rafmacalaba/datause-encoder-data huggingface dataset:
Furthermore, this result is robust to the use of various proxies or indicators for openness. Regarding the direction of causality, the evidence points in favor of a “virtuous cycle,” by which higher openness produces growth improvements and vice versa.’ As to the channels through which openness affects economic growth, empirical studies show that this mechanism not only works through raising domestic investment, but also, most importantly, through efficiency gains (growth in total factor productivity).° Countries with more open and less distorted foreign trade sectors exhibit faster TFP growth rates over the long run. Furthermore, TFP growth improves after trade-liberalization reforms. However, the evidence also points out that the degree of improvement is not homogeneous, as some countries (for example, Chile) benefited from trade liberalization more than others (Mexico), possibly because the extent oftrade liberalization and the depth of other structural reforms were greater in the former group of countries.

In this section, two sets of indicators of openness to international trade are presented, namely, policy-instrument indicators and outcome-based indicators.

1. Policy indicators oftrade liberalization ‘Policies that present obstacles to trade are of two broad categories: tariff (and para-tariff charges) and quantitative restrictions. Both should be evaluated together, given that one type of restriction has often substituted for the other. The higher the average level and dispersion of tariffs and para-tariffs (for example, duties and customs fees), the more distorted the trade outcome. As recommended by the literature,° the measure used to evaluate the level of tariffs and para-tariffs is the weighted average rate of tariff and para-tariff charges, using as weights their respective shares in * See Harrison (1996) and Easterly, Loayza, and Montiel (1996). * See Edwards (1995). -

© See Pritchett and Sethi (1993).

7 Actually, the weights are given by the respective shares in total imports of some 120 developing countries in 1985 (see UNCTAD (1994)). Using the country’s own import shares as weights leads to an underestimation of the tariff (or quantitative restriction) because these shares are negatively affected by the restrictions.

* The methodology used to construct these indices, as well as most indices in this document, is the following. The index is a weighted average of the principal components of its corresponding indicators, in which the weights are given by the share of the indicators’ variance explained by each principal component. See Theil (1971) and Demetriades and Luinte] (1996).

3
Under this light it should also be interpreted the finding that the PACI is highly correlated with the main perception-based measures of corruption, but it has a different scale. It was shown earlier that the scales become comparable once the log of the PACI is taken. If the PACI indeed approximates the probability that a transaction is corrupt with respect to a world average, then the _exponential_ of the WB-CCI and the TI-CPI would be approximately proportional to levels of corruption. Thus, further work in this direction might help clarify the scales of existing perceptionbased indicators.

Other venues for future research could be foreseen. First, the availability of the PACI spurs a reassessment of the literature on the causes and consequences of corruption. As mentioned earlier, more data would allow the computation of the PACI for separate sectors of the economy. It would also be interesting to study the differences among several versions of the same indices, computed by focusing on different jurisdictions (along the lines of the presentation in Table 1). Since they all measure the same concept of corruption, they are expected to provide similar results. Thus, any variance among them could be explained by sampling error but also, possibly, by the violation of one or more of the maintained assumptions—the bigger the violation, the bigger the divergence. Tests for the validity of the maintained assumptions could be developed by leveraging on the magnitude of such observed differences.

Lastly, the same intuition behind the use of judicial statistics in this article, could be applied to other domains. The essential ingredients needed for this are data culled from different jurisdictions that convey information on crimes committed in a given country by actors residing in another country. Some types of financial crimes may possibly lend themselves to a treatment based on the methodology introduced in this paper.

29
Table 1. Features of global debt waves

|||Wor|ld||A|dvanced e|conom|ies||EMD|Es||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Wave:|First|Second|Third|Fourth|First|Second|Third|Fourth|First|Second|Third|Fourth|
|**Magnitude**|||||||||||||
|(Percent of GDP)|||||||||||||
|Total|176|192|218|262|190|216|263|300|96|100|120|206|
|Government|56|65|75|97|56|70|91|120|55|48|39|63|
|Private|120|127|143|165|134|147|172|179|42|52|81|142|
|**Speed**|||||||||||||
|(Percentage points of|GDP)||||||||||||
|Total|3.0|1.7|3.0|4.8|3.2|2.3|6.3|3.5|2.1|0.8|1.5|8.7|
|Government|1.0|0.9|1.1|2.1|0.9|1.2|2.9|2.3|1.5|-0.3|-1.8|2.6|
|Private|2.1|0.8|1.9|2.8|2.3|1.1|3.4|1.3|0.6|1.1|3.4|6.2|
|**Coverage**|||||||||||||
|(Percent of countries)|||||||||||||
|Total|91|73|51|83|96|93|94|65|88|68|39|89|
|Government|90|62|32|85|80|69|72|76|93|60|22|87|
|Private|68|74|83|72|93|83|89|53|58|72|81|77|

Note: There have been four waves of global debt since 1970: first wave over 1970-89, second wave over 1990-2001, third wave over 2002-09, and fourth wave since 2010. Magnitude, speed, and coverage refer to, respectively, the stock at the end of each wave, an average annual change in debt during each wave, and the share of countries with rising debt over each wave. In computing the magnitude and speed, aggregates are weighted averages using current GDP in U.S. dollars as weights.

