rafmacalaba/datause-encoder-data huggingface dataset:
- 49 -

relatively inelastic (what is likely to be a normal situation for many Caribbean products in the short run) the exporter bears the major part of the freight bill and the price he receives falls below the "zero transport cost" price by approximately the full extent of shipping costs. It also follows that the fall in quantity traded below that exchanged in the absence of transport cost is determined by supply. When supply is inelastic, the quantity reduction due to freight cost is small; however, the revenue loss to the exporter may be considerable.

With a less than perfectly inelastic supply, any reduction in the elasticity of demand shifts some of the transport burden to the importer. When demand and supply schedules have equal elasticities, shipping costs are shared equally by buyers and sellers. As demand grows progressively less elastic, the share of freight costs born by the buyer rises, with the elasticity of demand determing the extent to which quantity exchanged falls below that traded in the absence of freight charges. The key point to note is that the Caribbean countries are generally relatively small suppliers (as measured by world traae) of the products they export so the dem4nd,for these items should be ouite elastic. As Figure 8 shows the more elastic the demand, with any given supply schedule, the greater will be the incidence of transport costs (or any changes in these costs) on Caribbean exporters.
7 graduated from IDA borrowing and 0 for eligible borrowers. We therefore used a logit estimation model. We run pooled logit regressions and correct standard errors for clustering by country. We also test the model with a probit estimator and find similar results. All data come from the World Development Indicators (WDI) database, unless stated otherwise.

As the graduation status of a country for the coming year cannot be determined by data for that same year, the IDA decisions are based on historical data. This means that the model has to reflect a two-year lag between the IDA graduate status in a certain fiscal year and the datapoints. For example, it is assumed that the FY2018 graduation decisions are based on calendar year 2016 data, as they are the most recent data available at the time the determination is made.

This analysis treats graduation and reverse graduation symmetrically. In practice, graduation and reverse graduation are not fully symmetric. Reverse graduations usually occur because of a crisis resulting in a major fall in income per capita and/or loss of IBRD creditworthiness, whereas decisions on graduation are affected by a broader set of factors. Notwithstanding this limitation, our analysis helps reveal whether IDA graduates differ systematically from IDA borrowers in ways consistent with the IDA graduation policy and, as demonstrated in the following paragraphs, explains graduation decisions reasonably well.

# _Explanatory variables_

In this model we are using the data that proxy for the issues analyzed when graduation decisions are made. In some instances, the data used in the model were not available when the decision was made. There are also variables that have been used for recent graduation decisions but are not available for most years used in this study and are therefore excluded.

Regressors in the model include two income-related indicators: the ratio of GNI per capita to the operational cutoff and (the log of 1 plus) the number of consecutive years in which GNI per capita has exceeded the operational cutoff. If income falls below the cutoff in any given year, this indicator resets at 0. The number of consecutive years is capped at 9, and a separate variable captures (the log of 1 plus) the number of years in excess of 9 that a country has been above the cutoff but not graduated. This captures the notion that a country that has been above the cutoff for so long may become less likely to graduate due to other factors. For example, there may be a negative element not included in the model that explains why a country has not graduated despite its income status.

We proxy for IBRD creditworthiness with the Institutional Investors Creditworthiness Index. It is based on surveys of international bankers, with scores potentially ranging from 0 (highest risk of default on sovereign debt) to 100 (lowest risk). Creditworthiness and income per capita have been historically the
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participating at the time of the second interview (two years after program intervention began). Moreover, some randomly sampled households from PRIME catchment villages participated in regular microfinance.

In addition to this sample survey of households, the InM carried out a similar survey in areas of 3 districts that were targeted for the PRIME intervention by 2010, but had not received it by 2008–09. Out of 11 upazilas in 3 districts, 4 were selected as control upazilas. Out of 40 villages in these 4 upazilas not yet covered under PRIME, 27 were selected from which 618 PRIME-eligible households were randomly drawn based on the village census. Thus, the total sample of households selected for the study was 5,207, of which 1,520 participated in PRIME, 1,718 participated in regular microfinance, and 1,968 did not participate in any program.

Table 2 shows the distribution of 5,207 households by program participation status and by district in the greater Rangpur region. The sample distribution was further disaggregated by PRIME and non-PRIME areas. Three groups were identified by program participation status: PRIME-only, regular microfinance only and non-participants (in PRIME, non-PRIME, or control villages).<sup>12</sup> In all areas, 33 percent of target households were regular microcredit program participants, 29.2 percent PRIME participants, and 37.8 percent non-participants.

In PRIME areas, the shares of PRIME participants, regular microfinance participants, and non-participants were 33.2, 33.8 and 33.0 respectively. In non-PRIME villages, 26.5 percent of the ultra-poor participated in regular microfinance programs while 73.5 percent were non-participants.

> 12 Although non-participants may include non-eligible households from PRIME or control villages, they were not sampled in these surveys.

