rafmacalaba/datause-encoder-data huggingface dataset:
major difference is not ordinal but cardinal: once we assume that opportunities to learn start well before primary school, low-learning countries see their _LAYS_ values drop much farther—in some cases, to less than 2 years of quality-adjusted schooling.

**Figure 3.4: Average years of schooling of the cohort of 25‐ to 29‐year‐olds, unadjusted and adjusted for learning using the** **_LAYS_ adjustment with different assumptions about when learning starts**

<!-- Start of picture text --> Years of schooling LAYS (leaning starts at Grade 0)<br>LAYS (learning starts at Grade -3) LAYS (learning starts at Grade -6)<br>Source: Authors’ analysis of TIMSS 2015 and Barro‐Lee data.<br>Notes: Numeraire country is Singapore. Correlation coefficients between the three LAYS measures exceed 0.99.<br>Spearman rank correlations between the three LAYS measures exceed 0.99.<br>15<br>10<br>Years<br>5<br>0<br>Australia Bahrain Botswana Canada Chile Egypt, Arab Rep. England Hong Kong SAR, China Hungary Iran, Islamic Rep. Ireland Israel Italy Japan Jordan Kazakhstan Korea, Rep. Kuwait Lithuania Malaysia Malta Morocco New Zealand Norway Qatar Russian Federation Saudi Arabia Singapore Slovenia South Africa Sweden Thailand Turkey United Arab Emirates United States<br><!-- End of picture text -->

# **4. Robustness of** **_LAYS_ to the data source used**

An important aspect of the _LAYS_ approach is that it depends on the metric used to measure relative learning. There are at least four ways in which this statement is true. First, the particular units of the assessment used matters. Consider, for example, a transformation of test scores that preserves the average score across countries but changes the standard deviation of country averages around that average. The “distance” between any country and the top 15
**<u>Table 6: Gazelles by Firm Size and Ownership</u>**

|**Type of ownership**|**Private or**|**co-operative**|**Pu**|**blic**|
|---|---|---|---|---|
|**Size**|#|%|#|%|
|**Micro**|517|44.42%|22|28.21%|
|**Small**|334|28.69%|19|24.36%|
|**Medium**|176|15.12%|10|12.82%|
|**Large**|135|11.60%|20|25.64%|
|**Very Large**|2|0.17%|7|8.97%|
|**Total**|1164|100%|78|100%|

Source: LMMIS, CSA To understand what factors affect the probability that a firm will gazelle, i.e. grow jobs very rapidly, a Probit regression model is used. Two sets of regressions are carried out – one covering all gazelles in the data set (see Table 6) and the other differentiating between gazelles on the basis of size and ownership (see Table 7). For ease of comparison with the analysis in Section III.A, the same set of variables, relating to agglomeration, business environment and infrastructure are used.

The following estimating equation is used: ���� ������������������, �������� ��������������, ��������������������� ��� ��� �����

> Where, the dependent variable [defined for firm _i_ in industry _j_ and city _k_ (between year � and ��1)] equals 1 if the firm is  gazelle and 0 otherwise. Agglomeration includes localization and industrial diversity, business environment includes licensing fees and taxes, and infrastructure includes the costs of transport and of electricity.

**<u>Table 7: What explains the emergence of Job Superstars?</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|||||||
|Localization|0.0284*|0.0156|0.0329|0.0156|-0.0034|-0.0343|
||[0.016]|[0.020]|[0.021]|[0.030]|[0.032]|[0.042]|
|Herfindal|0.0868**|0.0647|0.0970*|0.7783***|0.5927***|0.5814***|
||[0.044]|[0.050]|[0.051]|[0.122]|[0.145]|[0.147]|
|Taxes||-0.0353***|-0.0306|-0.0444**|0.0062|-0.0198|
|||[0.011]|[0.020]|[0.023]|[0.024]|[0.028]|
|Licensing||0.0477***|0.0483***|0.0256|-0.0396*|-0.0167*|
|||[0.015]|[0.016]|[0.019]|[0.021]|[0.022]|
|Transport|||0.0119|0.0148|0.0207|0.0188|
||||[0.021]|[0.024]|[0.026]|[0.030]|
|Electricity|||-0.0188|-0.0213|-0.0245|0.0162|
||||[0.021]|[0.024]|[0.026]|[0.032]|
|Constant|-1.0425***|-1.0193***|-0.9319***|-0.3550|-1.2082***|-1.1554**|
||[0.104]|[0.205]|[0.215]|[0.389]|[0.421]|[0.585]|
|City|**×**|**×**|**×**||||
|Year|**×**|**×**|**×**|**×**|||
|Industry|**×**|**×**|**×**|**×**|**×**||
|#|9,984|9,422|9,211|8,591|8,591|8,542|
|�<sup>�</sup>|0.000567|0.00244|0.00262|0.0274|0.105|0.118|

