rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7 OLS Estimations based on country specific variables**

|**EDUCATION INDEX 1**|**predicted**<br>**(lnOP1990)**|**Δpredicted**<br>**(lnOP1990-2000)**<br>**1985 arrival**<br>**1990-2000**|**(1/10) Δpredicted**<br>**(lnOP1990-2000)**|**predicted**<br>**(lnOP1980)**|**Δpredicted**<br>**(lnOP1980-1990)**<br>**1980-1990**|**(1/10) Δpredicted**<br>**(lnOP1980-1990)**<br>**1975 ar**|**predicted**<br>**(lnOP1990)**<br>**rival**|**Δpredicted**<br>**(lnOP1990-2000)**<br>**1990-2000**|**(1/10) Δpredicted**<br>**(lnOP1990-2000)**|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|**Log of GDP per capita**|**0.112*****<br>**[0.0205]**|**-0.0432*****<br>**[0.00841]**||**0.0999****<br>**[0.0500]**|**-0.0493***<br>**[0.0273]**||0.0369<br>[0.0227]|**-0.0208****<br>**[0.00886]**||
|**Openness Index**|-0.00006<br>[0.000650]|0.00015<br>[0.000257]|0.00001<br>[1.69e-05]|-0.00019<br>[0.00124]|-0.00004<br>[0.000703]|-0.00002<br>[2.38e-05]|-0.00015<br>[0.000684]|0.00025<br>[0.000299]|0.00001<br>[2.61e-05]|
|**Log of Distance to the US**|**0.142*****<br>**[0.0463]**|**-0.0229*****<br>**[0.00724]**|**0.00258*****<br>**[0.000895]**|0.0923<br>[0.0623]|-0.0278<br>[0.0311]|0.00272<br>[0.00198]|**0.0913****<br>**[0.0443]**|**-0.0239****<br>**[0.0117]**|0.000705<br>[0.00113]|
|**English**|0.0566<br>[0.0446]|**-0.0220****<br>**[0.0108]**||**0.123***<br>**[0.0731]**|-0.0317<br>[0.0408]||0.0393<br>[0.0422]|-0.0136<br>[0.0108]||
|**Military conflict**|**-0.0977****<br>**[0.0464]**|**0.0222***<br>**[0.0126]**|-0.000152<br>[0.00129]|-0.00147<br>[0.127]|-0.0196<br>[0.0776]|-0.00133<br>[0.00214]|-0.0358<br>[0.0508]|0.00546<br>[0.0147]|0.000404<br>[0.00163]|
|**Communism index**|-0.0399<br>[0.0733]|**0.0414****<br>**[0.0205]**|**0.00458***<br>**[0.00242]**|0.0406<br>[0.152]|-0.0327<br>[0.0876]|-0.00267<br>[0.00238]|-0.0353<br>[0.0697]|0.0123<br>[0.0191]|0.00166<br>[0.00207]|
|**Predicted Log of OP in 1990**|||**-0.0285*****<br>**[0.00419]**||||||**-0.0280*****<br>**[0.00938]**|
|**Predicted Log of OP in 1980**||||||**-0.0519*****<br>**[0.00460]**||||
|**Constant**|4.291***<br>[0.454]|0.699***<br>[0.103]|0.175***<br>[0.0226]|4.583***<br>[0.672]|0.987***<br>[0.320]|0.335***<br>[0.0279]|5.494***<br>[0.431]|0.432***<br>[0.144]|0.184***<br>[0.0563]|
|**Observations**|71|71|71|72|72|72|72|72|72|
|**R-squared**|0.637|0.639|0.695|0.282|0.142|0.844|0.221|0.163|0.377|

***** p<0.01, ** p<0.05, * p<0.1 Robust standard errors in brackets**

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(zaat/biraderi); leaving just 10% of marriages exogamous with respect to clan and caste. Exchange marriage clearly facilitates endogamy by forging a double union across the same two families, but this does not seem to be the sole motivation for the arrangement in rural Pakistan. The incidence of watta satta in our sample (using the looser definition, which is more appropriate here) is 48% among women married to a blood relative, 28% among those married to an unrelated member of the same caste, but is still 23% among those having no blood or caste affiliation to their husband.

