rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1 Introduction

The degree of mobility in income is often seen as a measure of the equality of opportunity in a society, and of the flexibility and freedom of movement in the labor market (Atkinson, Bourguignon and Morrisson, 1992). Greater mobility makes the distribution of lifetime incomes more equal for a given level of single period income inequality. On the other hand, Jarvis and Jenkins (1998) note that too much mobility may represent income fluctuations and economic insecurity. Gottschalk and Spolaore (2002) formalize this trade-off in a model with both aversion to inequality and aversion to unpredictability of incomes, finding the socially desirable level of mobility will be less than a level at which there is full reversal of ranks over time. Nevertheless, in many developing countries, the concern is more likely to be that there is too little, rather than too much, mobility. In particular, Piketty (2000) surveys recent theoretical work which finds that the presence of credit constraints can give rise to the possibility of “lowmobility traps”, whereby households who need to borrow to finance investment can take a long time to build up wealth.

Measurement of the degree of mobility using panel data on earnings is complicated by the presence of measurement error, and by non-random attrition from the panel. A simple measure of mobility is the slope coefficient from a regression of current period earnings on lagged earnings (e.g. Jarvis and Jenkins, 1998; Fields et al. (2003)). Classical measurement error causes the well-known attenuation bias towards zero in the estimated slope coefficient, leading one to overstate the degree of mobility. The existing literature has attempted to overcome the measurement error problem through the use of instrumental variable methods.<sup>1</sup> Instruments for lagged income have included lagged expenditure (e.g. McCulloch and Baulch, 2000), subjective measures of well-being (Luttmer,

> 1 An alternative method used in developed countries has been the use of administrative data from payroll records, where measurement error
.

**Table 2. Selected Features in Collective Voluntary Pension Plans**

||**United Kingdom**|**New Zealand**|**Italy**|**USA**|**Canada/Quebec**|
|---|---|---|---|---|---|
||(2012)|(2006)|(2007)|(2012)|(2014)|
|Automatic<br>Enrollment|Mandatory|Mandatory|Mandatory, but<br>implemented as<br>"opt‐in"|Optional,<br>but<br>considered<br>a<br>safe<br>harbour<br>(mandatory<br>in<br>Simple plans)|Mandatory|
|Requirement on the<br>minimum<br>number<br>of<br>employees<br>for<br>opting for a plan|250+<br>2012;<br>50+<br>2014;<br>30+<br>2016;<br>30‐ 2017|No|No|No, but 100‐ can<br>opt for SIMPLE<br>plans|5+|
|Period<br>for<br>opting<br>out|30 days|Between 14 and<br>56 days|180 days|90 days|60 days|

Source: national sources As shown in Figure 4, coverage of funded schemes in Italy is inferior to the rest of the countries with automatic enrollment. In fact, only a small percentage of participants in voluntary pension schemes in 14
Descriptive overviews of this data including population, employment opportunities, and income distributions are shown in Appendix A – Detailed Data Description.

Rio de Janeiro is the second largest city in Brazil, with more than 6 million inhabitants and the second largest GDP (IBGE, 2021a, 2021b). However, despite being relatively prosperous, it is characterized by high levels of inequality and large fragmentation (Ferreyra & Roberts, 2018), the latter being attributed to both morphological and geographic constraints (Ricky Burdett et al., 2013) as well as historical policies (Lago, 2015). Economic activity in Rio is heavily concentrated in the historic city center (Centro), with slightly lower levels of employment in directly adjacent areas and much lower levels across the rest of the city. This is contrasted with the population distribution across the city, which is relatively dense globally and extends across the municipal area with decreasing density as the city extends westward. The wealthiest residents are concentrated in low-density areas along the Southern coastline. Income tends to decrease the further North and West away from Centro, with the poorest residents generally living in the furthest North or Western parts of the city, except in the cases of some favelas with high concentrations of extremely poor residents in small areas of otherwise higher income regions.

