rafmacalaba/data-use-mentions-extended huggingface dataset:
correspondence between the policy data and the trade data. To measure policies that inhibit the free flow of data across borders, we use ECIPE’s **Digital Trade Restrictiveness Index (DTRI)** as developed by Ferracane et al. (2018). Specifically, we use the component of that database that captures restrictions on cross-border data flows in the importing country.

Moving towards more general measures of governance, we first take in the **World Bank’s Regulatory Governance** data. Specifically, we use the overall regulatory governance score based on the various components identified by the World Bank, again for the importing country. We also use data from the **Doing Business** project, specifically their index measures of the ease of doing business in three areas: **investor protection, contract enforcement,** and **resolution of insolvency** , all for the importing country.<sup>14</sup> Again, we cannot enter the measures individually in the regressions as they are strongly correlated. We therefore take a simple average to produce a single measure of the business environment, as we believe it is relevant to TFiS. Finally, we use the **regulatory quality** and **government effectiveness** indices from the **World Bank’s Worldwide Governance Indicators** , all for the importing country. For similar reasons as those previously mentioned, we take a simple average of the two indices and use the result as an aggregate indicator of governance as we believe it might be relevant to TFiS.

The final element of the gravity model data set is a set of controls for geographical and historical linkages between countries, all of which come from the CEPII distance data set. Specifically, we include the logarithm of bilateral distance, along with dummies for country pairs that were ever in a colonial relationship, those that had a common colonizer, and those that have a common language as measured on an ethnographic basis. Finally, we include a
# **Figure 4: The working-age population could become more skilled due to demographic change, if educational attainment rates are constant**

Share of working-age population with at least 9 years of schooling if educational attainment rates constant (percent) <!-- Start of picture text --> 2015 2050<br>High-income OECD<br>Upper-middle-income countries<br>Lower-middle-income<br>Swaziland<br>South Africa<br>Namibia<br>Lesotho<br>Botswana<br>15 25 35 45 55 65 75 85<br><!-- End of picture text --> Source: Authors’ estimates Note: Data from UN (2015) and GMD household surveys. The data for upper-middle-income countries and high-income OECD are simple averages. Projections presented for SACU countries and two potential educational attainment convergence targets. High income OECD, upper-middle income, and lower-middle income country groupings are based on the World Bank Group’s income based country classification.

While an understanding of the mechanisms and demographic trends can provide a sense of how policy impacts may differ across the region, a purely analytical approach may not provide any insight on their relative magnitudes, possibly confounding policy priority setting. These assessments are additionally complicated by the fact that the demographic changes can induce general equilibrium effects whose magnitude cannot be clearly identified in a multi-agent and multi-sector economy. However, widely cited work on the growth impact of changes in agestructure generally rely on empirical estimation of growth accounting relationships.<sup>6</sup> These provide some robust estimates of the importance of demographic changes on income per capita and savings, but do face some constraints. For example, since these empirical studies rely on historical variation in data to drive results, the studies may not necessarily find statistically significant impacts on economic indicators, even if the intuition would suggest otherwise. This may be why early empirical work for Africa, as in Bloom and Sachs (1998), found no significant impact of demographic change on growth. More recently
8 the expectation that it will ultimately be used with a source of geocoded data (we will return to this issue in the next section).

The most recent release of the HCPM also contains a clustering module that explicitly incorporates optimization routines as part of the clustering formation process (see below), and allows the user to choose from three different clustering algorithms. The HCPM currently consists of two independent modules: a customer location module and a loop design module.

_Customer location module._ The local exchange telephone network must connect every customer to a local central office switch. A critical component in the design of such a network is the definition of a 'serving area', which consists of a group of customers served from a common remote terminal. Then feeder plant connects every serving area to the central office and distribution plant connects every customer in a serving area to a 'serving area interface'. The customer location module first groups individual geographic locations of customers into clusters, to form serving areas based on engineering considerations. These considerations include a distance constraint, so that no customer is farther from a potential terminal location than is allowed by the maximum copper distance, and the maximum number of customers in a serving area, which depends on the capacity of the largest terminal.

A divisive clustering algorithm successively splits new clusters from a main cluster that initially contains every customer location. Clusters are evaluated on the basis of the relative distance of customers from the line-weighted centroid of the new and old clusters. After an initial clustering process, two different optimization algorithms look for ways to re-assign customers to clusters, so as to reduce the total distance from the cluster centroids, while satisfying the maximum distance constraints.

