rafmacalaba/datause-encoder-data huggingface dataset:
- The skill composition of Latvia’s exports to the EU indicates a high concentration of exports produced in low and medium-low labor skill industries and a low proportion generated by skill-intensive industries (Taxonomy II). The upper skill segment, which is developing rapidly in more dynamic transitional economies (it grew by 2.5 times in Hungary in 5 years and accounted for 17% of Hungarian exports in 1999), is almost nonexistent in Latvia, which lags behind all European economies, including the NMS’s (Graph).

**Figure 9 Exports to the EU by Labor Skill Requirements:**

**Comparison between Latvian and Hungarian Patterns of Development**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1995 1996 1997 1998 1999 1995 1996 1997 1998 1999<br>Latvia Hungary<br>High-skill industries Medium-skill/white collar workers industries<br>Meduim-skill/blue collar worker industries Low-skill industries<br><!-- End of picture text --> _Source_ : Austrian Institute of Economic Research, available at www.wifo.ac.at

- As the graph below indicates, Latvia is more heavily reliant on labor intensive exports, compared not only to existing EU member states, but also compared to Slovakia and Hungary.  Hungary’s share of labor intensive exports is comparable to that of Finland and Ireland. Hungary is also the only NMS with an overrepresentation of technology driven industries in exports to the EU, while Latvia increasingly specializes in labor intensive industries (which require mainly low skilled labor) and its exports of technology driven industries are minimal.

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from the central bank (Levy, 2005). Thus a central bank role in ensuring arms length transactions between governments and the banking sector would enhance monetary stability regardless of the degree of decentralization of the fiscal system.

Available empirical evidence suggests that such arms length transactions are more difficult to achieve in countries with a centralized structure of governance than under a decentralized structure with a larger set of players. This is because a decentralized structure requires greater clarity in the roles of various public players, including the central bank. No wonder one finds that the four central banks most widely acknowledged to be independent (Swiss Central Bank, Bundesbank of Germany, Central Bank of Austria and the United States Federal Reserve Board) have all been the products of highly decentralized federal fiscal structures. It is interesting to note that the independence of the Bundesbank is not assured by the German Constitution. The Bundesbank Law providing such independence also stipulates that the central bank has an obligation to support the economic policy of the federal government. In practice, the Bundesbank has primarily sought to establish its independence by focusing on price stability issues. This was demonstrated in the 1990s by its decision to raise interest rates to finance German unification in spite of the adverse impacts on federal debt obligations (see also Biehl, 1994).

The Swiss Federal Constitution (article 39) assigns monetary policy to the federal government. The federal government has, however, delegated the conduct of monetary policy to the Swiss National Bank, a private limited company regulated by a special law. The National Bank Act of 1953 has granted independence in the conduct of monetary policy to the Swiss National Bank although the bank is required to conduct its policy in the general interest of the country. It is interesting to note that the Swiss National Bank allocates a portion of its profits to cantons to infuse a sense of regional ownership and participation in the conduct of monetary policy (Gygi, 1991).

This paper also examined empirically some additional questions on the impact of fiscal decentralization on monetary stability. These included the impact of fiscal decentralization; growth of the money supply; control of inflation; and inflation and macroeconomic balances. Regression results reported in Table 2 show that growth of the money supply is primarily determined by central bank independence and fiscal decentralization has an insignificant positive impact. Similarly, fiscal decentralization has a positive but insignificant impact on price inflation (Table 3). Finally, the impact of fiscal decentralization on inflation and macroeconomic balances was found to be insignificant (Table 4).

