rafmacalaba/data-use-mentions-extended huggingface dataset:
icy and Institutional Assessment (CPIA) from the WDI), and sovereign wealth funds (country economists).

- **Macroeconomic indicators** : Core measures to calculate ratios, real variables and general indicators of economic health include real and nominal GDP, exports, public debt, the consumption price deflator, real and nominal exchange rates, average interest rates, credit spreads on public debt, public expenditures and revenues, the current and capital accounts of the balance of payments, credit ratings and default episodes (MPO, WDI, WEO, Bank of Canada, Bloomberg), and non-performing loans (WDI).

The methodology aggregates the above mentioned variables into a single index. The weights are estimated via a standard dimension reduction technique (e.g. dynamic factor models or principle components) using data starting from 2002. The advantage of this technique is that weights are neutral and completely dependent on the data. This methodology controls for country characteristics, where individual series might have a different weight across countries in each index (see Appendix A for more details on the methodology).

The proposed index measures whether country debt vulnerabilities are increasing or abating: (i) an increase in the vulnerability index highlights risk build-up, (ii) an increase in the vulnerability index relative to its trend highlights risk build-up with respect to its history, or (iii) an increase in the country index relative to regional trends to identify large outlier countries within a region. An increase in the resilience index implies that countries are building up buffers against possible unexpected shocks.

Principal components analysis (PCA) is used for extracting the vulnerability index from the annual data module. PCA is appealing because of its computational advantages and asymptotic properties in large data sets (see Bai (2003)).

After estimation, a fundamentals vulnerability index is generated. Figure 5 illustrates build-up of public and private debt vulnerabilities within each country using the proposed index. It is important to note that an increase in each of the variables used in the index point to
# **Table 1. Logistics Indicators (Possible Set)**

# _Indicators of time_

- Total time for trade-related procedures (average and maximum)

- Customs inspection clearance time (average and maximum)

- Technical control clearance time (average and maximum)

- Time for trade document procedures (average and maximum)

- Inland transport time

- Additional time due to Container Security Initiative

- Vessel turnaround time (average)

- Time to resolve customs appeals (average and maximum)

- Vessel waiting time to obtain berth

# _Indicators of cost_

- Total cost for trade-related procedures

- Port- and terminal-related charges

- Total cost for trade document procedures

- Border control costs

- Inland transport cost

- Additional cost due to Container Security Initiative

# _Indicators of complexity and risk factors_

- Total number of documents per trade transaction

- Number of signatures per trade transaction

- Criteria for customs inspection

- Percentage of containers inspected

- Level of customs inspection

- Damage or pilferage as percentage of value of container

- Shutdown of port due to natural disaster and labor dispute (days)

- Whether the port is a signatory to the Container Security Initiative

- Speed (inland transport by trucks) (kilometers per day)

- Frequency of vessel calls at port

- Number of agencies that have the power to inspect goods

- Number of times consignments are typically inspected

- Percentage of containers electronically scanned

- Percentage of containers physically inspected

Source: World Bank, Global Logistics Indicators Survey, 2005.

# **Table 2. Additional Possible Logistics Indicators for Landlocked Economies**

- Waiting time at border crossings (average and maximum)

- Inland freight cost (through transit country)

- Harmonization of documents with transit country

- Number of transit countries crossed

- Number of borders crossed

- Whether there is free transit access for vehicles across borders

13
boreholes|% pop||40.6|52.4||
|Access to surface water|% pop|37.4|13.4|2.7|13.0|
|Access to flush toilets|% pop|4.9|1.8|4.4|40.8|
|Access to improved latrines|% pop|9.9|13.2|21.9|1.4|
|Access to traditional latrines|% pop|50.1|13.2|16.0|30.4|
|Open defecation|% pop|40.3|71.7|58.5|14.3|
|Revenue collection|% sales|62.7|85.0|94.5|100.0|
|Distribution losses|% production|34.3|16|18|26.8|
|Cost recovery|% total costs|56.0|100|100|80.6|
|Labor efficiency|connections per<br>employee|158.6|125|200|368.7|
|Total hidden costs as % of revenue|%|162.7|33|6|140.4|
|||**Burkina Faso**|Countries<br>water re|with scarce<br>sources<br>O|ther developing<br>regions|
|||Mid 2000s||||
|Residential tariff|U.S. cents per m<sup>3</sup>||**76**|60.26|30 600|
|Nonresidential tariff|U.S. cents per m<sup>3</sup>||**215**|120.74|. – .|

_Source:_ Derived from the AICD national and utility database (www.infrastructureafrica.org/aicd/tools/data).
# _4.1.3 Additional contextual information for grades 4 and 8 (NALO 2018)_

