rafmacalaba/datause-encoder-data huggingface dataset:
2 2NP crucial, since it allows to predict household-level income changes (∆yi<sup>2</sup> = yi2 −ŷi1 ) apart from the aggregated poverty dynamics obtained by the parametric approach.

# **3. Data and empirical approach for validation**

I use the SEDLAC database for Peru as the primarily source information to validate income poverty dynamics. SEDLAC is a harmonized dataset of LAC’s household surveys compiled by the World Bank and the Center for Distributive, Labor, and Social Studies (CEDLAS, for its acronym in Spanish) at the _Universidad Nacional de La Plata_ in Argentina.<sup>4</sup> The main objective of the SEDLAC dataset is to allow for cross-country comparisons of household incomes and a wide range of socioeconomic measures in LAC. The data cover more than 300 surveys in 18 countries in the region and represent more than 90 percent of the population in LAC.

To validate the non-parametric approach, I select a panel subsample of the SEDLAC data in Peru. This panel follows the same 1,075 households in a five-year period from 2007 to 2011, which allows us to validate the technique in periods of different lengths of time that include the same households. Validations are done for two periods. The first period covers one year from 2010 to 2011, while the second is a five-year period between 2007 and 2011. In this validation, the poor are defined as individuals with a harmonized per capita income lower than $4 per person per day poverty line in 2005 $PPP/day.

In all cases, mobility estimates are done by comparing the actual income in the second round and the predicted one—that arises from applying the method proposed in this paper using time invariant characteristics—in the first round. To avoid life cycle events that may invalidate the main time invariance assumption of the method, all estimates are based on data from households— as opposed to individuals—whose head is between 25 and 65 years of age (805 households). I apply 50 repetitions to the procedure and test how sensitive these results are to changes in the number of repetitions. I also choose γ = 0.5 and test the sensitivity of results to other values of γ. Following Cruces et al. (2015), I randomly divide all panel datasets into two subsamples and treat each subsample as a cross-section. Therefore, I estimate the coefficients in one of these subsamples in the first round (403 households) and apply those coefficients to the second subsample in the

> 4 Bourguignon (2015) and Gasparini, Cicowiez, and Escudero (2013) present a detailed description of the SEDLAC data.

7
Of the 30 SALs approved during the period considered, only one -- Burundi SAL-I -- does not include any specific measures to address the social impact. Since the analysis of social impact in Presideil.t's Reports is fairly general, it is usually not possible to assess if the proposed interventions appropriately and adequately address the social impact. Furthermore, the President's Reports do not, generally, provide details of the design of the interventions, their implementation mechanisms, and their financial arrangements which makes it difficult to assess their likely effectiveness.

9. Most countries have proposed interventions for some (or all) of the following three groups (see the attached summary table): (1) The newly unemployed comprising retrenched public sector employees and civil servants. Measu - to reach this group have included severance payments, resettlement to rural areas, public works, retraining, and credit schemes.

(2) The chronically poor comprising people who have been in poverty for a long time -- certainly prior to the adjustment program. Some adjustment policies may further impoverish them. Measures for this group have included social expenditure reallocations and targeted interventions such as subsidized distribution of food, public works, retraining, nutrition, and credit schemes.