Exchange marriage might also serve as a mechanism for alleviating the financial burden of dowry. If dowry is principally a price for husbands (a view recently challenged by Botticini and Siow, 2003), the advantage of marrying ones daughter into ones son’s wife’s family is clear: it establishes a double-coincidence of wants, obviating the need to exchange money or wealth at the time of the joint marriage. In rural Pakistan, however, dowry values are relatively modest (median in our sample is 12% of median annual nondurable household consumption expenditures) and dowry assets are generally controlled by the woman herself rather than delivered directly to her in-laws (see World Bank, 2005). Moreover, in an analysis reported elsewhere (World Bank, 2005), we find no evidence that the value of dowries are lower in watta satta marriages.<sup>5</sup> In sum, then, it would appear that exchange marriage is not driven mainly by considerations of dowry avoidance.

# 2.3 Measuring Marital Discord

As already mentioned, the vast majority of women in rural Pakistan live in close geographical proximity to their natal families. Consequently, the parents’ home is, for most, a potential exile from their husband’s household, albeit not a costless one.<sup>6</sup> In our sample, 19% of women report that they had returned to their natal home at least once during their marriage due to an estrangement from their husband. These periods of estrangement are generally short, the modal duration being less than a month.

Estrangement is probably the clearest expression of the intensity of marital discord. Given the considerable psychic penalties, it is also not a decision taken lightly:

## "Sometimes I thought of going back to my parents but then I did not want

status.

> 5 Since low status households are more likely to undertake exchange marriages (see below), and would tend to provide smaller dowries, the OLS estimates reported by us in the World Bank study are likely to be biased in favor of a negative watta satta effect. Despite this, we cannot reject the null of no effect.

> 6 In the Indian context studied by Bloch and Rao (2002), where village exogamy is the norm, a wife’s return to her natal household is an even more drastic and unlikely course of action.

5
sample of countries covered in the SCI by one-fifth, from 146 countries , to include high-income countries. As such, to our knowledge, the SPI offers the most complete coverage of country statistical capacity to date, in terms of a combination of a larger number of indicators, more time periods, and more country coverage.

Existing efforts in building indexes to assess statistical capacity have focused on practical details such as data collection, organization, and legal issues.<sup>7</sup> There are just a couple of academic studies that rigorously discuss the underlying theoretical principles that are indispensable for the construction of a reliable, transparent, and consistent statistical capacity index. Sanga, Dosso, and Gui-Diby (2011) discuss the technical framework behind the African Statistical Development Index. Most recently, Cameron _et al._ (2021) propose a new theoretical framework for improving the SCI that discusses a statistical index’s desirable characteristics and its technical foundations (including aggregation methods and axiomatic properties). Both these studies, however, provide limited discussion of the conceptual foundation regarding data usage and data production behind their indexes. We address this limitation in this paper, and we build on the technical framework offered in Cameron _et al._ (2021) to provide a new index that is both conceptually clearer and more comprehensive.<sup>8</sup> This paper consists of five sections. We discuss in the next section the conceptual motivations behind the SPI (Section 2.1) and its construction (Section 2.2) before comparing it with the SCI (Section 2.3). We subsequently present the empirical analysis in Section 3 where we offer detailed

> 7 For example, the UNECE, in a recent Global Assessment report, discusses only the legal basis, description of the statistical system, data source, and processing of the target country (UNECE, 2014). The FAO, in its guidelines for assessing country capacity in producing agricultural statistics, provides instructions on completing the questionnaires and on compiling the assessment indicator (FAO, 2014), but pays no attention to the axiomatic principles of these indicators. The U.S. Census Bureau developed and recently updated (2017) the Tool for Assessing Statistical Capacity (TASC) with a primary objective of measuring the overall capacity of an NSS by providing a breakdown of the areas of strength and weakness. However, the focus of this instrument is on measuring the capacity of an NSS to conduct household-based surveys and censuses.

> 8 We return to more discussion on the differences between our paper and Cameron _et al_ .’s (2021) study in Section 2.2.

