rafmacalaba/datause-encoder-data huggingface dataset:
# Table I.11. Robustness Analysis for the Formal Job Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.080|0.081|0.070|0.100*|0.096*|0.103*|0.080|0.081|0.070|0.100*|0.096*|0.103*|
||(0.069)|(0.063)|(0.069)|(0.058)|(0.049)|(0.057)|(0.069)|(0.063)|(0.069)|(0.058)|(0.049)|(0.057)|
|Obs. left|267|233|258|258|228|250|267|233|258|258|228|250|
|Obs. right|635|473|591|877|897|872|635|473|591|877|897|872|
|Mean values (Non-RAMV)|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.108|0.029|0.117|0.067|0.094|0.091|0.101|0.171|0.090|0.068|0.055|0.060|
||(0.107)|(0.142)|(0.119)|(0.080)|(0.065)|(0.072)|(0.125)|(0.168)|(0.152)|(0.092)|(0.085)|(0.086)|
|Obs. left|267|202|228|284|202|231|267|202|228|284|202|231|
|Obs. right|635|412|470|1010|1163|1105|635|412|470|1010|1163|1105|
|Mean values (Non-RAMV)|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.177|0.173|0.172|0.126|0.027|0.113|0.199|0.157|0.090|0.159|0.102|0.133|
||(0.144)|(0.148)|(0.136)|(0.129)|(0.110)|(0.125)|(0.162)|(0.195)|(0.152)|(0.162)|(0.099)|(0.167)|
|Obs. left|307|248|323|303|203|303|307|248|228|303|203|303|
|Obs. right|682|534|708|901|995|903|682|534|470|901|995|903|
|Mean values (Non-RAMV)|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|0.001|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
explained by Bourguignon and Ferreira (2005) allows a much richer analysis<sup>7</sup> . However, both methods base the identification of the determinants of differences across distributions of economic welfare on the comparison of _counterfactual distributions_ with observed ones. The empirical implementation relies on regression analysis (OLS and Quantile).

In section 4 we use measures of pro-poorness from the recent literature to ascertain the extent to which economic growth has been pro-poor in Cameroon. Ultimately, impact analysis entails a comparison of social states (or states of the world) represented by profiles of individual outcomes. In this paper, social states are interpreted as growth patterns represented by growth incidence curves (GICs). The social desirability of a pattern of growth depends on the chosen _social evaluation_ function. The social evaluation functions used here can be written as a weighted sum of points on the GIC. The specification of the relevant weights hinges on the underlying value judgments. Concluding remarks are made in section 5.

# **2.  A Profile of Growth, Inequality and Poverty**

In this section, we present a summary of the three datasets we use in the analysis. We also discuss the observed poverty outcomes and try to link them to changes in per capita expenditure and inequality.

# **Evolution of per capita Income and Inequality**

Table 2.1. Distribution of Per Adult Equivalent Annual Expenditure in Cameroon (1996-2007)

|Mean|Lowest<br>Decile|2nd|3rd|4th|5<sup>th</sup>|6th|7th|8th|9th|10th|
|---|---|---|---|---|---|---|---|---|---|---|
|1996 243262.2|2.89|3.78|4.88|6.66|7.26|8.04|7.99|10.45|13.94|34.10|
|2001 372742.6|2.64|4.00|5.19|6.79|6.67|8.59|10.07|11.56|15.59|28.90|
|2007 432894.2|2.70|3.95|4.74|6.22|7.74|9.30|10.65|12.87|16.64|25.19|

Source: Authors’ Calculations (using data from the 1996, 2001 and 2007 household surveys)

> 7 Within this framework outcome differentials are explained in terms of individual (or household) endowments (or characteristics) and the returns to those assets.

