rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2: Inclusion of collective action clauses in dollar-denominated emerging market central government bonds, by governing law (2008-2015)**

||WithCAC|Without CAC|
|---|---|---|
|Under U.S. Law|114|22|
|Under U.K. Law|56|12|

Source: Authors’ calculations based on Dealogic data.

The impact of first-time issuances on spreads is also relevant. Governments without a previous history of dollar issuances could incur a relatively higher cost of debt when issuing for the first time. Because of the regional linkages already described, we know that first-time issuers from the same region gravitate to a certain jurisdiction. Specifically, the data show that Eastern European and Sub-Saharan African governments have issued for the first time mostly under U.K. law, while Latin American countries did so under U.S. law (Table 3).

# **Table 3: Countries issuing for the first time central government dollar-denominated debt -** **<u>under either U.K. or U.S. law (1990 2015)</u>**

|Region|1990-2007|of which:<br>under U.K. law|2008-2015|of which: under<br>U.K. law|
|---|---|---|---|---|
|East Asia & Pacific|4|50%|2|<br>50%|
|Europe & Central Asia|13|92%|4|<br>100%|
|Latin America & Caribbean|10|0%|2|<br>-|
|Middle East & North Africa|4|50%|1|<br>100%|
|South Asia|2|100%|0|<br>-|
|Sub-Saharan Africa|4|50%|7|<br>86%|

Source: Authors’ calculations based on Dealogic data.

Only a few governments became first-time dollar bond issuers after the crisis, but most of those came from either Eastern Europe or Africa and issued under U.K. governing law. This close connection between the U.K.’s governing law effect and the U.K.’s status as the jurisdiction of choice for first-time sovereign dollar issuances is analyzed more formally in the results section.

The importance of SEC registration also deserves analysis. Firstly, some categories of investors simply exclude non-SEC registered bonds from their eligible universe. For example, it is common for some Exchange-Traded Funds whose investment strategies consist of tracking an underlying index to select only SEC registered bonds. Previous research has also shown that firms that opt out of SEC registration are precisely those that exhibit a deteriorating performance (Leuza, Triantis and Wang 2008). It is then reasonable to ask whether investors could have a negative perception of foreign governments with a reduced commitment to disclosure. The SEC requires that, when selling securities in the United States, foreign governments not only file for the initial registration but also submit an annual report in subsequent years. This filing (form 18-K) must include detailed information regarding the issuing country fiscal position, a description of all categories of bonds outstanding, and relevant economic policy and legal updates. Foreign issuers can nonetheless circumvent the SEC registration process by adhering to Regulation S, as long as their securities are offered and sold outside of the United States. A second reason to examine the effect of SEC registration is its close correlation with the choice of jurisdiction. Most bonds under U.K. law are sold 10
than ti;e central government, remains a minimum requirement. In several of the reviewed cases, su-h an evaluation could have been provided since most of the required informationi was contVined in the report but not integrated, or was available from sector work but not used (see boxes I11.2 and 111.3).

_am,k_ **WI.! fblic Spendinmg** on Education **hi Haiti and Tanzania** Haiti's education indicators resemble those of low-income African countries more than those of its Caribbean neighbors or Asian low-income countries. But unlike most Africaan countries, the Haitian educational system depends heavily on nongovernment financing sources. Public recurrent and investment expenditure for FY 1983 was about 9 percent of all government spending, equal to about 1.5 percent of GDP. Total education expenditure--public and private--amounted to more than 6 percent of GDP, of which only about 20 percent was funded from the Treasury. Thirty percent came fiom external sources and from local and foreign charities (official donor aid plus religious organizations and NC-Os); fifty percent came from parents.

Public spending on education in Tanzania represents a larger share of GDP, 2.4 percent in FY 1987. Although local authorities and parents spend some on primary education, these amounts are small relative to the total budget. Since the central goverrnent provides almost all education financing, total sector spending in Tanzania remains at about 2.4 percent of GDP despite education's similar share of the public budget (10 percent compared with 9 percent in Haiti).

One might conclude that Tanzania and Haiti have a comparable commitment to education and that this commitment is reflected in roughly comparable output indicators (gross primary enrollment rates of 78 percent of the age group in Haiti and 69 percent in Tanzania). This conclusion, however, would be inaccurate. Haiti's education sector receives almost three times more resources (as a percentage of GDP) than does Tanzania's, but with the same outcome, less equitably distributed, at the primary level. Thus, when all inputs are taken into account, the Tanzanian situaiicn still reflects acute underfinancing of the sector, while the primary issue in Haiti is one of inefficient and inequitable management of sector resources.

