rafmacalaba/datause-encoder-data huggingface dataset:
banked adults. However, a regression of accounts per adult on the year the household survey does not show a significant relationship.

Second, one might also imagine that the conversion factor would vary significantly with the level of economic development of the country, however there was not a significant difference in the means between the developing and developed sample nor was there a significant relationship when regressing the conversion factor on log of GDP per capita. This result seems surprising, given the anecdotal evidence that individuals in richer countries often hold more accounts per person. One possible explanation could be that though the typical adult in a developed country may have more accounts on than her developing country counterpart, the ratio of business and government accounts to individual accounts is likely higher in developing countries (due to fewer individual accounts) which drives back up the ratio of accounts to banked adults.<sup>21</sup>

# **_4.5 Estimating the Number of Unbanked Individuals in the World_**

To make a final estimate of the number of banked adults around the world, we divide the number of accounts in each country by the conversion factor. When the resulting number is greater than the number of adults in the country (as was the case in a few countries with higher than 3000 accounts per 1000 adults) the number of banked adults was set to the total number of adults. Assuming 3 accounts per banked adult on average puts the number of un-banked adults in developed countries at approximately 160 million or 19 % of all adults, and at 2.7 billion adults or 72 % of the adults in the developing countries.<sup>2223</sup> Table 17 shows how this breakdown varies using the high and low confidence interval values for the conversion factor.

> 21 Desk research into a few country examples showed a high ratio of government and business accounts in developing countries.

> 22 CGAP Occasional Paper no. 8 estimates the number of savings and loan account holders in non-banks to be 500 million, leaving 2.5 billion poor around the world who do not get services form non-banks. CGAP Occasional Paper no. 8 numbers are not directly comparable to the ones presented in this report as they don’t include commercial banks and because they add together loan and deposit accounts. Never the less, their estimate of 2.5bn unbanked poor is similar in magnitude to the estimate presented here of 2.7bn adults in developing countries (some of which may not be poor).

> 23 It should be emphasized that the percentages reported are of all adults, not percentages of households banked.

24
Table 2: Additional controls

|||OLS|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_log_(1 +_imp_)<br>(1)|_log_(1 +_exp_)<br>(2)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup><br>(3)|_log_(1 +_imp_)<br>(4)|_log_(1 +_exp_)<br>(5)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup><br>(6)|
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1694**|0.4309***|-0.2615***|0.6370***|1.2130***|-0.5760***|
||(0.0635)|(0.1216)|(0.0712)|(0.2145)|(0.3175)|(0.1561)|
|_log_ (1 + _material inputs_)|0.0029|0.0939|-0.0910|-0.1710|-0.1970|0.0259|
||(0.1013)|(0.1662)|(0.1140)|(0.1227)|(0.2142)|(0.1225)|
|_log_ (1 + _IT capital_)|4.8065|7.8370|-3.0306|12.0230***|19.9068**|-7.8838|
||(3.9050)|(7.3223)|(6.2491)|(3.4734)|(8.7868)|(7.4821)|
|_log_ (1 + _non_-_IT capital_)|-19.3898**|-31.3726*|11.9828|-31.2832***|-51.2645***|19.9813*|
||(7.2922)|(16.5164)|(11.0901)|(7.2316)|(15.6678)|(11.3884)|
|_log_ (1 + _tariffs_)|0.5462***|0.2371|0.3091***|0.5589***|0.2584*|0.3005***|
||(0.0725)|(0.1650)|(0.0989)|(0.0643)|(0.1500)|(0.0925)|
|First stage:|||||||
|_Replaceability * initial GDPpc * global robot stock_||||0.0027***|0.0027***|0.0027***|
|||||(0.0003)|(0.0003)|(0.0003)|
|Observations|888,813|888,813|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.6354|0.7742|0.3913|0.6318|0.7659|0.3889|
|Kleibergen-Paap rk Wald-F-stat||||105.254|105.254|105.254|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the baseline estimation sample. Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
<mark>from 1790 to 1840. See Allen and Gale (2000), Holmstrom and Tirole (1997) and Song and Thakor (2010) for theoretical contributions on the complementarity and co-evolution of banks and markets.</mark> However, this is not to say that financial structure is not important. Instead, the studies seem to suggest that since countries differ in their underlying institutions and follow different paths of development, there is great variation in the financial structure and not one optimal institutional structure fits all types of firms and economies. Lin (2010) outlines a New Structural Economics approach wherein there is an endogenously determined optimal financial structure at each stage of an economy„s development. <mark>Demirguc-Kunt, Feyen, and Levine (2011) revisit the bank versus markets debate using data on 72 countries from 1980 to 2008 to explore whether deviations from an optimal financial structure is associated with development. They conclude that different financial structures may be better at promoting economic activity at different stages of a country‟s economic development. Allen et al. (2011) provide a comprehensive historical review of financial structures in the US, UK, Germany and Japan and how they contributed to their development process. The experiences of these four countries which all had different but sophisticated financial systems suggest that a variety of financial structure can lead to higher growth.</mark> Moreover, financial structure also affects different firms differently. Demirguc-Kunt and Maksimovic (2002) suggest that at lower levels of financial development, firms‟ ability to obtain financing is affected in different ways by the two systems. A larger securities market is associated with better access of firms to long-term financing while banking development is more associated with availability of short-term financing. Morck, Yavuz, and Yeung (2010) find that in countries where banking systems are controlled by wealthy tycoons or families, there is less efficient capital allocation, more non-performing loans, more frequent banking crises, and slower income and productivity growth rates. Thus financial structure may have important implications for which firms and projects have access to finance.

