rafmacalaba/datause-encoder-data huggingface dataset:
world‘s biggest exporter of manufactured goods, increasing its share of the world market from about 8 to almost 12 percent. Japan‘s success was followed in the 1970s by a second generation of economies (Hong Kong SAR, China; South Korea; Singapore; and Taiwan, China), in the 1980s by a third generation (Indonesia, Malaysia, the Philippines and Thailand (ASEAN4)), and in the 1990s by a fourth generation (China and Vietnam), (Gill and Kharas 2007, p.81).

What is less well studied, however, is how this flying geese pattern evolved at the subsector level, and how the ―jumping‖ of an industry from one country to another evolved, and how Korea has ceded its dominance in labor–intensive sub-sectors to the third generation of geese— ASEAN4, and China and Vietnam. Using COMTRADE data we show graphically that:

- There is an inverse U shape in some subsectors where the lead goose loses comparative advantage to its followers (as in Akamatsu 1961 and 1962). Since Akamatsu‘s transformation cycle can last for over 100 years, the inverse U shape emerges only when simple measures such as share of exports in the sector are used to illustrate the pattern. Each specific sector may have several generations of countries playing the role of lead goose _sequentially_ in different periods as the country‘s endowment structure changes. This is reminiscent of Akamatsu‘s theory on the international dimensions of the flying geese model (Figure 2.1).

- In textiles, an upstream but labor-intensive industry, five generations emerged sequentially. Japan ceded to Korea in the 1980s, then China emerged in the 1990s but now its textile exports are losing steam as labor costs are rising and employment shares are declining. ASEAN4, in particular, Indonesia and Vietnam, and countries which can expand market share rapidly would have a better chance to benefit by following China (fig 2.6a).

- In the apparel and clothing sector, long ago in the 1970s, Japan lost its leading position to Korea, whose clothing exports show _a clear hump shape_ as it ceded its leading position to China in 1989. China emerged later than ASEAN4, but its low wages and efficient industrial clusters in many provinces enabled it to gain dominance. After many years in the dominant position, China is now losing its comparative advantage due to rising wages and will gradually cede its market shares to ASEAN4, Vietnam, and countries which can seize the opportunity to rapidly expand exports (fig 2.6b).

- In footwear and toys, China is recently losing market shares in the EU and the US markets (UBS investment), and their shares in total exports have been declining (fig 2.6c and 2.6d). This is consistent with what other studies found but these shares vacated by China have not yet been taken up by African countries (Maswana 2011, UBS 2011). There are large opportunities for other low-income countries to benefit from China‘s graduation from these labor-intensive industries.

25
by over 100 calories per day between 1983-2012, more than enough to account for the measured decline in caloric intake and equivalent to between a third and a half of the average rural-urban gap around 1998, there was substantial demographic change in the population that more than offset this effect. The share of children under age 15 fell from 40% to 30% during this period, and the analysis shows that they have both lower metabolic requirements and substantially lower activity levels, particularly in rural areas. Because the average TEE of adults is much higher than children, population-level demographic change of the magnitude that occurred in India during this period exerts very powerful upward pressure on population TEE, enough to offset reasonably large reductions in TEE for a given age/sex. Thus, although variation in TEE can help account for the caloric-intake household size relationship explored in (Deaton and Paxson 1998), and can explain some of the national and cross-state patterns of caloric intake in the 20052012 period, it appears unlikely that a large reduction in TEE could “explain” the decline in caloric intake measured in the NSS. To the extent that there was a real (as opposed to measured) decline in population caloric intake during the 1983-2005 period, an improving disease environment (as shown by (Duh and Spears 2017)) is a more likely explanation. The estimates of TEE reported here are also consistent with a large degree of measurement error in NSS caloric intake, which recent data suggests is large enough to plausibly explain much of the apparent decline.