All of the differences between boys and girls in grades 4 and 8 noted in this section are statistically significant at the p < .001 level. However, the effect sizes associated with these differences were generally small (appendix A1). NALO data indicate that 14.5 percent of grade 4 boys had repeated a year at school because of poor academic performance, compared to 6.6 percent of grade 4 girls (table A1.1). In addition, grade 4 boys reported engaging in a lower level of reading than grade 4 girls. For example, 22.6 percent of boys reported never having read a book, compared to 12.0 percent of girls. Conversely, 33.8 percent of boys have read more than 10 books, compared to 40.4 percent of girls. However, when asked whether mathematics is important in life, grade 4 boys and girls provided broadly similar levels of agreement (table A1.1). Among grade 4 immigrant students (those reported by their parents to have been born outside Saudi Arabia), boys were more likely to attend a private school (13.9 percent) than girls (2.6 percent) (table A1.2). However, the vast majority of both boys (85.0 percent) and girls (95.2 percent) attended government schools.

NALO data also show the differences in perceptions among teachers, school principals, and parents in boys’ and girls’ schools. Teachers of grade 4 boys are substantially less likely to agree that parents have a good understanding of their child’s current academic level, suggesting greater misalignment between student performance and parental understanding in boys’ schools compared to girls’ schools. This difference was associated with the largest effect size observed among all the selected NALO variables ( _φ_ = .302; table A1.3). Teachers in boys’ schools are also less likely
>agricultural sector|-0.007***|-0.014***|0.0001|-0.045***|-0.051***|0.0001|
|Access to electricity|-0.015***|-0.044***|0.0001|-0.118***|-0.147***|0.0001|
|Piped drinking water|-0.004**|-0.021***|0.0017|-0.052***|-0.069***|0.0017|
|Improved sanitation|-0.007***|-0.018***|0|-0.056***|-0.067***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A20: Identifying the $3.20 poor based on covariates of poverty**

|||_Colombia_<br>|_2017_<br>||||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)**|**(3)**|**(
changes in crop mix across regions could possibly be observed using remote sensing (and linked to farm size categories if there is a complete cadastral map linked to the registry), only survey data can provide economic information on costs, prices received, input and output market participation, profit, and cash flow. To obtain such information, the survey collected data on the cost of hired labor, purchased inputs (fertilizer, pesticides, and other chemicals for crop protection), and machinery services by crop for the 2021 and the 2022 agricultural season. In several cases, respondents instead provided total input cost or total cost of machinery services and purchased inputs either by crop or for all crops grown by the farm.<sup>24</sup> If inputs are reported at farm rather than crop level, we apportion them across crops using crop area as a weight.

As respondents provided consistent information only on output prices, we rely on a survey of community leaders and local dealers’ adverts, complemented by completed auctions for machinery services on the Prozorro sales platform, to compute regional prices for fertilizer, fuel, machinery services, and labor in 2021 and 2022.<sup>25</sup> Appendix table 2 shows that at the national level, prices for fertilizer more than doubled, those for diesel increased by 90%, and those for machinery services by 31%. Wages increased by 25% on average, with differences across regions (no increase in the North vs. 75% in the South).

> 23 See the discussion of regression evidence in table 7 below.

> 24 To separate different categories of input costs in cases where costs were reported in total, we use median cost shares in the sample as follows: If non-labor inputs are reported jointly, purchased material inputs (i.e., fertilizer and other chemicals) and machinery services account for 64% and 36% of the total, respectively. If labor and non-labor inputs are reported jointly, they account for 15% and
14000 Brazil<br>Chile<br>12000<br>Colombia<br>10000<br>China<br>8000<br>Source: World Bank calculations based on Comtrade.<br>EXPY<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> 27. Also, in Mexico and Central America the value of medium and high tech exports as a percentage of total export value is considerably higher than their value added as a percentage of

> 9 The PRODY index is a weighted average of the per capita GDPs of countries exporting a given product, and thus represents the income level associated with that product. The index represents a weighted average of per-capita GDPs. EXPY is a weighted average of the PRODY for that country, where the weights are simply the value shares f the products in the country‘s total exports.

> 10 The intuition behind the _PRODY_ index is that it would show which products are more 'sophisticated,' i.e. which products tend to be exported by higher-income countries.  It is clear then that the _EXPY_ metric would be highly coordinated with high-income countries.  But there are outliers such as China and India, whose _EXPY_ measurement is much higher than their GDP per capita measurements should indicate.