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infrastructure has increased production and transportation costs and reduced the job opportunities available to the poor. Together, these factors are contributing to a long-term decline in economic productivity. Meanwhile, Guatemala’s low tax revenues limit its capacity for redistributive fiscal policy and pro-poor spending. Guatemala’s high poverty rates and tight fiscal constraints underscore the critical importance of effectively targeting public spending.

# **_The Role of Data in Poverty Reduction_**

Effective poverty-reduction strategies require detailed information on the current geographic distribution of poverty and the characteristics of households living below the poverty line. Censuses and household surveys can shed light on a wide range of economic and social indicators. However, these methods are expensive and time-consuming, and implementing them requires considerable institutional capacity. Moreover, adverse local conditions such as violent conflict, high crime rates, or political instability may make traditional in-person surveying impossible in certain areas. As a result, policy makers must often base critical decisions on outdated or incomplete information.

Social scientists are increasingly using so-called “big data” analysis to supplement more traditional sources of information. Satellite imagery, logs from sensors (e.g. traffic, weather), smartphone applications, and cell phone data—the subject of this paper—have already yielded important insights in numerous fields. Unlike household surveys, which are specifically designed to address certain research questions, big data are usually collected in a non-research context, often as the byproduct of a commercial activity or public service. Analyzing big data requires new research methods, many of which are still in the early stages of their development. Emerging research methodologies based on Call Detail Records (CDRs) and advanced machine-learning techniques have especially promising applications in developing countries, as they can potentially generate reliable poverty data at a far lower cost than conventional household surveys.

CDR analysis can play a vital role by filling the spatial and temporal gaps left by traditional research methods. By making inferences based on cellular network usage, CDR analysis can reliably project the evolution of poverty dynamics over a specific timeframe. Unlike censuses and household surveys, CDR analysis is quick and relatively inexpensive and can be performed by a small team of statisticians using records that are already collected by Mobile Network Operators (MNOs).

Guatemala offers a prime example of the limits of traditional data collection. The country’s most recent Population and Housing Census, dates from 2002, and all national poverty data are derived from just four household surveys conducted over the past 25 years. For example, the most recent 2014 household survey ( _Encuesta Nacional de Condiciones de Vida_ , ENCOVI) covered

**3**
the whole script). The respondents were asked whether they wanted to change their ideal bequest allocation. I find that about 10 percent of respondents did so, leading to 52 percent of women and 40 percent of men favoring a gender equal bequest in this scenario. The share of respondents who change their preferences when the daughter is in need is not negligible but not large enough to suggest that economic need is a guiding principle behind respondents’ preferences.

Exchange expectations and equity considerations constitute another, not necessarily competing explanation: sons may be expected to take care of their parents when in old age and it may therefore be fair to compensate them with a higher inheritance share. In a longitudinal study of US women, Light and McGarry (2004) find that exchange is a common explanation for respondents to favor unequal bequest allocations among their children. Similarly, Tisch and Gutfleisch (2022) show that German respondents in an online experiment allocate a significantly larger gift to a child who helps their parents. Inter-generational exchange and equity considerations are not an obvious explanation for respondents’ preference for gender discriminatory law in the Tunisian context, though: 98 percent of respondents consider that a son has the duty to financially support his parents when they get old. The percentage of respondents who believe that a daughter has the duty to financially support her parents when they get old is 93 percent - almost the same.

Table 5 points out some gender differences with respect to these attitudes. Women are significantly more likely to believe that legal reform is necessary than men (24 versus 17 percent). Gender differences in family norms can motivate this finding: women are significantly less likely to adhere to the social norm that a man is responsible of his female family members. They are just as likely to expect a son to financially take in charge his elderly parents as men, but they are significantly more likely to expect daughters to do the same. Gender differences in intergenerational equity concerns may thus partially explain why women are more favorable towards reforming the law than men. Yet, even among women, a majority rejects legal reform and considers it unnecessary.