(3) Others including civil servants and prospective entrants to the labor force (e.g. graduates or school leavers) who may fail to find jobs as
|**Variables**|**Program**|**Standard**<br>**Error**|**Observations**|
|---|---|---|---|
|WPR for Male Household Members with Any<br>Livelihood Activity|-0.062|<br>0.05|4,193|
|Difference between Male and Female Household<br>Members WPR with Any Livelihood Activity|-0.271***|0.105|4,127|
|Ratio Female to Male WPR with Any Livelihood<br>Activity|0.370**|0.175|4,021|
|Log Total Formal Savings (Rs.) in the last 12<br>months-winsorized|0.108|0.511|4,316|
|Log Total Informal Savings (Rs.) in the last 12<br>months-winsorized|1.652|1.076|4,316|
|Log Total Savings (Rs.) in the last 12 months-<br>winsorized|-0.018|0.468|4,316|
|Share of Total Informal Savings to Total Savings in<br>the last 12 months-winsorized|-0.007|0.055|4,083|
|Log of Total Amount Borrowed for Outstanding<br>Loans|0.307|0.883|4,316|
|Logof Total OutstandingLoans|0.148|0.821|4,309|
|Average Annual Interest Rate of OutstandingLoans|0.040|10.295|987|
|Number of Fully Productive Loans in past 12<br>months|0.086|0.059|4,316|
|Number of Partially Productive Loans in past 12<br>months|-0.012|0.025|4,316|
|Number of Loans with No Collateral given in past 12<br>months|-0.009|0.122|4,316|
|Does the Household have a loan?(Y/N)|0.074|0.091|4,316|
|Has the Household given Any Collateral for Loan for<br>Loan in last 12 months? (Y/N)|-0.040|0.031|4,316|
|Has the Household taken Any Productive Loan in<br>past 12 months? (Y/N)|0.077|0.061|4,316|
|Total Irrigated land owned byHousehold(acres)|9.207|9.527|4,316|
|Total land owned byHousehold(acres)|12.267|9.602|4,316|
|Total land Leased byHousehold(acres)|-0.011|0.052|4,316|
|Total land owned and leased byHousehold(acres)|12.256|9.603|4,316|
|Total Number of Consumptive Assets Owned Now|-0.975|0.805|4,316|
|Total Number of Livestock Assets Owned Now|5.588*|2.896|4,316|
|Total Number of Productive Assets Owned Now|-0.759|0.583|4,316|
|Total Number of Productive Assets Owned Change|-0.173|0.175|4,316|
|Total Number of Livestock Assets Owned Change|-5.110|7.876|4,316|
|Total Number of Consumptive Assets Owned<br>Change|-0.344|0.349|4,316|
|<br>LogTotal Funds Invested in Enterprise|-1.113***|0.412|4,316|
|LogTotal Funds Borrowed for Enterprise|-0.298|0.208|4,316|
|Log Total Cash & Kind Revenue from Enterprises in<br>Rs.|-1.314***|0.466|4,316|
|Total Number of Nights Spent by all Members<br>OutsideHomelast year|8.072|7.603|4,316|

20
tend to grow at 2.2 percentage points less than during peace. Using World Health Organization data on 23 major diseases in populations distinguished by gender and age groups, Ghobarah, Huth and Russet (2003) find that civil war increases substantially the incidence of death and disability produced by contagious diseases. Soares (2005) provides an estimation of the welfare cost of violence in a sample of countries applying a willingness-to-pay approach to account for the health consequences of war. For instance, Soares estimates that the civil conflict in Colombia, by reducing life expectancy at birth by 2.2 years, produces a loss of 9.7% of GDP. Other studies focus on the neighboring effects of civil war. Murdoch and Sandler (2002) show that civil wars reduce also growth across an entire region of neighboring countries. Montalvo and Reynal-Querol (2006) explore the influence of refugees from civil wars on the incidence of malaria in the refugee-receiving countries. They show that for each 1000 refugees there are between 2000 and 2700 cases of malaria in the refugee receiving country.

The empirical literature on the aftermath of civil and international war is scarcer. It seems to indicate that countries do recover in the post-conflict period to at least to their pre-war situations. In a cross-country empirical analysis, Przeworski et al. (2000) finds that post-war economic recovery is rapid. Their results indicate that the average rate of growth during the five years following a war is 5.98 percent. They also find that wars cause more damage under dictatorships than under democracies, but, in contrast, recoveries are faster under dictatorships than under democracies. Barro and Sala-iMartin (1995) explain post-war recoveries --considering the examples of Japan and Germany following World War II-- arguing that whenever a war destroys a given production factor relatively more than other factors, the rate of return of the latter increases, thus creating the forces of convergence that spur rapid growth.