Although TEE is not usually considered by economists, the analysis suggests that there are interesting insights from quantifying it. Better measures of TEE could be used for the formulation and revision of poverty lines and equivalence scales, although given the difficulty of accurately measuring caloric intake or TEE it seems clear that anthropometric outcomes (e.g. stunting, wasting, BMI and weight gain over time) are more reliable for the evaluation of adequate nutritional status. Estimates of TEE could also be useful inputs into quantitative models of structural and demographic change since “subsistence” food requirements may vary with mechanization and household composition. In addition, quantifying TEE is a useful contri– bution to debates about estimated caloric intake and nutritional outcomes given the scope for measurement error, measuring both calories in, calories out, and net caloric surplus can shed light on biases in measurement. Timeuse and anthropometric surveys are available for many developing countries, and the potential availability of multiple waves of time-use data in developing countries could be particularly useful for this type of exercise, as it may 18
**Table 7: Estimated Interregional Trade Volume Effect of Road Network Upgrading ($US Million)**<sup>**30**</sup>

|Regional Pair|Current<br>Network<br>Trade|Upgraded<br>Network<br>Trade|Change|
|---|---|---|---|
|WestWest|2,838.1|7,300.7|4,462.6|
|West Central|647.3|1,593.1|945.8|
|WestEast|23.1|437.6|414.5|
|West Southern|4.2|50.6|46.4|
|West South|870.4|2,301.4|1,431.0|
|CentralCentral|199.6|1,361.6|1,162.0|
|Central East|308.1|590.4|282.3|
|CentralSouthern|133.3|215.5|82.2|
|CentralSouth|1,390.2|3,093.1|1,702.9|
|EastEast|724.0|6,169.8|5,445.8|
|East Southern|101.2|196.3|95.1|
|East South|871.3|3,010.3|2,139.0|
|SouthernSouthern|142.6|190.3|47.7|
|SouthernSouth|1,867.9|3,298.4|1,430.5|
|Totals||||
|Within Regions|3,904.3|15,022.4|11,118.1|
|AcrossRegions|6,217.0|14,786.7|8,569.7|
|Among West, Central,<br>EastAfrica|978.5|2,621.1|1,642.6|
|West, Central, East Africa<br>withSouth Africa|3,131.9|8,404.8|5,272.9|
|West, Central, East Africa<br>withSouthern Africa|238.7|462.4|223.7|
|Southern Africa with<br> South Africa|1,867.9|3,298.4|1,430.5|
|Total|10,121.3|29,809.1|19,687.8|

# **4.5  Implications**

Our results highlight the importance of a network perspective in evaluating road investment opportunities in Sub-Saharan Africa. The overall implications are illustrated by Figure 3, which presents two types of information: country road-quality indices and network road links, size-graded to reflect frequency of use by the 3,403 minimumdistance routes in our analysis. Figure 3 shows that the minimum-distance routes joining

> 30 By construction, as we have previously explained, total simulated trade prior to upgrading in Table 7 is the same as the calculated total for the IMFDOT data in Table 6.  Total simulated trade in Table 7 after upgrading ($29.8 billion) also matches the total econometric projection reported in Table 6

22
23

# **_Securities markets are relatively more important at higher income levels._**

The Global Financial Development Database allows for examining the relative size of financial institutions and financial markets around the world. The issue of financial structure – usually approximated by the relative size of bank credit and stock market capitalization – has been an important topic in the policy debate.

In a recent paper, which uses data that are part of the Global Financial Development Database, Demirgüç-Kunt, Feyen, and Levine (2012) examine the issue of financial structure empirically and find that as economies develop, use of services provided by securities markets increases relative to those provided by banks. In other words, as income increases, the marginal increase in economic activity associated with an increase in bank development falls, while the marginal increase in economic activity associated with an increase in securities market development rises. This work highlights the potential economic development costs of policy and institutional impediments to the evolution of the financial system.