Socio-economic data for Kinshasa was sourced from a Commuter Travel Survey conducted by the Japan International Cooperation Agency (JICA) in 2018, as part of ongoing work towards the Kinshasa Transport Master Plan (JICA, 2019). The survey was conducted for Traffic Area Zones (TAZ) and extended across the functional labor market of Kinshasa. However, the survey does not cover the full extent of Kinshasa’s provincial boundaries. In addition, population data from WorldPop<sup>8</sup> was also used to complement the survey data and provide more detailed population information.

Kinshasa is the capital and economic center of the Democratic Republic of Congo (DRC) with a population in excess of 14 million (World Bank, 2019) and
# **3.5 Inference**

As already alluded to, the error term _εd_ is likely to be correlated across neighboring districts, if only because geographically proximate regions experience similar productivity shocks over time. I use a nonparametric covariance matrix estimator or spatial HAC (Conley, 1999) to account for heteroskedasticity and spatial dependence. A familiar alternative to the spatial HAC is the clustered covariance estimator. But clustering standard errors by state or region assumes independence of errors across state or regional boundaries, a serious lacuna given the large fraction of districts bordering an adjacent state.<sup>25</sup> Bester et al. (2011) show that the asymptotic normal distribution, typically used to obtain critical values for inference in HAC estimation, is a poor approximation in finite samples. I thus follow their suggestion of bootstrapping the distribution of the relevant test-statistics. For this reason, inference should be guided by _p_ -values rather than standard errors alone, although I will follow convention and report both. In particular, bootstrapped _p_ -values are much less sensitive than standard errors to choice of the tuning or bandwidth parameter (i.e., the degree of kernel smoothing).<sup>26</sup> Both numerator, ∆ _wd_ = _<u>wd,</u>_ 09 _−_ _<u>wd,</u>_ 04, and denominator, _ψd_ , of the dependent variable in equation (8) are district-level summary statistics derived from micro-data. This gives rise to a particular form of heteroskedasticity and renders least-squares estimation inefficient. The standard solution is to use weighted least-squares, taking the inverse of the estimated sampling variances as weights. While the sampling variance of ∆ _wd_ is _σ_<sup>2</sup> <u>(</u> _<u>wd,</u>_ 09)+ _σ_<sup>2</sup> <u>(</u> _<u
- Dinkelman, T., Kumchulesi, G., and Mariotti, M. (2024). Labor migration, capital accumulation, and the structure of rural labor markets. _Review of Economics and Statistics_ , pages 1–46.

- Dinkelman, T. and Mariotti, M. (2016). The long-run effects of labor migration on human capital formation in communities of origin. _American Economic Journal: Applied Economics_ , 8(4):1–35.

- Docquier, F., Lodigiani, E., Rapoport, H., and Schiff, M. (2016). Emigration and democracy. _Journal of Development Economics_ , 120:209–223.

- Docquier, F. and Rapoport, H. (2012). Globalization, brain drain, and development. _Journal of Economic Literature_ , 50(3):681–730.

- Dustmann, C., Sch ̈onberg, U., and Stuhler, J. (2017). Labor supply shocks, native wages, and the adjustment of local employment. _The Quarterly Journal of Economics_ , 132(1):435– 483.

- Dustmann, C., Vasiljeva, K., and Piil Damm, A. (2019). Refugee migration and electoral outcomes. _The Review of Economic Studies_ , 86(5):2035–2091.

- Egorov, G. and Sonin, K. (2023). Why did Putin invade Ukraine? A theory of degenerate autocracy. Technical report, National Bureau of Economic Research, Cambridge, MA.

- Endoh, T. (2010). _Exporting Japan: Politics of emigration to Latin America_ . University of Illinois Press.

- Foged, M. and Peri, G. (2016). Immigrants’ effect on native workers: New analysis on longitudinal data. _American Economic Journal: Applied Economics_ , 8(2):1–34.

- Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider
Figure 3. Age-Dependency Ratio <!-- Start of picture text --> 100<br>90 Initial<br>Final<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Source: Health, Nutrition, and Population Statistics, World Bank 2012.<br>percentage of working-age population<br><!-- End of picture text --> Figure 4. Growth in the Share of Adults per Household <!-- Start of picture text --> 2.5%<br>All<br>2.0% Poor < US$1.25<br>Poor <US$2.5<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>-1.0%<br>-1.5%<br>percentage change<br><!-- End of picture text -->

**_Source:_** Authors’ calculations with data from SEDLAC, RIGA, and National Household Surveys.

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fossil fuels is the main source of revenue for many fossil fuel exporters which thus face significant fiscal risks. The MENA region is the most exposed while dependence is rising for some countries in the ECA region (Figure 7D). Diversification away from fuel exports will become more urgent, bringing additional challenges and uncertainties to these economies. In our analysis, we use the ratio of fossil fuel exports to GDP as the indicator for this type of transition risk.

# _Macro‐financial conditions_

We analyze macro‐financial conditions using the EIU country risk index and its subcomponents (macroeconomic, banking, and sovereign debt). The EIU Country Risk Index is part of the EIU Country Risk Model and measures the build‐up of sovereign, currency, and banking risks in 131 countries since 1997, using 77 quantitative and qualitative variables. In each category, countries are assigned a score from 0 to 100, where 0 is the lowest risk and 100 is the highest risk.<sup>13</sup>

# _Linking climate and macro‐financial risk measures_

This section highlights countries and regions that face the double jeopardy of elevated macro‐financial and climate related risks. We discuss the results for the four main categories of risks. Countries are classified as being at higher macro‐financial or climate‐related risk if they are above the sample median for the particular climate‐related or macro‐financial indicator. We also examine systematic relations

> 13 Using the MFR indices of macro‐financial risks yields broadly similar results. However, due to the confidentiality of these ratings these results are not shown in this paper.

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which those funds shall be spent.

Thus, while logic and experience suggest that basin organizations must have some financial resources and autonomy for decentralization initiatives to be implemented successfully, this is not a variable that can be arrayed unidirectionally. Rather, all other things being equal, we would expect to see the greatest prospects for success somewhere toward the middle of a spectrum where complete central government funding and control lie at one pole and complete basin-level funding and control lie at the other.<sup>7</sup>

> 6 This is distinct from the discussion in the previous section about the motivation for the decentralization policy.  A central government might adopt a decentralization policy out of sincere motivations and in consultation with local stakeholders, but nevertheless fail for other reasons to actually relinquish any control over resource management or establish local authority.

> 7 An optional further refinement of this variable would involve inquiring not only about the gross proportions of funding between central and local agencies but about how funding relates to resource management functions.  In this refinement, successful implementation of decentralization in water resource management would be linked to central government funding and control of functions that are best organized at a larger scale because of their high capital or technical requirements (e.g., conducting hydrogeological surveys or developing water quality standards) and basin-level funding and control of functions that are best organized on a smaller scale because of their time-and-place specificity or socio-

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Shown in Figure 1, the questions test understanding of three basic financial concepts: interest rate compounding, inflation, and risk diversification. The first two questions also require basic numeracy skills, while the third question requires familiarity with the definition of stocks and mutual funds.

Although the usefulness of these questions, at least when employed on their own, has been the subject of some debate, they form a useful basis of comparison across countries.<sup>2</sup> A summary of results from nationally representative surveys is shown in Table 1. These include those used in a series of studies focusing on financial literacy and retirement, including Lusardi and Mitchell (2011b) in the U.S., Fornero and Monticone (2011) in Italy, Bucher-Koenen and Lusardi (2011) in Germany, Sekita (2011) in Japan, Crossan et al. (2011) in New Zealand, Almenberg and Säve-Söderbergh (2011) in Sweden, Alessie et al. (2011) in the Netherlands, and Klapper and Panos (2011) in Russia. Additional surveys using these questions were conducted or analyzed by Cole et al. (2011) in Indonesia and the state of Gujurat in India, Behrman et al. (2010) in Chile, and a series of recent World Bank studies in Romania, Azerbaijan, Bulgaria, Bosnia and the West Bank and Gaza.