Sourc: "Haiti: Public Expenditure Review," Report No. 6113-HA (September 26, 1986); and 'Tanzania: Public Expenditure Review," Report No. 7559-TA (May 22, 1989).

25
# **_3.2. Sensitivity and Robustness Checks_**

To address the endogeneity issues that may arise from the two-way relationship between food insecurity and trust, we perform a series of robustness and sensitivity checks to validate the results. First, we use entropy matching to create a matched sample of food-secure and food-insecure respondents (Table 4). Table C1 in Appendix C shows that entropy matching perfectly balances the distributions of all covariates.

Table 4. The effect of food insecurity on vertical and horizontal trust: Entropy-matched sample.

|||Econom<br>|ic development<br>Lower-middle-<br>|rankings (GNI pe<br>Upper-middle-<br>|r capita)<br>|
|---|---|---|---|---|---|
||World sample|Low-income|income|income|High-income|
|Trust in national institutions||||||
|Food insecurity|-0.030***|-0.020***|-0.021***|-0.035***|-0.071***|
||(0.002)|(0.003)|(0.003)|(0.003)|(0.004)|
|Trust in friends and family||||||
|Food insecurity|-0.119***|-0.128***|-0.112***|-0.113***|-0.127***|
||(0.002)|(0.005)|(0.004)|(0.004)|(0.005)|
|Controls|_yes_|_yes_|_yes_|_yes_|_yes_|
|Number of observations|388,602|69,101|106,410|100,728|112,363|

_Notes:_ Models estimated using entropy-weighted individual-level data from the 2014-17 Gallup World Poll. Standard errors are in parentheses. References categories are single or never married, elementary education, employed fulltime, poorest quintile, rural area or farm, and the year 2014. Each model includes country and years fixed effects. Low-income countries are defined as those with a gross national income (GNI) per capita of $995 or less, lowermiddle-income countries are those with a GNI per capita of more than $996 but less than $3,895, and upper-middleincome countries are those with a GNI per capita of more than $3,895 but less than $12,055, and high-income countries are those with a GNI per capita of more than $12,055. * Significance at the 0.10 level. ** Significance at the 0.05 level. *** Significance at the 0.01 level.

Column 1 of Table 4 presents the results from the total sample after matching, while Columns 2 through 5 represent the matched sample decomposed into economic development rankings. Overall, the results are very similar when compared to the non-matched sample (Table 3): food insecurity in each income category is associated with a decrease in both dimensions of trust across 15
lIi INTRODUCnON Table 9 Population and Percentage Distribution by Geographic Region and by Income Group, 1985-2100

