rafmacalaba/datause-encoder-data huggingface dataset:
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# I. INTRODUCTION

Successive developments in the role of credit policy in the stabilization and adjustment strategy of developing countries have been toward greater recognition of the importance of interest rate channel in influencing the pace of financial sector reform,1 in the process of allocative efficiency,3 and in influencing aggregate private spending.' Indeed, the earlier emphasis placed on volume of credit as an instrument of policy, both in development literature and practice, as exemplified most notably _in_ the IMP approach to stabilization [see for instance, Polak and Argy (1971); Keller (1980)1, is now giving way to a new conventional wisdom about the virtues of positive real interest rate policy.' But ironically, as this view is gaining the status of a new orthodoxy there exists a growing body of theoretical research and empirical evidence which proport to return the quantitative channel of credit policy to the forestage.

Thus, there exists the important strand of literature on credit rationing which either in its micro context (Hodgman (1960, 1962); Jaffee and Russell (1976); and Stiglitz and Weiss (1981)1 or in its traditional "availability doctrine," context [Roosa (1951); Scott (1957); Lindbeck (1962)] has led to rationalize non-price credit rationing even in environments where interest rates are free of administrative control and regulation.$ Moreover, by shifting the spotlight from the saver/borrower decisions to that of the intermediary, i.e. the 1 On this see, McKinnon (1973); Khatkate (1972); Galbis (1977); and World Bank (1909).

2 See, for instance, Balassa (1982); and Roe (1982).

' See, Taylor (1979, 1981); van Wijnbergen (1983) and Dailami (1989).

See Sauv6 (1986) for current state of debate on interest rate policy in developing countries.

' See also, Baltensperger and Devinney (1985) for a recent survey and synthesis of credit rationing theory.
inspections, and price control measures. The large incidence of SPS measures and TBTs raise concerns for developing countries’ exports. The costs of compliance of SPS measures and TBTs are higher for lowincome countries, and can erode the competitive advantage that these nations have in terms of lower labor costs and preferential access (Cadot et al., 2012). Only a small proportion of these NTMs are still in the form of quotas and export restrictions, since most quantitative restrictions are illegal under WTO rules. In the cases where quantitative measures are allowed, they materialize in the form of nonautomatic licensing, often necessary to administer the importation of goods where SPS- and TBT-related issues are of particular importance. Pre-shipment inspections are, in many cases, necessary to assure the quality and quantity of a shipment, thus facilitating trade, but they do add to the costs of trading. Price control measures are one of the least used forms of NTMs. These barriers affect only a small share of goods and are largely related to anti-dumping and countervailing duties, as well as some forms of administrative pricing for staple food and energy or other sensitive products.

NTMs can have different effects on trade. “On the one hand, requirements on marking, labeling, and packaging, although adding to costs of production, generally are not discriminatory. On the other hand, quotas, voluntary export restraints, and non-automatic import authorizations often have much more significant effects” (Gourdon and Nicita, 2012). To analyze the extent to which NTMs constitute an important barrier to trade, we use the indicators constructed by Gourdon and Nicita (2012), which employ a newly collected dataset on NTMs constructed by the World Bank, the International Trade Center, and UNCTAD. The analysis relies on two indicators: the frequency and coverage ratio of NTMs. The frequency ratio accounts only for the presence or absence of an NTM, and summarizes the percentage of products to which one or more NTMs are applied. In more formal terms, the index for country _j_ is calculated as follows: where _D_ is a dummy variable reflecting the presence of one or more NTMs affecting product _i_ , and _M_ indicates whether country j imports good _i_ (also a dummy variable). To capture the relative value of the affected products, we use the coverage ratio. In formal terms, the measure is given by: where _D_ is defined as before, and Vi is the value of country j ’s imports of product _i_ . Table 6 presents the results.

Table 6. NTMs: Frequency and Coverage Ratio

||Frequency|Coverage Ratio|
|---|---|---|
|China|100%|100%|
|European Union|90.1%|86.2%|
|Indonesia|43.5%|36.8%|
|Japan|39.4%|59.5%|

Source: Gourdon and Nicita (2012) and authors’ calculations for China using data from UNCTAD accessed through WITS (2012).

It is important to acknowledge that this analysis assumes that all NTMs are applied on a nondiscriminatory basis. This implies that if a country applied a particular NTM to the imports of a product from a single country, it will do the same with the imports of the same product from all countries.

