rafmacalaba/data-use-mentions-extended huggingface dataset:
16 government (CU, column 1), adjusted central government (CA, column 2), and unadjusted consolidated public sector (PU, column 3). 7° Performing this robustness check for alternative public-sector measures is important given that differences in empirical results across different studies have often been attributed to differences in public sector definition. Surprising to us, the estimated results are remarkably robust across definitions of the public sector. Concerning the adjusted and unadjusted data, this is not all that striking given that we have dropped from the sample the observations corresponding to extreme inflation episodes. In any case, Table 6 shows only one exception that deserves discussion.”° The estimated coefficient on public saving is larger in the central government regressions than in those corresponding to the consolidated public sector. In fact, the “offset” coefficient is about 25% larger in the case of the central government, so that in the long run it reaches 72% and 95% in the CU and CA specifications, respectively, in contrast with the 58% and 69% that results from the PU and PA estimates. The straightforward explanation of this result is that there is a larger degree of offset between the central government and other public-sector levels (provincial and state governments and public enterprises) than between the consolidated public sector and the private sector. This in turn implies that studies of Ricardian equivalence based on a central-government definition of the public sector tend to overstate the public-private saving offset. 4.5 Additional explanatory variables In Table 7, we add other potential private saving determinants, excluded from the “core” set of explanatory variables because they are either less commonly used in the literature or not well justified conceptually. We consider each variable in turn. The first one is the current account deficit (relative to private disposable income). While popular in the literature, the current account deficit is a somewhat dubious regressor, as it is jointly determined with saving in countries and/or at time periods characterized by unrestricted access to net foreign lending
While the data cover the years 2000 to 2015, we focus on the most recent 10‐year period, between 2006 and 2015. Years prior to 2006 show several missing values for two of our variables of interest. Approximately 25% and 30% of city GDP per capita growth rate and city employment growth rate values, respectively, are missing for the year 2001. These percentages decrease for the subsequent years with only two missing observations for each variable for the year 2006. The period we chose has then the double advantage of being the most recent and complete.

Using the information in the data set, and following the classification provided in World Bank Report (2016), cities can be classified in three broad categories depending on their mix of economic sectors: Market towns, Production Centers, and Creative and Financial Services. Market towns are cities with a GDP per capita below $2,500 and where consumer services account for a large proportion of the economy; Production Centers include cities with a GDP per capita between $2,500 and $20,000 with their economy mostly focused on manufacturing, construction, and mining industries; and Creative and Financial Services refer to cities with a GDP per capita above $20,000 where high‐end services account for an increasing share of their economies.

This distinction of cities by major economic activity can help us understand how different the challenges could be for different types of cities. Figure 1 shows the distribution of these different types of cities by region. Blue represents Market Town cities, Orange corresponds to Production Centers, and Creative and Financial Services are shown in green. It is clear from this figure that the distribution of city types varies widely across regions. On one extreme, in South Asia and Sub‐Saharan Africa, cities are either Market Towns (blue) or Production Centers (orange). These cities are often starting their industrialization process, attracting population, and growing fast. They often struggle with the natural consequences of fast growth, such as how
family firms) perform better than their older counterparts and that (ii) family-owned but professionally-managed firms perform better than those which are both family-owned and family-managed. This reinforces the Bloom and Van Reenen (2007) findings that primogeniture is especially harmful for management practices, and provides additional evidence that it is family management rather than family ownership that is detrimental to firm performance.

Family-managed firms are on average 24.8% less productive than professionally-managed firms. Our prior results therefore suggest that strong contractual institutions should raise aggregate productivity by lowering the share of family-managed firms. Using our sufficient statistic approach that allows to us to relate changes in trust to changes in TFP through its effect on the share of professionally-managed firms, we find that a one standard deviation increase in trust increases TFP by 2.68% and that moving from the minimum trust region to the maximum increases TFP by 14.65%. Stepping back to the national-level where we can use the rule of law index directly, we find that a one standard deviation increase in a country’s rule of law increases TFP

> 8First, Amit et. al. (2015) examine how family ownership and management respond to regional variation in measures of institutional development in China. The institutional variables measure investment climate (e.g. taxes, bureaucratic and customs delays) and economic development (average firm market value) rather than trust. Their data have only 6 observations of these institutional outcomes across regions. Second, Akcigit, Alp and Peters (2016) have an empirical component to their paper that uses variation in trust across Indian states. However, their data only provides information on family ownership, while our data allows to us to explicitly measure whether or not firms are family-managed. They also only have 22 observations of trust (Indian states). In contrast, our data spans 134 regions in 11 countries which gives us enough power to cluster standard at the region-level to identify the effect of trust on managerial decisions.