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**Table 16: Number of foreign residents in municipalities where foreign residents concentrate to live**

||Total population|The number of<br>foreign resident|The number of<br>Brazilian<br>resident|The proportion of<br>Brazilian in total<br>population|
|---|---|---|---|---|
|Ota City,Gunma|149,599|7,203|3,390|2.3%|
|Oizumi Town, Gunma|41,284|6,166|4,704|11.4%|
|Iida City, Nagano|109,434|2,873|1,380|1.3%|
|Hamamatsu City, Shizuoka|596,988|21,434|12,712|2.1%|
|Iwata City, Shizuoka|90,128|4,392|3,301|3.7%|
|Kosai City, Shizuoka|44,601|2,549|1,818|4.1%|
|Fuji City, Shizuoka|242,392|4,494|1,850|0.8%|
|Toyohashi City, Aichi|372,986|15,417|9,655|2.6%|
|Toyota City, Aichi|344,549|11,381|6,251|1.8%|
|Ogaki City, Gifu|154,048|5,397|3,432|2.2%|
|Kani City, Gifu|96,203|3,854|3,015|3.1%|
|Minokamo City, Gifu|50,820|3,373|2,438|4.8%|
|Yokkaichi City, Mie|289,797|7,234|3,127|1.1%|
|Suzuka City, Mie|187,425|7,046|3,851|2.1%|
|Ueno City, Mie|59,626|3,041|1,999|3.4%|
|Total|2,829,880|105,854|62,923|2.2%|
|(National Data, whole Japan)|126,688,364|1,851,758|268,332|0.2%|

Source: The data from each municipality.

38
# **Table 12: Determinants of Unit Costs for Road Upgrading and Maintenance**

Dependent Variable: Log Road Project Unit Cost ($US/Km) from ROCKS

||(1)|(2)|
|---|---|---|
|Log Mean Rainfall|1.791|1.355|
||(2.44)*|(2.32)*|
|Percent Paved|-0.038<br>|-0.046<br>|
||(2.28)*|(4.58)**|
|Log Income Per Capita|0.709<br>|0.749<br>|
||(2.42)*|(2.67)**|
|Governance|0.309<br>(1.23)||
|West Africa|-0.849|-0.730|
||(1.32)|(6.24)**|
|Central Africa|-0.339<br>(0.49)||
|East Africa|0.139||
||(0.19)||
|Upgrading Categories|||
|New 2-Lane Highway|2.057|2.111|
||(2.58)*|(2.66)**|
|Widening and Reconstruction|1.503|1.416|
||(1.99)*|(1.88)|
|Upgrading|-0.154|-0.118|
||(0.23)|(0.18)|
|Strengthening|-0.306<br>|-0.329<br>|
||(0.45)|(0.49)|
|Reconstruction|<br>-0.341|<br>-0.337|
||(0.53)|(0.52)|
|Asphalt Resurfacing|-1.042<br>|-1.037<br>|
||(1.58)|(1.58)|
|Maintenance Categories|||
|Surface Treatment Resurfacing|-1.892|-1.906|
||(2.87)**|(2.89)**|
|Gravel Resurfacing|-2.785|-2.782|
||(4.29)**|(4.28)**|
|Bituminous Pavement Preventive Treatment|-3.278|-3.281|
||(5.01)**|(5.02)**|
|Unsealed Preventive Treatment|-4.389|-4.375|
||(6.71)**|(6.69)**|
|Routine Maintenance|<br>-4.876<br>|<br>-4.877<br>|
||(7.35)**|(7.35)**|
|Grading|<br>-5.899|<br>-5.893|
||(7.81)**|(7.80)**|
|Constant|3.306|4.989|
||(0.94)|(1.63)|
|Observations|465|465|
|R-squared<br>|0.86|0.86|
|Absolute value of t statistics in parentheses|||
|<br>significant at 5%; ** significant at 1%|||