7
# Appendix I.A.13 Historical Demographic Data, France 1851-1982

|Year|1851|1866|1876|1881|1886|1896|1901|1911|
|---|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|10.23|9.09|8.47|8.04|8.08|7.85|7.81|7.68|
|Population aged 20 to 59/<br>population over 60 years old|5.30|4.76|4.42|4.25|4.30|4.22|4.17|4.18|
|Percentage of population<br>over 60 years old|10.15|11.20|11.84|12.31|12.18|12.49|12.72|12.81|
|Percentage of population<br>under 15 years old|27.31|26.95|27.11|26.73|26.96|25.98|25.70|25.46|
|Women over 60 years old/|||||||||
|Men over60years old|1.17|1.09|1.07|1.06|1.02|1.10|1.15|1.20|
|Year|1921|1931|1936|1946|1954|**1962**|1975|1982|
|Population aged 15 to 64/|||||||||
|population over 65 years old|7.43|7.09|6.54|6.10|5.67|5.25|4.42|4.72|
|Population aged 20 to 59/<br>population over 60 years old|3.94|3.91|3.70|3.39|3.30|2.91|2.65|2.86|
|Percentage of population<br>over 60 years old|13.90|14.22|14.91|16.05|16.13|17.09|18.94|18.48|
|Percentage of population<br>under 15 years old|22.41|22.62|24.44|21A1|23.74|26.36|22.64|20.70|
|Women over 60 years old/|||||||||
|Menover60yearsold|1.25|1.26|1.29|_IA3_|1.54|1.52|_IA3_|_1A5_|

Ratios calculated using data found in Mitchell, B. R. 1990 "Intemational Historical Statistics: Europe 1750-1988".
<!-- Start of picture text --> 8 Manufacturing<br>~<br>=~ 2 aa<br>/ ~ io \<br>/ or /<br>/ 8 \ / \ A<br>} S87 \ / \ /<br>\ $.| \ \ 4 AN<br>y- NS 1 : \ \ ad \<br>Nene / 33 Neem ee<br>\ SN ~ fs oe = \<br>Nh son 9 —<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>——— Average (watd.)<br>——— perc. 90 (watd.)<br>—---—- Rest of firms (below perc. 90) (wgtd.)<br><!-- End of picture text -->

<!-- Start of picture text --> Services<br>+<br>a<br>=<br>4 hi<br>=n /~ —<br>x /<br>E AN /<br>= ass<br>3 \v=2s FS P2 -<br>ee \/---7<br>Q<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>——— Average (watd.)<br>——-— perc. 90 (watd.)<br>~---- Rest of firms (below perc. 90) (wgtd.)<br><!-- End of picture text --> <!-- Start of picture text --> Mining<br>o<br>A<br>= a<br>ny S In i \<br>=a / \ ! \<br>x eat’ } oe a<br>2a i‘ ! / \ x ~<br>ra Zt I \ ><br>3. *y N i *.<br>— q<br>2<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>——— Average (watd.)<br>——-— perc. 90 (watd.)<br>—---- Rest of firms (below perc. 90) (wgtd.)<br><!-- End of picture text -->
is higher with the quadratic form, which we retain for our analysis. For stage 2, both terms in the quadratic functions are statistically significant, and the adjusted is higher than in the log-linear specification reported in Table 2, and thus it is our preferred specification. No coefficients are RR<sup>2</sup> statistically significant for Stage 3 and the Adjusted is lower than in Table 3, which leads us to keep the log-linear form for our Stage 3 indicator. RR<sup>2</sup>

**Table 4. Regression results: Quadratic specifications**

|RHS Variables:|Stage 1 LHS<br>Variable:<br>Bank accounts|Stage 2 LHS<br>Variable:<br>Digital payments|Stage 3 LHS<br>Variables:<br>Mobile money|
|---|---|---|---|
|log_gdppc|-0.234<br>(0.179)|-0.654***<br>(0.167)|-0.132<br>(0.139)|
|log_gdppc_2|0.0231**<br>(0.0101)|0.0428***<br>(0.00949)|0.00593<br>(0.00789)|
|year2017|0.0346<br>(0.0255)|0.0803***<br>(0.0238)|0.0890***<br>(0.0198)|
|Constant|0.633<br>(0.781)|2.624***<br>(0.731)|0.753<br>(0.608)|
|Observations|149|149|149|
|AdjustedR<sup>2</sup>|0.575|0.417|0.144|
|Min. of log_gdppc||6.70||
|**Predicted value at minimum**|**0.116*****|**0.206*****|**0.181*****|
|20<sup>th</sup>percentile of log_gdppc||7.64||
|**Predicted value at 20**<sup>**th**</sup>**perc.**|**0.207*****|**0.169*****|**0.136*****|

<u>Source: Authors’ calculations. Notes: Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).</u> LHS = Left-Hand Side Variables in equation (1). RHS = Right-Hand Side variables in equation (1).