23
# **Table A1:**

**Estimating the death toll and measuring the downward bias in using the sample based approach, by comparing it to the reconstruction approach. Sources: Siblings' mortality data. Cambodia Demographic and Health Survey, 2000 and Heuveline 2001(b)**

||**Population**<br>**in 1970**|**size**<br> <br>|**Death probab**<br>**1970-1974**|**ilities**<br><br>|**Deaths**<br>**1970-197**|**4**|**Remainin**<br>**in 1975**|**g alive**|**Death probab**<br>**1975-1979**|**ilities**<br>|**Deaths**<br>**1975-1979**||**Deaths**<br>**1970-1979**||**Excess de**<br>**1970-1980**|**aths**|**Ratio sampl**<br>**vs. reconstr**|**e**<br>**uction**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**From Heuv**|**eline**<br>|**From Table 1**|**(**|**1)*(3)**<br>|**(2)*(4)**|**(1)-(5)**|**(2)-(6)**|**From Table 1**||**(7)*(9)**<br>**(**|**8)*(10)**<br>|**(5)+(11)**<br>**(**|**6)+(12)**|**From Heuv**|**eline**|**(13)/(15)**<br>**(**|**14)/(16)**|
|**Cohort**<br>**born in**|Males|Females|Males|Females|Males|Females|Males|Females|Males|Females|Males|Females|Males|Females|Males|Females|Males|Females|
||1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|
||||**25-29***||||||**30-34**||||||||||
|**1940-44**|244900|249800|0.08|0.03|19445|7319|225455|242481|0.18|0.08|39973|18501|59418|25820|95900|47700|0.62|0.54|
||||**20-24**||||||**25-29**||||||||||
|**1945-49**|313500|311800|0.09|0.03|28873|9479|284627|302321|0.17|0.09|48757|26453|77630|35932|134600|60300|0.58|0.60|
||||**15-19**||||||**20-24**||||||||||
|**1950-54**|411800|404600|0.10|0.03|41633|12421|370167|392179|0.23|0.07|83843|26472|125476|38893|191800|92100|0.65|0.42|
||||**10 to 14**||||||**15-19**||||||||||
|**1955-59**|490200|479200|0.06|0.02|27402|9009|462798|470191|0.20|0.06|93439|30233|120841|39242|220400|110600|0.55|0.35|
||||**5 to 9**||||||**10 to 14**||||||||||
|**1960-64**|582000|564000|0.03|0.03|18391|15792|563609|548208|0.08|0.05|44243|27575|62634|43367|224300|134700|0.28|0.32|
||||**0 to 4**||||||**5 to 9**||||||||||
|**1965-69**|715700|691200|0.03|0.03|24262|22049|691438|669151|0.07|0.05|45220|36067|69482|58117|229600|180500|0.30|0.32|
|**Total**|||||||||||||||||||
|**Born**<br>**1940-69**|2758100|2700600|||160007|76069|2598093|2624531|||355475|165302|515482|241371|1096600|625900|0.47|0.39|

* Age range at the beginning of the five year period Data for the baseline population in 1970 and for the excess deaths between 1970-80 are taken from Table 5-1, p. 122 of Heuveline 2001(b).

**Notice:** This table aims at measuring the extent of the downward bias implied by the sample approach used in this paper.

The author believes that for estimating the death toll in absolute numbers, the demographic reconstruction approach is more appropriate. This table should be read together with the appendix.