The existing research and policy work does not provide enough guidance to justify targeting a particular financial structure for a particular country. However, if market or bank development is too skewed compared to what one could expect given their level of economic development, the above research findings provide a reason to dig deeper: one would need to find out if taxes, regulations, legal impediments or other distortions are leading to excessive reliance on banks or markets. Facilitating a shift from a bank-centric system to a more market-based system through a policy is never an easy task. Actively intervening to ―develop‖ markets is likely to be problematic. Interventions should be more along the lines of enabling environment and reducing impediments. Even in systems with relatively strong role of the state in the economy, shifts in financial sector structure do not occur overnight. China (Box 1.3) is a case in point: despite policy intentions and reforms aimed at promoting non-bank financial institutions and markets, the financial system remains very much dominated by large banks, and in some ways have become even more bank-centric during the recent period of rapid credit growth.

# **6. Conclusions**

This paper has presented the Global Financial Development Database, an extensive dataset of financial system characteristics around the world since 1960s. The database is a one-stop, cleaned-up database that builds on previous efforts, in particular the data collected and the categorization of variables proposed by Beck, Demirgüç-Kunt, and Levine (2000, 2010).

The dataset can be used to illustrate cross-country and time-series patterns in financial systems. The data can be used to better assess linkages between finance and economic development and to assess the efficacy of different financial policies and regulations. The database can be used to analyze financial sector development and trends in 205 jurisdictions around the world. The
Policy Research Working Paper 6204

# **Abstract**

Competing theories increasingly support the positive role of social capital in small loan default costs of group lending; at the same time, potential group collusion may increase loan delinquencies. Findings from the available literature are mixed on the role of the various attributes of group lending. But past studies suffer from estimation bias due to the unobserved sorting behavior of group members and their other attributes. This paper attempts to resolve that estimation bias by utilizing longitudinal data from 297 Grameen Bank groups since their inceptions. A dynamic lagged dependent model with correction for time-varying heterogeneity of group and individual behavior is applied to estimate the effect of group liability in the Grameen Bank. The results suggest that group liability matters in both loan disbursement and repayment, with women less of a credit risk than men and women’s groups more homogeneous than men’s. Finally, the benefits of social capital outweigh the costs of group collusion, especially for women’s groups, thereby reducing overall default rates. The risk-pooling behavior of diverse men’s groups increases men’s repayment behavior. Overall, group lending as practiced by Grameen Bank appears to increase repayment rates.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
productivity occupations, with limited access to capital, and with earnings that are relatively low on a global scale. On the other hand, one could argue that there is also a great dispersion across many of these socioeconomic characteristics among MICs. In brief, MICs are countries that made substantial progress in social and economic outcomes, but still lag significantly behind the rich countries in most social and economic indicators.

Most of the social indicators mentioned above are positively correlated with countries’ income per capita. The simplest way to establish thresholds for MICs would be on that basis. The World Bank classifies a country as a MIC if its income per capita (Gross National Income, in accounting terms) is greater than $1,005 and less than $12,275.<sup>3</sup> This group of countries represented about 55 percent of all the economies for which the WDI has data for the year 2010. Broadly speaking, it does represent the middle class and the distribution of countries across these income categories looks evenly distributed (Figure 1)—at least for the categories above lowincome status. In this figure, the MIC class is disaggregated into “lower middle-income” (LMIC—up to $3,975) and “upper middle-income” (UMIC). The average incomes vary exponentially; however, the log levels follow a fairly straight line across income categories (line in Figure 1).<sup>4</sup>

**Figure 1: Distribution of countries by income per capita, 2010**

<!-- Start of picture text --> 35 Log of Group's 12<br>30 Average GNI per<br>10<br>capita (RHS)<br>25<br>8<br>20<br>6<br>15<br>4<br>10<br>2<br>5<br>0 0<br>LICs LMICs UMICs HICs<br>% of Countries<br><!-- End of picture text --> Source: World Development Indicators.