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# 8.1 Earnings Analysis

Table 27 shows the means and standard deviations of the variables used in the regional earnings functions. The estimated functions in Table 28 reveal that there are similarities as well as important differences in the structure of rewards to human capital across locations. The similarities pertain mainly to years of general education and years of work experience, while the main differences have to do with years of VTE and the role of diplomas.

The insignificance of years of primary schooling as a determinant of wage-earnings is observed in every location. The contributions from a year of secondary and higher schooling are in the 12%-15% range in all iocations. In urban areas, both years of current and previous job experience are important determinants of wage-earnings. In Abidjan, a year of current job experience adds '0% to earnings, against 7% in other cities, but the earnings-experience profile peaks much sooner in Abidjan. A year of previous job experience adds 8% to earnings in Abidjan as well as in other cities.

The effect of VTE on earnings differ a great deal across locations. Secondary VTE adds 7.3% to earnings per year of education in Abidjan, but no additional premium is received for diplomas. In the other cities equation, the coefficient of years of secondary VTE is not significantly different from zero, but the corresponding diploma variable
Looking at a sample of developing countries, these authors find a positive relationship between financial development and poverty if financial development is measured by the ratio of M3 to GDP. Inoue and Hamori (2012) find similar results considering deposits in a panel of 28 Indian states. Kraay (2004), by contrast, studying the association between the change in absolute poverty and the ratio of M2 to GDP in a sample of developing countries, finds that financial development is not associated with a faster decline in poverty.

# **B.   Finance and Property Rights**

Financial institutions operate in settings where information is often asymmetric. On the one hand, entrepreneurs seeking financing normally have more information about their projects than their banks do. On the other, projects that may have different probabilities of success are indistinguishable from the viewpoint of a financial institution. This information asymmetry requires banks to screen applications so as to grant loans only to the most promising projects (Singh, 1992).

Stiglitz and Weiss (1981) have shown, however, that lenders cannot rely simply on increasing the interest rate. Raising the interest rate charged on loans may adversely affect the composition of the pool of borrowers. Lenders would like to be able to identify borrowers who are more likely to repay, since the expected return to lenders depends on the probability of repayment. Those who are willing to borrow at high interest rates, however, are willing to do so because if their project succeeds it will yield a high enough return to cover the high financing costs, but also because they perceive their probability of repaying the loan to be low (and hence are riskier). Under these conditions, for a given expected return, an increase in interest rates will induce low-risk projects to drop out first, leaving only the riskier ones in the pool.

More efficient exchange of information could reduce the cost of screening borrowers. Bank branches bring banks closer to their potential clients and by hiring local staff they can tap into local knowledge to discriminate between high- and low-risk borrowers. GuillaumontJeanneney and Kpodar (2011) show that extending bank coverage may change their initial observation that the poor may not benefit from increased credit. Their results show a positive and significant coefficient for the interaction term between the credit ratio and the number of bank branches per km<sup>2</sup> . In advanced countries, databases centralizing information on borrowers are often established by the private sector or maintained by central banks. These registries collect information on the standing of borrowers in the financial system and make it available to lenders. The system improves transparency, rewarding good borrowers and increasing the cost of default.

As an alternative, lenders could also impose a cost if the entrepreneur defaults by requiring collateral. As higher risk projects entail a greater probability of failure, the same amount of 5
# **3. Data**

An intensive effort allows us to put together the most comprehensive micro-level database on remitting behavior currently available, comprising of data on 33,000 immigrants from developing countries from 14 surveys in 11 OECD destination countries. These countries were the destination for 79% of all global migrants to OECD countries in 2000 (Docquier and Marfouk, 2005). The focus on destination country data sources allows us to look directly at the relationship between education and remittance sending behavior by analyzing the decision to remit by the migrants themselves. It also enables us to capture the remittance behavior of individuals who emigrate with their entire household, whereas using household surveys from the remittance-recipient countries would typically miss such individuals. Since more-educated individuals are believed to be more likely to emigrate with their entire household than lesseducated individuals (Faini, 2007), it is apparent that using surveys from migrant-sending countries will not be appropriate for examining the relationship between remittances and education.