Organski and Kugler (1977, 1980) analyze the economic effects of the two World Wars on a sample of mainly European countries. They find that in the “long run” --15 to 20 years-- the effects of war are dissipated in both losers and winners, typically returning to pre-war growth trends. Miguel and Roland (2005) analyze the impact that the U.S. bombing on Vietnam had on the country’s subsequent economic development. They compare the heavily bombed districts to the rest and find that U.S bombing did not have a lasting negative impact on poverty rates, consumption levels, infrastructure, literacy, and 3
small young businesses often fail, while in others, more mature large firms tend to decline. By contrast, in transition economies there is more variability in the size structure of new firms than among those that exit the market. A large number of new businesses entered the market, filling different niches of activities that were largely underdeveloped under central planning, while job destruction occurred more evenly in firms of different sizes; many large obsolete firms closed, but so did many relatively small new ventures. In transition economies, country effects account for 20.3 percent of the variation in job destruction by exiting firms, but only 6.5 percent of the variation in job creation by entering firms. This is suggestive of cross-country differences in the enterprise restructuring and its impact on firm closure and downsizing.<sup>9</sup> To summarize, the analysis of variance of job flows suggests a significant role for the size composition - a factor that was not considered in previous studies - as well as differences across and within regions. Technological and market structure characteristics (e.g. the industry effects) seem to play a relatively smaller role in explaining cross-country differences in job flows.

> 9See Haltiwanger and Vodopivec [2003] and World Bank [2004].

22
**-** **<u>Table 9: Predicting growth acceleration and deceleration panel data</u>**

|**Variable**|**Dep. variable:**<br>**growth accel**|**dummy of**<br>**eration**|**Dep. variable:**<br>**growth dece**|**dummy of**<br>**leration**|
|---|---|---|---|---|
||**Odds ratio**|**p-value**|**Odds ratio**|**p-value**|
|Savings|1.152|.000|.929|.000|
|Investment in fixed capital|||.956|.062|
|Foreign direct investments net flow|1.146|.000|.811|.000|
|GDP deflator|||1.010|.016|
|Consumption|1.051|.004|||
|Government consumption|.904|.000|||
|Trade|||.980|.008|
|Minor conflict|||1.744|.045|
|Major conflict|.435|.064|||
|LR (chi2)|127.6|.000|97.4|.000|
|N|825||647||
|Note: fixed effect logit regression.<br>Source:Arbache andPage (2007b)|||||

# **_D. Good luck or good policy - How robust is the recent growth?_**

Today’s Africa is clearly different from the Africa of the early 1990s, when it was coming out of the declines after the first two oil price shocks, the debt problems, and stagnation of the adjustment years. Thanks to the recent acceleration of growth, there is definitely a higher economic base to work with. But how sustainable is that growth? Are the main contributing factors good luck or good policy? As the growth diagnostics so far indicate, the answer hinges not only on policy and governance but also on economic fundamentals, such as factor accumulation and productivity as well as trade and export diversification—and the significance of the changes in these fundamentals. This section evaluates Africa’s recent growth against the lessons learned in the previous sections.

**_Commodity prices and terms-of-trade._** External circumstances have certainly been favorable since the mid-1990s: the global economy has been expanding at 3.2 percent a year, global trade expanded by 40 percent a year, and the share of FDI in the world GDP nearly doubled from 1.15 percent in 1995 to more than 2.23 percent in 2005. As a result of greater demand, commodity prices, including oil prices, have been pushed to new high levels. Hence, the better economic performance in the recent period is certainly partly due to the higher export prices of many African countries (figure 7). Higher oil prices now benefit about 8–10 oil-exporting countries in Sub-Saharan Africa, as discussed in the previous section. Non-oil commodity prices have also risen significantly. Of the 40 commodity prices monitored regularly, only cotton prices declined from the high prices of the 2003 drought year. Hence, gains from higher export prices for commodities such as gold, aluminum, copper, and nickel more than offset the losses from higher oil import bills in several oil-importing countries, such as Burundi, Ghana, Guinea, Mali, Mozambique, Rwanda, Uganda, Zambia, and Zimbabwe. Overall, compared with the previous major oil price cycle during 1973–1980, the aggregate terms