10
In terms of change in the FLFP rate, however, there is a drop in FLFP in recent years across all educational categories, in both rural and urban areas. This indicates that irrespective of one’s educational attainment, the incentive for females to participate in the labor force has declined over the period. The FLFP rate had declined across all levels of education, from illiterates to college graduates, during 1993-94 to 2011-12. These two groups, illiterates and those with a college education, who initially held the highest participation rates, are also the groups that experienced the largest drops in FLFP rate during this period. In rural areas, there was an 11.5 percentage point decline in the FLFP rate among illiterate females, and for college educated females the decline was 8.0 percentage points (Table 9). In urban areas, there was a decline of 5.0 percentage points and 4.0 percentage points, respectively.

**Table 9: Change in FLFP Rate According to Level of Education**

||Rura <br>|l(%)<br>|Urba <br>|n(%)<br>|
|---|---|---|---|---|
||93-94 to 11-12|04-05 to 11-12|93-94 to 11-12|04-05 to 11-12|
|Illiterate|-11.5|-12.3|-5.0|-6.4|
|Below Secondary|-2.4|-8.3|1.1|-3.3|
|Secondary & HS|-4.8|-9.4|-4.1|-4.2|
|College &Higher|-8.0|-13.0|-4.0|-6.5|

Source: Authors’ own estimate based on NSSO data.

Here we have an intriguing situation, wherein both educational attendance and attainment are associated
significant inequities persist between urban and rural areas. The national Gini coefficient<sup>1</sup> rose from 0.35 percent in 1997 to 0.41 percent in 2006, in part because Uganda’s growth path has created greater opportunities in urban areas in the central and western parts of the country, while more remote rural areas, particularly wide swathes of the north and east, , have not grown as fast. Nrthern Uganda, which has now emerged from conflict, recorded high and persistent levels of income poverty at 60 percent of the population. While new opportunities are opening up in the post-conflict environment, agricultural production has yet to reach levels that would have a sustained impact on poverty.

Uganda is on track to meet at least two of eight Millennium Development Goals (MDGs). The country is close to halving poverty and has made substantial progress toward achieving universal primary education and addressing gender inequality. Uganda may even achieve the targets for combating HIV/AIDS, malaria, and other communicable diseases, ensuring environmental sustainability, and developing global partnerships. MDGs on reducing child mortality and improving maternal health, however, are unlikely to be met. Despite progress, Uganda ranked 156 of 179 in the fiscal 2009 Human Development Index (HDI) of the United Nations Development Programme (UNDP). The low overall ranking reflects the low starting point for Uganda along many development related indicators.

Uganda has one of the youngest and most rapidly growing populations in the world. The country’s population growth rate, at 3.3 percent in 2010, is well above the African average. Population growth has been consistently high aside from during the AIDS epidemic in the early 2000s. The total fertility rate is estimated at 6.7 children per woman according to the government’s data and 6.4 according to UN data. About half (48.7 percent) of Uganda’s population is below 15 years of age, well beyond Sub-Saharan Africa’s average of 43.2 percent and
three steps. If the remaining countries have _tk >_ 1 _._ 65 then there are two groups. Otherwise, repeat steps 1-3. It may be the case that the remaining economies are divergent and that there is no additional club.

- If _c∗_ = 0, then the requirement to be added to an existing group will be very conservative (relative to _−_ 1 _._ 655% critical value).

# **Converging in levels vs growth rates**

Applying the PS routine to our market-exchange rate adjusted data produces 5 convergence clubs ( **Table 3** ). The parameter _γ_ = 2 _β_ is between 0 and 1 in the majority of cases. This suggests that the convergence clubs uncovered show a tendency for relative convergence, or the reduction of the size of the gap in relative productivity levels over time, but not necessarily full convergence to the same level of output per worker. However, this does not rule out a substantial closure of productivity gaps over time—instead, it implies that over time productivity growth rates will align, alongside smaller productivity gaps between members of clubs. Full convergence in levels is a very strict condition not met even by advanced economies, where productivity gaps have declined considerably over the past 50 years, but persistent smaller gaps remain. Applying the PS log-t test to advanced economies yields a _γ_ of just 0.15, in line with the results for many of the convergence clubs identified for in Table 3. Estimated over the period 1970-2000, there are considerably fewer members of the two highest convergence clubs, which as noted in the main text is a result of fewer EMDEs displaying fast-convergence characteristics.