Even so, careful attention should be paid to differences in the wording of the questions and answer options (as noted in the footnotes to Table 1). In other cases, the spirit and wording of the questions may be very similar, but the numbers used can affect the mathematical difficulty of the required calculations. Insofar as it is possible to generalize, it appears that higher-income countries perform better than lower-income countries on average.

> 2 Cole and Fernando (2008) note that the ability to answer these questions is highly predictive of financial decisions, while Lusardi and Mitchell (2007) find that the first question on compounding interest happens to be the
>|1987-88<br>|National<br>|3200<br> <br>|2349<br>|2350<br>|2341 Month<br>|0-4.99<br>|f 22<sup>i ttl</sup>|
|Guatemal|a Guatemalan<br>Survey of Family<br>Health|1995<br>|Regional<br>|<br>4792<br>|2817<br>|2854<br>|2814 Month<br>|0-4.99<br>|The survey covers 4 departments (out o<sup>n oa).</sup>|
|Guyana<br>|LSMS<br>|1992-93<br>|National<br>|5340<br>|590<br>|589<br>|587 Month<br>|0-4.99<br>|i h<sup>83 % f th</sup>|
|Indonesia|Indonesian Family Life Survey|1993<br>|RegIonal<br>|7224<br>|1250<br>|1371<br>|1233 Month<br>|0-4.99<br>|The survey covers 13 provnces tat represent<sup>o e</sup><br>population.|
|Morocco|LSMS<br>|1990-91<br>|National<br>|3323<br>|2121<br>|2121<br>|2120 Month<br>|0-4.99<br>||
|Nepal<br>|LSMS<br>|1996<br>|National<br>|3373<br>|1597<br>|1603<br>|1586 Month<br>|0-3.99<br>||
|Nicaragua<br>|LSMS<br>|1993<br>|National<br>|4200<br
Figure 7: Political Influence Concentration Curve, example <!-- Start of picture text --> Namibia (2004)<br>Senegal (2011)<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Share of individuals from the top<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>Share of individuals from the bottom<br>.1<br>0<br><!-- End of picture text --> index for all countries in our sample. There is no clear pattern among the two indices: political concentration may be very high or very low for each level of poverty. Nevertheless, we can see some regional patterns. Almost all countries in Europe and Central Asia and North Africa and the Middle East are found in the bottom left quadrant: these are countries where both the poverty level and the political influence concentration indices are relatively low with respect to their averages (represented, respectively, by the vertical line at about 0.35 and the horizontal line right above 0.7 in Figure 8). The largest share of countries in Sub-Saharan Africa are instead mostly positioned in the upper-right quadrant, where both the political influence concentration index and the headcount poverty index are higher than average. Furthermore, almost all countries in Latin America have lower poverty rates than average, but higher political influence concentration.

One piece of empirical evidence is worth noticing. Although theory defines the lower bound for _I_<sup>Π</sup> being zero when the political power is equally shared among all individuals in the society, the data show that almost all countries have a level of _Political Influence Concentration Index_ higher than 0.5 (the only two exceptions are and the Czech Republic, with 0.
# **II. In-service teacher training in Ecuador**

In 2011, Ecuador enacted a new education law ( _Ley Orgánica de Educación Intercultural_ ) that provided the education sector with a renewed vision of the country’s educational processes and actors. The law recognizes and gives high value to the work of teachers and introduces important elements to promote and develop the teaching career. For instance, Chapter 4 of the law specifies that teachers in Ecuador shall have free access to professional development opportunities, such as in-service training, to continuously update their knowledge and pedagogical practices. Chapter 10 establishes the need for the creation of a National University of Education, whose objective would be to support and develop the teaching profession and to better train new teachers. The law also establishes a more transparent teaching career path, whereby promotions are based on obtaining satisfactory results in teaching evaluations and on active participation in continuing education programs.