There is also a literature suggesting a link between financial structure and growth of certain types of industries. For instance, concentrated banks tend to be associated with more mature, less risky firms whereas equity markets or fragmented banking systems are associated 56
of isolation is magni fi ed in regions with greater agricultural potential. In contrast, low return nonfarm jobs, paying equal to or less than median agricultural wage of a village, are driven by local demand and are distributed much more evenly across geographical space. Finally, access to smaller rural towns with population of about 5,000 exerts little in fl uence on nonfarm activities except for non-tradeable services work. The empirical results thus highlight the need for improved connectivity of regions with higher agricultural potential to urban centers for stimulating growth in high return wage employment and self-employment in non-farm activities in Bangladesh.

While our empirical analysis is done in the context of Bangladesh, the fi ndings have wider applicability in the context of developing countries. The results highlight the need for examining both farm and urban linkages simultaneously. Since the relative importance of the linkages is likely to vary across countries, country speci fi c estimation of the impact of these linkages will facilitate informed policy making with respect to investment in infrastructure and agriculture development.

# References

1. Bakht, Z. and S. Khandker, 2006, “The Impact of Rural Roads", Policy Research Working Paper #3875, World Bank.

2. BARC/FAO,1998, Bangladesh Land Resources Information System (BLRIS) Geographic Information System(GIS) and Agro-Ecological Zones (AEZ) Database for Agricultural Development in Bangladesh Status in October 1998 (http://www.fao.org/landandwater/swlwpnr/reports/y_sa/banglade/home.htm).

3. Fafchamps, M. and F. Shilpi, 2003, “Spatial Division of Labor in Nepal," Journal of Development Studies, Vol. 39, No. 3, pp. 23-66.

22
# **1. INTRODUCTION**

Although Brazil is now one of the world’s top ten largest economies, high levels of poverty and inequality continue to pose major challenges for the country. Like in most countries, Brazil’s development is spatially uneven. People in metropolitan areas tend to be better off than people in urban (non-metropolitan) areas, and people in urban areas tend to be better off than people in rural areas. Between the five major regions, the North and Northeast regions lag behind the South and Southeast where economic centers like São Paulo are booming. In 2002, the Northeast was home to 28% of the country’s population but accounted for 50% of the country’s poor, making its poverty rate of 38% the highest in the country. On the other hand, the Southeast was home to 43% of the population but accounted for 25% of the poor and had a poverty rate of 13%. Although recent decreases in inequality have been encouraging, the Gini index has nonetheless remained high – above 55 – over the last 25 years.

These persistently high levels of inequality in Brazil have raised many economic and social concerns. First, there are concerns that high levels of inequality may compromise economic efficiency and growth. For instance, credit and insurance market failures may prevent poorer households from investing in and contributing to the economy at an optimal level, thereby undermining efficiency and growth. Also, inequality in political influence may lead to an inefficient allocation of resources for the public services necessary for greater output. Furthermore, lower social cohesion and greater crime may increase the cost of doing business (World Bank, 2003). Second, there are concerns that high initial levels of inequality may undermine the poverty reduction potential of growth. Based on empirical studies, the growth elasticity of poverty tends to be low in countries with high levels of initial inequality. (World Bank 2003; Ravallion 2004). Third, there are concerns that addressing the inequality of opportunities is fundamental to the pursuit of social justice, and doing so constitutes a development objective in itself (Roemer 1998, World Bank 2003 & 2005).