# **4.2.2 Gifting as a Private Second-Best Alternative?**

From the evidence presented so far, it is clear that the majority of Tunisians oppose a national reform introducing gender equality in inheritance. One of the most commonly held beliefs is that reform is simply not necessary. Considering this finding, I turn to the following 27
interested in the year of recovery, the academic year 2000/01. Since the 1999 violence occurred mainly in the summer and fall of 1999, we can assume with a certain degree of confidence that the academic year of 1998/99 was not affected by conflict, while the academic year of 1999/00 started under the wave of violence. Almost all schools were destroyed during the violence in 1999 and most teachers escaped to West Timor during the 1999/00 academic year. Therefore, very few children were able to attend school that year. The very few that attended school were in makeshift schools in IDP camps or in the open air. We report in table 1 average school attendance rates, disaggregated by gender and age groups, across these three academic years. The sample includes children aged between 7 and 12 years old over the three years considered: the sample is aged 7-10 in 1998, 8-11 in 1999 (during the violence) and 9-12 in 2000.

In general, we notice that attendance rates have increased over time and are generally higher for girls. The difference in outcomes between boys and girls is not statistically significant, but is nonetheless an interesting result. The higher attendance rates observed among girls in 1998/99 show evidence for a gender catching up phenomenon, which started before 1999. In the post-violence year, the difference in attendance rates between girls and boys becomes statistically significant, particularly for the younger cohort. This seems to suggest that educational outcomes among girls recovered faster in the post-conflict period.

In addition to the analysis of school attendance, we explore also the impact of the 1999 violence on school grade deficit. The grade deficit is computed as follows: The TLSS 2001 does not allow us to distinguish whether the grade deficit is due to drop outs, delayed entry or grade repetition. We restrict our sample to only those children that attend all three years consecutively in order to isolate the consequences of the violence on the sample of individuals that do not drop out.

The attendance rate over the three years for all children aged 8-11 in 1999 is on average around 72 percent (table 1). The attendance rate for those that attended all three years is lower. Table 2 shows the average grade deficit over the three years. Two main patterns are visible. First, the average grade deficit – which was already significant in 1998/1999 – increased during the conflict. Second, young girls tend to have a higher grade deficit than young boys while old girls show a lower grade deficit than older boys. The differences are however not statistically significant.

[Table 2 about here] 14
15 (1.11) (1.87) + O.54*(log(yt)-log(yt-l)) (5.22) R<sup>2</sup> -0.67, DW-1.74, 1966-1987 A small negative real wage elasticity, significant at a ten percent level, and an income elasticity substantially below one. Next year's capital stock depends on this year's and on total fixed capital formation:

-

- (6) Kt+1 (1-0.04)*Kt + (Iprt+IG.t)

The assumption of a four percent depreciation rate is based on SPP data with some correction for the likely understatement of true depreciation in the national accounts in inflationary periods due to historical cost accounting practices.

The intertemporal part of the model is rounded out by the current account identity:

- (7) CA yt + Xojl*Poj2.,S*E/PGNP - i*Bf,SE/PNP - Cpr,t - ipr,t . - t

- B.2 Allocation of Expenditure and the Real Exchange Rate

The part of the model presented in the previous section focused mostly on intertemporal trade. It thus dealt with the allocation of current production and expenditure over the present and future periods, rather than over domestic and foreign goods. In line with that focus, the most important relative price variable was the real interest rate. In this section, the focus is intratemporal trade, allocation of expenditure over current Mexican versus current future goods, and the relevant relative price variable is the real exchange rate.

Consider first the allocation of domestic expenditure over foreign and domestic goods. We do this by explicitly estimating import demand equations for the different import categories. The volume of capital goods imported, ecap, depends on the relative price of capital goods imports versus investment goods in general ( the GNP deflator for investment) and on aggregate investment: (7) log(Mcap) 14.11 - l.l2*log(P.caP SE/PINV) - 0.74*1og(Pc.p,$( 1)E( 1)/PINV( )) (5.93) (5.63) (2.72) + 0.7l*log(Ipr+Ig) - 0.88*MQUSH(-l) (5.77) (4.52) R<sup>2</sup> _0O.94, DW-1.40, 1970-1987 The variable MQUSH is the fraction of imports covered by QRs; it is included in an admittedly crude attempt to capture to many changes in trade regime that have taken place in Mexico over the sample period. The results
**Figure 10. Percent input cost increases by economy and sector from the US$25/ton CO2e carbon tax, 2030**