In 2008, the MINEDUC began making important efforts to promote in-service teacher training through its Comprehensive <mark>System for the Development of Education Professionals</mark> (SIPROFE), overseen by the MINEDUC’s Undersecretariat of Professional Development. SIPROFE’s main objective is to improve the country’s education quality through the provision of training to teachers and school administrators. To deliver in-service teacher and administrator training, the SIPROFE offers a variety of courses, including remedial and developmental courses, orientation to new teachers, and school management. The duration of courses varies between 8 and 60 hours, although most courses have a duration of either 20 or 40 hours (MINEDUC 2016). Some courses are sequential (that is, are a prerequisite for others) to promote and incentivize the sustained participation of teachers. Training programs under the SIPROFE have traditionally been delivered through agreements with universities and pedagogical institutes ( _Institutos Superiores Pedagógicos_ , ISPED) that satisfy predetermined quality and service delivery requirements.<sup>4</sup> Data from the MINEDUC suggest that during 2010–18, the SIPROFE program delivered in-service teacher training to
We use granular data on FDI projects in several African countries matched with spatial data on Internet infrastructure roll out to estimate the effects of Internet connectivity on sectoral FDI in Africa. Our identification strategy leverages the plausibly exogenous variations in the staggered arrival of submarine fiber Internet cables that brought high-speed Internet to the continent and the spatial variations in access to the terrestrial cable network.

First, we show that the arrival of high-speed Internet played a crucial role in stimulating FDI to Africa. The effects are, however, largely concentrated in the service(s) sector, with finance, technology, health and retail subsectors as main beneficiaries. The probability of receiving FDI, as well as the number and value of FDI in these services (sub)sectors, increased with access to fast Internet. Second, we show that the effects of high-speed Internet connectivity on FDI pertain largely to subnational districts with better access to complementary infrastructure such as roads and electricity. In particular, we find consistent evidence on the complementary role of electricity access on the impact of Internet connectivity on investment. This is plausibly due to the key role electricity plays in powering digital equipment and the general effect of electricity on technology adoption. Hence, access to electricity services amply the role of high-speed Internet in attracting investment. Third, we provide suggestive evidence that an increase in the quality of governance and market expansion–resulting in high sales (return on investments)–as potential mechanisms through which high-speed Internet connectivity induces FDI.

Overall, the findings of the paper underscore the importance of quality infrastructure provision in attracting investments to developing and emerging economies. In addition, the results highlight the complementarities in the economic impact of infrastructural services. Thus, future research on the impact of infrastructural services should pay particular attention to these complementarities.

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requires projections of the export share; while this has been rising rapidly in the past, it fell with during the 2008 Global Financial Crisis and never recovered. Given the aim to rebalance the economy away from exports and investment towards consumption, we simply project a constant value for the share parameter.

The base year data for 2010 was constructed in 2013 since then, the macro variables for 2011-13 is now available; these include the GDP, investment and current account surplus. The current account surplus has fallen, and the unusually high share of investment in GDP has risen even more after 2010. We take these into account in setting the savings rate and current account balance as share of GDP for these years.

# _Parameters_

The rate of productivity growth is another factor that has a large effect on the base case growth rate of the economy but has little impact on the difference between cases. Total Factor Productivity growth at the industry level in the 1982-2000 period show a very wide range of performance as estimated by Cao et al. (2009), ranging from -10% to 5% per year. The Domar-weighted productivity growth for all industries was 2.7% for 1982-2000. To keep the base case as simple as possible we ignore this wide range of observed

> 24 The projections for crude oil prices are taken from the EIA’s _Annual Energy Outlook 2010_ , Table 12, which is available on their web page:  http://www.eia.gov/forecasts/aeo/.

‐81‐
−2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10<br>s s s<br>Autocracy Democracy Polity Score<br>−10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10<br>s s s<br>1 1<br>0<br>0 0 −1<br>−1 −1 −2<br>−2 −2 −3<br>3 0 0<br>2 −1 −2<br>1 −2 −4<br>0<br>−3 −6<br><!-- End of picture text --> _Source_ : Authors’ analysis based on data from the Polity IV project and structural breaks estimated using the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

All indicators describing a country’s political institutions from the Polity IV database are significantly lower (or higher) in the decade before a slump occurs and then gradually return to normal levels thereafter. Figure 2 shows that, ten years before a slump, countries tend to

> 12In this case, ‘normal’ refers to all observations other than the 21 years around the downbreak.

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