After geocoded customer locations have been grouped into clusters, it is necessary to further process the location data, so that they can be used by the loop design module. HCPM defines a grid on top of every cluster and then subdivides each grid into a large 10 To geocode a customer or a wire center location (or
to displacement in the Republic of Yemen in a critical welfare dimension. In part, this is likely due to a well-targeted increase in humanitarian assistance, which further offers support to a growing body of literature demonstrating that social protection schemes that reach world. These indicators are used for these purposes in part because they have been shown to be very sensitive to shocks in well-being in many settings across the world (e.g., Headey and Ecker 2012; Vaitla et al. 2015; etc.).

> 4Bohra-Mishra and Massey (2011) use panel data in Nepal on the location of residence to track displacement over time, but do not have panel information on the well-being of households (e.g., food access metrics, expenditure, etc.). Additionally, Hoogeveen et al. (2019) reach forcibly displaced populations primarily after their first displacement and analyze the decision to return.

4
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
tion that explains how much of the variation in each country’s GDP growth rate is a result of the constructed measure.

I include the estimated global GDP factor in a structural factor-augmented VAR (FAVAR) model with data on the real price of oil, U.S. oil consumption, and U.S. and non-U.S. oil production. The FAVAR models the dynamic relationships between these endogenous variables and links them to exogenous shocks, including U.S. energy efficiency and U.S. oil supply shocks. This framework allows me to examine the impact of exogenous changes in, for example, U.S. oil production and U.S. oil consumption on the global business cycle—a difficult task as movements in other endogenous variables tend to confound these effects in the data. The identification of shocks is based on a combination of sign and elasticity restrictions in line with Kilian & Murphy (2012). For example, I impose the restriction that a positive U.S. oil supply shock increases U.S. oil production and decreases real price of oil on impact. The decline in the oil price, in turn, increases U.S. oil consumption and decreases non-U.S. oil production. At the same time, global economic activity—as represented by the global GDP factor—increases because the cost of production falls. Similarly, a positive U.S. energy efficiency shock decreases U.S. oil consumption on impact as firms require less oil to produce a given level of output. This decline in oil consumption leads to a decrease in the real price of oil, and, consequently, increases the global GDP factor and decreases U.S and non-U.S. oil production.

The sign restrictions are sufficiently flexible to capture a wide spectrum of effects. For example, some studies argue that improvements in energy efficiency may not translate to a one-to-one reduction in energy use or may even increase energy consumption as the real price of oil falls—a
organizational dynamics and bureaucratic behavior, but at the same time its analytical power is somewhat more limited than that of the principal-agent theories. Leadership is seen to be key'<sup>4</sup> , as is "a strong sense of mission, effective managerial practice, and high expectations about employee performance".,<sup>5</sup> The perspective builds on the sociological traditions that have tended to see organization as an organic entity and have emphasized '° See Heyman (1988) and Miller (1994).

> 3' A large-scale survey-based study of senior bureaucrats and politicians in five western countries found that "policymaking by bureaucrats is characterized by continuity, stability, and predictability" in clear distinction to the views of politicians who see themselves as "advocates, partisans, and tribunes" (Aberbach, Putnam, and Rockman, 1981, p. 25 6-7).

12 See Moe (1984), Moe (1990), Simon (1991), and Fukuyama (1995).

1 3 They are "far better at explaining why bureaucrats shirk, subvert, or steal than they are at explaining why bureaucrats behave as 'principled agents' - workers who do not shirk, subvert, or steal on the job even when the pecuniary and other tangible incentives to refrain from these behaviors are weak or nonexistent" (Dilulio, 1994, p. 277). Herbert Simon also provides a critique of the principal-agent variant labeled transactions cost analysis (Simon 1991).

14 Dilulio (1994). Other influential organization theorists including Selznick (1957) and Wilson (1989) have also underscored critical roles of leadership in accounting for organizational performance. IS See Grindle (1997, p.491).

_5_
(i) the eects of parents on children as captured by IGC ( _β_ ) are heterogeneous across families; and (ii) the parameter _β_ can be positively (for convex mobility function) or negatively (concave mobility function) correlated with parental education. Bingley and Cappellari (2019) develop a decomposition method that allows for heterogeneous _β_ and arbitrary correlation between _β_ and _Sf_<sup>_p_.Theyshowthat,forsiblingcorrelationinincome,relaxationoftheimplicit</sup> assumptions in equation (5) makes a big dierence.

To the best of our knowledge, we are the rst to implement the Bingley and Cappellari (2019) approach for estimating the intergenerational share in sibling correlation in education, and we do it for a large number of developing countries (53 countries) using comparable data from the DHS surveys. We provide a brief discussion of the Bingley and Cappellari (2019) approach below, and refer the reader to the original paper for details.