> 9The direction of this bias intuitive: countries with
. Additional controls: Ethno-linguistic fragmentation, income inequality, (log) population, size of extractive industries, democratization (current), demographic dummy variables, economic openness (current), religious make-up variables, regional dummies (see Appendix A for details). * Significant at the 10% level, ** significant at the 5% level, ***significant at the 1% level. CPI score divided by 10 to aid interpretation. OLS results are -0.009 (0.0055); 0.19*** (0.06); 0.14* (0.081), for the interaction term-for models (1), (2) and (3) respectively. See Appendix A for details of variable descriptions. _Source: The Author_ Overall, the finding of the regression analysis suggests that in a high-income democracy context, consistent with the model, in which retrospective evaluation of incumbents is most likely to be feasible, a higher EDD/NEDD ratio is associated with less rent extraction, as the model introduced above and its antecedent

> 36 As membership of the EU is confined to high-income democracies in this dataset (Persson and Tabellini’s dataset and measures were developed before the 2004 enlargement of the EU) the non-EU component cannot be used, as no non-highincome democracy is a member and, therefore, there is no variation in this variable.

27
Regional unemployment also played a role in the Central West and North, while leadership approval mattered in the South and Southeast (Panels J, K, and L). This is in line with the greater economic dynamism and better labor market conditions in the South of Brazil (Brazilian Institute of Geography and Statistics, 2021). However, increase in economic pessimism and dissatisfaction with living standards played a role in the decline of SWB in all regions.

Since the last GWP survey included in our data set was conducted in November of 2020, it is possible that reactions to the 2020 COVID-19 pandemic are also driving the declines in SWB and differences in these declines across groups. Since in the next section we focus on the role of the economic crisis in the mid-2010s and the loss of approval of the governments prior to Bolsonaro’s election in 2018, we conducted a robustness check on the analysis of the decline in SWB with data up to 2018. We find very similar results, which are available upon request.

# **5. Declining SWB and Voting Behavior**

Did the plunge in experienced welfare following the economic and political crisis in the mid-2010s influence the voting behavior in Brazil during the 2018 general elections? Since the work of Kramer (1971), several economic studies have linked the state of the economy to election outcomes which either reward or punish the incumbent party. Lewis-Beck & Stegmaier (2007) put forward two main explanations for the relationship between the state of the economy and voting behavior. First, voters can punish (or reward) the incumbent for a deteriorated (improved) personal economic situation. In developing countries, changes in one’s perceived personal economic situation have been found to be particularly strongly correlated with support for the incumbent (Lewis-Beck & Stegmaier, 2007). Job losses related to automation and globalization are also often mentioned as important reasons for the election of Donald Trump in the United States in 2016 (e.g., Autor et al., 2017). Second, voters
the costs they must pay for land and labor and the risks they will face at a given location. Taking the hedonic land and labor market pricing gradients as given, firms will chose a profit maximizing location. New firms will take into account the competitive effects of whether they want to locate close to or far from incumbent firms.

Firms are not tied to any one location on the map. If certain areas experience repeated horrible weather, then rents of such land should decline and the most productive firms should be less likely to locate there (Rentschler et al., 2023). Such areas would likely be populated by less productive firms. In this sense, the equilibrium hedonic land price gradient sends signals about land quality.

Given that it is costly for a firm to migrate, firms have strong incentives to research the risks and challenges that each location faces. In the United States, there has been a growth in climate science information firms that provide location specific “climate report cards” for different geographic areas regarding their flood and fire risk. Such sufficient statistics are based on academic models and recent geocoded natural disaster realization data. In the short run, we expect that if such tailored risk report cards are introduced in the developing world this will accelerate the learning process and that geographic site selection will be better informed about the recent shocks and the expected future shocks (Burlig et al., 2024).