The majority of the empirical literature on immigrants has used data from either Census or labor force surveys. However, neither contains information on remittances. Instead, we must use special purpose surveys of immigrants. We have pulled together all of the publicly available datasets we are aware of,<sup>7</sup> along with six additional surveys that are not publicly available, but which other researchers were generous enough to share. Table 1 provides an overview of our comprehensive database of migrants, outlining a summary of the datasets, sample population, and survey methodology. Our database covers a wide range of populations, covering both nationally representative surveys such as the New Immigrant Survey (NIS) in the United States (drawn from green card recipients) and the Spanish National Survey of Immigrants (ENI), which draws on a neighborhood sampling frame, as well as surveys which focus on specific migrant communities within the recipient country, such as the Black/Minority Ethnic Survey (BME) in the United Kingdom and the Belgium International Remittance Senders Household Survey, which surveyed immigrants from Senegal, Nigeria and the Congo. In all cases, we keep only migrants who were born in developing countries.<sup>8</sup>

> 7 Exceptions include longitudinal surveys of immigrants from Canada and New Zealand, which can only be accessed through datalabs in these countries, and so are not included here.

> 8 High Income countries are defined based on the World Bank Country Classification Code, April 2009.

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24 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity use the digital elevation model from the NASA Shuttle Radar Topographic Mission (SRTM) version 4, with a resolution of 90 meters (Jarvis, Reuter, Nelson, & Guevara, 2008), to calculate slopes; and the Global MODIS Raster Water Mask (Carroll, Townshend, DiMiceli, Noojipady, & Sohlberg, 2009a, 2009b) to account for the presence of bodies of water. We implemented the process to define the potential urban extent in ArcGIS from the extracted urban extents for 1996, considered as the real urban extents. We first determine the “minimum-bounding circle” that contains the real urban extent and locate its centroid. Then we use the r��,� estimated from equations (5) and (6) with the buffer function to obtain the estimated circles for each date. In all cases we use the estimated radius for the first date (1996). This is because we observed contraction of the potential urban extents when using the minimum-bounding circle for the first date (1996) and the estimated ratio for the following two dates (2000 and 2010). Second, we defined the developable terrain. Initial tests with a 15% slope threshold proved to produce fragmented patches of developable land in many cities, resulting in potential urban extents that were extremely small compared to reality. While architectural development guidelines in developed countries typically use the 15% slope threshold as constraint threshold for urban development (Saiz, 2010), given that the Andean Mountain Range crosses many countries in Latin America, in the region, urban development guidelines have been more tolerant and cities have grown in areas with considerably steeper slopes. An analysis of the distribution of slope values in a sample of urban extents extracted for the year 1996 suggested that in fact the median slope in the areas considered is 21%; hence, in the analysis we use a threshold of 20% slope to delimitate the undevelopable land areas. Finally, we manually select the largest patch within the circle as the potential urban extent for each date. Figure 3 shows an example of an actual urban extent and its potential shapes in the three dates.

**Figure 3.** Lima 1996 extracted urban extent outline in dark red and potential urban extent areas for 1996 (light red), 2000 (orange), and 2010 (yellow). Base map: © Google.
First, we estimate the key parameters of our NEG model using detailed data for 2007 and market access based on the road network that includes the NEN. The equilibrium solution of this model provides an accurate empirical description of the observed spatial distribution of income and wage levels. We then use this model to simulate the (real) income and wage levels which would have existed in 2007 had the NEN not been constructed – that is, the spatial distribution of our outcome measures if market access had not been significantly improved. By comparing these income levels with those under our original equilibrium solution we arrive at estimates of the short-run impacts of the NEN up to 2007.<sup>7</sup> Given an explicit NEG general equilibrium framework, we are able to fully capture the impacts of the NEN road network expansion, the totality of which would be missed by other impact evaluation approaches. Under the model, improving a link in a road network not only benefits the immediate vicinity, but also areas further away whose trade and travel passes through that link. Better transport infrastructure thus changes the relative strength of agglomeration and dispersion forces in the entire country. This shapes the geography of market access and consequently the spatial distribution of wage and income levels. Moreover, our NEG model incorporates the full set of price changes resulting from transport cost reductions, so that we can measure both nominal and real quantities, for instance real urban wages. However, we enhance the theoretical NEG model, in which wages are determined solely by market access via the wage equation, by also allowing for (empirically estimated) variations in the efficiency of labor both across prefectures and between the urban and rural sectors.

Our results suggest that Chinese real income in 2007 was approximately 6 percent higher than it would have been, had the NEN not been built. As already indicated, this is a short-run estimate that does not take into account possible additional effects associated with the longrun mobility of labor. It is also a one-off level effect, as opposed to a permanent growth rate effect, as a result of a boost to productivity levels. The estimated impacts of China's NEN investments vary both across prefectures and between urban and rural areas. In all but one prefecture, overall estimated real income has increased.<sup>8</sup> The largest real income gains, however, have been concentrated in the East of China, so that, contrary to its objectives, the NEN has, thus far, done little to alleviate disparities between the coastal and interior

> 7 Our estimates of impact exclude those (temporary) impacts associated with the actual construction of the NEN itself (e.g., employment of construction workers).