# (3.3) Decomposition of Sibling Correlation: Bingley and Cappellari (2019) Approach

In the context of our set-up, Bingley and Cappellari (2019) replace equation (5) by the following random coecient specication: where _β_<sup> ̄</sup> is average eect of parental education and _βf_ is deviation of family _f_ from the mean. This specication thus incorporates heterogeneity in the eects of parental education captured by the parameter _βf_ . If we relax only the heterogeneity assumption but retain the assumption that the magnitudes of the parental eect is independent of the level of parental education, we have the following decomposition: But as we discussed above, there are plausible theoretical models that suggest that _βf_ is correlated with _S_<sup>_p_Usingaresultontheexactvarianceoftheproductoftworandom</sup> _f_<sup>.</sup> variables due to Bohrnstedt and Goldberger (1969), Bingley and Cappellari (2019) derive the 14
the researcher developed a set of codes and categorized the relevant interview data across all research sites following the coding system. Finally, the researcher analysed and linked the different codes together into cohesive, overarching themes related to social cohesion and refugee-host relationships. Once this analytical process was completed, the qualitative data was integrated with quantitative results. Our rich qualitative data enabled us to interpret the results of survey, explore causal relationships for the patterns we observe and to substantiate our data through narratives. In order to protect the privacy of interviewees, all respondents are either anonymized or given pseudonyms in this paper.

# **4.3 Identification Strategy**

Focusing on the Uganda sample (because of data availability), we first explore the correlation between host community attitudes towards refugees and refugee-host interactions. We use OLS regressions of the index of the perception index ( _Pi_ ) on the interaction index ( _Ii_ ) using individual level data: Each observation is a respondent from the host community. We control for the perception index among other household members ( _PHH_ ) and among neighbours ( _PEA_ ) to account for the fact that perceptions are partly formed at the group level through discussions, gossips, and common narratives.<sup>10</sup> The geographic area considered as a neighbourhood when creating _PEA_ is provided by the Enumeration Areas of the Uganda national census by the Ugandan Bureau of Statistics (UBOS).<sup>11</sup> We also control for a vector of socio-economic and demographic variables _Xi_ to limit the risk of omitted variable bias. We use two approaches to select the variables included in _Xi_ . First, we selected a list of variables that could explain perceptions or interactions based on our knowledge of the literature (see Tables A.3 and A.4 in the Appendix for descriptive statistics). We include enumeration areas fixed effects ( _μEA_ ) or household fixed effects ( _μHH_ ) to control for unobserved characteristics that are constant within EAs/households. Second, we implement the double LASSO method of
For the meta-analysis, we collect the b coefficients from empirical studies of interest rate passthrough. In doing so, we focus on studies that estimate the interest rate pass-through using equation 1 or its extended specification as in equations 2 and 4.

The vector _X_ appearing in equations 2-5 typically includes variables capturing the overall macroeconomic environment and bank-level characteristics. For instance, Holton and d’Acri (2015) include into vector _X_ macroeconomic variables such as the government bond yield, unemployment rate, and inflation rate. Van Leuvensteijn et al. (2013) and Leroy and Luccote (2015) control for bank competition using the aggregate Boone and the Lerner indices, respectively, both as a stand-alone control and in an interaction with the money market rate (see equation 4). Studies also control for market expectation of future interest rates, including the spread between the government bond yield and the money market rate (Cifarelli and Paladino, 2016; Gregor and Melecký, 2018), the forecast of the money market rate (Banerjee et al., 2013), and the Volatility Index (VIX), which reflects the expectation of the market’s volatility over the next 30 days (Grigoli and Mota, 2017).

As for bank-level characteristics, researchers mostly control for credit risk using the ratio of nonperforming loans (Gambacorta et al., 2015; Grigolli and Mota, 2017; Gregor and Melecký, 2018) or determinants of credit risk such as the business climate indicators (Mihaylov, 2016) or the price of credit default swaps (Gambacorta et al., 2015). Other typical bank characteristics include bank assets, bank capital, the leverage ratio, the loans to deposits ratio, and the liquidity ratio (Horváth and Podpiera, 2012; Holton and d’Acri, 2015; Havránek et al., 2016).

The empirical literature, however, does not typically control for broader country context, which is understandable for bank-level studies for a single country. For instance, overall financial depth, capital market development,
their capacity to generate earnings. Lower earnings could also be related to a deceleration in the high growth rates that emerging economies used to experience, as they became more mature and reached higher GDP per capita levels.

