rafmacalaba/datause-encoder-data huggingface dataset:
We also calculate the largest autoregressive root or dominant root in the univariate autoregressive process for each inflation series. In the long run, the effect of a shock will be dominated by this largest root. The dominant roots confirm that core and headline inflation are more persistent than food price inflation. The sum of autoregressive coefficients gives a similar result: high persistence across inflation series and higher persistence of core inflation.

Yet another measure of persistence is the impulse half-life, the number of periods it takes for the impulse response to fall below 0.5 following a unit shock. This measure also confirms the higher persistence of core inflation (see Appendix B). The half-life for headline inflation is estimated at around 4 quarters, and for core inflation around 5 quarters. These findings are consistent with the earlier results of Ball, Chari and Mishra (2016), who similarly document the shorter duration of food price shocks.

We estimate a VAR model to identify the timing relationship between food price inflation and core inflation.<sup>19</sup> We treat food and core inflation as endogenous and fuel inflation as exogenous (as given largely by global economic conditions).<sup>20</sup> We estimate specifications with 2, 4 and 8 lags. Food price inflation has a larger and more consistent impact on core inflation than vice versa. The impact is significant for two quarters. Fuel inflation does not impact food or core inflation at a quarterly frequency. Granger causality tests in Table 5 further suggest that food inflation Granger causes core inflation. This result is robust to different lag lengths. In contrast, there is no evidence that core inflation Granger causes food inflation. Put differently, past values of food inflation help predict core inflation, but past values of core inflation do not help predict food inflation.

**Table 5. Granger Causality Wald tests (VAR model)**

|**Dependent**<br>**Variable (y)**|**Explanatory**<br>**Variable (x)**|**F**|**df**|**df_r**|**Prob >**<br>**F**|**Does x granger**<br>**cause y?**|
|---|---|---|---|---|---|---|
|||**Lag l**<br>|**ength 2**<br>||||
|Food Inflation|Core Inflation|2.4129|2|81|0.096|No|
|Core Inflation|Food Inflation|3.4081|2|81|0.0379|Yes|
|||**Lag l**<br>|**ength 4**<br>||||
|Food Inflation|Core Inflation|3.3246|4|73|0.0147|Yes|
|Core Inflation|Food Inflation|3.3435|4|73|0.0143|Yes|
|||**Lag l**<br>|**ength 8**<br>||||
|Food Inflation|Core Inflation|0.69824|8|61|0.6917|No|
|Core Inflation|Food Inflation|2.9597|8|61|0.0073|Yes|

Note: Granger causality is based on 5% significance level; "No" indicates that we fail to reject the null hypothesis: x does not granger cause y.

> 19 Data is from 1997q1 to 2019q4. We use standard splicing method to expand our CPI 2011-12 inflation series which starts from 2012 onwards. Prior to 2012, CPI-IW inflation is used for each component—food, fuel, and core. 20 India being a net importer of fuel, its base price is determined globally, and retail prices by the base price and taxes. During the earlier period in the analysis, the retail price was administered.

10
54 Table A2.23: Number of households using LPG and estimated average quantity purchased per month by purchasing households in Peru

|**Quintile**|**Number o**<br>**Rural**|**f user households**<br>**Urban**|**(percent)**<br>**Total**|**Kg purchased**<br>**Rural**|**per household**<br>**Urban**|**(per capita)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|50,837 (5.5)|79,367 (40)|130,204 (12)|6.3(1.4)|8.6(1.8)|7.7(1.6)|
|**2**|125,742(18)|365,296(70)|491,038(40)|7.5 (1.8)|9.1 (2.0)|8.7 (1.9)|
|**3**|132,221 (35)|761,581 (85)|893,802 (70)|8.0(2.2)|9.7(2.2)|9.5(2.2)|
|**4**|110,440(53)|1,082,078(92)|1,192,518(86)|8.7 (2.8)|9.8 (2.5)|9.7 (2.5)|
|**5**|61,001 (62)|1,448,440 (89)|1,509,441 (88)|8.7(3.8)|9.6(3.1)|9.6(3.2)|
|**All**|480,241(21)|3,736,762(85)|4,217,003(63)|7.9 (2.4)|9.6 (2.6)|9.4 (2.6)|

_Source:_ Authors’ calculations. _Note_ : See notes for Table A2.3.