|||Po|pulation|(mill|ions)|||Perce|ntage<br>of|<br>world<br>p|<br>opula|tion|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region<br>or|||||||||||||
|income<br>group|1985|1990|2000|2025|2050|2100|<br>1985|1990|2000|2025|2050|2100|
|World|4,844|5,285|6,204|8,415|10,03|5<br>11,330|100.0|100.0|100.0|100.0|100.0|100.0|
|Less devetoped<br>(a)|3,666|4,074|4,939|7,078|8.71|6 10,020|<br>75.7|77.1|79.6|84.1|86.9|88.4|
|More devetoped<br>(a)|1,179|1,211|1,26|5 1,336|1,31|9 1,310|<br>24.3|22.9|20.4|15.9|13.1|11.6|
|Africa|559|<br>651|<br>870|1.587|2,27|5 2,962|<br>11.5|12.3|14.0|18.9|22.7|26.1|
|East Africa|231|<br>271|<br>367|<br>700|1,03|4 1,383|<br>4.8|<br>5.1|<br>5.9|<br>8.3|10.3|12.2|
|West Africa|204|<br>238|<br>320|<br>602|<br>873|1,152|<br>4.2|<br>4.5|<br>5.2|<br>7.2|<br>8.7|10.2|
|North<br>Africa|124|<br>142|<br>182|<br>286|<br>368|<br>427|<br>2.6|<br>2.7|<br>2.9|<br>3.4|<br>3.7|<br>3.8|
|America|667|<br>721|<br>826|1,040|1,14|6 1,192|<br>13.8|13.6|13.3|12.4|11.4|10.5|
|Latin<br>America<br>and Caribbe|an 399|<br>441|<br>525|<br>703|<br>814|<br>864|<br>8.2|<br>8.3|<br>8.5|<br>8.4|<br>8.1|<br>7.6|
|Northern<br>America|268|<br>280|<br>302|<br>336|<br>332|<br>328|<br>5.5|<br>5.3|<br>4.9|<br>4.0|<br>3.3|<br>2.9|
|Asia|2.823|3,100|3,664|4,901|5,72|8 6,288|<br>58.3|58.6|59.1|58.2|57.1|55.5|
|East and Southeast<br>Asia|1,65|0 1,781|2,038|2,503|2,71|9 2,822|<br>34.1|33.7|32.8|29.7|27.1|24.9|
|South<br>Asia|1,05|8 1,186|1,45|4 2,108|2,61|4 2,977|<br>21.8|22.4|23.4|25.1|26.0|26.3|
|Southwest<br>Asia|115|<br>132|<br>172|<br>290|<br>396|<br>490|<br>2.4|<br>2.5|<br>2.8|<br>3.4|<br>3.9|<br>4.3|
|Europe<br>and U.S.S.R.|771|<br>787|<br>813|<br>848|<br>843|<br>842|<br>15.9|14.9|13.1|10.1|8.4|<br>7.4|
|Oceania|25|<br>27|31|<br>39|42|<br>44|0.5|0.5|0.5|0.5|0.4|0.4|
|Income group(b)|||||||||||||
|Low|2,711|3,004|3,627|5,165|6,343|7,315|56.0|56.8|58.5|61.4|63.2|64.6|
|Lower-middLe|580|<br>648|<br>792|1,145|1.416|1,609|12.0|12.3|12.8|<br>13.6|14.1|14.2|
|Upper-middle|422|<br>462|545|<br>748|<br>891|<br>989|8.7|8.7|8.8|8.9|8.9|8.7|
|High|767|<br>789|<br>830|<br>884|869|<br>869|15.8|14.9|13.4|<br>10.5|8.7|7.7|
|Nonreporting norgnember|365|<br>382|<br>410|<br>474|515|<br>548|7.5|7.2|6.6|5.6|5.1|4.8|

a. "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zeatand, and Japan. "Less developed" comprises the rest of the world.

b. Criteria for income groups are given in World Bank 1989. See tisting in Exhibit 1.
<!-- Start of picture text --> oe, a a$ A /<br>fa e i} - : — -<br>\e, = | \ i | } ;<br>‘<br>——<br>a\ i t ee wv i t —<br>va || . a is | r<br>=) . . — = - i<br>[ s i I ’ q » ; , -~ a -<br>TM<br>. ¥ / \ : (pi - : , aa *” as *.<br>rm 07 June 2022<br>*<br>i ow: -_ 5<br><!-- End of picture text -->
have extremely high endemicity percents (89.6%, 88.1% and 84.4%, respectively). The maxima are even respectively. Molluscs, Fungi and other species more closely resemble Arthropods and Plants in the relative compactness of their occurrence regions

|Re<br>re<br>Re<br>ee<br>eees<br>re<br>Re<br>ee<br>eees<br>aes<br>aes<br>a|
|---|
||
|es<br>aes<br>a|
|es|
|aes<br>eeeG<br>[Russianfederation |<br>|<br>|<br>|<br>ee<br>eees<br>a<br>fArmgentina<br>||<br>aes<br>aes|
We conducted two rounds of follow-up surveys of these firms. The first, intended to measure shortterm effects, was taken between April and August 2016, corresponding to a period of approximately six months after the end of the investment readiness program and judging, and enables us to measure short-term effects. The overall survey response rate was 79.2 percent, and does not differ significantly between treatment (79.9%) and control (78.5%). In addition, we collected information on operating status, number of employees, and whether negotiations for an outside investment had occurred for a further 12 percent of firms, resulting in basic data being available for 92.2 percent of firms. The second follow-up survey took place between August 2017 and March 2018, corresponding to an average of two years since the intervention. Catalini et al. (2017) show that 75 percent of firms that receive venture capital financing in the U.S. receive their first financing within the first two years after incorporation, so this timing covers a window where we should expect many firms to receive external financing if they will ever do so. The overall survey response rate for this second follow-up was 85.0 percent, and again does not differ significantly between treatment (86.2%) and control (83.7%), with data on firm operating status and receipt of equity available for 94.5% of firms. Appendix 8 shows no significant difference in response rates by treatment status, and that treatment and control firms remain balanced on baseline observable data for those responding to the survey.