43
10-C

**Figure** **_2.c_**

<!-- Start of picture text --> Trade Shares of GDP<br>Tanzania: 1970-88<br>50-<br>40<br>30<br>204<br>10 _<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>Exports of Goods and Services/GDP %i<br>Imports of Goods and Services/GDP % I<br><!-- End of picture text -->
_(Box 4)_ pastoral and agro-pastoral systems, household objectives often revolve around food security issues and the importance of keeping cattle as a capital and cultural asset. Livestock are a key way by which households can generate cash for their needs. The results of modeling work suggests that subdivision often results in substantial reductions in livestock numbers, partially because households have to sell more animals to generate the cash needed, with serious longterm consequences on herd sizes and food security. Subdivision also tends to reduce wildlife numbers too. In the situations where subdivision does occur, the models indicate that households with more livelihood options, even if they are relatively poor, may be able to offset some of the negative impacts of subdivision. Results also underlined the fact that loss of key dry-season grazing resources can have serious household repercussions. Some households may be able to compensate for this loss by increasing the area cropped, but there are many households for whom this would not be feasible.

In Kajiado, subdivision seems to be inevitable, but more information on the likely ecological and household impacts that may result would be of value, so that the deleterious effects can be ameliorated through appropriate action. Such action could take the form of encouraging group ranch members to form grazing associations, or promoting the development of other economic activity that can provide other options for agro-pastoralist households that are faced with declining livestock numbers and productivity. Leasing programs are operating in parts of Kajiado district, in which agropastoralists are compensated directly for not erecting fences that can hinder wildlife movements in and out of protected areas that generate substantial levels of tourist revenue. The impacts of subdivision are not the same in all circumstances, but differ in relation to particular household, economic and ecological characteristics. Competition for land in Kajiado, as in many other areas, is intense and likely to increase in the future because of increasing populations and changing household aspirations. The results of the integrated assessment work have been presented at local community meetings, and one thing is clear: responses to what may be highly complex situations need to be flexible and adapted to local conditions.

Sources: Boone et al. (2005); Thornton et al. (2006a) 36
channels of indirect reporting. The LIC-DSA coverage increases the debt stock that is reported in official statistics by an average of 5 percent of GDP; the degree of dispersion fluctuates between -15 and 30 percent.

**Figure 5: indirect-direct reporting debt stock gap (in percent of 2019 national GDP)**

<!-- Start of picture text --> 30.00<br> 25.00<br> 20.00<br> 15.00<br> 10.00<br> 5.00<br> -<br> (5.00)<br> (10.00)<br> (15.00)<br><!-- End of picture text --> Source: WB/IMF 2019 DSA and authorities’ websites. Notes: author’s elaboration.

This example shows that the degree of mismatch across different sources may be significant. Previous work has highlighted the role of variation in coverage and debt definitions as the main driver of gaps between different debt statistics. This paper studies these factors but extends the list of potential causes to also include recording errors (often amplified by decentralized and non-automated recording processes) and data manipulation by external agents to comply with standardization and comparability goals. “Hidden debt” is also touched upon, but analysis regarding this channel faces severe data limitations and is not addressed in this paper.

# _4.1. Sectoral and instrument coverage_

Public debt data is typically presented as aa matrix with instrument coverage on the horizontal axis and sector coverage on the vertical axis (Figure 6). This tool is useful for mapping a country’s progress, although it is difficult to rank the level of coverage solely based on this matrix as it implies a ranking along both axes that may not apply to all countries. For instance, if this matrix were used to assess coverage in LIDCs, no country would register instrument coverage beyond D1, as currency/deposits are not included in LIDC debt reports.<sup>26</sup> Similarly, LIDCs have scattered coverage in terms of sectors and this may not fit with the hierarchical structure of the matrix, for example, a country may cover sub-national debt (GL3) but may not report its extra-budgetary debt (GL2).

## **Figure 6: public debt coverage by instrument and sector**

> 26 The treatment of cash as debt is a legacy of the gold standard and has been recently questioned from an economic (Abbas, 2020) and a legal viewpoint (Kumhof et al, 2020).
# **Table 8.  Is SADC Different?  Another Look The dependent variable is ln(FDI per capita + constant)**

|ln(schoolingt-1)|0.109|
|---|---|
||(0.631)|
|ln(phone densityt-1)|0.308***|
||(2.711)|
|the adult share of populationt-1|2.560|
||(1.263)|
|urban sharet-1|0.091|
||(0.148)|
|Ln(population)|-0.015|
||(-0.228)|
|Relative exchange ratet-1|-0.002*|
||(-1.743)|
|Ln(GDPpct-1)|0.329**|
||(2.149)|
|Mean GDPpc growth in the previous 5 yrs|0.077***|
||(5.839)|
|S.D. GDPpc growth in the previous 5 yrs|-1.388*|
||(-1.862)|
|Rulelawt-1|0.018|
||(0.341)|
|Trade/GDPt-1|0.639***|
||(3.924)|
|SADC|1.928|
||(1.347)|
|SADC * ln(phone densityt-1)|-0.293*|
||(-1.791)|
|SADC * Ln(GDPpct-1)|-0.386*|
||(-1.696)|
|SADC * Trade/GDPt-1|0.683*|
||(1.939)|
|Number of observations|3,187|
|Adjusted R2|0.464|

Note: ***, **, and * represent statistical significance at the 1, 5 and 10 percent levels. Standard errors clustered at the country level.