7
> _ I.e. _

Figure 1.C.21 COCOA SUPPLY RESPONSE UNDER POPULATION AND INCOME GROWTH WITH ZERO FOOD IMPORTS <!-- Start of picture text -->
1.2-<br>Li -/\<br>0.  -<br>0.6-<br>0.7-<br>0.6-<br>0.5-<br>0.4-<br>0.3<br>0.2<br>0.1<br>s97  1997  2007  2017  2027  2037<br>PERIOD  MDPOINTS<br>* o 90  U  *  140  o  30  I O  X<br><!-- End of picture text -->
# **Figure 4**

<!-- Start of picture text --> Productive component of public spending<br>(in % of total expenditure)<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>Fast growing countries Comparison group<br>Core public spending<br>(in % of total expenditure)<br>65<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>Fast growing countries Comparison group<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> 47
types means banks restrain their ability to obtain future informational rents and in their model, interest rates are lower and lending is higher than in the case with lower information. This is because information sharing means lower market power. Lower market power translates into an expectation of lower interest rates and raises the incentive to perform on the part of borrowers. Thus defaults are lowered and lending rises.

Several papers discuss how markets have responded to information failures by developing institutions to deal with them. Good examples of institutional innovations that have evolved in response to informational asymmetries are credit registries and credit bureaus which facilitate information sharing among lenders (see Jappelli and Pagano, 2000 for a survey). Credit bureaus operate on principles of reciprocity and are typically voluntary mechanisms. Public credit registries are databases created by public authorities and managed by central banks. These registries generally record bank loans and lines of credit. In some countries, there are specialized registers for certain classes of debt (e.g. those that record the real collateral assisting housing mortgage loans). Data are obtained by lenders who are required to report and who in turn obtain a return flow of data to use in their lending decisions.

Since lenders are required to participate in public credit registries, their coverage of bank loans tends to be universal, though they may fail to include data provided by credit card companies and other financial companies and they may not record loans below a certain size. A credit bureau’s information is limited to members. They tend to provide reliable data -timeliness and honesty is forced by threatening expulsion from the group in case of non-compliance. They also provide information on consumer loans and small-business loans. Credit bureaus allow better pricing and targeting of loans, level the 5
as formula-based allocations and term limitations, intergovernmental social transfers in Mexico are found to respond to political motivations (Abbott et al., 2017; HernandezTrillo and Jarillo-Rabling, 2008). Given the increased political interests for road infrastructure and its potentially transformative impact on the economy (Blankespoor et al., 2017), we focus on highway investment and political outcomes.

In fact, both the PRI and PAN presidencies were characterized by large road infrastructure programs. In 1988, President Carlos Salinas (PRI) launched the ambitious _Programa Nacional de Solidaridad_ (Pronasol), which devoted $15 billion USD to the construction of 5,800 km of highways in six years (Foote, 1997). Similarly, the PAN governments of Vicente Fox and Felipe Calderón’s road construction and rehabilitation investments were massive. Under President Calderón, for example, the first phase of the _Programa National de Infraestructura_ (NIP) allocated $26.5 billion USD to highway projects over the 2007-2012 period. The second phase of the NIP (2014-2018) was initiated by the PRI government of President Enrique Peña Nieto and aimed at investing $30 billion USD in road infrastructure. Our goal in the rest of the paper is to carefully examine whether political motivation affected the geographic distribution of these important infrastructure investments.

# **III. Data**

## **A. Road Data**

Our data effort with a collection of sheet the gathering begins maps produced by American Automobile Association (AAA). These maps provide accurate information on major road segments in Mexico at different dates. We import the road type information from the paper maps into the digital DeLorme GIS layer road geometry for 2015.<sup>8</sup>

> 8 This provides precise GIS road information for major roads (those reported on AAA maps) that can be used in the analysis.