Migration in response to climate change induces reallocation of activity. For example, weather change induced migration of labor leads to reallocation of factors across space (Cai et al., 2016; Cattaneo and Peri, 2016; Mueller et al., 2014), as evidenced among localities in rural India that experienced higher-than-normal local rainfall. Such migration shifted labor towards non-agricultural non-tradable sectors (e.g., construction, retail, and education sectors) and consequentially increased productivity in the agricultural sector and led to an increased demand for non-tradables (Emerick, 2018). Higher temperature in India also led to reallocation of agricultural
21 Figure 2.2 shows households' desired improvements in their housing conditions. The most common wish is to move to a larger apartment (30% of Vladimir and 26% of Gorodets households); 11 and 7% respectively would like to exchange their apartments for two or several so as to split with relatives; 6 and 8% would like to move into more convenient housing or apartments on par with their present ones. Very few households (only 1% in each city) would consider moving into smaller or less convenient apartments (e.g., to reduce the utilities payments).

The proportion of households actually planning to improve their living conditions in the near term is 21% in Moscow, 24% in Vladimir and 15% in Gorodets (Fig. 2.3), which means that the overall picture in 1996 had not changed much from 1994.

**Fig. 2.2.** Household Motivations in Changing Housing (% of households polled)

<!-- Start of picture text --> 40<br>35-<br>30 * to move to a larger apartment<br>30 _ 26.7 0 to move away from relatives<br>26<br>El to move to an equivalent<br>20 - apartment<br>* to moto to a more convenient<br>10.9 apartment<br>M to move in with relatives<br>10 - 6.8<br>5 4.3 4.2 E to move to a small apartment<br>1.4 1.2 10<br>0~~~~~~~~~~~~~~~<br>Vladimir,1996 Gorodets,1996<br>Sources: Vladimir and Gorodets Longitudinal Household Survey, December 1996<br>Fig. 2.3. Households' plans to improve their housing conditions in the near future<br>(% of households polled)<
>fob</sup> (j)� c in (6), we see that ...rm j ...nds it pro...table to serve country c if xc<sup>fob</sup> (j) > �Fc=� c. For each destination, there is therefore a minimum value of xc<sup>fob</sup> (j) that is consistent which non-negative pro...ts. Under the assumption of a log-normal distribution for ac(j), this yields a Tobit structure where the minimum value of xc<sup>fob</sup> (j) in country c serves as a theoretically-consistent censoring point.

The main advantage of the Tobit estimator is that the correction procedure relies solely on the link between theory and observed trade ‡ow data. For robustness, we will also use a Heckman correction approach similar to Helpman et al. (2008). To avoid identi...cation based on functional form, the Heckman selection model requires identifying at least one variable that a¤ects export participation but not export intensity. From (7) and (8), we see that this requirement is ful...lled by a variable that in‡uences solely ...xed costs of exporting. In line with Helpman et al. (2008), we will use measures of entry costs based on World Bank data that may plausibly satisfy this requirement. As a benchmark, we will also present the results of OLS estimates of (12) on non-zero export revenues.

14
estimating the model’s coefficients, estimates for areas are generated by calculating _E_ � _G_<sup>_−_1</sup> ( _ysar_ )�, conditional on estimated parameters _β,_<sup>ˆ</sup> ˆ _ση_<sup>2,and</sup><sup>_σ_ˆ</sup> _ε_<sup>2.Weincludebothsurveyweightsand</sup> population weights, with the latter taken from WorldPop estimates rather than the censuses. The software calculates measures of uncertainty using 100 parametric bootstrap replications. For more details on the model, we refer readers to Tzavidis et al. (2018) and Molina and Rao (2010).

As with any regression model whose main goal is prediction, EBP models can be prone to overfitting. This is especially true in our case, where we have several hundred possible features from which to choose. To help prevent overfitting, we select features using lasso, implemented using the R package _glmnet_ (Friedman et al., 2010). We select the optimal lambda using cross-validation.