The constant in the dependent variable is 1.4.

30
degree of inequality of opportunity is higher than at the basic education level, as shown by a higher dissimilarity index. Inequality of opportunity in enrollment for both age groups varies significantly by region, although the disparities have declined over time. For example, in 2000, rural Upper Egypt presented the lowest HOI at about 76 percent and 48 percent for 6-14 and 17-17 age groups respectively. On the other hand, urban Lower Egypt had the highest HOI at about 92 and 75 percent for 6-14 and 17-17 age groups. Similar improvements can be noticed in 6<sup>th</sup> and 9<sup>th</sup> grade on-time completion rates. On time completion of 6<sup>th</sup> and 9<sup>th</sup> grade rates show similar improvements over the period, although the HOI for both is lower than that of enrollment (Figure 16). The changes in HOI in the education sector are mostly a result of the expansion of coverage for children overall (the scale effect) rather than the distribution effect. Inequality of opportunity in enrollment for both age groups varies significantly by region.

**Figure 16: Human Opportunity Index for Educational Enrollment and Attainment**

<!-- Start of picture text --> 100 Enrolment ages 6-14<br> Complete 6th grade on time<br>90 Enrolment ages 15-17 Enrolment ages 15-17 Enrolment ages 6-14<br> Complete 6th grade on time HOI = 80%<br>80 Complete 9th grade on time<br>Complete 9th grade on time<br>HOI = 60%<br>70<br>60<br> HOI = 40%<br>2000 2008<br>50<br>HOI = 20%<br>40<br>10 20 30 40 50 60 70 80 90 100<br>Coverage (%)<br>Equality of Opportunity (%)<br><!-- End of picture text --> _Source_ : DHS 2000 and 2008; HIECS 2000 and 2008 used for the imputation of household consumption Parental education variables are consistently the most important factors explaining variation in enrollment rates in Egypt, at both the basic and secondary education levels. Mother and father’s educational attainment combined explains more than half of the variation in the probability of being 31
temporary structure. Only 58 percent had a toilet, 51 percent had drinking water and 40 percent had electricity (ibid).

These failures of service delivery reflect how schools function as a mechanism of patronage for Sindh’s feudal elite. In many cases, schools serve more as an extension to a landlord’s estate in a village than as a functioning public service (Gazdar, 2000). School buildings are frequently used for non-educational purposes, and politically-appointed teachers are protected from discipline and rarely attend schools (Babur, 2016); given the high rate of one-teacher schools, these high rates of absenteeism exacerbate school closures.<sup>3</sup> As a result of these governance failures, enrollment remains low in Sindh. According to the PSLM survey, 2010-2011, the Net Enrollment Rate (NER) at primary level (ages 6 to 10) in rural Sindh was 54 percent, compared to 67 percent in rural Punjab. Data from our baseline survey, conducted in three representative districts of Sindh, reveal a similar picture: fewer than half (49.8 percent) of school-age children in surveyed households were enrolled in school, and the majority of those unenrolled had never attended school **(Table 1)** .<sup>4</sup>

> 3 During the period of this study, the inadequacy of schooling facilities was exacerbated by extensive flooding in both 2010 and 2011. The 2010 floods destroyed an estimated nine percent of Sindh’s educational facilities and damaged an additional 19 percent and severely disrupted the livelihoods of about 865,000 Sindh households (Asian Development Bank and World Bank, 2011). The 2011 heavy rains resulted in the extent of damage similar to that of the 2010 floods (Asian Development Bank and World Bank, 2012). Schools which were not damaged were converted into temporary shelters for displaced people, disrupting teaching for several weeks. Overall, 66 percent of schools in the villages sampled for this study reported being affected by the floods at the time of the baseline (EMIS, 2011/2012). 4 Although low enrollment often reflects limitations of demand as well as supply, recent evidence suggests that the latent demand for education in Sindh is high; Barrera-Osorio et al. (2017), in an experimental evaluation, found that provision of schooling through public-private partnership to under-served communities in rural Sindh increased enrollment by 30 percentage points for treatment villages.