Does this alleged inability of countries to progress from MIC to high-income status (HIC) imply that there is a “trap”? There have only been about 200 years of modern economic history: what has been the evolution of MICs and what would be a reasonable timeframe for progress to high-income status? This paper surveys the empirical evidence for various definitions of a MIC trap. The closest paper in the literature that we have found is Felipe et al (2012); however, we take a different approach to look at the empirics of whether the MIC to HIC transition is different

> 3 This is based in “Atlas” dollars, where the “Atlas” measurement smoothes out short-term changes in countries’ exchange rates.  In years, where the currency has been fairly stable, the “Atlas dollar” GNI per capita is very close to a GNI per capita based on market exchange rates. More information is available at http://data.worldbank.org/about/country-classifications.

> 4 Felipe et al, 2012, use a methodology to extrapolate these thresholds to Geary-Khamis PPP dollar thresholds, so as to be able to track the evolution of countries across categories using the longer term Maddison data set.

3
Table 4: Village land inequality and social network density

||(1)<br>Befor|(2)<br>e land tra|(3)<br>nsfers|(4)<br>Aft|(5)<br>er land tra|(6)<br>nsfers|
|---|---|---|---|---|---|---|
||Kinship|Ethnicity|Neighbor|Kinship|Ethnicity|Neighbor|
|Density / SD|-0.068***|-0.019|-0.107**|-0.038*|0.005|-0.071*|
||(0.021)|(0.023)|(0.041)|(0.020)|(0.020)|(0.035)|
|Constant|0.645***|0.572***|0.662***|0.542***|0.457***|0.557***|
||(0.043)|(0.066)|(0.077)|(0.041)|(0.059)|(0.066)|
|Observations|50|50|24|50|50|24|

Note: The dependent variable is the Gini coefficients in land in each village. It varies between zero and one. Source: Author’s own calculations on the Gambia Networks Data 2009.

Table 5: Land transfers and social proximity

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Ethnic component|0.000<br>(0.001)||||-0.001<br>(0.005)|
|Family component||-0.005<br>(0.008)|||-0.002<br>(0.009)|
|Family link|||0.045***<br>(0.009)||0.045***<br>(0.010)|
|Neighbor link||||0.035***<br>(0.011)|0.026**<br>(0.010)|
|Constant|0.037***|0.041***|0.031***|0.023***|0.020**|
||(0.006)|(0.008)|(0.005)|(0.004)|(0.007)|
|Observations|54,144|54,144|54,144|21,266|21,266|

Note: The dependent variable is the absolute amount of land transferred from i to j measured in hectares. Standard errors clustered by village are shown in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Source: Author’s own calculations on the Gambia Networks Data 2009.

of between four and eleven points, depending on the network density measure used.

Whether land transfers are more common between socially proximate households is also investigated. To this effect _|wij|_ is regressed on the five measures of social proximity described earlier: co-ethnicity _Dij_<sup>_E_,membershipinthesamekinshipcomponent</sup><sup>_D_</sup> _ij_<sup>_K_,beingdirectly</sup> related _L_<sup>_K_</sup> _ij_<sup>,andbeingneighbors</sup><sup>_LD_</sup> _ij_<sup>.Resultsshowthat</sup><sup>_D_</sup> _ij_<sup>_E_and</sup><sup>_D_</sup> _ij_<sup>_K_donotpredictland</sup> transfers, but _L_<sup>_K_</sup> _ij_<sup>and</sup><sup>_LD_</sup> _ij_<sup>do,suggestinghomophilyinlandtransactionsalongthosetwo</sup> dimensions (see table 5).<sup>23</sup>