32
# **<u>Table 2: Russia’s RCA Index, Export Values, and Export Share</u>**

|||**2007**|||**2012**||**2007-12**|**2012-07**|
|---|---|---|---|---|---|---|---|---|
|**HS Chapters –Sectors**|**Exports (US$,**<br>**Millions)**|**Export Share**|**RCAI**|**Exports (US$,**<br>**Millions)**|**Export**<br>**Share**|**RCAI**|**Annual Average**<br>**Growth**|**RCAI**<br>**Difference**|
|01-24 Food & Agriculture|16,516|2.6%|0.33|33,490|3.3%|0.47|15.2%|0.14|
|25-26 Non-fuel Minerals|4,684|0.7%|0.45|11,230|1.1%|0.68|19.1%|0.23|
|27 Mineral Fuels|433,031|66.9%|4.21|737,707|72.1%|4.28|11.2%|0.07|
|28-40 Chemicals|34,329|5.3%|0.37|57,834|5.6%|0.48|11.0%|0.11|
|44-48 Wood|22,997|3.6%|1.29|19,983|2.0%|1.1|-2.8%|-0.19|
|72-83 Metals|94,530|14.6%|1.52|88,810|8.7%|1.41|-1.2%|-0.11|
|84-89 Machinery &|||||||||
|Transportation|26,626|4.1%|0.1|37,422|3.7%|0.13|7.0%|0.03|
|Miscellaneous|14,748|2.3%|0.14|37,372|3.7%|0.29|20.4%|0.15|
|**Total**|**647,461**|**100%**||**1,023,849**|**100%**||**9.6%**||

_Source_ : COMTRADE data. _Note_ : HS refers to chapters of the Harmonized System of trade and tariff data of the World Customs Organization.

0
Figure A1 in the appendix presents the gender-disaggregated distribution of employment outcomes across the two study regions for the _analytic_ and _main survey_ samples.

The average man in the analytic sample is significantly more traditional than the average woman (Table 4). This is because the average gender role attitudes index for the 1,103 men was 42.75, compared to the average of 40.31 for the 1,375 women questioned on their gender norm perceptions (see Table 1). The table shows the proportion of sampled men and women in the various households who agreed with different statements relating to gender role attitudes. Average gender norm indices are shown in parentheses below the percentages. The last column depicts the significance of differences in responses given by men and women.The table shows in general that both gender agree women should perform household chores, care for children and dependents, while men should manage women workers on the farm (over 95% of both gender agree). That notwithstanding, we see significant differences between men and women with respect to making all important business and financial decisions.

A summary of the answers to the questions on personality traits for men and women in the survey is presented in Figure 1. On average, men tend to show higher averages than women with most traits e.g., enjoyment in planning activities that others do, enjoying having influence over people, etc. The only exceptions are with respect to being organized and an affinity for multitasking, where women report higher preferences than their male counterparts. Women and men display similar traits with respect to planning tasks carefully (92%); enjoyment with improving past performance (86%); as well as expecting the best in uncertain times (77%). About two-thirds of the respondents – men and women – reported a lack of confidence in identifying genuine friendships; were hesitant about trying new things; and disliked juggling different things at the same time.

Table 5 presents the mean difference test for the personality groups as well as gender role attitudes index for the analytic sample. Differences in gender role attitudes between men and women are highly statistically significant, with men being more traditional than women (see Table 3). There are significant differences in personality groups between men and women as well. While men and women show similar levels of impulsiveness and optimism, men have higher measures of work centrality, compared to women. Men are also more tenacious, have a higher desire to achieve, and also have stronger power motivations. Women, on the other hand, demonstrate higher levels of polychronicity, have greater desire for control in their lives and businesses, and also show greater strengths in being organized, compared to their male counterparts.