The results reported in Tables 3 and 4 are interesting and confirm the results shown in Figures 1- 4. To assess whether countries can leapfrog over stages of payment-systems development at low levels of economic development, it is informative to compare the predicted incidence of the three stages of payments systems at the minimum and 20<sup>th</sup> percentile of (log) GDP per capita. At the minimum level of development observed in the common sample, the estimated prevalence of bank accounts is the lowest (11.6%) compared to the prevalence of digital payments (20.6%) or mobile money (16.1% in the preferred log-linear specification reported in Table 3). These results are weaker at the 20<sup>th</sup> percentile, with banking being the most prevalent payment technology (20.7%), followed by digital payments (16.9%) and mobile money (13.5% in the preferred log-linear specification reported in Table 3). Hence there is evidence of relative leapfrogging between the poorest economy and the 20<sup>th</sup> percentile, a finding that was graphically reported in Figure 4 above.

13
remain butare weakened.'<sup>9</sup> We also try using the bank regulation variable from Barth, Caprio, and Levine (2001) - which measures the restrictions on banking organizations engaging in securities, insurance, real estate activities, and ownership of nonfinancial firms - in place of our ECON FREEDOM variable, and the main results are strengthened. However, our results are weakened if we include both the regulation and freedom variables, likely due to collinearity from including two variables measuring regulation. In addition, we try including a measure of education (secondary school enrollment percentage, taken from World Development Indicators), and our results continue to hold in most cases. As well, we try including variables for Government Consumption (from Intemational Financial Statistics) and Composite Risk (taken from the Wall Street Joumal/Heritage Foundation Governance data), and the main results are robust, albeit weakened.

> In addition, we try altering the data sample in several ways. We try excluding the effects of the Asian crisis by dropping all observations from Asia for 1997 and 1998. As a further measure, we try excluding all Asian

> observations. The main results remain robust, but weakened by the reduced numbers of observations. Similarly,

> we find the results to be robust when the Transition nations are excluded. We also experimented with extending the lag structure beyond measuring the exogenous variables with a one-year lag to further mitigate the potential endogeneity problem. We try measuring the dependent variables as two- and three-year averages or growth rates to create additional temporal separation between the endogenous and exogenous variables. The results are robust, but weaker with two-year averages/growth rates, and are no longer significant with three years because of the

> substantially reduced numbers of observations.

> Finally, we try running the models specifying fixed-effect dummies for each nation in place of the national-level control variables (which do not vary much within a nation) and the regional dummies (which are

> constant for each nation). The fndings for the GDP GROWTH and BKLENDGDP regressions remain robust, but the SME employment regressions become insignificant due to the smaller numbers of observations.

> 5. Conclusions

> We test the effects of the relative health of community banks on economic growth and investigate two

> potential transmission mechanisms for these effects. The relative health of community banks is measured by the total market shares of these banks and their weighted-average efficiency ranks - indicators of how well they

> perform relative to other banks within the same nation. The potential transmission mechanisms are through

> 19 We view international trade primarily as an endogenous variable that reflects the vitality of the economy, rather than as exogenous to economic growth.

24
economic growth. Since the results for economic growth are largely similar to those of the inclusive growth equation, we can confirm the robustness of the impact of housing finance development on inclusive growth.

Comparing table A.8 in the appendix to table 4.2, the results remain the same independently of the estimation method. However, governance indexed by political stability produces either positive or negative (and significant) impacts on inequality, depending on the method.

# **5. Conclusion**

This paper used a new database on mortgage depth and penetration across countries and panel data econometric techniques to investigate housing finance determinants and the potential of housing finance to contribute to inclusive growth in Africa. It begins by using a benchmarking exercise with a decomposition approach to describe and present the typology of housing finance systems in Africa.

The statistical analysis mainly revealed that housing finance performance varies significantly based on the decomposition of country characteristics. In addition, our analysis emphasized that housing finance systems in Africa are generally nascent and based more on banks than on markets.

The paper’s econometric analysis leads to the following key findings.

First, stock market capitalization and urban growth are the main determinants of housing finance development in Africa. Specifically, stock market capitalization is one of the main determinants of housing finance development in Africa; while credit to the private sector is non-significant and negatively correlated to housing finance in some countries but it is significant and positively correlated to housing finance in others. Hence, the assumption that banking development is a catalyzer of housing finance should be reconsidered.