Several complementary factors are important in determining the observed spatial distribution of welfare across the major regions and areas of Brazil. One factor is the concentration and availability of skilled and unskilled labor in a particular location. Since migration is not restricted, the sorting of certain attributes may occur over time to produce variation in the concentration of household attributes in a region and area. Other factors, such as the quality of infrastructure, the distance to and size of markets, and the ability of local government to finance public investments and to create the right incentives for private sector development, can influence the scope of opportunities available and the rate of return to attributes, such as occupational specialties or education.

In exploring the factors that may be driving this spatial inequality of welfare within Brazil, a fundamental question is whether the observed differences are due primarily to the spatial concentration of 1
The first-order conditions of the Core household’s problem are: where, for brevity, the superscript _C_ is dropped for consumption and labor variables. Equation (9) is a static optimality condition describing the composition of the household’s consumption basket between tradable and non-tradable goods. Equation (10) is a static optimality condition describing the labor choice of the consumer. Equations (11), (12) and (13) are Euler equations for Periphery bonds, Core bonds and money holdings, respectively.

The expected life-time utility of a representative Outsider household is given by: Households face a sequence of budget constraints where _b_<sup>_O_</sup> _P t_<sup>istherealPeripherybondholdingsofcountryO’shouseholds,</sup><sup>_bO_</sup> _Ct_<sup>istherealCorebond</sup> holdings of country O’s households.

The first-order conditions of the Outsider household’s problem are: where for brevity, the superscript _O_ is dropped for consumption and labor variables.

10
||Ta<br>Probit C|ble 5: In-S<br>lassifier|ample an<br>Random|d Out-of-S<br>Forest|ample Pre<br>Linear C|dictive P<br>lassifier|ower<br>Threshol|d Classifier|
|---|---|---|---|---|---|---|---|---|
|Cutoff|In-Sample|Out-Sample|In-Sample|Out-Sample|In-Sample|Out-Sample|In-Sample|Out-Sample|
|**Model 1**|||||||||
|1990|0.342|0.430|0.405|0.375|0.274|0.418|0.312|0.335|
|1991|0.324|0.392|0.404|0.381|0.296|0.380|0.296|0.330|
|1992|0.340|0.404|0.376|0.390|0.309|0.407|0.292|0.345|
|1993|0.360|0.327|0.366|0.354|0.341|0.357|0.309|0.296|
|1994|0.353|0.297|0.381|0.367|0.341|0.333|0.310|0.284|
|1995|0.354|0.289|0.385|0.324|0.332|0.345|0.282|0.352|
|1996|0.338|0.286|0.358|0.350|0.327|0.306|0.300|0.299|
|1997|0.334|0.290|0.368|0.336|0.329|0.305|0.276|0.327|
|1998|0.331|0.299|0.367|0.380|0.326|0.295|0.275|0.358|
|1999|0.359|0.231|0.385|0.363|0.334|0.258|0.286|0.380|
|2000|0.354|0.280|0.379|0.343|0.328|0.238|0.288|0.305|
|Average|0.344|0.320|0.379|0.360|0.322|0.331|0.293|0.328|
|**Model 2**|||||||||
|1990|0.321|0.438|0.371|0.427|0.290|0.362|0.211|0.309|
|1991|0.336|0.444|0.404|0.394|0.276|0.422|0.208|0.303|
|1992|0.329|0.414|0.373|0.400|0.267|0.389|0.218|0.322|
|1993|0.346|0.368|0.404|0.378|0.287|0.317|0.224|0.287|
|1994|0.348|0.312|0.384|0.328|0.296|0.341|0.212|0.299|
|1995|0.345|0.338|0.386|0.380|0.284|0.330|0.214|0.271|
|1996|0.339|0.369|0.360|0.341|0.293|0.385|0.202|0.313|
|1997|0.320|0.308|0.360|0.355|0.287|0.315|0.217|0.345|
|1998|0.323|0.309|0.369|0.386|0.297|0.288|0.211|0.325|
|1999|0.339|0.279|0.350|0.338|0.320|0.308|0.238|0.343|
|2000|0.325|0.266|0.349|0.348|0.296|0.272|0.236|0.327|
|Average|0.334|0.350|0.374|0.370|0.290|0.339|0.217|0.313|
|**Model 3**|||||||||
|1990|0.324|0.486|0.429|0.406|0.285|0.361|0.204|0.347|
|1991|0.292|0.472|0.448|0.367|0.260|0.432|0.196|0.357|
|1992|0.326|0.443|0.472|0.365|0.273|0.355|0.195|0.262|
|1993|0.323|0.422|0.425|0.383|0.267|0.374|0.195|0.351|
|1994|0.325|0.506|0.410|0.347|0.260|0.370|0.201|0.312|
|1995|0.307|0.516|0.416|0.330|0.245|0.408|0.201|0.244|
|1996|0.300|0.449|0.386|0.356|0.251|0.370|0.189|0.302|
|1997|0.287|0.499|0.380|0.278|0.245|0.443|0.204|0.236|
|1998|0.282|0.364|0.347|0.326|0.257|0.324|0.178|0.324|
|1999|0.279|0.444|0.362|0.317|0.252|0.394|0.202|0.332|
|2000|0.281|0.446|0.375|0.328|0.242|0.337|0.200|0.367|
|Notes: This ta<br>splittingthe sa<br>Average|ble reports the<br>mple at the ind<br>0.302|minimized valu<br>icatedyear. M<br>0.459|e of the predict<br>odel 1,2,and 3<br>0.405|ion loss functio<br>are as defined<br>0.346|n, for in-sample<br>in Table 4.<br>0.258|and out-of-sa<br>0.379|mple predictiv<br>0.197|e performance,<br>0.312|