The follow-up surveys focused on measuring changes in the firm in three domains. The first is whether or not the firm is still operating (regardless of whether or not it has been sold to another owner). The second is investment readiness, where we focus on three aspects identified by Mason and Kwok (2009): (1) willingness and interest in taking on equity investment; (2) general investability, as measured whether there is a viable business of interest to investors in terms of employment, sales, and profits; and (3) whether the firm has put in place specific measures investors want to see before making investments, such as separation of outcomes, revenue projections, knowledge of customer acquisition costs, tracking key metrics of traction, and covering intellectual property. The third and final domain looks at steps towards receiving external funding and then external financing received. Steps towards financing include contacting outside investors, making pitches, working with mentors or experts to help obtain financing, and entering into negotiations. Receipt of external financing considers new debt and equity investments, as well as receipt of incubator and accelerator grants.

23
across, but only within indicators. Making an error of 0.5 in the percentage of rural inhabitants is large for that indicator, but tiny for the population indicator. We therefore undertake a series of transformations of the absolute errors to make them comparable across indicators. We take three approaches.

Our core measure is created by constructing z-scores that are the value of the absolute error minus the mean value across the distribution of absolute errors within the indicator, all divided by the standard deviation of absolute errors across the distribution of absolute errors within the indicator. The interpretation of this z-score is a unitless measure of how many standard deviations away from the benchmark data an official’s assessment is. Comparisons across indicators therefore take into account the empirical distribution of errors for each indicator. They compare the scale of an official’s error relative to the errors made by all other officials in our data set.

One issue with the above measure is that it does not take into account potential skewness of the underlying indicators. Table A2 provides information on the Pearson moment coefficient of skewness for each of the indicators. It is evident that the non-fraction indicators exhibit right-skew, with positive coefficients larger than 1 in all cases. For non-fraction indicators, a test for skewness relative to the normal distribution is conducted based on D’Agostino et al. (1990) and Royston (1992). The test statistic is approximately normal under the null hypothesis and allows for standard hypothesis tests. Thus, if the p-value associated with the test statistic is less than or equal to 0.05, we classified the variable as significantly skewed and replace the indicator with its log, consistent with Duggan and Morton (2004). For those that are not significantly skewed, no transformation is applied. The ‘skewness-adjusted’ z-score is calculated on this set of values.

A third approach is to use the ordinal rank of the absolute error within the entire distribution of absolute errors within the indicator (again consistent with Duggan and Morton, 2004). We rank all errors according to size, give each a corresponding rank, and divide these ranks by the maximum rank within the distribution of absolute errors within the indicator. The rank-proportion scores are therefore free of distributional assumptions. They can be interpreted as the percentile of error made by an official relative to other in our data set.

# **Measuring the incentive environment**

To construct our management indices, we followed the best-practices laid out in the World Management Survey literature (such as in Bloom and Van Reenen, 2007; Bloom et al., 2012). Management interviews were ‘double blind’ so: (i) managers were not told in advance they were being scored or shown a score grid; (ii) enumerators were given no information on the organization’s performance. Only managers (directors) were enumerated the World Management Survey and the measures of organizational management practices 38
4 one path, follows ir -ividuals in that path to their destinations, and estimates how frequentl) individuals end up in each of these destinations. Which approach to use depends on what one is seeldng to learn.