Second, countries with more highly developed housing financial systems tend to have lower levels of inequality than those with bank-based financial systems. Hence, well-developed housing finance systems are associated with inequality reduction.

Third, human capital appears to be the main factor for reducing inequality. In fact, there is a significant negative impact of human capital on inequality, suggesting that higher levels of economic development and human capital are associated with lower levels of inequality. These results are in line with the theoretical and empirical literature, which assumes that investing in human and physical capital is a good tactic to reduce inequality.

Fourth, housing finance depth is contrary to inequality reduction in a post-conflict country. The reason could be the same as highlighted above since the unequal access to and distribution of money favor the wealthy more than the poor.

Overall, to develop SSA housing finance markets, this study suggests that governments should focus on market capitalization and mortgage markets development.

19
Kee et al. (2007). An elasticity of substitution lower than the one used in this paper (7.5) will enhance the welfare benefits of increased variety--other things equal-- because it implies that goods are more differentiated. The sensitivity of the welfare results to the choice of substitution parameters and the size of the extensive margin are important issues that we plan to examine in future.

Finally, because we do not know the exact channels through which China and India will grow in the next fifteen years, we undertake three simulations that are alternatives to the neutral high-TFP scenarios, and which allow us to investigate whether China and India’s export growth might create more competition for developing or for industrial countries. We first study the implications of positive productivity shocks of 2 percent per year in the relatively capital and skill-intensive sectors considered in the case studies of Winters and Yusuf (2006): metals; electronics; machinery and equipment; automobiles, and commercial services in China and India. Then, we consider shocks that augment the stocks of human and physical capital, and could be expected to shift the composition of China’s exports towards goods more intensive in human and physical capital, and hence more competitive with the exports of the industrial countries. We first assess the impacts of a 2 percentage point annual increase in the stock of physical capital in China and India. Then, we compute the effects of a 2 percentage point annual increase in the stock of human capital in China and India.

The macroeconomic closure of the simulation model assumes constant employment, perfect mobility of skilled and unskilled labor between sectors, and none between regions. Since we look at long run effects, we have doubled the elasticity of substitution between imported goods from different sources and between composite imported and domestic goods from the values used in the GTAP 6 Data Base.

The effects on key variables of higher growth in China and India, and higher growth with and without increased variety and quality of exports are presented in Table 4. These impacts are presented for real incomes (welfare); for export volumes; and for terms-of-trade effects. For each variable, the effect depends upon whether the income increases in China and India result in intensive-margin growth of the same exports (“Growth”), or whether export growth is accompanied by expansion in the range of products exported, and improvements in their quality (“Growth, Variety and Quality”). Increases in real income presented are measures of equivalent variation in 2001 dollars. Export expansion is presented 17
the incidence is relatively low, but the amounts received are significantly higher than those of the vulnerable population. It is noteworthy that among the upper class only 2.6 percent receive cash transfers, but they receive a monthly average of $173.

**Figure 5. Incidence and average amount of monetary transfers by social class** _Percentage of households and monthly dollars, at PPP_

<!-- Start of picture text --> 27.3%<br>$173.1<br>21.5%<br>16.8%<br>14.6% $93.5<br>$94.6<br>$87.5 $84.3<br>$77.7<br>7.0%<br>2.6%<br><!-- End of picture text --> <!-- Start of picture text --> Source : Author’s estimations based on ENIGH and MPS 2010<br><!-- End of picture text -->

**Figure 6. Incidence of monetary transfers by destination and socioeconomic class** _Percentage of households_

<!-- Start of picture text --> 15.7%<br>14.4%<br>9.1%<br>7.1%<br>5.2%<br>4.4% 4.2%<br>2.6% 2.9% 2.9%<br>1.2% 1.3%<br>0.2% 0.4% 0.9% 0.6%<br><!-- End of picture text --> Among the vulnerable population (both moderate and extreme), Figure 7 shows that direct transfers from _Oportunidades_ aimed at incentivizing the enrolment and assistance to all levels of education (i.e., scholarships comprised under the heading of Education) are the most important transfer in terms of coverage, and increased notably during the 2000s (Figure 8). Such conditional scholarships are highly 20
Berger, A., R. Rosen and G. Udell, 2002, The effect of market size structure on competition: The case of small business lending, _Board of Governors of the Federal Reserve System Working Paper_ Berger, A. and G. Udell, 1995, Relationship lending and lines of credit in small firm finance, _Journal of Business_ 68, 351-381 Besanko, G. and D. Kanatas, 1996, The regulation of bank capital: do capital standards promote bank safety?, _Journal of Financial Intermediation_ 5, 160-183

