rafmacalaba/datause-encoder-data huggingface dataset:
# **7. References**

Arellano, M. & Bond, S. (1991). “Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations.” _The Review of Economic Studies_ , 58, pp. 277-297.

Atkinson, A. B. (1975), “The Economics of Inequality” (Oxford: Clarendon Press).

- Badev, A., T. Beck, L. Vado & S. Walley (2014), “Housing finance across countries: New data and analysis,” World Bank Working Paper Series.

- Beck, T., A. Demirgüç-Kunt & R. Levine (2004), “Finance, inequality, and poverty: Cross-country evidence.” <mark>NBER Working Paper No. 10979. Issued in December 2004.</mark>

- Beck, T., S. Munzele Maimbo, I. Faye & T. Triki (2011), “Financing Africa: Through the crisis and beyond,” World Bank, Washington, DC.

Becker, G. S. (1975), “Human Capital,” NBER and Columbia University Press, New York.

- Besley T, S. Coate, G. Lowry (1992). “The economics of RoSCAs,” Centre for International Studies, Discussion Paper No. 149. Princeton University, NJ.

- Blundell, R. & S. Bond (1998). “Initial conditions and moment restrictions in dynamic panel data models.” _Journal of Econometrics_ , 87(1), pp.115-143.

- Bond, S., A. Hoeffler & J. Tample (2001), “GMM Estimation of Empirical Growth Models,” University of Oxford.

- Buckley, R. & R. Madhusudhan (1984), “The Macroeconomics of Housing’s Role in the Economy: An International Analysis,” Presented to the American Real Estate and Urban Economics Association;

Buckley, R. (1996), “Housing Finance in Developing Countries,” McMillan, London.

- Calderon & Serven (2008), “The Effects of Infrastructure Development on Growth and Income Distribution”, The World Bank Policy Research Working Paper (WPS3400).

- Chen, J. & A. Zhu (2008), “The Relationship between Housing Investment and Economic Growth in China: A Panel Analysis Using Quarterly Provincial Data,” China National Social Science Foundation Working Paper.

- Christiansen, L., J. De Weerdt & Y. Tod (2013), “Urbanization and poverty reduction - The role of rural diversification and secondary towns,” Policy Research Working Paper 6422.

- Dübel, Hans-Joachim (2007), “Does housing finance promote economic and social development in emerging markets?,” Housing Finance Impact Study for International Finance Corporation.

- Econsult Corporation (2009), “Potential economic and fiscal impacts of a Pennsylvania housing trust fund,” The Housing Alliance of Pennsylvania.

- Egert, B. & D. Mihaljek (2007), “Determinants of house prices in Central and Eastern Europe,” _Comparative Economic Studies_ , vol. 49(3), p.p. 367-88.

- Erbas, S. & F. Nothaft (2002), “The role of affordable mortgages in improving living standards and stimulating growth: A survey of selected MENA countries,” IMF Working Paper.

- Fox & Sohnesen (2012), “Household enterprises in Sub-saharan Africa – Why they matter for growth, jobs and livelihoods.” World Bank Policy Research Working Paper;

- Freire, Mila, M. Gautier and O. Hassler (2006), “Review of Argentina’s housing sector: Options for affordable housing policy,” World Bank Working Paper

28
2005; Lee et al. 2011). For instance, in a recent study, Ardagna and Lusardi (2010) worked with GEM microdata from 37 countries, including eight DCs, and showed that stringent entry regulation, soft contract enforcement rules, and labor market rigidities play an important role in hindering entrepreneurship and strengthening the adverse impact of risk aversion. Moreover, inefficient regulation may hinder the growth of small firms in DCs because they may fear the effects of red tape and higher taxes (De Soto 1989). The regulatory framework often involves counterproductive policy measures that were originally intended to support small firms but actually prevent firms’ growth. Indeed, the presence of subsidies addressed to SMEs may push entrepreneurs to keep the size of the firm unchanged - or at least below a given threshold - to maintain eligibility for government funding (Little, Mazumdar, and Page 1987; Mitra and Pingali 1999).