The aggregate counterpart of a deterioration in the debt servicing capacity of firms was an increase in the share of debt-at-risk. In 2016, the share of nonfinancial corporate debt-at-risk in emerging economies stood at 24 percent up from 15 percent in 2008. China, where the share of debtat-risk increased from about 15 to 32 percent, was largely responsible for this increase. Nevertheless, nonfinancial corporate debt-at-risk also increased in other emerging economies. In 2016, debt-at-risk amounted to more than a fifth of total nonfinancial corporate debt in several large emerging economies, including Argentina (22 percent), Brazil (30 percent), India (23 percent), and Turkey (20 percent) (Beltrán et al., 2017).

In addition to debt-at-risk, the Corporate Vulnerability Index (CVI) constructed by Feyen et al. (2017) can provide a comprehensive measure of firms’ debt repayment capacity. Whereas debt-atrisk measures the share of debt held by firms with an ICR below a threshold, the CVI calculates the share of debt held by firms which present vulnerabilities in multiple indicators at the same time. These indicators include the leverage ratio, return on assets, the market-to-book ratio, and the ICR itself, among others. According to the CVI, the overall vulnerability of emerging market firms steadily increased after 2008, particularly accelerating after 2013, and then stabilized in 2016. Trends were not homogenous across emerging regions. In some regions, such as Eastern Europe and Central Asia as well as South Asia, vulnerability increased around 2008 and 2009 and remained stagnant since then. In contrast, in Latin America and the Caribbean and Sub-Saharan Africa the risk of financial distress did not become elevated until around 2012.

Whereas indicators such as debt-at-risk and the CVI can provide useful insights on how corporate vulnerability increased over time, their levels do not provide information
The program received approximately 6,000 applicants. Interested applicants had to submit a one‐page written proposal for a new business idea. The document had to be written in English, although communication materials were also provided in Juba Arabic. In addition, the applications required proof of their South Sudanese nationality and documents needed to open a bank account. This application process was designed to incentivize positive self‐selection into the sample. In this sense, the program participants may be better equipped to use the cash grant successfully to improve their business or employment situation than the average population. From the received 8,240 applications, 4,699 were found to be eligible.<sup>2</sup> From these eligible applicants, 1,200 were randomly selected to receive the grant and 1,200 were randomly selected for study in the control group, with equal proportions per state and by gender. The baseline survey was conducted between January to March 2015 and data were collected from 1,144 treatment participants and 1,148 control participants. Approximately 4.5 percent of initially identified study participants could not be tracked and did not participate in either the baseline survey or the program. The baseline survey was concluded prior to the commencement of the one‐week training that was held across the 6 states between April and May 2015.

_<mark>Figure 1 Treatment streams of original and new intervention.</mark>_ <!-- Start of picture text --> Business Startup Competition<br>(Participants interviewed at<br>baseline, Feb‐Mar 2015,<br>N=2,292)<br>Program cancellation<br>(Participants reached in endline<br>survey, Sep‐Dec 2017, N=1,507)<br><!-- End of picture text --> <!-- Start of picture text --> Randomly<br>selected Eligible<br>applicants<br>(N=
br>91|<br>126|
|Share of Staple Food||||||||
|Calories from Wheat||||||||
|(percent)||||||||
|1961-65|i6|<br>7|<br>__|<br>16|<br>0.6|<br>7|<br>0.3|
|1979-81|24|<br>14|<br>__|<br>18|<br>2|<br>7|<br>3|

Sources: International Maize and Wheat Improvement Center, i985 CIMMYT World Wheat Facts and Trends (Mexico, 1985); The International Rice Research Institute, World Rice Statistics 1985 (Manila, 1986); World Bank, World Development Report 1985 (New York: Oxford University Press for the World Bank, 1985).

aFigures are for 1968.
693.3|***|
|Netproduction value(Rs/acre)|13,145.3|14,565.9|11,051.3|***|
|**Land-related investment**|||||
|Invested in soil & water conservation (y/n)|0.28|0.39|0.12|***|
|<br>... if yes, cost (Rs)|141.48|203.08|50.67|***|
|No. of family days invested in 2007|4.78|6.78|1.83|***|
|Access to private irrigation (y/n)|0.50|0.54|0.44|***|
|Access to public irrigation (y/n)|0.17|0.17|0.18||
|Number ofplots|9,285|5,532|3,753||

_Source:_ Household questionnaire from the 2008/9 West Bengal survey.

* significant at 10%; ** significant at 5%; *** significant at 1% (based on simple t-test for the mean difference between own land and barga land). Private irrigation includes ponds, wells, and bore wells.

18