- Boot, A., 2000, Relationship banking: what do we know? _Journal of Financial Intermediation_ 9, 3-25

- Brick, I., E. Kane and D. Palia, 2004, Evidence of jointness in the terms of relationship lending, _Rutgers University Working Paper_

- Brick, I. and S. Ravid, 1985, The relevance of debt maturity structure, _Journal of Finance_ 40, 1423-1437

- Brown, M., T. Jappelli and M. Pagano, 2006, Information sharing and credit market performance: firm-level evidence from transition countries, _CEPR Working Paper_

- Calvo, G., A. Izquierdo, and L. Meijia, 2004, On the empirics of sudden stops: the relevance of balance sheet effects, _NBER Working Paper_ 10520

- Caprio, G. and D. Klingebiel, 2003, Episodes of systemic and borderline financial crises, _World Bank Working Paper_

- Chang, R. and A. Velasco, 2001, A model of financial crises in emerging markets, _Quarterly Journal of Economics_ 116 (2), 489-517

- Degryse, H. and S. Ongena, 2006, Competition and regulation in the banking sector: a review of the empirical evidence on the sources of bank rents, _Tilburg University Working Paper_

- Degryse, H. and P. Van Cayseele, 2000, Relationship lending within a bank-based system: Evidence from European small business data, _Journal of Financial Intermediation_ 9, 90-109

- Demirguc-kunt, A. and E. Detragiache, 1998, The determinants of banking crises: Evidence from developing and developed countries, _IMF Staff Papers_ 45

- Demirguc-Kunt, A. and V. Maksimovic, 1998, Law, finance and firm growth, _Journal of Finance_ 53 (6), 2107-2137

- Dermirguc-Kunt, A. and V. Maksimovic, 1999, Institutions, financial markets, and firm debt maturity, _Journal of Financial Economics_ 54, 295-336

25
variation in average household demographic attributes across countries. Two coarse indicators, average age and household size, reveal significant variation across countries (Figure 1) and regions (Table 1). In terms of average household size, the data used in our analysis indicate that average household size within regions ranges from 2.4 people in high-income countries to 5 in Sub-Saharan Africa and 5.1 in the Middle East and North Africa.<sup>4</sup> Across countries, average household size ranges from 2 in many high-income countries to above 9 in Senegal and Mali (based on the data used in this paper). In terms of variation in age, 20 percent of the population in upper-middleincome countries was 14 years of age or less in 2021; this proportion doubles (41%) when considering low-income countries.<sup>5</sup> Given that this age category on average needs fewer calories to meet basic needs, the per capita assignment differentially treats countries in a manner that is systematically correlated with income level.

The aim of this paper is to understand the implications of relaxing the per-capita allocation assumption for the profile of global poverty and the identification of poor people by contrasting it with a commonly used alternative allocation rule (i.e., dividing household resources by the square root of household size). We show that the regional profile of extreme poverty is relatively insensitive to changing the household-resource-allocation rule, but the within-country identification of who is poor is highly sensitive to the change (reclassifying the poverty status of 270 million people across the world). We also provide evidence that the probability of being poor as identified by the square-root allocation is more strongly negatively correlated (compared to

> 4 Other data sources indicate even greater dispersion in household size. UN-DESA (2019a) estimates that countries range from slightly more than 2 people to slightly more than 8 people. These differences have clear geographic patterns with the average person living in a household with 6.9 people in Sub-Saharan Africa, compared to 3.1 in Europe (Kramer, 2020). For detailed breakdown by age, see UN-DESA (2019b). 5 The proportion of the population 0-14 years in a country is monotonically decreasing in income classification (i.e., low-income, lower middle-income, upper middle-income, high-income). World Development Indicators, <u>https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS?most_recent_value _desc=false [Last accessed July 23, 2022.]</u>

5