The expenditure share of LPG, averaged across all households, was low and did not exceed 2.5 percent (Table A2.24). For user households, however, the expenditure share reached 6 percent for the lowest quintile in urban areas. The share declined with quintile level for both rural and urban users.

Table A2.24: Shares of total household expenditure on LPG for all households and for purchasing households (percent) in Peru

|**Quintile**|**All househol**<br>**Rural**|**ds (% of total e**<br>**Urban**|**xpenditure)**<br>**Total**|**Us**<br>**Rural**|**er households (**<br>**Urban**|**%)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|0.2|2.5|0.7|4.5|6.3|5.6|
|**2**|0.6|2.3|1.3|3.1|3.2|3.2|
|**3**|0.9|2.1|1.8|2.6|2.5|2.5|
|**4**|1.1|1.8|1.7|2.1|2.0|2.0|
|**5**|1.0|1.1|1.1|1.7|1.2|1.3|
|**All**|0.6|1.7|1.3|2.7|2.0|2.1|

_Source:_ Authors’ calculations.
Panel A are taken from the 2001 Population Census for India, while those in Panel B are derived directly from the manufacturing surveys. These correlations are unweighted and treat each industry independently. We select the specific attributes listed due to their frequent use in studies of Indian manufacturing and its spatial variations.<sup>6</sup> Within Panel A, several factors display strong univariate correlations across all four columns: literacy rate, strength of household banking sector, urbanization rate, and consumption per capita levels. Population density also comes across very strongly for the organized sector, while infrastructure access in the form of paved roads has a univariate link for the unorganized sector. Of course, these factors are likely to be highly correlated among themselves, and so we turn shortly to a multivariate analysis.

Within Panel B, the first three rows consider the organized sector’s share of local manufacturing activity. When using employments or output, there is strong correlation of a larger organized sector share in the district to greater average input counts by local plants in both sectors. On the other hand, we observe in the fourth through sixth rows of Panel B a weaker connection to the degree to which the local unorganized sector contains a high share of femaleowned businesses relative to male-owned businesses (i.e., the relative composition of the local unorganized sector). The negative connections observed in the fourth column are likely a consequence of greater local female ownership encouraging more entry on the margin by smaller and frequently household-based female entrepreneurs (Ghani, Kerr, and O’Connell 2013b). These marginal entrants tend to have smaller businesses and likely lower input counts.

The final three rows of Panel B provide perhaps the most interesting result, which is a null finding. One might have expected that substantial local industrial diversity would have been associated with greater counts of distinct inputs used by firms, an argument that dates back to Jacobs (1969) and others. To assess, we calculate a Herfindahl-Hirschman Index (HHI) of the 6 Appendix Table 1 provides descriptive statistics on district-level traits. The construction and definition of most traits are fairly intuitive. Infrastructure variables are calculated as the percentage of villages that report access to that form of infrastructure (e.g., electricity, paved roads). Travel time to one of India’s ten largest cities is calculated from Lall, Wang, and Deichmann (2011) based upon the driving time from the central node of a district. The ten cities are Ahmedabad, Bangalore, Bhubaneshwar, Chennai, Delhi, Guwahati, Hyderabad, Kolkata, Mumbai, and Patna. The strength of household banking variable is calculated the share of households that report a banking relationship.

11
**Table 4:  Number of Enterprises and Workers by Enterprise Size Group‐ Uganda Business Registry 2001 and 2010**

|**Enterprise Size group**|**Nu**|**mber of enter**|**prises**<br>**Avg Annual**|**% share**|**of total**|
|---|---|---|---|---|---|
||**2001**|**2010**|**.**<br>**% Growth**|**2001**|**2010**|
|Less than 5 workers|151,879|428,111|20.2|92.2|93.5|
|5‐20 workers|11,330|26,623|15.0|6.9|5.8|
|21‐100 workers|1,274|3,016|15.2|0.8|0.7|
|More than 100 workers|257|342|3.7|0.2|0.1|
|Total|164,740|458,092||100.0|100.0|
||**N**|**umber of wor**|**kers**|**% share**|**of total**|
|**Enterprise Size group**|**2001**|**2010**|**Avg. Annual**<br>**% Growth**|**2001**|**2010**|
|Less than 5 workers|236,725|642,876|19.1|48.2|59.9|
|5‐20 workers|95,441|218,670|14.3|19.4|20.4|
|21‐100 workers|50,044|112,611|13.9|10.2|10.5|
|More than 100 workers|108,797|98,828|‐1.0|22.2|9.2|
|Total|491,007|1,072,985||100.0|100.0|