18
The survey is nationally representative of the formal private sector. It is built based on a stratified random sampling frame designed by the WBES team. Three variables are used to construct the strata: firm size, sector, and geographic area within a country. Under the WBES sampling framework, firms are divided into three categories according to their size: small, medium-sized, and large. Small firms are those with 5-19 full-time employees; mediumsized firms have 20-99 full-time employees; and the large ones have more than 99 full-time employees. The industries are classified according to the ISIC Revision 3.1 classification at 2-digits. The regions within a country are defined by the WBES team. The database also includes sampling weights that can be used to mimic nationally representative samples in the empirics.

The WBES collects data on a broad range of variables related to firm production, performance, and the business environment in which firms operate. It has information on variables related to production such as sales, capital, labor, materials, investment, exports, and manager’s education, among others. Due to the lack of information on prices at the firm-level, we use the consumer price index from the World Bank’s World Development Indicators to deflate sales, capital, materials, and investment, thus transforming nominal values into 2010dollar values. Firms’ labor is equal to the number of permanent employees that work for the firm. The survey collects data on the percentage of firms’ sales that are exported. Last, a firm’s managerial capability is measured by the number of years of experience of the manager. The novelty of the WBES is that it also collects information on technology adoption at the firm-level. Thus, at every wave, firms are asked whether they use a business email to communicate with clients and suppliers and whether they have a business website in order to carry out their operations. We exploit this variation in time, across countries, sectors, and firms to estimate the effects of digital business solutions on productivity and factor demand.

To construct the estimation sample, we first compiled all the WBES waves available from 2002-2019. This creates a sample of 145,626 observations, which corresponds to 118,868 firms, operating in the manufacturing or service industries. Table 6 in section A of the Appendix 9
# **Appendix I:  Correlation Results**

## **Table I.1 Central Bank Independence and the Broader Governance Environment**

|Variables|Independence of<br>central bank|<br>Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|
|---|---|---|---|---|
|Independence of central bank|1||||
|Corruption Perceptions Index|0.472***|1|||
|Governance Pillar Score|0.346***|0.690***|1||
|Government Effectiveness|0.440***|0.928***|0.720***|1|
|GDP per capita|0.383***|0.803***|0.609***|0.784***|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.2  Country Risk and the Broader Governance Environment**

|Variables|Independence of<br>central bank|<br>Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|Country risk<br>score|
|---|---|---|---|---|---|
|Country risk score|-0.484***|-0.828***|-0.638***|-0.880***|-0.712***|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.3  Transparency of Reserve Management Policies and the Broader Governance Environment**

|Variables|Independence of<br>central bank|<br>Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|
|---|---|---|---|---|
|Transparency of reserve management policies|0.298***|0.117|0.082|0.084|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.4 Transparency of Reserve Management Policies and Country Risk**

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inadequate financial and legal structure, lack of liquidity, low auditing and accounting standards, low transparency, and weak corporate governance. The second strand argues that Asian economies lack enough “safe” assets in which to allocate their excess savings and, therefore, invest in assets from developed economies. But financial underdevelopment can manifest in other forms too, with implications for the geographical composition of a country’s external balance sheet. In particular, the higher degree of financial development of the North affects not only its availability of financial instruments for both domestic and foreign savers, but also the sophistication and depth of its financial markets and institutions that are able to invest abroad and diversify risk internationally. Therefore, given these disparities in the development of financial markets, we expect different integration patterns of the EAP region with the North, the South, and itself across the different investment types. Specifically, EAP can be expected to be more connected through arm’s length financing with the North, and in relative terms more connected through FDI with the South and itself.

This paper contributes to the discussions in the literature by providing a comprehensive analysis of how EAP economies have been financially integrating across borders using different types of financial investments in an integrated framework. To do so, we assemble a novel dataset encompassing bilateral cross-border portfolio investments, syndicated loans, mergers and acquisitions (M&As), and greenfield investments (the latter two jointly known as FDI) between 1990 and 2014. We then analyze EAP’s connections with itself, the North, and the South. This helps us complement our understanding of where EAP stands in its financial integration process across all types of commonly used cross-border financial flows, taking into account all regions of the world. The bilateral dimension of our dataset allows us to analyze the financial integration process along the intensive margin (value of cross-border bilateral investments) and the extensive margin (number of connections/partners), as well as contrast EAP’s connectivity with the North vis-à-vis the South. It 3
in panel B of Table 4. The estimated intercept is _larger_ for the farm households and the difference is statistically significant at the 10 percent level (standard errors clustered at the PSU level). The evidence in favor of a larger intercept in the farm households is stronger when we add controls to the regression; for example, the difference is significant at the 1 percent level when number of children is added to the specification (see online appendix Table A.5). This conclusion is also supported by other available evidence on India; for example, the All India Debt and Investment Survey 1991 (NSS 48th round) shows that the assets of farming households (“cultivators”) are higher than those of the nonfarming households (see P. ii, NSSO report No. 491, 1998).