In a developing country context, a prominent role is played by the wide diffusion of bribing, which may abort any chance of growth of a fragile new entrepreneurial activity<sup>3</sup> . For instance, Fisman and Svensson (2007), using data collected from 126 Ugandan firms, show that a 1 percent increase in the bribery rate implies a reduction of 3 percent in firm sales growth. Obviously, corruption may amplify the hampering role of credit constraints (see above) when it involves bank officials who are responsible for screening the entrepreneurial initiatives (Beck, Demirgüç-Kunt, and Maksimovic 2005).<sup>4</sup> Finally, the lack of an adequate infrastructural endowment including roads and railways, basic utilities such as electricity and water supply, and ICT networks is singled out by the literature as a significant shortcoming that prevents young and small firms in DCs from growing (Aterido, Hallward-Driemeier, and Pagés 2009; Goedhuys and Sleuwaegen 2010<sup>5</sup> ; Ghani, Kerr, and O’Connell 2011).

7
Forest cover has increased but forest quality has declined. Unlike most of its neighbours, Vietnam has in all probability stopped the decline in its forested area and the country's total forest coverage also compares favourably with that of almost any other country. However, Vietnam has not been successful in protecting its rich and closed forests and the quality of its forest has deteriorated over time (Figure 15). Vietnam falls short of many other countries in protected area coverage. World-wide, the average country has 6.5 percent of its total land area within protected areas. Vietnam has about 3 percent of its area under protection and no coherent strategy is in place to protect even this small remaining area of highly biodiverse forest.

Figure 15. Trends im the composition of Vietnam's Forest Cover <!-- Start of picture text --> 2= 46 fp. vevsedeufecentvesetetgeepenetesieate footieTeneelesdteeeed<br>————— Fenny RR ai. Renee<br>RE ees | RESy ec sad FA een IR<br>1990 2000<br>O Poor/open/regenerating forests - Classes IIIal, Ia2, Ha, ITb<br>G) Medium quality/partially-closed forest - Class IIIb<br>Rich/closed forest - Class IV<br><!-- End of picture text -->

Source: NEA/ World Bank/ DANIDA (2002).

Marine fish catches are unsustainable. More attention is also needed to the protection of its marine resources. The destruction of Vietnam’s mangrove forest, mainly caused by expansion of shrimp farming, has caused a fast reduction in fish stocks leading to a steep decline in the fish catch per effort (Figure 16), threatening the sustainability of the marine fisheries sector.

Figure 16. Marine catch in Vietnam 1991-2001 <!-- Start of picture text --> 4000000 a CRN - — — 1<br>. j&xccatch per effort (t/H P) . a ‘ Lo.9<br>. —@@Total catch (t . - 0.8<br>3000000 ” a aca : Sa<br>a oe ee a 2<br>2500000 7 | | 7 . — oo - ot ‘Loe<br>2000000 . _ - a, Pe + 0.5<br>1500000 aa an i ; | we) 5 o-4<br>1000000 . ary aan ~ Pp. ee tool OR Dee ' 2 ar)<br>ttt - “ : oo fF 0.2<br>0<br>: nn on one L to<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br><!-- End of picture text -->

