, 1.486<br>0.0310, 0.028|Sectoral GVA shares, Brazil, 1970-2007|
|_ξ_|0.6204|Annual cost of corruption, Brazil, 2010|
|_ψ_|1.390|Own estimates|
|_foat_, _fomt_, _fost_<br>Mean<br>Average growth|Diferent values<br>0.131, 0.702, 0.206<br>0.0100, 0.0287, 0.0495|Number of workers per frm in each sector|
|_λt_<br>Mean|Diferent values<br>-0.834|Infrastructure stock, Brazil, 1970-2007|
|Average growth|0.0038||
|_ei_, _Am_1970, _fq_|1, 1, 0.1|Normalizations|
show SMEs to be important job creators. Haltiwanger, Jarmin and Miranda (2013) also find a strong role for SMEs in job creation in the US.

> 5 See Love and Mylenko (2003), Beck et al. (2005), Galindo and Micco (2005), and IADB (2007).

> 6 A credit bureau is defined in the World Bank Doing Business database as a private firm or nonprofit organization that maintains a database on the creditworthiness of borrowers (individuals or firms) in the financial system and facilitates the exchange of credit information among creditors. Credit investigative bureaus that do not directly facilitate information exchange among banks and other financial institutions are not considered.

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_X_ includes location-specific variables representing agglomeration economies, size of market, infrastructure accessibility, and local labor costs.

The conditional logit approach has a variety of disadvantages, such as the property of independence of irrelevant alternatives (IIA). Unfortunately, however, it is difficult to overcome the problems with our data. No individual-specific data are available, except for sub-sector classification.

24. The main data come from the INE Estatisticas Territoriasis, 2014, in which 593 firms were “newly” registered in 75 districts over the 3-year period: 2009-2012 ( **Table 2** ). Agglomeration economies are expected to be captured by the number of firms that already existed in the same district. To mitigate the possible endogeneity issue, the 2008 data are used. Transport costs and road density are calculated based on geo-referenced road network data reflecting the condition in 2009. While road density is expected to capture local transport connectivity, transport costs to a port can explain access to the global market.

25. Economic theory tells that local labor costs are an important factor determining firm location and agglomeration economies. Local wage data come from one of the household surveys that are regularly collected by the Ministry of Agriculture. Using the 2008 Agriculture Sample Survey, the average monthly wage is calculated for each district. In case there is no data available in a given district, data at the closest district are used as a proxy. The average monthly wage is about 2,700 metical or US$110. The current minimum wages
For Brazil estimations we also include state fixed effects (São Paulo and Ceará) and graduation year (2015 and 2016) fixed effects. Standard errors are clustered at the program level.

We estimate this equation following the same two-stage approach described above. Throughout, all regressors except for those in the Q<sup>d</sup> vectors are held fixed **.** Estimates of the second-stage coefficients on the LASSO-selected quality determinants measure these determinants’ contributions to student outcomes, net of the contributions from the student, her peers, or other program and institution characteristics.

## **4.3 Variance decompositions**

We conduct a Shapley–Owen R-squared decomposition of the second-stage regressions (Shapley 1953; Owen 1977; Huettner and Sunder 2012). For the program-level regressions, we quantify the fraction of explained variation attributable to the following sets of variables: quality determinants (overall and per category); student, program, and HEI characteristics (as captured by the corresponding PCA scores), field fixed effects, and country fixed effects. For the individual-level regressions, we additionally quantify the fraction of explained variation attributable to student and peer administrative variables (as captured by the PCA scores) as well as, for Brazil, state and graduation-year fixed effects.

# **5 Estimation results**

## **5.1 SCP Quality Determinants**

### **Using program-level data**

### **_Academic Outcomes_**

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Table 1 Estimates of Structural Parameters

|Parameter|Estimate|Description|
|---|---|---|
|Discount factor _β_|0.95|Predetermined|
|Depreciation rate _δ_|0.0991|Computed from BEA and rm data|
|Expenditure shares|||
|Food and beverages|0.313|National accounts|
|Textiles and apparel|0.052|National accounts|
|Minerals|0.025|National accounts|
|Metals|0.025|National accounts|
|Other manufactures|0.211|National accounts|
|Non-traded goods|0.374|National accounts|
|Production function pa|rameters||
|_αL_|0.619<br>(0.036)|ACF regression|
|_αK_|0.283<br>(0.029)|ACF regression|
|_vm_|0.410<br>(0.003)|Computed from rm data|
|Stochastic processes of|shocks _A_|and _b_|
|_ρa_|0.902<br>(0.008)|AR(1) regression|
|_σ_<sup>2</sup><br>_a_|0.118<br>(0.00009)|AR(1) regression|
|_b_|0.318|AR(1) regression|
||(0.156)||
|_ρb_|0.777<br>|AR(1) regression|
||(0.116)||
|_σ_<sup>2</sup><br>_b_|0.044<br>(0.00011)|AR(1) regression|
|_P_(_bt_ =_high|bt_ =_high_)|0.827|Tauchen-Hussey approximation|
|_P_(_bt_ =_high|bt_ =_low_)|0173|Tauchen-Hussey approximation|

Notes: list of estimates and source of each parameter value. Standard errors
estimated losses as a result of theft, robbery, vandalism or arson that occurred on establishment’s premises calculated as a percent of annual sales or the total annual value of the losses?” In some instances the total value of losses is reported, and in that case the share of losses due to crime over total sales is manually calculated. The main advantage of having a share of the losses over total sales as a measure of crime is that it provides a unit free measure given the range of measurement issues regarding currency exchange rates and inflation prevalent in crime measures using local currency units. We make no distinction between a firm that has experienced no crime and a firm that has experienced crime but incurred no losses. Both firms get a zero value for the dependent variable. The variable averages 0.72% in the sample with a standard deviation of 3.9%. Using country averages across all firms, Azerbaijan has the lowest amount of crime losses at 0.20% of sales, while El Salvador has the highest with 1.73%. Data source and description of the variable can be found in table A1, with summary statistics in table A2.