10
**Figure 5: Annual growth of government expenditure, percent average for 1997–2007**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Source: Eurostat.<br><!-- End of picture text -->

**Figure 6: Annual growth of government revenue, percent average 1997–2007**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Source: Eurostat.<br><!-- End of picture text --> 23. Bibow (2012) argues that one of the core reasons behind the current crisis of the Euro Zone is the lack of wage coordination which is normally critical in preventing asymmetric shocks and sustaining a monetary union (Mundell (1961)). In fact, since 1996, Germany established its own new lower norm of zero nominal unit labor cost inflation resulting from a consensus between the trade unions, workers‘ representatives and employers that wage restraint was pivotal to preserve Germany‘s competitiveness, reduce unemployment and prevent further relocation of labor to Eastern Europe and other low-wage countries. Most European countries, especially on the periphery, found themselves unable to emulate German wage restraint, 12
physical activity levels and the disease environment that may have lowered the demand for calories, but reach no firm conclusion.<sup>1</sup> This article presents estimates of Total Energy Expenditures (TEE) for the Indian population over the 1983-2012 period to provide insight into this puzzling finding. Total energy expenditures are the total calories required for maintaining one’s weight level given the body’s metabolic processes and physical activity levels. Anthropometric data from three waves of India’s National Family Health Survey (1998, 2005, 2015) are used to estimate Resting Energy Expenditures (REE) that capture metabolic requirements related to height, weight, age and gender for individuals at rest. Detailed time-use data from India’s 1998-1999 Time-Use Survey, matched to activity factors in (FAO/WHO/UNU Expert Consultation 2001), are used to estimate activity levels (AL) that capture the additional energy used for physically intensive activities. The large set of variables in common between these surveys and the NSS employment surveys (Schedule 10) that were conducted between 1983 and 2012 are used to estimate individual TEE. This quantification provides insight into the evolution of population TEE and the contributions of changes in demographics, occupation and industry, work status, and laborintensive domestic tasks.

The main finding is that population level TEE in India is fairly flat between 1983 and 2012 and actually increased slightly over the 1983 to 2005 period before declining slightly between 2005 and 2012. The quantification highlights two reasons for flat TEE over time. First, individual and household characteristics that predict lower Activity Levels (AL) also tend to predict higher Resting Energy Expenditures (REE) for a given demographic composition. For example, higher levels of education, higher levels of expenditure, and less labor-intensive occupations are all associated with lower AL but higher REE. Because TEE is computed as the product of REE and AL (FAO (2001)), as discussed later, the effect of changes in education, expenditures, and occupation on TEE tend to be smaller than their effect on AL. Overall,

> 1Note that the “puzzle” documented by (Deaton and Dr`eze 2009) for India has also been documented in other contexts, e.g. (Du et al. 2002) for China and (Clark et al. 1995) for Great Britain during the Industrial Revolution. Literature on measurement error and the disease environment in the Indian context are discussed below. Other contributions include (Basole and Basu 2015), who focus on the role of prices and food budgets squeezed by rising expenditures on fuel, education and medical goods, (Patnaik 2010), who argues that higher relative prices for food lowered demand, and (Gupta 2013), who argues that conspicuous consumption (Veblen goods) could explain lower food intake.

2
# **1 Introduction**

The 2008 Global Financial Crisis (GFC) has prodded policy makers to reconsider the role of the state in the financial sector, including state ownership of financial institutions. The financial turmoil prompted extraordinary government intervention, including in addressing a set of market failures and procyclicality in lending. One type of initiative involved using state-owned financial institutions (SOFIs) to ramp up financing to the private sector and smooth out the credit supply over the economic cycle. Nonetheless, the heated and long-standing debate over the rationale for state intervention and the advantages and shortcomings associated with state ownership in finance has continued. On one hand, such state intervention can be beneficial to address domestic market failures, achieve social/ developmental goals (positive externalities) and mitigate procyclicality in lending. Conversely, political capture, inefficiencies and inherent contradictions between a profit oriented banking mandate and a social/ developmental policy mandate are often deemed as evident shortcomings of state-ownership in the financial sector. Yet data on SOFIs are sparse and inadequate to assess and evaluate some important aspects of their operations such as mandates, instruments, performance, governance and monitoring.