33
|(7)|0.752***<br>(0.089)<br>0.807***<br>(0.054)<br>-0.088***<br>(0.006)<br>0.002<br>(0.142)<br>-1.041***<br>(0.293)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.582<br>ded in all specifcations. The<br>China_j_ is an interaction term<br>mber of foreign competitors is<br>y the number of other French<br> from the Penn World Tables.<br>-country pairs are reported in|
|---|---|
|(6)|-0.167<br>(0.150)<br>-1.586***<br>(0.312)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.543<br>ar and a constant are inclu<br> _j_ in year_t_. Tariff_fjt ×_ <br>ndahl index, where the nu<br>gn competitors is proxied b<br>og of the GDP of country_j_<br>ard errors clustered by frm|
|ucts<br>(5)|0.758***<br>(0.089)<br>0.806***<br>(0.054)<br>-0.088***<br>(0.006)<br>-0.185<br>(0.129)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.582<br>ffects by frm, country and ye<br> _f_ when exporting to country<br>variable is the frm-level Herf<br>ex, where the number of forei<br>arkets. GDP_jt_ is the natural l<br>eteroskedasticity-robust stand|
|exported prod<br>(4)|-0.492***<br>(0.136)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.542<br> by frm _f_ in year _t_. Fixed e<br>he tariff measure faced by frm<br>mpetition_fjt_(N_◦_countries)<br>the frm-level Herfndahl ind<br>each frm in the destination m<br>al Financial Statistics (IFS). H|
|og number of<br>(3)|0.889***<br>(0.093)<br>1.024***<br>(0.059)<br>-0.090***<br>(0.006)<br>No<br>Yes<br>Yes<br>Yes<br>34,327<br>0.560<br>rted products to country _j_ <br>d Thailand. Tariff_fjt_ is t<br>_j_. The extensive margin co<br> _fjt_(N_◦_frms) variable is<br>the product level faced by<br>untries from the Internation|
|L<br>(2)|0.672***<br>(0.091)<br>0.900***<br>(0.058)<br>No<br>Yes<br>Yes<br>Yes<br>34,327<br>0.555<br>thm of the number of expo<br>, Philippines, Singapore an<br>and zero otherwise, China<br>tensive margin competition<br>average import volumes at<br>of France and importer co|
|(1)|1.080***<br>(0.093)<br>No<br>Yes<br>Yes<br>Yes<br>34,327<br>0.547<br>ndent variable is the logari<br>sia, Korea, Japan, Pakistan<br>ination of exports is China<br>the Asian markets. The ex<br>petition_fjt_ variable is the<br>roup using producer prices|
|Dependent variable|Extensive margin competition_fjt_(N_◦_countries)<br>Extensive margin competition_fjt_(N_◦_frms)<br>Intensive margin competition_fjt_<br>Tariff_fj,t−_1<br>Tariff_fj,t−_1_×_China_j_<br>Country level controls<br>Firm fxed effects<br>Country fxed effects<br>Year fxed effects<br>Observations<br>_R_2<br>_Notes_: The regressions are OLS estimations of equation (3) for the period 1999-2005. The depe<br>destinations are China and other Asian countries that already integrate WTO such as India, Indone<br>between the frm level tariff measure and a dummy variable equal to one when the country of dest<br>proxied by the number of countries exporting the same product line that the French frms towards<br>frms exporting the same product line towards the Asian markets. Finally, the intensive margin com<br>RER_jt_ is the bilateral real exchange rate between France and China and countries in the control g<br>parentheses. _∗∗∗, ∗∗,_and_∗_indicate signifcance at the 1, 5 and 10 percent levels respectively.|

21
16

# _5.2 Urban road networks_

Road provision at the urban level differs greatly from that at the national level. Holding income and density unchanged, urban road length increases less rapidly than population; its elasticity with population is 0.8 (see table 4). The elasticity of urban road length with per capita income is not constant, as it is at the national level, but increases with income (see figure 2), and the elasticity of road length with population density is much stronger at the urban (-1.0) than the national level (elasticity of 0.0 to -0.3).32 Urban population densities are often low when per capita income is high, but population densities vary widely in high-income cities (figure 3). This wide variation reflects the impact of historical paths of development on urban form. Cities that have experienced much of their population growth when auto ownership levels have been high have lower densities than other cities. In addition, high relative land prices are likely to raise population densities. The nonlinearity between urban population density and income shown in figure 3 contributes strongly to the nonlinearity between per capita roads and per capita income at the urban level in figure 2.

**Figure 3. Per Capita Income and Population Density in 35 World Cities**

<!-- Start of picture text --> 100,000<br>Bandung Guangzhou HK A<br>A A Seoul A<br>Jaka London A Osaka<br>10,000 AAToy London<br>Surabaya Maila ayo<br>(A<br>1,000 .*tN<br>Perth Phoenixl<br>A Actual<br>- Equation<br>100<br>100 1000 10000 100000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text --> _Source:_ **Ingram** and Liu, "Vehicles, Roads, and Road Use." 32 These results are based on panel data from 35 cities in developing and industrial countries, and the elasticities take into account the strong negative relation between per capita income and population density at the urban level.
- 20 -

# Interest Rates: A Two-Sector Banking System

The difference between the deposit ratas in the state and the private banks is evident in the data of Table 4. Given this d'fference, it is surprising that state banks were able to collect deposits. Some have argued that the deposits at state banks were insured by the government, but the failure of the foreign banks was unlikely and this argument is not convincing. A better explanation is that the government required public enterprises to keep deposits with the state banks. In July 1984, deposits of state enterprises, government agencies, and financial institutions were equal to about 46 percent of state bank time deposits and 71 percent of their demand deposits, (Balifio and Sundararajan, 1985.) Another explanation is that borrowers who benefited from the preferential loans (granted only by the state banks) were forced to keep balances with the state banks.