**Table 5: Number of workers by enterprise age group‐ Uganda Business Registry 2001 and 2010**

|**Enterrise ae rou**|**Nu**|**mber of ent**|**erprises**<br>|**% share**|**of total**|
|---|---|---|---|---|---|
|||||||
||**2001**|**2010**|**g.**<br>**Growth**|**2001**|**2010**|
|<5 yrs.|95,336|251,198|18.2|57.9|54.8|
|5-10 yrs.|14,322|70,121|43.3|8.7|15.3|
|More than 10 yrs.|10,083|127,363|129.2|6.1|27.8|
|Total|119,741|448,682||||
||**N**|**umber of w**|**orkers**|**% share**|**of total**|
|**Enterprise age group**|**2001**|**2010**|**Avg. Annual %**<br>**Growth**|**2001**|**2010**|
|<5 yrs.|194,782|458,787|15.1|60.7|46.6|
|5-10 yrs.|58,945|172,215|21.4|18.4|17.5|
|More than 10 yrs.|66,966|354,322|47.7|20.9|36.0|
|Total|320,693|985,324|23.0|100.0|100.0|

39
# **Table S16. Relationship between the Economic Complexity Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.036***<br>(0.00)|0.002*<br>(0.00)|0.002<br>(0.00)|||||
|Open Data Barometer<br>||||0.028***|0.000|0.000||
|Score||||||||
|||||(0.00)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.035|
||||||||(0.00)|
|Trade (% of GDP)|||0.000|||-0.001||
||||(0.00)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (% of|||-0.012|||0.008||
|<br>GDP)||||||||
||||(0.02)|||(0.02)||
|Manufacturing value<br>added (% of GDP)|||0.020***|||0.002||
||||(0.00)|||(0.01)||
|School Enrollment,|||0001|||0008**||
|Primar (% ross)|||-.|||.||
|y g|||(0.00)|||(0.00)||
|Year 2014|||||0.006|0.003||
||||||(0.01)|(0.01)||
|Year 2015|||||0.018|0.012||
||||||(002)|(002)||
|Year 2016|||||.<br>-0.017|.<br>-0.026||
||||||(0.02)|(0.03)||
|Year 2017||-0.011|-0.017||-0.021|-0.025||
|||(0.01)|(0.02)||(0.02)|(0.03)||
|Year 2018||-0.019|-0.031|||||
|||(0.02)|(0.03)|||||
|Year 2019||-0.016|-0.030|||||
|||(0.02)|(0.03)|||||
|Year 2020||-0.031|-0.023|||||
|||(0.03)|(0.03)|||||
|Year 2021||-0.038|-0.055|||||
|||(0.03)|(0.04)|||||
|Year 2022||-0.040|-0.071|||||
|||(0.03)|(0.05)|||||
|Constant|-1.753***|||-0.658***|||-1.153***|
||(0.18)|||(0.12)|||(0.17)|
|Sigmau||0.71|0.52||0.7|0.47||
|_<br>Sigmae||0.14|0.14||0.1|0.1||
|_<br>R2|0.378|0.982|0.982|0.411|0.992|0.992|0.405|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|119|119|119|94|94|94|85|
|No of observations<br>Note: * p<0.10, ** p<0.05<br>Development Indicators (W|833<br>, *** p<0.01.<br>DI), Open D|833<br>Standard erro<br>ata Watch (OD|833<br>rs are clustered<br>IN), Global Da|340<br>at the country<br>ta Barometer (|340<br>level. Data fro<br>GDB), and Op|340<br>m the Worl<br>en Data Bar|85<br>d Bank's World<br>ometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

65
can stem from the recognition that in case the private sector has limited capacity to manage FX risk an elimination of the direct FX risk from a government’s balance sheet can shift the FX risk to private sector’s balance sheets and possibly increase the value of government’s contingent liabilities.