The situation can thus be summarized as follows: an essential resource is not allocated equitably between small producers, a situation there are reasons to suspect is inefficient; the 23
6) _Tax simplification_ . These initiatives are a form of indirect business support to SMEs and are aimed at improving firm performance through formalization. Economic theory suggests that formal firms grow by accessing credit markets and by taking advantage of economies of scale. A tax simplification program could affect outcomes such as employment and profits through two intermediate outcomes: 1) formalization rates and 2) access to credit. The causal chain could be simplified as follows: the necessary conditions for a tax simplification program shifts informal entrepreneurs from an equilibrium characterized by low productivity and profits to another where they face fewer constraints to growth (as a result of formalization). Plenty of studies concentrate only on final outcomes and thus shed little light on the mechanisms associated with tax simplification/formalization (and consequently offer little policy guidance). The underlying assumption is that formal firms are less credit-constrained than their informal counterparts and, therefore, formalization is an effective way to help entrepreneurs. Indirect support to SMEs may include policies regarding business registration, property registration, and regulatory frameworks (Fajnzylber et al., 2011; Monteiro and Assunção, 2012; McKenzie, 2013).

The various result chains shown in Figure 1 are thus useful in providing the rationale behind the types of interventions considered in this review.

# **3. Inclusion Criteria and Search Methods**

This review focuses on studies that evaluate policies aimed at supporting SMEs in LMICs (as defined by the World Bank). The focus on LMICs is justified, firstly, because private firms in these countries tend to be more labor intensive and less innovative and, consequently, are the main employers of a large proportion of the labor force (for example, Acz and Amoros, 2008; Cravo et al., 2012). Secondly, restricting the scope to LMICs helps identify the binding constraints that SMEs might face in similar institutional contexts.

A common definition of SMEs does not exist. This review mainly uses the most common criteria used to classify SMEs based on employment information. The cut-off used to define SMEs is 250 employees as Beck et al. (2005), Ayyagari et al. (2007), Cravo et al. (2012), Kushnir et al.

8
With the huge expansion in bibliographic data and easier (electronic) access to those data, most researchers have become very aware of how much others have cited their work. Bibliometric measures, such as Hirsch’s (2005) famous _h_ -index, are now routinely calculated from these data and used to assess the performance of individual researchers, universities and journals. Recruiting, promotion, tenure, employer choice and funding decisions are increasingly influenced by these measures.

As economists coming fresh into bibliomerics, we were surprised how little attention has been given to the logically-prior conceptual issue of what the theoretically ideal measure would look like. Citations are clearly only of interest as an observable indicator for a latent, but more important, concept of “scholarly influence” (more often called “scientific impact” in the literature, but we prefer our more modest term). Yet it is rare to find any explicit discussion in this literature of how one expects citations to reflect scholarly influence. And without being explicit about that relationship it is hard to understand what exactly the _h_ -index (or the variations on it since Hirsch’s paper) is measuring.

This paper proposes a new class of measures of the impacts of a publication record. Our point of departure from past work is that we build the bibliometric measure on a theoretical “influence function” that embodies our priors about how citations reflect influence. The assumed properties of this function then determine the bibliometric measure, thus making the measure’s conceptual foundation transparent.<sup>2</sup> While the properties we postulate for the influence function appear to be quite natural, we find that widely-used bibliometric measures have one or more properties that are inconsistent with those assumptions. We argue that these are counter-intuitive properties for a measure of the overall impact of a publication record.

We also recognize explicitly that reasonable people need not agree on the properties of the influence function. Then the question arises as to whether one can derive robust orderings of two or more publication records without knowing the precise way in which citations reflect

> 2  The idea that measures of performance should be built on a precise formulation of the objective function is not, of course, new. The most prominent example we know of has been in a strand of the economics literature on the measurement of income inequality, in which the measure is defined as the loss of aggregate social welfare due to inequality, where social welfare is the sum of individual “utilities,” each of which is a stable function of own income; the utility function is taken to be unobserved, and so a matter for prior judgment. The properties of the inequality measure are thus derived from the prior (ethical) assumptions made about social welfare. Dalton (1920) first suggested this approach. An influential formalization and development was provided by Atkinson (1970).

2