6
# ����� �������������,�

� � ������ ��������������,��� ���������������,� ���������������,� ���,� where i = 1...N and t =1....T. ����� �������������,��� represents the rate of youth unemployment at time t-1 for country i which allows to model the youth unemployment as a dynamic process and thus captures its persistent effect. �����������,� captures the perceived corruption variable. For the purpose of this study we rely on the results of surveys conducted by international organizations among local agents from both private and public sectors to evaluate how they perceive corruption in their countries. More specifically we use the Transparency International (TI) index, which as Treisman (2000:400) notes: “constitutes a “poll of polls” compiled by a team of researchers (...) using information from up to 12 individual surveys and ratings”. �����������,� is the vector of control variables containing macroeconomic, political and social covariates. ��,� is comprised of two components; a country specific component potentially correlated with some of the explanatory variables and an independently and identically distributed vector of disturbances. We use a slightly unbalanced panel of 92 countries (both developed and developing countries) over the period 1985-2008 to account for data availability restrictions.

Table 1 provides the results of various specifications of the relationship between youth unemployment and corruption. Specifications 1 to 3 assume no endogeneity (reverse causality) between youth unemployment and corruption practices. We note that while the lagged youth unemployment variable is positive and statistically significant confirming the high persistence effect of this variable, no support can be found to the effect of lagged corruption. As pointed out by several authors, in dynamic panel models where endogeneity of certain variables is suspected, the results of OLS and fixed effects estimations will be biased upwards and downwards, respectively. To correct for this bias (finite sample bias), the specifications 4 through 9 follow Arellano and Bover (1995) and Blundell and Bond (1998) to estimate the equation above using the system GMM approach. As suggested by Roodman (2006), we will use two lags for the system GMM estimators and we will apply the Windmeijer (2005) finite sample correction for standard errors (Heid et al., (2011)). All GMM estimators use robust standard errors and assume the lagged corruption variable as endogenous. Notice that when we account for the dynamic effect, the lagged corruption displays now a positive and statistically significant coefficient suggesting a positive impact on the increase of the youth unemployment. This impact remains significant regardless of the specification we use in our model. Equally important, we note the higher persistence of the rate of youth unemployment regardless of the specification chosen. In line with the empirical literature findings, the coefficient of the system GMM falls between the coefficient of the lagged Page | 13
that can answer questions like is relative educational mobility higher or lower in the rural areas? To this end, we calculate weighted IGME (henceforth WIGME) and weighted ES (henceforth WES): where _πk_ is the proportion of children in our sample with father having _k_ years of schooling.

# **(4.2) Data and Variables**

We use panel data from the Indonesia Family Life Survey (IFLS) for our analysis of educational persistence across generations in rural and urban areas in Indonesia. The first wave of IFLS was fielded in 1993, and the second, third, fourth and fifth waves were fielded in 1997, 2000, 2007 and 2014, respectively. At the time of the first wave, 7,224 households were interviewed and it represented 83 % of the national population of Indonesia covering 13 of the 27 provinces (Frankenberg et al., 1995). In the subsequent waves, the sample size grew because others joined the sampled households either through marriage or births.

Our focus is on the children of 18-40 years age cohorts in IFLS 2014 wave. This is motivated by three factors. First, we are interested in intergenerational educational persistence in rural vs. urban Indonesia during the 1990s and 2000s, and most of the children in the 18-40 age cohorts went to school during this period. Second, we can use the earlier rounds of IFLS to estimate the investment equation (8) above for most of these age cohorts which allows us to understand the role played by economic forces such as returns to education and school quality in the observed rural-urban differences in intergenerational educational mobility. Third, only the 2014 wave contains data on Raven’s scores for all adults which is necessary for our analysis of the biases from unobserved ability heterogeneity.

Returns to education for parents determine the gradient of household permanent income with respect to educational differences across households. The existing literature on returns to education provides estimates of labor market returns, and may capture only a small part of household permanent income we are interested in. To address this issue, we take advantage of the panel data on consumption expenditure in the first 3 rounds to calculate 15
**_Energy demand models for policy_** **_<u>formulation</u>_**

incorporate traditional energy in energy demand modeling exercises in developing countries. Ignoring an important energy source from analysis due to data constraints or limitations of the analytical framework does not provide a realistic or correct picture.