> 10The package is a spin-off of the EMDI package developed by Ifeanyi Edochie and colleagues and available for download at: https://github.com/SSA-Statistical-Team-Projects/SAEplus.

> 11The level at which we are interested in predicting outcomes is the area, described above.

11
750|666,451|69,921|273,538|419,491|590,419|
|Electricity|9,793|<br>3.507|13,300|5|0|9,168|4,138|
|Construction|65,811|66,048|131,859|0|0|0|131,859|
|Commerce Restaurants<br>& Hotels|277,033|44,849|321,882|0|0|275,706|46,177|
|Transport,<br>Storage<br>& Communicatio|n 62,612|30,539|93,151|2,751|0|54,092|41,810|
|Financial<br>Services|104,286|12,436|116,722|3,028|G|44,312|75,438|
|Other Services|181,055|58,753|239,807|146|0|60,621|179,333|
|Total|1,100,05|0 699,133|1,799,182|105,821a|316,314|1,015,447|1.205.871'|

a) includes 17,363 million pesos of imports going directly to final demand.

Source: Sistema de Cuentas Nacionales de Mexico, Tomo 1, Resumen General, pp, 106, 138. The figures include indirect taxes and subsidies. When imports are deducted from the total in the last column, it **is** equal to the total in the first column.
, 15(2), 245-295.</mark>

- <mark>Afridi F., Dinkelman T., Mahajan K. (2018). Why are Fewer Married Women Joining the Work Force in India? A Decomposition Analysis over Two Decades. Journal of Population Economics 31 (3): 783–818.</mark>

- <mark>Ahmed, T., & Sen, B. (2018). Conservative outlook, gender norms and female wellbeing: Evidence from rural Bangladesh. World Development, 111, 41-58.</mark>

- <mark>Akter, S., & Chindarkar, N. (2020). An empirical examination of sustainability of women’s empowerment using panel data from India. The Journal of Development Studies, 56(5), 890-906.</mark>

- <mark>Amir, S., Kotikula, A., Pande, R., Bossavie, L. L. Y., and Khadka, U. (2018). Female Labor Force Participation in Pakistan: What Do We Know?. World Bank, Washington, DC.</mark>

- <mark>Anukriti, S., & Dasgupta, S. (2017). Marriage markets in developing countries. The Oxford Handbook of Women and the Economy, 97-120.</mark>

- <mark>Arnab, A. T., & Siraj, M. S. (2020). Child Marriage in Bangladesh: Policy and Ethics. Bangladesh Journal of Bioethics, 11(1), 24–34.</mark> <u>https://doi.org/10.3329/bioethics.v11i1.49193</u>

27
in  exporting  firms  relative  to  domestic  firms  to  be  insignificant.  Thus,  our  findings  using matched  employer  employee  data  suggest  a  quite  different  picture  of  the  links  between trade  liberalization  and  wages  than  is  obtained  by  analyzing  the  data  at  a  more  aggregate (firm)  level.  Consistent  with  the  models  of  Helpman,  Itskhoki,  and  Redding  (2010)  and Davidson,  Matusz  and  Schevchenko  (2008),  we  find  that  the  workforce  composition improves  systematically  in  exporting  firms  in  terms  of  innate  (time  invariant)  ability  and  in terms  the  quality  of  the  worker-­‐firm  matches.   This  finding  also  serves  to  explain  the difference  between  the  results  at  the  firm  level  and  those  at  the  worker  level.  If  average worker  ability  improves  systematically  in  exporting  firms  following  trade  liberalization,  and this  change  in  not  taken  into  account,  it  will  appear  that  trade  liberalization  leads  to  a differential  wage  improvement  for  workers  at  exporting  firms  even  when  this  is  not  the case.

The  remainder  of  this  paper  is  organized  as  follows.  In  Section  2,  we  present  a  background discussion  on  Brazil’s  trade  policy  reforms  and  describe  the  data.  We  present  the  empirical methodology  and  estimation  results  for  the  aggregate  (firm-­‐level)  analysis  in  Section  3  and for  the  analysis  at  the  worker  level  in  Section  4.  Section  5  concludes.