40
Then, in the second stage, the total changes in factor shares are split into factor substitution and technical change and the share-based bias measures are plotted against relative prices, to test the inducement hypothesis. Similar two-stage CES approaches are used by Kawagoe, Otsuka and Hayami (1986), who also apply the model to the USA and Japan, Thirtle (1985), who considers US wheat production, and Karagiannis and Furtan (1990) who use Canadian data. Binswanger (1974, 1978) pioneered a similar path, applying the translog cost function, and using relative factor prices to explain the residual factor shares, net of factor substitution. The "isotech' analysis of Nordhaus (1973), which has been extended by McCain (1977) and Wyatt (1986) is similar to the Hayami and Ruttan approach, but has not been taken on board by agricultural economists. These developments are more fully outlined by Thirtle and Ruttan (1987).

However, the "traditional model", developed by Binswanger and many others uses a time trend in estimating the biases of technical change. Clark and Youngblood (1992) argue that the time series properties of all the variables must be established prior to estimation, since the "parameter estimates for the traditional model are valid only if all independent variables are stationary and the dependent variables are driven by a deterministic time trend." They correctly suggest that estimates of the biases of technical change should be based on measures such as research expenditures or publications. This is common practice in the duality-based cost and profit function approaches to technical change in agriculture, which are conveniently summarised by Evenson and Pray (1991, pp. 185-194). The 'technology variables", such as R&D and extension expenditures, that shift the flexible functional form over time, are included in the specification of the 'meta-profit" function.

The combination of duality and flexible functional forms employed in studies of this type increases the level of technical sophistication considerably, but is far more demanding in terms of data. The alternative approach, based on the two-stage CES function is parsimonious in this respect and the functional form itself gives rise to estimating equations that allow direct testing of the inducement hypothesis.

4
11

**Table** 6: **Changes in RCA indexes in environmentally sensitive goods, 1970-90**

|Regionr/Countries|**RCAs**<br>above I<br>1970|1990|Goods in which RC<br>Increased<br>1970-90|As<br>**Decreased**<br>1970-90|
|---|---|---|---|---|
|InduMral|28|23|(no.)<br>8|30|
|**Austria**|21|22|27|10|
|Fnand|13|14|24|13|
|Norway|20|16|11|26|
|Sweden|15|16|23|14|
|Gerrany|18|**18**|16|'I|
|**Japan**|**is**|S|8|**30**|
|US|14|10|17|21|
|Developing|10|15|30|7|