The obvious solution to the problem of eliminating the FX risk from sovereign balance sheets is development of domestic government bond markets. This would entail gradual elimination of the constraints due to e.g. the lack of benchmark issues, institutional investors, and a liquid secondary market. Institutional factors are crucial in this respect, see. e.g. Burger and Warnock (2004), Santos and Tsatsaronis (2003), and Turner (2002), who stress among others the role of creditor-friendly policies and laws, sound debt management and public governance for domestic government bond market development. Since institutional factors show high persistence it is not likely that the sovereigns and debt managers in particular could eliminate FX risk inherent to their balance sheets in a rapid manner.<sup>4</sup> Moreover, the potential size of the domestic market might still not su¢ ce for accommodating the demand for funds from the government and other entities in the domestic economy. The practical experience regarding relaxation of the domestic market’s constraints in selected countries<sup>5</sup> is summarized in World Bank (2007).

This paper focuses on management of the FX risk in a government debt portfolio by choosing the optimal currency composition for the unhedged sovereign debt. The latter refers to an open, short position of a sovereign in a foreign currency. Such position occurs if the sovereign, after matching the ...nancial characteristics of its asset and liabilities, is exposed to FX risk. The FX risk is usually considered one of the major risks in EMEs. It is also signi...cantly higher than in industrialized economies, as the EMEs are more exposed to external shocks, such as those relating to the terms of trade and the cost of ...nancing, in addition to ...nancial contagion or natural disasters, see Caballero and Cowan (2006), and Claessens (2005). The importance of ...nding optimal currency benchmarks for sovereign debt portfolios has

> 4There are also transitional issues for many economies, relating to macroeconomic policies, that impact on exchange rates and overall monetary conditions.

> 5These are the 12 countries selected by the WB’s Treasury for piloting its debt management diagnostic and associated advisory work.

4
In Table 4 we report how the pandemic outcomes are related to country characteristics, government policy delays, and vulnerabilities. We proceed step by step. For the pandemic spread and the mortality, we first include only basic country characteristics and _DelayI_ and _DelayD_ in columns (1) and (1’). We add vulnerabilities in columns (2) and (2’). In columns (3) and (3’), we then add longitude and latitude to examine the robustness of the previous specification to further geographical controls. We allow the interaction of _DelayD_ with vulnerabilities to see how delay in government policies interact with inherent vulnerabilities in columns (4) and (4’). We do not interact _DelayI_ with vulnerabilities because _DelayI_ has less impact, and in exploratory runs, we do not find consistently interaction effects between _DelayI_ and vulnerabilities. For columns (2), (2’), (4), and (4’), we have tried both the OLS and the SAC specification, and to save space, we report only the preferred specification—that is, the OLS when the two SAC parameters ( ) are both insignificant, and the SAC otherwise. ρρ, λλ Most of the results in columns (1) and (1’) that link country characteristics and policy delays to pandemic severity make sense. More populous countries tend to have higher pandemic mortality rate. Richer countries have higher pandemic spread, reflecting the role of higher density and more international trade; they also have lower mortality rate, possibly due to better healthcare facilities. Perhaps surprisingly but upon reflection not so, _DelayD_ is associated with lower pandemic spread. As discussed earlier, reverse causality for _DelayD_ is serious: worse pandemic spread would lead to faster government response and a shorter _DelayD_ , resulting in spurious negative correlation between _DelayD_ and pandemic spread. In contrast, _DelayI_ is positively associated with pandemic mortality rate. Given the effects of _DelayI_ is likely under-estimated, the causal effects of _DelayI_ are likely positive on pandemic mortality. Increasing _DelayI_ by one SD is associated with an increase in pandemic mortality rate by 0.004, or 17% of its mean, a pretty large effect.