Table 4. Interest Rates in Indonesia

|Year|(1)<br>State<br>banks<br>6 month<br>deposit<br>end of<br>period|(2)<br>Private<br>banks<br>6month<br>deposit<br>end of<br>period|(3)<br>Singapore<br>MMR|(4)<br>State<br>Banks<br>Lending Rate|(5)<br>UsingCPI|(6)<br>If<br>X<br> GDP Deflator|(7)<br>Real Deposit<br>Rate<br>(1-5)|
|---|---|---|---|---|---|---|---|
|1972|15.0||3.57||6.42|13.59|8.58|
|1973|12.0||11.69||31.03|32.94|-19.03|
|1974|12.0||8.90||40.63|47.31|-28.63|
|1975|12.0||4.39||19.07|12.47|-7.07|
|1976|12.0||4.15||19.84|14.46|-7.84|
|1977|9.0||4.76|9.0|11.02|12.87|-2.02|
|1978|6.0||5.93|9.0|8.11|11.08|-2.11|
|1979|6.0|16.75|7.76|9.0|20.59|32.51|-14.59|
|1980|6.0|18.45|10.98|9.0|18.53|29.15|-12.53|
|1981|6.0|18.05|11.54|9.0|12.24|18.50|-6.24|
|1982|6.0|18.35|7.92|9.0|9.48|5.13|-3.48|
|1983|6.0|18.63|7.11|9.0|11.79|13.20|-5.79|
|1984|16.0||7.67|12.0|10.46|11.27|5.54|
|1985|18.0||5.38|12.0|4.73|6.49|13.27|
|1986|16.0||4.27||5.83|||
|1987|||3.89||9.28|||

Source: IFS.
These risks associated with marriage are prevalent in Malawi and clubs take active actions to prevent them. The most extreme measure is the actual membership prohibition to unmarried women, especially younger ones. Recall that, in Table 1, we used data from the TCS to show that 40 percent of clubs openly deny membership to unmarried women. Also, an additional 27 percent of clubs would accept unmarried women only after a careful screening of her permanence in the area and only under very restrictive conditions. In many cases, the requirements imposed to unmarried women are impossible to meet and actually act as binding prohibitions in themselves. For instance, a common condition among these clubs is to require (intangible) proofs of “unlikely marriage,” proof that the applicant will not get married in the short-middle term. This last requirement is almost never satisfied by young women.

In practice, thus, tobacco clubs are particularly reluctant to accept younger unmarried women as members. Notice that not all unmarried women are denied membership but only those that are young enough to pose a real marriage risk. Since there are no formal age requirements in the club constitutions, in order to determine the age cut-off we first performed interactive interviews (Udry, 2003) during the pilot phase of the Tobacco Clubs Survey in May and June. We concluded that many burley clubs specifically require unmarried women to be over 45 years old for eligibility. We then added a question to the core questionnaire of the TCS (Module B, see Appendix 1) to quantify the proportion of clubs admitting unmarried women under 45 years old: it turns out that only 3 percent of the clubs do.

Given this scenario raised within the clubs’ regulations, we propose to use the constraint on unmarried females under 45 years old as an instrument for tobacco club membership. For robustness, we also report results for different age cut-offs as well (see below). The instrument should clearly help explain tobacco club participation conditional on other covariates: since young unmarried women are more likely to get married, they are thus riskier for the club and should find it more difficult to be accepted as members. We test this below during the estimation of the first stage of the model.

production will be seriously reduced.