<!-- Start of picture text --> 14 10a. South Asia 40<br>12 35<br>30<br>10<br>25<br>Input cost increase (percent) (LHS) 8<br>20<br>6<br>15<br>4<br>10<br>Sector's share in total output (percent) (RHS) 2 5<br>0 0<br>14 10b. Afghanistan 40 14 10c. Bangladesh 40<br>12 35 12 35<br>10 30 10 30<br>25 25<br>8 8<br>20 20<br>6 6<br>15 15<br>4 10 4 10<br>2 5 2 5<br>0 0 0 0<br>14 10d. Bhutan 40 14 10e. India 40<br>12 35 12 35<br>10 30 10 30<br>25<br>25 8<br>8 20<br>20 6<br>6 15<br>15 4<br>4 10<br>10<br>2 5<br>2 5<br>0 0<br>0 0<br>14 10f. Maldives 40 14 10g. Nepal 40<br>12 35 12 35<br>10 30 10 30<br>25 25<br>8 8<br>20 20<br>6 15 6 15<br>4 10 4 10<br>2 5 2 5<br>0 0<br>0 0<br><!-- End of picture text --> 21
McDonald, B. (2006) _From Compensation to Development: Involuntary Resettlement in the People’s Republic of China_ , University of Melbourne, PhD thesis.

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Meehl, G., Stocker, T., Collins, W., Friedlingstein, P., Gaye, A., Gregory, J., Kitoh, A., Knutti, R., Murphy, J., Noda, A., Raper, S., Watterson, I., Weaver, A., and Zhao, Z.C. (2007) Global climate projections _._ In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.), _Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change_ , Cambridge University Press, Cambridge and New York: 747-846.

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Migration DRC (2007) _Global Migrant Origin Database_ (Updated March 2007). Development Research Centre on Migration, Globalisation and Poverty, viewed 28 November 2008, <http://www.migrationdrc.org/research> Mortreux, C. and Barnett, J. 2009. Climate Change, Migration and Adaptation in Funafuti, Tuvalu. _Global Environmental Change_ : in press.

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Viewed 10 December 2008, <http://www.timesonline.co.uk/tol/news/world/article759319.ece> 58
if the stock market liberalization occurs before or during the lifetime of the spell.

In Subsection 5.3 and Online Appendix D, we also use alternative measures of stock market liberalization. First, we construct a dummy variable based on the dates related to the first signs of liberalization that can sometimes precede the official liberalization dates. The source of these alternative dates for stock market liberalization events is Bekaert, Harvey, and Lundblad (2005). Second, we introduce two versions of a continuous variable capturing the stock market liberalization intensity at the industry-country level. Both versions are based on the proportion of domestic equities the foreign investors can acquire. We compute them from the data on the market capitalization of firms included in the IFC Investable index and IFC Global index available in the Emerging Stock Market Factbooks of International Finance Corporation.

As for other financial variables, our proxies for the level of domestic banking and stock market development in the liberalizing countries are the ratio of private credit to GDP and the ratio of stock market capitalization to GDP, respectively. Both variables are from the database by Beck, Demirg ̈u ̧c–Kunt, and Levine (2000). The industry measure of dependence on external finance at the ISIC 3-digit level is from Braun (2003). This measure goes back to the seminal paper by Rajan and Zingales (1998) and captures the share of capital expenditures that cannot be financed by internally generated funds. It is computed as ratio of the capital expenditures minus cash flow from operations to the capital expenditures for the median publicly listed company in a given US industry, using the financial data on US firms from Compustat.<sup>11</sup>

# **3.2 Distance to comparative advantage**

Distance to comparative advantage captures how far is a given product from the comparative advantage of a given country. Building upon the notion of “product chain of comparative advantage” within the Heckscher-Ohlin theoretical framework (see Online Appendix A2 as well as Jones 1956-57; Bhagwati 1972; Deardorff 1979) and previous

> 11 The idea of using data on publicly listed US companies to compute a generic industrial measure of dependence on external finance relies on the argument that these companies face relatively frictionless financial markets. Consequently, the need for external finance of a median US public company captures the true technologically determined need for external finance at the industry level that is unaffected by various financial and contractual frictions facing smaller firms or firms in less financially developed countries. See Rajan and Zingales (1998) and Braun (2003) for a more detailed discussion.

8