Columns (2) and (2’) add vulnerabilities, and columns (3) and (3’) further add longitude and latitude as controls to examine the robustness. We obtain two main findings. First, the share of elderly and obesity prevalence are both associated with lower pandemic spread, perhaps because the elderly and the obese are less physically mobile and have fewer social interactions, reducing the risks of passing the virus. On average, neither the share of elderly nor obesity prevalence is significantly associated with higher pandemic mortality rate. Second, and in contrast, urbanization ratio and SO2 are both positively correlated with pandemic spread (and not with pandemic 17
**Table 5.3. Impact of PqTR reduction on repetition rates**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.266***|0.253***|
||(0.010)|(0.006)|
|Time|-0.014|-0.001|
||(0.014)|(0.009)|
|Treatment|0.007|-0.016|
||(0.011)|(0.012)|
|DiD (Treatment and Time)|-0.028*|-0.021|
||(0.016)|(0.015)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01 2017/18 compares Repetition rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares Repetition rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._

**Table 5.4** focuses on repetition rates in lower primary (Grades 1-4). Again, we find significant impacts from the 2017 allocation of teachers, with lower primary repetition rates reduced by 3.5 percentage points, but we do not observe similar impacts from the 2018 deployment.

**Table 5.4. Impact of PqTR reduction on repetition rates in lower primary**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.276***|0.269***|
||(0.011)|(0.007)|
|Time|-0.014|-0.005|
||(0.015)|(0.010)|
|Treatment|0.016|-0.026**|
||(0.013)|(0.013)|
|DiD (Treatment and Time)|-0.035**|-0.017|
||(0.017)|(0.016)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019_ _Note: Robust standard errors in parentheses. *p<0.10, ** p<0.05, *** p<0.01_ _2017/18 compares lower primary Repetition rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares lower primary Repetition rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._ 19
# **(6.1) Rural-Urban Gap: Understanding the Higher Correlations in Urban Areas**

The magnitude of sibling and intergenerational correlations are in general larger in urban areas. This is true in 1993 for both men and women, also for men in 2006. It seems puzzling, because the schooling infrastructure and financial sector are expected to be more developed in urban areas. However, there are a number of factors that may help explain the observed higher persistence in educational attainment in urban areas compared to the rural areas.

Most of the schools, both primary and secondary, in rural areas are public schools and thus tuition free. The public primary schools also provide mid-day meals. The absence of tuition costs and provision of mid-day meals help the poorer households (parents with lower education) to send their children to schools. Moreover, the private market for supplementary tutoring is not developed in rural areas. A private market for quality tutoring could potentially give an advantage to the children of richer (and more educated) parents, creating inequality in educational opportunities. The above factors combined together weaken the link between parental income and children‘s educational attainment in the rural areas. In contrast, there has been dramatic growth in private schools and supplementary private tutoring in urban areas in India in last couple of decades (Kingdon (2007), World Bank (2009)). According to one estimate the share of enrollment in private secondary schools in urban India was about 30-40 percent in 2002. Thus parental income and access to credit have become increasingly important in urban India for children‘s education, creating more prominent role for parental education and family background. This raises the worry that the inequality in access to education may accelerate in the urban areas in the coming years.

Another important factor is the differences in returns to education. The available estimates for 1993/94 shows that while returns to primary education were higher in rural areas, returns to higher education were higher in urban areas (Duraisamy (2002)). The recent estimates indicate that the rural-urban gap in returns to education has increased after the liberalization (Aslam et al. (2011)). The returns to one more year of schooling in 2007 for self-employed is estimated to be 9.8 percent in rural areas and 34 percent in urban areas. For wage employment for men, it is 6.3 percent in rural areas, but 32 percent in urban areas. For female wage employment, the corresponding returns are 8 percent (rural) and 44 percent (urban) (see Aslam et al. (2011)).<sup>42</sup> As noted by many observers, the economic growth in India after economic liberalization has been both skill-biased and urban-biased,

> 42 The skill biased nature of economic growth can also be seen from growth of wages across different schooling levels. The average wage in 1999/2000 for someone with a college degree was 73 percent higher than someone with high school degree, and 67 percent higher for someone with high school degree compared to someone with middle school education (based on NSS data).

27