Europe and Central Asia (ECA) is a region that offers interesting insights on the debate (see Box 1 on a historical perspective).<sup>1</sup> Prior to the financial crisis, several countries in the western part of ECA encouraged a high degree of foreign entry in their banking sectors to increase access to finance based on funding from foreign parents, improve stability and overall banking efficiency through spillovers to domestic banks, and prepare for joining the European Union. In parallel, the creation of a single European market led to a decrease in direct support to national economies in the member states of the European Union and a limitation on direct government intervention. As the regulatory powers of the European Commission (EC) increased, SOFIs in the western part of Europe have come under increased scrutiny and have started relying more on market financing to avoid violating state aid rules. By contrast, as they transitioned from planned to market economies, several countries in the eastern part of ECA did not manage to attract foreign investors and actively pursued a high degree of domestic and (often) state ownership in their financial systems. A

> 1 According to the World Bank definition (see for instance Gould and Melecky, 2017), the countries included in ECA are: Albania, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Lithuania, Luxembourg, Macedonia, Moldova, Montenegro, Netherlands, Northern Cyprus, Norway, Poland, Portugal, Republic of Serbia, Romania, Russian Federation, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Tajikistan, Turkey, Turkmenistan, Ukraine, United Kingdom, Uzbekistan.

3
branches per 100,000 adults. The data are available in the _World Development Indicators_ (WDI) for 183 countries from 2004-2018 (Table 1). Our proxy for financial development is significantly and positively correlated with other financial development measures commonly used in the literature, such as domestic credit to the private sector, the number of automated teller machines, account ownership, or the IMF’s financial development indices (Table 2).

While there are many dimensions of informality, we focus on one for the purposes of this study: output informality (Elgin et al. 2021). For our analysis, output measures of informality are preferable to employment measures since finance is typically accessed to fund working capital or investment in physical or human capital, not employment. Specifically, output informality is proxied by Dynamic General Equilibrium (DGE) modelbased estimates of informal output in percent of official GDP, as detailed in Elgin and Öztunal ı (2012) and Elgin et al. (2021). DGE-based informal output as a share of GDP is available for up to 156 economies for 1990-2018. This measure of informality has been used in several previous studies of informality, including Elgin, Elveren, and Bourgeois (2021); Elgin and Iyidost (2021); Elgin, Williams, Oz-Yalaman and Yalaman (2022); and Granda-Carvajal and García-Callejas (2022). As a measure for the purposes of this study, it is preferable to the MIMIC measure of informality since the MIMIC measure includes, in its construction, indicators of financial development (specifically, cash demand). There are, however, limitations of the DGE-based estimates on informal output, such as reliance on a specific functional form or the choice of benchmarking (Elgin et al. 2021). In section 4.2, we run a series of robustness checks to address some of these limitations.

# **2.2 Correlation between informality and banking sector development**

The scatter plot in Figure 1 indicates a negative relationship between the average level of output informality and the average number of commercial bank branches (per 100,000 adults) over the period 2004-2018. A large group of emerging market and developing economies are concentrated in the upper left corner where output informality is high, but the number of commercial bank branches is limited.

This negative relationship is further confirmed by t-tests where the sample is split into country-year pairs with above-median and below-median shares of informality. We test for statistically significant differences in the simple average of the number of commercial bank branches for country-year pairs with above-median informality and those with below-median informality. On average, every 100,000 adults in countries with abovemedian informality have access to about 11 commercial bank branches— about half the number in countries with below-median informality. The t-test suggests that these differences are statistically significant.

Of course, this association between informality and financial development could simply be driven by a set of confounding factors, such as per capita incomes or the quality of 6
than blanket policy structure. This is especially critical in the case of Mexico, South Africa, and Turkey as was also indicated in the results.

One observed weakness of the CGE framework or at least in its application (including also the studies reviewed in the literature review section) is the lack of institutional analysis. Supporting institutions are known to go hand in hand with policy reforms and the inclusion of institutional setups with the various simulations in the CGE experiments would enhance the performance of the policy. For example, a water market liberalization simulation in the CGE analyses in all 4 countries was undertaken under the assumption that the market would perform without any added transaction cost. It is not obvious that the many individual small users would participate in that water market and how well would they perform. It is suggested that alongside the establishment of a water market the CGE framework could also include an institutions such as a Water User Association. Such institution represents the many members in setting the quantity and price to be traded and increases the market efficiency.

And finally, the cloud of climate change was casted over all 4 country case studies. It is obvious that additional analysis of impact of and adaptation to climate change is needed. In addition, more emphasis is necessary on policy interventions that could incentivize adaptation responses. The way climate change was handled in both the macro-micro linkage studies and the other studies that were reviewed, seem to be mechanistic and based on simplistic assumptions, such as fixed yield reduction, fixed water supply reduction, etc... Future research could benefit from handling both the impact of and adaptation to climate change endogenously in the farm or household model within the CGE framework.

43