**Scurce: DeAived** from United nations COMTRADB **datbase. Includes a** total of 3S thred4iit SrrC **categories as** defind in **footnaote** I **nd annex table 1.**

Product level analysis gives some indication of a shift in trade towards developing countries in a number of products included in the category of sensitive goods. Although industrial countries continue to have a comparative advantage in more of the categories of sensitive goods studied (23), the index declined in 30 categories. This means that developing countries increased their index in most categories. Between 1970 and 1990 the number of industries in developing countries with indexes above one increased by half (Table 6). Developing countries are gaining market share in industries like iron and steel, fertilizers, wood products, and chemicals. This is likely to reflect both their increasing participation in world trade and increasing production in the developing countries of many of the basic industries that belong to the sensitive category. As mentioned the share of non-industrial countries in world exports of manufactures, for example, increased from about 8 % in 1970 to over 18 % in 1990. The role of lower environmental costs in this is likely to be negligible. Many of these industries, such as iron and steel, tend to be part of early stages of industrialization.

Data on foreign direct investment (FDI) flows of environment-sensitive industries do not suggest any systematic trend towards an increasing pollution intensity of foreign direct
22 Table 4: Effects of GM cotton adoption on national economic welfare as of 2001 (equivalent variation in income, 2001 US$m)

||Welfare ch<br>|anges due to e|ffects of:<br>f|Total|
|---|---|---|---|---|
|**_Adopters as of 2001:_**|resource<br>re-<br>allocation|new<br>technology|terms o<br>trade<br>change|welfare<br>change|
|United States|-47|485|-114|324|
|China|-18|214|-34|162|
|Australia|2|63|-39|26|
|South Africa|-1|2|1|2|
|**_Non-adopters as of 2001:_**|||||
|Other high-income countries|46|0|101|147|
|Eastern Europe and Central Asia|0|0|5|5|
|Southeast Asia|-15|0|51|36|
|South Asia|4|0|10|14|
|Middle East and North Africa|5|0|9|14|
|Sub-Saharan Africa (excl S.|-4|0|-13|-17|
|Africa)|||||
|Latin America and Carib.|7|0|22|29|
|**World**|-22|764|0|742|

Source: Authors’ GTAP model simulation results
outcome from our growth accounting regressions could be related to factors which are orthogonal to GDP, such as government policies and the quality of regulation and governance. The two results could be reconciled with a growth story in which infrastructure constraints, if left unaddressed by governments, can slow the transition towards the long-run growth path, but do not ultimately affect the long-run rate of growth. Governments of poor countries in East Asia may do a better job of addressing these constraints than governments of poor countries elsewhere.

It is interesting to compare our results with those of Seethepalli et al (2007). As in Estache et al. (2005), these authors compare a “benchmark” (without infrastructure) production function estimated at the steady state with the same specification including infrastructure variables (these include physical indicators for telecom, electricity, roads, sanitation and water).<sup>13</sup> Using data for East Asian countries<sup>14</sup> , they find that virtually all dimensions of infrastructure positively influence GDP per capita when controlling for education and investment. The cross-country regressions have similar controls, while our growth accounting estimations both investment and changes in the quality of labor are captured in the APO calculations of TFP growth rates, making comparison meaningful. Our growth accounting results in the Philippines, Indonesia and Thailand tends to support their result that telecom and road infrastructure enhances productivity conditional on investment and education, but not so for electricity. But our results from South Korea and Singapore, where no infrastructure impact has been found, suggest that individual countries could be at variance with the cross-country results of Seethepalli et al (2007). And our growth regressions provide much weaker results than those obtained by Seethepalli et al. One of the reasons might be the fact that they do not control for the potential endogeneity of infrastructure stocks. While they argue that the use of stocks rather than flows mitigates the problem of reverse causation, countries may have unobserved characteristics that lead them both to have higher infrastructure stocks and higher growth. The fact that fixed effects estimations are not carried out reinforces the concern that this may bias the results (Holtz-Eakin, 1994).

> 13 This approach parallels our interpretation of our results in terms of infrastructure productivity greater than or less than that of other types of capital.

> 14 Australia, Cambodia, China, Fiji, Indonesia, South Korea, Laos, Mongolia, Papua New Guinea, Philippines, Singapore, Thailand, Tonga, Vanuatu, Vietnam.

22