# **2. Data   and   Policy   Background**

Our  main  data  are  administrative  records  from  Brazil  for  formal-­‐sector  workers  linked  to their  employers.  We  combine  this  worker-­‐level  information  with  complementary  data sources  on  firm-­‐level  exporter  status  and  information  on  industry-­‐level  trade  protection during  Brazil’s  main  trade  policy  reform  period.

# **2.1.   Brazil’s   Policy   Reforms**

The  1990s  were  a  period  of  dramatic  policy  reform  in  Brazil,  providing  a  particularly appropriate  setting  in  which  to  study  the  impact  of  trade  liberalization  on  wages.  As compared  to  the  gradual  process  of  globalization  in  many  developed  countries,  Brazil’s trade  reform  occurred  over  a  relatively  short  period
# **Figure 8. EAP: Health and education**

_East Asia and the Pacific made great progress towards education and human development outcomes, including child survival, nutrition and education outcomes. Despite clear progress in the region, some countries still face significant challenges and serious education and humanresource shortfalls._

# **A. Health expenditure**

# **B. Life expectancy, by country**

<!-- Start of picture text --> Percent of GDP Years<br>14 Latest OECD World EAP 85 Latest OECD World EAP<br>12 80<br>10<br>75<br>8<br>6 70<br>4<br>65<br>2<br>0 60<br>C. Under-5 mortality rate D. Quality of math and science education<br>Number per 1000 live births Score<br>2016-17 EMDE OECD EAP<br>80 Latest OECD World EAP 6<br>5<br>60<br>4<br>40<br>3<br>20 2<br>0 1<br>0<br>E. Country capacity to retain or attract F. Tertiary gross enrollment ratio<br>talent<br>Score Percent<br>2016-17 EMDE OECD EAP Latest OECD World EAP<br>6 80<br>5 70<br>60<br>4<br>50<br>3 40<br>2 30<br>20<br>1<br>10<br>0 0<br>Vietnam Thailand Cambodia China Solomon Islands Mongolia Philippines Fiji Papua New Guinea Malaysia Indonesia Myanmar Lao, PDR Timor-Leste China Vietnam Malaysia Thailand Fiji Mongolia Indonesia Philippines Timor-Leste Cambodia Solomon Islands Myanmar Papua New Guinea<br>...<br>Malaysia China Thailand Vietnam Fiji Mongolia Indonesia Philippines Cambodia Myanmar Timor-Leste Papua New Lao, PDR China<br>Solomon Islands Malaysia Indonesia Thailand Lao, PDR Philippines Cambodia
A number of studies have examined post-recession policy responses to these challenges. Galasso (2008) runs simulations based on the political determinants of social security contribution rates and retirement ages, and concludes that the retirement age is likely to increase in each country, regardless of political constraints. However, according to Andersen (2008), indexing benefits and retirement ages to longevity does not ensure financial viability of the social security system. Recognizing this, Poterba (2014) suggests that individuals bear a larger share of the responsibility for determining their retirement security.<sup>2</sup> According to Papke (2003), allowing participants a choice of investment options may increase their retirement savings. However, Lusardi (2003) reports that little is known about how households make saving plans and how they collect information to make savings decisions. A survey of households by the Board of Governors of the Board of Governors of the Federal Reserve System (2014) found that almost half of respondents have given little or no thought to retirement savings. Among those who have, many either do not plan to retire, expect to keep working into retirement to pay for expenses, or do not know how they will pay for their retirement.<sup>3</sup> Nearly a third had no retirement savings or pension. These retirement planning challenges were exacerbated by the recession, which resulted in many respondents delaying their planned retirement.<sup>4</sup> One way firms have responded is by shifting from defined benefit pensions to defined contribution pensions, where employees have to decide how much money to put into their retirement funds and how to allocate their pension wealth. However, such plans do not go without criticism, taking into account insights on cognitive limitations from the behavioral economics and finance literature.<sup>5</sup> For instance, Banks and Oldfield (2007) report that the

> 2 The author reviews how designing public policy toward retirement security must recognize both the heterogeneity among households saving for retirement,