For those interested in the success of particular training programs, or the outcomes and labor market destinations of graduates of these programs, then the follow-up is the most appropriate methodology. Not all of these course completers will be employed in a training-related job; some will be working in occupations not utilizing skills acquired in the program, while others will be unemployed or currently outside the labor force. The typical policy questions examined are, "Does this training program prepare individuals for the jobs they obtain?' or "Is this training program economically worthwhile?" For those curious about how individuals working in particular occupations of interest obtain their training, the reverse tracer study is the most appropriate methodology. Without maldng assumptions about the importance of one particular training institution, the reverse tracer study probes all the alternative training paths followed by workers employed in these occupations. Identifying the range of training paths utilized by workers in a given set of occupations serves to widen the effective range of policy options for training provision, particularly with regard to the occupations under study. The kdnds of policy questions the reverse tracer study examines are, "Where do skilled and semi-skilled workers obtain their training?" or "What is the most cost-effective means of preparing workers for skilled or semiskilled occupations?"
is not to say, however, that the indices are orthogonal to these properties either. For example, it is increasingly difficult for large districts with lots of employment to have an exceptionally undiversified industrial base, while it is easier for large districts to maintain specialization in one industry. Thus the connections of our indices to these properties are intriguing, and we quantify these relationships below.

# **3. The Evolution of Indian Specialization and Diversity**

This section describes the levels and trends in the specialization and diversity of Indian districts when considering both manufacturing and services. Duranton (2013b) considers specialization and diversity in Colombia across multiple sectors, but these types of analyses have otherwise been very rarely undertaken. We devote attention to showing the patterns as they compare to our longer series in the prior work that considered manufacturing only.

_Levels, Trends and Persistence of Indian Specialization and Diversity_ Table 1a begins with descriptive statistics for our indices by survey. Panel A provides the specialization index, and Panel B provides the diversity index. Within each panel and survey year, we provide the mean, standard deviation, min, max, and median values across our 554 districts. The statistics are calculated after the winsorization noted earlier. In addition to these annual values, we calculate the average specialization and diversity for a district across the three periods. We also calculate the change in specialization and diversity for a district in relative terms to its initial value. The last two columns provide summary statistics for these metrics. Similar depictions are found when measuring changes relative to average values over the period.

Looking at 2005, the average specialization value is 10.66, and the median value is 6.55. This suggests that, for the average Indian district, the maximum degree to which one industry’s employment exceeds its national share is around 10-fold (e.g., the industry constitutes 10% of the district’s employment relative to 1% nationally). There is a wide variance in this metric, with the standard deviation greater than the mean. The lowest winsorized level in 2005 is 2.9 (e.g., a 3% local share combined with 1% nationally), while the maximum winsorized level is 80 (e.g., a 80% local share combined with 1% nationally).

11
indices that result from this procedure. The indices can be expressed as linear combinations of the underlying sector‐specific indicators, and hence their use in a regression context is equivalent to imposing linear restrictions on the coefficients of the individual infrastructure indicators. We define the synthetic infrastructure quantity index IK1 as the first principal component of three variables: total telephone lines (fixed and mobile) per 1000 people (Z1/L), electric power installed capacity expressed in MW per 1000 people (Z2/L), and the length of the road network in km. per 1000 people (Z3/L). Each of these variables is expressed in logs and standardized by subtracting its mean and dividing it by its standard deviation. All three infrastructure stocks enter the first principal component with roughly similar weights: The index accounts for almost 80 percent of the overall variance of the three underlying indicators. As a robustness check, we compute an alternative index of infrastructure quantity, IK2, which uses main telephone lines instead of the combined main lines and mobile phones employed in the first index.<sup>9</sup> _Financial openness_ is approximated by the data on holdings of foreign assets and liabilities from Lane and Milesi‐Ferretti (2001, 2007). Specifically, we use summary measures of financial openness: where _FA_ and _FL_ refer to the stocks of foreign assets and liabilities —expressed as a ratio to GDP. Note that _FA_ and _FL_ include stocks of assets and liabilities in foreign direct investment, portfolio equity, financial derivatives and debt (portfolio debt, bank and trade‐related lending).<sup>10</sup> On the other hand, given that international trade in debt instruments may be driven by special factors, we also consider the

> preferable, but unfortunately they do not exist, at least with any significant coverage across countries and time periods.

> 9 The correlation between the two synthetic quantity indices is over 0.996. This is unsurprising given the similarly high correlation between the two indicators of telephone density underlying the respective synthetic indicators. 10 In this paper we also evaluate the role of the structure of external capital in driving the long‐term growth performance of countries. Hence, we will break down our outcome measure of financial openness into equity‐ and loan‐related foreign liabilities. While the former includes the foreign liability position in foreign direct investment and portfolio equity, the latter includes only the debt liability position. The same calculation is performed for the ratio of foreign assets and liabilities to GDP.

8