> 8 The exception is Changji Hui Autonomous Prefecture, located in north-eastern Xinjiang. But the decline in real income is only 0.53 percent.

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Table 5: Financial Development and Productive Structure In Panel A, the dependent variable is the share of total manufacturing value added in sectors at the top quartile of inventory over sales in column (A1), while in column (A2) the dependent variable is the share-weighed value of liquidity needs. In Panel B, the dependent variable is the share of total manufacturing value added of each sector in every country. Regressions in Panel A include a non-reported constant. Regressions in Panel B include a country and industry specific effect (non-reported). Regressions in Panel A are 2SLS where the measure of financial development (private credit) has been instrumented by a country’s legal origin. Panel B reports both the OLS (B1) and 2SLS results (B2). The instruments in 2SLS are the same as in Panel A. Standard errors are robust to heteroskedasticity in both Panels, while Panel B also allows for correlations in the errors within countries.

# Panel A: Cross-country regressions

|Dependent|Variable|
|---|---|
|Variables<br>Share of<br>most vulnerable<br>(A1)|Share weighed<br>liquidity needs<br>(A2)|
|Private Credit<br>0.289*<br>(0.087)|0.029<br>(0.020)|
|Obs<br>58|52|
|Panel B: Productive structure regressions<br>Dependent variable is the share of each industry in every|country|
|OLS|IV|
|(B1)|(B2)|
|Interaction (Liquidity needs<br>0.060|0.134*|
|X Private credit)<br>(0.032)|(0.051)|
|Obs<br>4035|4035|

Note:*=Significant at 5% confidence. Robust standard errors in parenthesis.

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cotton futures contract. However, the present very low level of liquidity of the contract is likely to discourage use of this contract for hedging purposes.

Although the New York number I cotton futures contract represents a cross-hedge, it was effective in decreasing volati1ity in the simulations. Moreover, the New York Cotton Exchange has added additional serial months to the Cotlook World Cotton Futures contract--for which settlement is based on the Cotlook A Index--to increase the trading and hedging opportunities for market users. In addition to the regular cycle months of March, May, August, October, and December, two spot or serial months from the January, February, April, September, and November cycle will also be available. The Exchange anticipates that the addition of rolling spot months will increase the contract's liquidity and afford hedgers and speculators a more viable trading vehicle.

# **11.1 Risk Minimization (Ex-Ante Risk-Minimizing Hedaes)**

We turn now to analyzing the risk management prospects for FPA cotton. We will assume throughout this section that the objective of the hedger is simply to minimize risk.

The FPA hedging decision can be thought of as a portfolio selection problem in which the hedger selects the optimal
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equations simple, the scale effect associated with the volume of exports is measured in logarithms. The expected effect is unchanged; beef export prices would decease with the market share. Finally, the dummy variable for years before 1997 is introduced to account for the negative effect of the BSE crisis. It is plausible that after the crisis beef exporting developing countries might begin bearing additional export costs, which are likely to reduce their competitiveness in the international market.

# **_Consumer choice_**

On the demand side a simple discrete consumer choice model is applied to the EU beef and veal import market, essentially to estimate the demand elasticity. Following the industrial organization literature (e.g., McFadden, 1974; Trajtenberg, 1989; Berry, 1994; and Nevo, 2001), suppose that a representative importer _i_ = ,1 L , _I_ decides to purchase one unit of beef from a variety of country-brands _j_ = ,0 L , _J_ , and maximizes the following linear indirect quasi-utility function:<sup>29</sup> where<sup>_x_</sup> _j_<sup>and</sup> Δξ _jt_ are a set of brand-specific characteristics and a brand-specific deviation from the mean brand valuation. Assume that products are not perfectly substitutable, and the country-specific quality of beef is considered unchanged over time. When the idiosyncratic error term ( ε 2 ) is independently and identically distributed according to Type I extreme value distribution, such as exp( − exp( −ε )) , Equation (5) yields the conventional market share equation:

> 29 In practice, it is less likely that consumers are directly involved in the arbitrage process in the international trade market. Rather, wholesalers purchase from exporters. However, their buying behavior is naturally predetermined by consumer preferences.