# **Determinants of club membership**

As noted in the main text, initial productivity, governance quality, education, and economic complexity are all statistically significant drivers of club membership in a multinomial logit regression. Further details of these regressors are provided in **Table 4** . To preserve degrees of freedom, 34
Jalisco (center-south) and Yucatan (South). Airlift capacity, along with its high level of human capital, good governance, was critical to Intel’s plant location in south-of-Mexico Costa Rica and is important to the long established computer industry in Guadalajara, Jalisco.<sup>11</sup> Yucatan also benefits from the shallow water port of Progreso that offers easy access to US ports in the Gulf of Mexico as well as those in Central and South America and the Caribbean. It is perhaps not surprising that in 2003, Yucatan had the second highest concentration of maquila employment of a non-border state exceeded only by Jalisco. The port of Veracruz, the entry point for Mexico’s first globalizing influence in the 16<sup>th</sup> century, remains Mexico's most important with extensive road and rail networks that connects the central and southern states, again to the Gulf of Mexico ports. Given this ready water access, _all other endowment equal_ , a southern pole or a southeastern corridor enjoying the same benefits of proximity would seem as plausible than the region being left behind.

In fact, to date, there is very little evidence that either the 1985 unilateral trade liberalization, or NAFTA has led to a correlation of growth with distance from the border. At the wage level, Hanson (1997) himself did not find that the North-South wage gradient steepened after 1985. Using state per capita income data, Rodriguez-Pose and SanchezReaza (2005) find no relationship at any time period and our background regressions confirm this.<sup>12</sup> Esquivel (2000) finds no relationship of distance with either level of income or growth. Chiquiar (forthcoming) finds a relationship of changes in growth with distance, but this appears to be a result very particular to the 1970-1985/1985-1999 difference that does not survive breaking the period into a post GATT (1985-1993) and post NAFTA periods 1993-2002.<sup>13</sup> In sum, the geographical patterns of
the difference is not statistically significant at any point during or after the end of the original loan maturity (Figure 2c). Flexible borrowers repay a lower fraction of the principal amount throughout the original maturity, as both extension and no-extension passes are used, and this gap does not close after the end of the original loan period (Figure 2d).

We can draw the following conclusions: First, since default only appears _after_ the end of the _original_ loan period (Fig. 2a and b), only extension passes (rather than no-extension passes) are associated with negative repayment behavior. Second, because the share of flexible borrowers that _ever_ missed a payment is _not_ different compared to that of standard borrowers (Fig. 2c), but the share of flexible borrowers who miss a payment after the end of the original period increases (Fig. 2b), we conclude that the flexible borrowers driving the difference in default rates by missing scheduled payments after the original loan period also missed payments during the original loan period. Third, the lack of treatment effects on loan renewal is consistent with the repayment behavior above as the set of borrowers driving the additional default (only statistically significant at the end of the loan cycle) were already behind on their loans and likely to be ineligible for a follow-on loan.

Next, we examine business, financing and stress-related outcomes using the follow-up survey (Table 3 and 4). Column 1 reports the ATE described in Equation 2. There are no impacts on key outcomes such as sales, profits, or investment (Table 3). Column 6 reports the p-value of a difference in volatility (std. deviation) in sales and profits between the Flexible and Standard Contract groups, but none of the differences is statistically significant. Borrowers of the flexible loan appear to have slightly more businesses and to have started a secondary business. A new enterprise typically is an indication of risktaking, but of course could also be a diversification strategy, and thus we are not able to infer whether the
advantages. First, it allows for cleaner econometric identication and interpretation of the results as our instrument utilizes public school construction guidelines that were applied for GSSs constructed after the 1980s.<sup>9</sup> This also alleviates exclusion restriction concerns that arise if our instrument were to predict other public goods. Second, focusing on the shorter exposure (to GSS) periods is likely to better isolate supply-side eects since GSS construction probably impacts a range of demand factors over a longer time span. It is nevertheless reassuring to note that all of our main results hold in the full sample of villages, both in terms of statistical and economic signicance, and several of these results are in fact stronger (Appendix Table II).<sup>10</sup> Table I presents summary statistics for the nal sample. Two and a half percent, or 232 villages, in this sample received a GSS between 1981 and 2001.<sup>11</sup> Conditional on existence,

> 9We are not aware of similar guidelines used in previous years. To the extent they were, we are reluctant to use the 1981 population (the earliest available census data at the village-level) to construct population rank for earlier years. Focusing on villages which did not receive secondary schools prior to 1981 also allows us to better control for village-level baseline data prior to the construction of a public school. For villages with pre-existing secondary schools, it is harder to discern whether dierences in the baseline data arise from selection into villages or the exposure to the secondary school. While we could have also excluded villages which received girls'/boys' primary schools prior to the 1980s, this is too severe a restriction and would eliminate most of our sample. Finally, we are also concerned with pre-existing secondary schools since we believe they are more likely to reect village wealth, size, or inuence. We therefore also exclude villages whose neighbours' have pre-existing secondary schools, since that could have spillover