11
importance of an (by economists) understudied link between labor and land markets, thereby improving our understanding of the structural transformation process in developing economies. Migration, by driving contractual change, can alter the efficiency of the agricultural sector, and have implications for equity and access. Second, we present three decades of national-level descriptive statistics on the nature of the relationship between tenants and landlords for India, one of the most populous country in the world which have a significant share of the population in agriculture. Third, we introduce a novel instrumental variable strategy for emigration: stripping the pull instrument from distance-related components strengthens exogeneity and can be applied in other contexts.

The rest of the paper is structured as follows. In section 2, we provide background on migration in India. Section 3 introduces the data. Section 4 presents descriptive statistics. Section 5 outlines a theoretical framework. Section 6 presents the empirical identification strategy. Section 7 presents the results. Section 8 concludes.

# **2 Setting: Migration in India**

India is often thought of as a country with low internal migration. Several studies point to India’s low internal labor mobility and localised labor markets, which are associated with regional wage gaps, efficiency losses and high local vulnerability (Jayachandran, 2006; Topalova, 2010; Chari et al., 2021). In 2001, merely 2.8% of India’s population consisted of individuals who had moved permanently to a different district in the last five years. In contrast, 9% of Brazil’s population had moved permanently to a different municipality in the last five years, and 10% of China’s population had moved to a different prefecture in the last five years (Kone et al., 2018). Research into the barriers to migration and labor mobility pointed at the role of social networks (Munshi and Rosenzweig, 2016), political and cultural inter-state differences (Kone et al., 2018) and restrictive labor laws (Adhvaryu, Chari and Sharma, 2013).

However, internal migration has been on the rise in India. According to 7
<!-- Start of picture text --> Percentage of respondents who actually complained<br>in education<br>towers<br>ee<br>Seep a mn<br>a<br>“ens<br>a<br>i<br>Litroaras<br>Slovenia<br>Kacakhasan<br>Armenia<br>Kones<br>(Estonia<br>Conch Pine<br>Grose Brae<br>Sra<br>0 10 20 30<br>Percentage of respondents<br><!-- End of picture text -->
Table 2: Summary Statistics of Main Outcomes

||(1)|(2)<br>F|(3)<br>ull Sample|(4)|(5)|(6)<br>WhatsApp|(7)<br>CATI|(8)<br>P-val.Dif.|
|---|---|---|---|---|---|---|---|---|
|Variable|Obs.|Mean|St.Dev.|Min|Max|Mean|Mean|(6) vs (7)|
|_Panel A: Outcomes_|||||||||
|Completed survey (%)|599|0.51|0.50|0|1|0.298|0.720|(0.000)***|
|Consistency with baseline survey|||||||||
|Stress high (%)|207|0.60|0.49|0|1|0.574|0.619|(0.535)|
|Physical punishment is effective (%)|207|0.68|0.47|0|1|0.735|0.647|(0.207)|
|Consistency with follow up survey|||||||||
|Stress high (%)|169|0.62|0.49|0|1|0.566|0.638|(0.376)|
|Physical punishment is effective (%)|169|0.58|0.50|0|1|0.642|0.552|(0.275)|
|Reasons to not complete the survey|||||||||
|Refusal to response (%)|294|0.67|0.47|0|1|0.686|0.619|(0.275)|
|Connectivity problems (%)|294|0.33|0.47|0|1|0.314|0.381|(0.275)|
|_Panel B: Sensitive information measures using data from..._|||||||||
|Knowledge incorporation survey|||||||||
|Stress high (%)|305|0.43|0.50|0|1|0.584|0.370|(0.001)***|
|Physical punishment is effective (%)|305|0.54|0.50|0|1|0.539|0.546|(0.912)|
|Baseline survey|||||||||
|Stress high (%)|358|0.31|0.46|0|1|0.295|0.324|(0.558)|
|Physical punishment is effective (%)|358|0.48|0.50|0|1|0.500|0.456|(0.407)|
|Follow - up survey|||||||||
|Stress high (%)|265|0.17|0.38|0|1|0.202|0.147|(0.243)|
|Physical punishment is effective (%)|265|0.37|0.48|0|1|0.341|0.390|(0.413)|

_Notes:_ This table shows summary statistics of the main outcomes used in this paper. Panel A shows statistics of main outcomes, which were constructed as described in table A8 in the Appendix. Panel B presents statistics on measures of sensitive information using different sources (knowledge incorporation surveys, baseline, and follow up data). Construction of these sensitive items and data details are presented in table A9 in the Appendix. Differences in number of observations to estimate the consistence measures are due to availability of data in each survey. For example, 305 participants completed the midline survey and 358 has baseline information in our sample. However, to estimate consistence, we only have information in both samples for 207 respondents.

30