# **Box 3: Structural models of transport fuel demand**

The structural models generally determine the transport fuel demand using a two-stage process. In the first stage, the demand for transport services is related to the distance traveled by passengers (indicated by passenger-kilometers) and freight transport (indicated by ton-kilometers). For these two types of transport demand, the basic theories of consumer demand and producer demand are used.

For passenger demand, it is assumed that individuals maximize their utility through optimal selection of their goods and services operating within their budget constraint. The demand function is derived from the constrained optimization of the utility function. This yields the demand function of the following form: PT = f1(I, Pp, W, D) (1) Where PT is the passenger transport demand function, I is the real income Pp is the price of passenger transport, W is the price vector for other goods and services, D is a vector of other demographic variables.

For freight transport, let us assume that the industry using the transport services minimizes its cost. Let the cost function be denoted by C(Pf, Po, X, Q) where (2) Pf is the price for freight transport (per ton kilometer) Po – the price vector for other inputs, X is a vector of fixed factor quantities, Q is the level of output.

The cost minimizing demand for freight is obtained by differentiating the cost function with respect to Pf, which yields the demand equations of the following form: FT = f2(Q, Pf, Po, X) (3) Given the demand for PT and FT, the demand for specific fuels is obtained assuming appropriate separability of functions. It is now considered that the utility or cost function contains the relevant passenger or freight demand, the price of the relevant fuels and other factor inputs or variables. The demand function for a specific fuel is obtained by differentiating Eq. 3 with respect to its price and takes the following form: Demand = g(PT, FT, P) (4) The two-stage econometric model for transport fuel demand is thus obtained. Source: Berndt and Botero (1985).

22
|Descriptor|2005|2006<br>2007<br>2008<br>2009<br>2010<br>2011|
|---|---|---|
|Country-Specific Aliernative Scenario||Fillin yellow cells, based on alternative scenaria|
|||Default is baseline|
||lf no alternative|scenario is assumed, hide line 72 (and fn 9) in “Table_SR." and lines 21|
|Macroeconomicanddebtdata|and40(an|dfn4)in"Table_GF"andfollowinstructionsinsheet"Data_chart".|
|<br>Public sectorbalance|<br>22,129,288||
|Public sector expenditure|346,050,170||
|Public sectorinterest expenditure|43,284,000||
|Public sectorrevenue (and grants)|323,920,882||
|Gross domestic product, currentprices|481,813,761||
|Gross domestic product, constantprices|143,254||
|ConsumerPrice Index|||
|Exchange rate, national currencyperU.8. Dollar, end ofperiod|1,460,000||
|Exchange rate, national currencyperU.S. Dollar, average|1,378,148||
|Domestic counsumer price index|428 684||
|BaseMoney|37,515,500|55,266,208<br>70,389,353<br>82,179,921<br>90,220,797<br>98,613,666)<br>102,994,998|
|Netforeign assets|26,845,485|0<br>i}<br>i}<br>i}<br>a<br>0|
|Foreign-currency denominated debt(expressedin localcurrency)|109,470,697|153,436,540|163,581,444 168,966,200 182,648,028 194,083,213 205,137,490|
|Privatization receipts (negative)|-1,957,671||
|Recognition of implicit or contingent liabilities|a||
|Other(specify, e.g. bank recapitalization)|a||
|Amortization onMLT public sector debt|17,089,000||
|Short-term public sector debt|in}||
|Interestpaymentsonforexdebt|10,116,000||
Table C.2. Descriptive Statistics – Municipal Baseline Controls