The evidence in panel B of Table 4 also accords well with a substantial body of related evidence available on the nonfarm activities in India. Note that it is likely to have _Y_ 0<sup>_pn_</sup> _< Y_ 0<sup>_pf_</sup> if the low-end nonfarm occupations are primarily low productivity residual activities and provide the last resort for the poorest households. Lanjouw and Murgai (2011) use three rounds of NSS data (1983, 1993/94, and 2004/2005) and show that nonfarm employment is positively associated with rural poverty in India, consistent with the observation that nonfarm employment involves primarily low productivity economic activities (see also World Bank (2011)).

This evidence on the intercepts of the income equation provides an explanation for the higher intercept for farm households in the intergenerational mobility equation as discussed earlier.

# **(7) Evolution of Mobility: Evidence on the Younger Generation in Rural China**

The rural economy and educational policies in China went through significant changes in recent decades. The changes include gradual relaxation of Hukou restrictions, increasing returns to education as the labor market matured, accelerated structural change in favor of the nonfarm sector, and a more prominent role for private educational expenditure after the fiscal decentralization (for an excellent discussion, see Fan et al. (2020)).<sup>41</sup> There is credible evidence that the economic changes have adversely affected intergenerational mobility of the younger generation in China. Fan et al. (2020) find that intergenerational _income persistence_ has increased over time; they report an IGE estimate of 0.390 for the

> 41The share of tuition and miscellaneous fees in educational expenditure rose from 4.42 percent in 1991 to 18.59 percent in 2004 (Hannum et al. (2008). The spread of better quality public schools to the rural areas has accelerated. All these changes would increase the magnitude of _θ_ 2 in the extended Becker-Tomes model for the younger generation.

28
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3

# **Projecting demand for new infrastructure**

The model developed below is from Fay<sup>(2000)andseeksto askthequestionofwhat</sup> infrastructure levels will be required in the<sup>future,eitherasconsumptiongoods,oras</sup> input into production function.

## **_A model of Infrastructure demand_**

We develop a model to estimate future demand<sup>forinfrastructure,whereinfrastructure</sup> services are demanded both as consumption<sup>goodsbyindividualsandasinputsintothe</sup> production process by firms. On the consumption<sup>side, the amount of service demandedis</sup> a function of income and prices: Demand for a particular type of infrastructure<sup>serviceI by individual j is a functionof j's</sup> income, Yj, and the price of infrastructure<sup>serviceI, ql.Aggregatingover the population,</sup> national per capita demand of infrastructure<sup>serviceforconsumption,lC,willthenbe</sup> given as: where Y/P is income per capita.

On the production side, each individual firm's<sup>demand for infrastructure serviceI willbe</sup> based on a profit maximization decision<sup>which yields the usualfirst order condition:</sup> where Y, is output of good i by the firm, and<sup>wiis the priceof that good.</sup> To go any further, we must adopt a specific<sup>functionalform for theproduction function.</sup> Assuming a Cobb-Douglas, we can rewrite the first<sup>order conditionas:</sup> where K is physical capital (excluding infrastructure),<sup>L is laboror human capital,andI is</sup> the flow of infrastructure services consumed<sup>bytheindividualfirminthe productionof</sup> good i. Solving for Ii yields the derived demand<sup>for infrastructureservicesof firm i:</sup>
American Depository Receipt on a U.S. stock exchange for a non-U.S. firm).<sup>19</sup> Thus, I have included a dummy variable for cross-listing that takes on a value of one if a firm is listed on an exchange outside of its nation of domicile and zero otherwise.

# _4.2. Industry Information_

Differences in industry classification are avoided by using the issuers’ (one digit) primary SIC code as an industry indicator. Industry dummies are included to account for any industry fixed effects.

# _4.3. Macroeconomic Information_

To control for the impact of other potential sources of funds for firms, I include savings, domestic credit, and foreign direct investment. Savings is calculated as the difference between gross domestic product and consumption. Domestic credit is the level of credit extended by financial institutions domestically. Foreign direct investment is included to control for the effect provided by the more stable of the two global capital flows on capital issuance. This inclusion is important given the fact that the impact of foreign direct investment is likewise beneficial for alleviating financing constraints (Harrison et al., 2004). To control for business cycle effects, I include GDP growth.

Fiscal burden, from Heritage Foundation, is used to control for the tax implications of debt in the bank-lending route of FPI analysis. This variable takes into consideration the proven relation between taxes and lending in a multinational setting (Desai, Foley, and Hines, 2004).

Controlling for the investment environment, I include investment, law and order, and corruption, which are indices reflecting the investment risk facing foreign investors, the level of legal development, and the level of corruption, respectively, in a country. All three indices are created by the International Country Risk Guide and collectively reflect the investment environment of a country. The inclusion of proxies for the extent to which a country’s investment environment attracts investors follows the method of Beck et al. (2002) and Beck et al. (2005), as well as many other examinations of access to financing in 13