23
# **Table 3. Correlates of Digital Payment Prevalence in 2021**

|DEPENDENT VARIABLE: Share adults making or receiving payments,<br>2021|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|EXPLANATORY VARIABLES:|||||||||
|Overall Restrictions on Banking Activities (2016)|-4.146***|-3.657***|-2.068**|-3.657***|-1.631|-3.715***|-0.213|0.979|
||(1.257)|(1.372)|(0.976)|(1.352)|(1.055)|(1.392)|(2.255)|(2.153)|
|Made or received a digital payment (% age 15+) (change 2017-2014)|-31.777|-40.239|19.330|-25.521|30.309|-40.042|-5.431|-4.573|
||(25.193)|(28.288)|(29.474)|(30.140)|(25.711)|(28.617)|(35.654)|(39.478)|
|Banks' assets (% GDP) (2019)|0.246***|0.262***|0.156***|0.226***|0.085**|0.271***|0.315***|0.362***|
||(0.056)|(0.059)|(0.042)|(0.062)|(0.040)|(0.050)|(0.057)|(0.054)|
|GDP per capita growth (mean over 2015-2019)||1.181|0.298|0.559|0.476|1.206|-2.593|-2.739|
|||(1.262)|(1.039)|(1.395)|(0.947)|(1.273)|(1.772)|(2.012)|
|log(population) (2019)||-2.199|0.688|-1.809|1.149|-2.273|-2.823|-0.769|
|||(1.497)|(1.069)|(1.476)|(0.995)|(1.508)|(2.389)|(2.295)|
|Inflation, consumer prices (annual %) (2019)||0.032|-0.006|0.048*|0.073***|0.031|-0.455|-0.373|
|||(0.030)|(0.027)|(0.028)|(0.024)|(0.031)|(1.077)|(0.805)|
|Mean years of schooling (2019)|||5.423***||||||
||||(0.828)||||||
|Access to electricity (% of population) (2019)||||0.243*<br>(0.131)|||||
|Individuals using the Internet (% of population) (2019)|||||0.768***<br>(0.117)||||
|Mobile cellular subscriptions (per 100 people) (2019)||||||-0.027|||
|||||||(0.114)|||
|Enablers data economy (2020)|||||||2.226*||
||||||||(1.130)||
|Safeguards data economy (2020)||||||||2.451***|
|||||||||(0.749)|
|Constant|82.853***|112.571***|4.748|86.904***|1.256|116.682***|76.409**|<br>23.809|
||(8.797)|(23.163)|(22.535)|(25.221)|(20.650)|(27.806)|(32.288)|(42.485)|
|Observations|83|83|83|83|83|83|44|44|
|Adjusted R-squared|0.387|0.389|0.624|0.402|0.622|0.382|0.407|0.466|

Notes: Parameter estimates are unweighted ordinary least squares treating each country or economy as the unit of observation. Standard error of the estimates, in parentheses, are robust to heteroskedasticity of general form. *, **, *** denote statistical significance at the standard confidence levels of 10%, 5%, and 1%.

35
considered is still limited in our sample, so we should in principle not expect large differences among groups of countries. Also, we estimate the impact of each characteristic separately to keep the number of parameters and degrees of freedom at bay. Although existing studies have also not considered a full set of interactions (see Broda, 2004, for an example) using this stepwise analysis we cannot be completely sure that the differential effects associated with a particular characteristic are not actually due to a different characteristic that is correlated with it. Nevertheless, as it will be shown below, some suggestive differences emerge that indicate that some specific factors may affect a country’s vulnerability to external shocks and seem to confirm some of the intuition on the role of these characteristics.

By increasing the relative importance of trade as a fraction of GDP, trade openness can make a country more vulnerable to fluctuations in the international demand or prices of its main products. It also may reduce the degree of import substitution, exposing countries to fluctuations in the international prices of its main import products and reducing the degree of diversification. We explore the impact of openness on the dynamic response of output to external shocks by interacting with an indicator variable (that takes the value 1 if a country is open in a given year) all the variables on the right hand side of the output equation of the benchmark specification (the last equation in the system represented by equation (1)). The dummy variable is constructed using the liberalization dates compiled by Sachs and Warner (1995) and extended by Wacziarg and Welch (2003). The results of this exercise are reported in Figure 6, which compares the dynamic responses of the real per capita GDP of open and closed low-income countries to each external shock. In general, trade openness does not seem to affect much the dynamic response of output to external shocks, and only three interesting differences emerge. First, the initial impact of commodity price shocks is larger in open low-income countries, which is consistent with a higher exposure resulting from the higher relative importance of trade in these countries, but their persistence is shorter than in closed low-income countries. This difference in persistence seems to indicate that, although less exposed initially, closed economies probably lack appropriate mechanisms to deal with the impact of these shocks. Second, the impact of a shock to the international real interest rate is larger in open economies, which is consistent with these economies being more synchronized with the international business cycle. Finally, an aid shock seems to have a larger impact on closed economies, suggesting that aid flows play a larger role on countries that are only partially integrated with the rest of the 24
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