||Year|Observations|Average|SD|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Panel A. Confict and Violence_|||||
|Homicide rates (per 100,000 inh.)|2017|1,098|10.07|46.91|
|Number of robberies|2017|1,098|200.24|1,218.37|
|_Panel B. Public Finance_|||||
|Revenue|2017|1,098|49,932.28|218,740.95|
|Expenditure|2017|1,098|49,655.99|220,118.23|
|Capital Expenditures|2017|1,098|42,716.56|188,503.99|
|Central Government Transfers (SPG)*|2017|1,098|19,218.23|62,347.32|
|SPG in education|2017|1,098|7,769.44|41,083.67|
|SPG in health|2017|1,098|6,138.15|15,432.11|
|SPG in sewage and water|2017|1,098|1,298.5|2.304.58|
|SPG in child nutrition programs|2017|1,098|151.73|269.86|
|SPG in children|2017|1,098|133.75|246.87|
|_Panel C. Poverty and inequality_|||||
|Subsidized Regime Affliates|2016|1,098|14,330.32|86,453.46|
|Rural index (% Ruralpopulation)|2017|1,098|0.55|0.24|
|_Panel D. Economic Growth_|||||
|Night Light Density|2009|1,098|3.85|7.21|
|_Panel E. Previous Regularized Population_|||||
|Number of Applicants PEP 1 (August 2017-Oct. 2017)|2017|1,098|36.63|293.95|
|Number of Applicants PEP 2 (Feb. 2018-June 2018)|2018|1,098|58.3|454.21|

_Notes:_ *SPG stands for _Sistema General de Participaciones_ and represents central government transfers to municipalities. Variables are expressed in millions of Colombian pesos, except for expenditures, which are expressed in thousands of Colombian pesos. _Data source:_ (i) homicide rates (per 100,000 inh.) and number of robberies come from the Colombian National Police; (ii) revenue, expenditure, and capital expenditure come from the municipal panel of the Center for Economic Studies at Universidad de los Andes (CEDE); (iii) central government transfers (SPG), and SGP in education, health sewage and water, child nutrition programs, and children come from the Colombian National Planning Department; (iv) subsidized regime affiliates come from the Colombian Ministry of Health; (v) rural index (% rural population) comes from the municipal panel of CEDE; (vi) night light density comes from the National Oceanic and Atmospheric Administration (NOAA); and (vii) number of applicants PEP1 and PEP2 come from Migracio ́n Colombia 67
of beneficiaries entailed cross-tabulation of the indicator to determine whether the respondent had ever used the maternal health voucher by household wealth index among women who participated in the 2010–11 survey. Analysis of health service utilization that considered births occurring before and after the voucher program was initiated among voucher clients (women who had used the maternal health voucher) and nonvoucher clients. The impact of the program on health service utilization was determined by the difference-in-differences estimate—that is, the difference in changes over time between voucher and nonvoucher clients (Gertler et al. 2010).

The difference-in-differences estimate was obtained both from a simple comparison of changes in proportions utilizing services as well as from an estimation of multilevel, randomintercept, logit models, due to the hierarchical nature of the data. The multilevel logit models include an interaction term between the indicator of whether a voucher or nonvoucher client gave birth and the period of occurrence (birth before or after the program started). The models controlled for maternal age at birth, education level, marital status, place and duration of residence, religious affiliation, poverty status, parity, birth order, and sex of child.

The impact of the program on out-of-pocket expenses involved a simple difference-indifferences comparison of changes in the proportions paying for delivery, and an estimation of multilevel, random-intercept, logit models predicting the likelihood of paying for delivery at any public or private facility. With respect to equity, the analysis examined the gap in the use of maternal health services by poor and nonpoor women in villages with and without a voucher client in the five years preceding the 2010–11 survey. The impact of the program on reducing inequity was examined by performing a simple comparison of the difference in the proportions of poor and nonpoor women using the services in villages with and without a voucher client, and an estimation of multilevel random-intercept models.

The multilevel logit models included interaction terms between the survey year and whether a voucher client was resident in the village at follow-up. The models control for maternal age at birth of the most recent child, level of education, marital status, place and duration of residence, religious affiliation, poverty status, and the number of children born at any time (parity). The basic form of the multilevel, random-intercept, logit model with interaction terms is given by Equation [1]: 17