23
As the mobile monitors were attached to motorcycle taxis, the location of the mobile measurements might be systematically related to time trends in pollution (e.g., taxi drivers might be more likely to drive through some specific city neighborhoods at the time of day when traffic, hence pollution, is highest or lowest). To address this potential concern of non-random spatial location of the mobile monitors across hours of the day and days of the year, we net out hour and date fixed effects using the readings from the stationary monitors.<sup>26</sup> We run the following regression using the readings from all our stationary monitors _k_ in order to recover hour _b_ and date _c_ where _ln_ ( _PM_ 2 _._ 5 _k,h,d_ ) is the log of the PM2.5 reading from monitor _k_ recorded on calendar date _d_ and hour _h_ . We include static monitor fixed effects _λk_ since we do not have a balanced panel. We then net out these time fixed effects from the readings of our mobile monitors. To do so, we compute the pollution residuals _em,h,d_ ˆ as the log of the raw measurements from our mobile monitors at GPS coordinates _m_ at time _h_ of date _d_ , net of the hour and calendar date fixed effects estimated from the stationary monitors: ˆ _em,h,d_ captures residual pollution variation across locations conditional on a particular hour of the day and a particular calendar date. As such, this allows us to document systematic _spatial_ variation in pollution within the city.

# **5.2 Grid Cell Approach**

To create the neighborhood-level measures of firm density, pollution and road size, we adopt a grid cell approach. The next administrative unit after sub-counties are parishes. Specifically, our 52 sub-counties comprise 179 sampled parishes. Following Ahlfeldt et al. (2015), Carozzi and Roth (2020) and Michaels et al. (2017), we split parishes in our sample into grid cells of 500 _m ×_ 500 _m_ , drawing grid cells on all selected parishes.<sup>27</sup> Each road, pollution measure and firm are attributed to a cell using their geo-coordinates.<sup>28</sup>

> 26While stationary monitors are useful for recovering the _time_ variation in pollution, we cannot rely on them to recover the _spatial_ variation in pollution within sub-county without making very strong assumptions on the decay of pollution with distance from the stationary monitor. In fact, we decided to use both stationary and mobile monitors precisely to be able to document both the spatial and temporal variation in pollution at a granular level. Sullivan and Krupnick (2018) discuss the unreliability of using only stationary monitors. In using the stationary monitors to identify time fixed effects across all the sub-counties in our study, we are assuming that time trends do not vary spatially across the sub-counties in our sample.

> 27For more details on the calculation of the grid cells and the robustness of our approach see Appendix A.2.

> 2818% of firms interviewed in the survey fall slightly outside the boundaries of the corresponding sampled sub-counties, often by just a few meters. We still include these firms in our estimation sample by adding grid

17
The publications dataset was then merged with data on standard growth variables, governance indices and release of survey data to arrive at a panel dataset and a country-level dataset on 175 countries for the period 1985 to 2004. Table A2 in the Appendix provides a detailed description of variables and their sources. Summary statistics for the data used in the paper are presented in Table 1.

To assess the volume of economics research covered in this paper relative to total economics research, Figure 1 presents a characterization of the nature of research over the 20-year span of our data. We split the set of articles into three groups: theoretical contributions, country-specific case studies, and cross-country analyses. During the 20-year period, the number of publications in all 3 groups increased. Nevertheless, there has been a very small reallocation of research across these 3 groups. There is an increase in the share of cross-country empirical work at the expense of the other two, but theory (40 percent) and single-country empirical papers (50 percent) continue to account for 90 percent of all publications in the field. Therefore, the papers examined here represent the vast majority of empirical work in the field, and almost half the output of the discipline as a whole.