# _2.2 Explanatory variable_

The main variable of interest is the number of police per 100,000 of population. The variable is obtained from United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems. The variable is typically lagged by 1 year to reduce potential simultaneity bias between crime losses and police presence, however due to data constraints, for some countries the lag is not exactly one year previous, but a few years previous in certain cases. The specific details of the number of lags are available in table A3 in the appendix. The sample mean for the number of police is 288, while the standard deviation is 108. Macedonia has the highest number of police with 480 police per 100,000 of the population while Hungary has the lowest with 90 police per 100,000 of population. The country average for
GLD=Loan Guarantees, On lending Derivatives; MP=Monetary Policy; SD=Segregation of Duties; SC=Staff Capacity. Sample covers 17 low-income countries.

C. Unweighted averages. The range of the index is from 0-15, 0=least transparent and 15=most transparent. D. De facto exchange rate regime from the Exchange Rate Regime Classification of Shambaugh (2004) is used to determine whether a country has a pegged or flexible exchange rate. The original classification has four categories: “1” reflects no fluctuation at all, “2” indicates movements within 1 percent bands, “3” indicates movements within 2 percent bands, and “4” indicates a one-time devaluation with 0 change in the remaining 11 months of the year. Shambaugh (2004) assesses these movements against relevant base currencies. The constructed dummy variable indicating a pegged exchange rate regime was defined to equal 1 for countries classified as 1, 2, 3, or 4. A value of 0 is assigned to flexible exchange rates—i.e., exchange rates that routinely fluctuate outside a 2 percent band. Based on 31 LICs.

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hypothetical nature of the contingent valuation surveys can be kept to the minimum, as it is real project under preparation.

A multiple-stage stratified random sampling approach was adopted to select sample in Qiubei County. In the first stage, based on the project area and beneficiaries, geographical boundaries (e.g. districts, urban and rural) were determined so that the sample can cover all the geographical areas and spatial representations of the project. Qiubei County was therefore selected in this first stage. After considering the survey cost and the population covered in Qiubei, a total sample size of 500 was determined. In stage two, Qiubei County was further divided into several functioning zones based on their relations to the project implementation and the impacts by the project, and a list of representative functioning zones were chosen to conduct the survey. In stage three, those selected functioning zones were further divided into communities, and a number of communities were randomly selected to conduct the survey. In stage four, a list of households in each community was obtained with the help of local project workers, and the team randomly picked households from the list to ensure the principle of representation in sampling. The sample sizes were determined to be proportional to the population sizes of different categories. **Table 2** illustrates the detailed sampling statistics in Qiubei. We obtained totally 507 relatively well-filled questionnaires. Among them, 245 coming from the urban areas: 100 coming from the Qiubei county town (County government area, Donghu, Zhenxing, Xiazhai, Qiaotoucun / Xiaoxinzhai / Shuizhai), and the rest of sampled respondents from rural area (82 from 8 villages within the lake area, 125 from 56 villages around the lake, 200 from remaining rural districts in the county).

Specifically trained enumerators from Yunnan Environmental Information Center conducted in-person interviews. Care was taken that only the head of a household was interviewed, who should be well aware of overall income and expenditure of the household, and who can decide about additional expenditures. Neutrality as well as anonymity of
a way to quantify from accounting and market data.

Price theory was so far removed from the real world that many came to accept that prices did not matter, a conviction that was buffered by surveys by Oxford Keynesians that purported to show that interest rates does not influence the investment decisions of businessmen, and regressions which showed that the price elasticity of demand for exports is so low that the Marshall Lerner equilibrium condition does not hold. The skepticism about prices accounts for the popularity at that time of Harrod-Domar and input-output models, that dispensed with prices in their description of economic mechanisms.

Micro economists were so fascinated by the implications of market failures stemming from scale economies, externalities and the second best character of equilibria that their students could well think that Adam Smith's invisible hand works so poorly that their chief task, should they become policy makers, would be to correct its working.

> 22 Krugman (1995), p 360 in _Handbook of Economic Innovation and Technical Change,_ ed., Stoneman, Oxford: Blackwell pp 342-36.

> 23 The Phillips machine that is on show in London's Albert and Victoria Museum, that was used in teaching at the time. illustrates the model's simple plumbing.

24 The Giffen paradox; and the Slutzky conditions.

15
(2.323)|
|High consumption of<br>h dtib|0.071|0.006|-0.046|0.043|-0.006|-0.009|
|ome proucon)|(1.693)|(0.313)|(1.674)|(1.170)|(-0.399)|(-0.411)|
|R'<br>N||0.7420<br>1160|||0.6660<br>5173||

a) Dummy variables shown in this table are interacted with the dunmny for the specified time period in the table.

b) Defined as the ratio of consumption of home production to production to purchased food 2 0.6.

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