# **3. Two Facts on the Geography of Academic Research**

## _3.1._ **Stylized Fact 1: The Research-Wealth Relationship**

Three figures and a table present the basic facts about empirical economics publications. Figures 2 and 3 and Table 2 use the total number of publications over the period 1985-2004 and relate it to average GDP of countries. Here, we disregard time variation in the data, and take 20-year averages of all variables. Figure 4 returns to the panel aspect of the data and looks at the effect of data dissemination on publications.

Figure 2 plots publications aggregated over the 20 years for all countries in the database and by geographical region, as classified by the World Bank (Table A3 in the appendix summarizes the number of publications for every country in our database, and also indicates the number of papers that were published in one of the top 5 economics journal). Countries in different geographical regions are shown in the seven columns—one each for every geographical region. For every region, 7
Given the weakness of budgetary management and the strength of the "economic" ministries relative to those undertaking social functions, it might be thought desirable for both social insurance and social assistance costs to be met from a separate fund. However, this hypothecation would have to be based on public choice logic, rather than on the insurance principle. flypothecation on public choice grounds evades the real issue, which is that the government has not established control over all its branches and has not been able to set and enforce priorities in determining their command over resources (Allan 1994).

# **VI. The Informal Economy and Insurance**

In section II it was argued that the extent to which insurance contributions will be seen as savings will depend in part on households' propensity to save. A severe fall in real income accompanied by the expectation of a future increase in income will engender a low propensity to save, and bring transitional difficulties in getting workers or their employers to pay insurance contributions. It was also argued that a low propensity to save could be reflected in a growth in informal, unmonetized economic activity.

The poor quality of Moldovan national accounts makes these propositions difficult to document. The available estimates of GDP are based on output data and cannot be reconciled with income and expenditure data. Information on the proportion of the population in poverty is also problematic.

23
knowledge on financial issues and also to address the specific financial education needs of high school students. More specifically, the program aimed at providing students with tools to overcome financial challenges they were facing or about to face, such as family finances, entry into the labor market or further studies, and preparing for financial independence.

The themes are taught through 72 case studies called didactic situations (SDs) that include theoretical and applied content, exercises and activities, as well as self-evaluative questions. SDs make use of texts, stories, images, and tables to convey the material in an accessible way. SDs also contain “experiment” sections that are designed to make the material relevant to students’ daily life. For example, when discussing the international economy, students are asked to identify the imported products they use in everyday life. Each SD concludes with a short outline of what students are expected to learn.

Teacher guidelines explain how to integrate the financial education case studies into the regular curriculum, and teachers have discretion over the order in which the cases are taught. The program trained teachers in advance through an introductory seminar that lasted 1 to 2 days, a reference DVD, and made available a training website with distance education material which remained active throughout the program. Teacher training was also important for generating interest and support for the program, which ultimately led to a large fraction of teachers applying the financial education material (see section 6.1 for discussion of take-up rates).

# **4. Research Design, Sampling, and Timeline**

# **4.1. Research Design**

We use a randomized control trial to measure the causal effects of the high school financial education program. Treatment was assigned at the school level. Treated schools received financial education material and teacher training. Control schools did not receive any material or training, but participated in surveys and testing in the same manner as the treated schools. One eleventh grade class in each school participated in our study. Treated classes received financial education during the second semester of eleventh grade (fall 2010) and the second and third semesters of financial education throughout twelfth grade (spring 2011 and fall 2011), the last year of high school. Students in the sample were between 15 and 17 years of age at the start of the intervention.

In addition to the school financial education program, we also study the impact of a complementary parent intervention in treatment schools.<sup>9</sup> During the second semester of financial education (spring 2011), parents of students in treatment schools were invited to participate in a school workshop. At the workshop, they were directed to watch either a financial education video (workshop treatment group) or a health education video (workshop control group) through random assignment. Parents learned which video they had been assigned to

> 9 Due to political constraints, we were unable to extend the parent intervention to control schools.

9