rafmacalaba/datause-encoder-data huggingface dataset:
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30
**Table 1: Strengths and Limitations of Mobile Data Collection Client Options**

|Data Collection<br>Client / Interface|Data<br>Transfer<br>Method|Strengths|Limitations|
|---|---|---|---|
|Call Center|Voice|**-**Simple voice call (users will be<br>familiar with the interaction)<br>- Operators can troubleshoot during<br>the call, e.g. clarify questions, correct<br>misinterpretations)<br>- Opportunity to build relationship<br>with respondents over time<br>- Good for setting where literacy is<br>low|**-**Requires additional management<br>of call-center operations<br>- Requires a reliable network signal|
|Interactive Voice<br>Response (IVR)|Voice/<br>Keypad|- Simulates a voice call<br>- Good for settings where literacy is<br>low|- Cumbersome nature of keypad<br>based menu navigation<br>- Requires a reliable network signal|
|SMS|SMS|- Works on any GSM mobile phone<br>(Does not require installation)|- Requires a network signal<br>- Requires that users have a cue-card<br>to guide text input (changes to<br>questions on an ongoing basis may<br>be difficult)<br>- Possibility of lost or dropped<br>messages<br>- Limited to 160 characters of data<br>- Relatively high cost per byte of<br>data sent compared to GPRS<br>- Data collection efforts with high<br>expected data volumes will require a<br>commercial SMS gateway provider<br>(entailingadditional costs)|
|USSD|USSD|- Works on any GSM mobile phone<br>(Does not require installation)<br>- Widely used in East Africa for mobile<br>transactions<br>- Real time communication between<br>user and application|- Requires partnership with mobile<br>service operators<br>- Requires a consistent network<br>signal<br>- Relatively high cost compared to<br>SMS and GPRS|
|Electronic Forms<br>(Java or other<br>application)|GPRS|- Automated data upload from phone<br>to server<br>- Real-time visualization and<br>monitoring of results<br>- Branching, skip logic and enforced<br>validation at time of data entry<br>- Asynchronous: As data can be saved<br>to the phone, can be used in areas<br>without mobile coverage (data is sent<br>to server once in range of a signal)|- Requires smartphone and<br>additional installation of software|

4
8 country’s exports in a particular year.<sup>5</sup> We estimate the regressions with standard errors clustered across cross-section (White cross-section covariance method).

Using this framework, we run regressions on 46 countries for the period 1996-2012. The choice of the period of analysis and sample of countries is determined by the availability of data on trade in value added from the OECD-WTO TiVA database used to undertake the main analysis in Section III. Unless otherwise stated, the data sources and country coverage are given in the Appendix.

# **B.   The Decline in the REER Elasticity of Manufacturing Exports**

Table 1 presents our results on the REER elasticity of exports in our sample. As the dependent variable, we use growth of (real) total exports in goods and services in Panel A, growth of exports in goods in Panel B, and growth of manufacturing exports in Panel C.<sup>6</sup> We find that the REER elasticity decreased over time for total, merchandise and manufacturing real exports. Panel A shows that the elasticity of total exports has declined by nearly half, from -1.42 for the period 1996-2003 to -0.70 for the period 2004-2012. Panels B and C also report a decreasing pattern using growth of real exports of goods and manufacturing sectors. The elasticities are estimated to be -1.38 and -1.29 in the period 19962003 and -0.63 and -0.62 in the period 2004-2012 for goods and manufacturing exports, respectively.<sup>7</sup> Consistent with findings in the literature (Eichengreen and Gupta, 2013), total exports tend to display higher REER elasticity as the real exchange rate impacts services exports more powerfully than

> 5 In this specification, the country specific average growth rates should be captured by the country fixed effect. While not reported here, we also find that results are robust to alternative sets of controls, such as GDP in nominal US$ terms and GDP per capita.

> 6 We follow the literature here and focus first on gross exports. We turn to the impact of exchange rate changes on value-added exports in Table 4.

> 7 However, in additional regressions not reported here, we find weaker results using total export volumes from World Economic Outlook (IMF) database. Specifically, the point estimates of the elasticity are smaller and the decreasing pattern is less steep than in Table 1. As common to this literature, results also tend to be sensitive to the methodology through which real export series are obtained from nominal values. We come back to this point in Section III.B.
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Galal & Nauriyal 20 On the one hand, the Philippines's telecom sector reportedly makes the highest rate of return, which is consistent with the notion that the country did not successfully resolve the information, pricing or the commitment problems. On the other hand, the sector made the lowest rates of return in Argentina and Chile. The Chilean case is easier to explain because it is the country which we judged to have reasonably resolved the three regulatory problems. In Argentina, the explanation may reside with the existence of two suppliers of the service, which may have enabled the regulators to extract more information.

**Table 7. Returns on net worth before and after reform, (percent annual averages)**

|Country|Pre-reform|Period|Post-reform|Period|
|---|---|---|---|---|
|Argentina|-5.7|1985-88|7.7|1991-93|
|Chile|6.7|1983-86|13.8|1987-91|
|Jamaica|13.5|1982-87|20.5|1988-91|
|Malaysia|--|1982-86|14.0|1987-92|
|Mexico|9.5|1982-89|22.4b|1990-93|
|Philippines|15.5|1980-85|25.7|1986-91|
|Venezuela|-10.9|1986-89|21.7|1991-93|

-- information not available.

a. Profit before taxes over net worth for 1990 only.

b. Estimate based on World Bank projections of revenues and expenses for TELMEX.

Sources: Hill and Abdala (1994); Galal (1994); Spiller and Sampson (1993); World Bank (1993, 1990); Wellenius et. al (1994); Esfahani (1994); Clemente (1994).

From the perspective of the consumers, they undoubtedly benefitted from expansion (especially in Chile, Argentina, Jamaica, Mexico and Venezuela), as compared with countries where the rate of expansion fell (the Philippines and Malaysia). Not surprisingly, pending demand -- the ratio of applications for phone service to phones installed -- declined in Argentina and Mexico and increased in the Philippines (table 8). But pending demand also increased in Chile, Jamaica, and Venezuela, despite rapid growth in the number of phone lines; this is probably because, as the prospect of actually obtaining a phone improves, more people apply, so that the ratio of applicants to phones initially surges. Thus, the two countries with the highest pending demand (the Philippines with 79 percent, and Jamaica with 81 percent) have very different stories to tell: slow system growth in the Philippines; rapid growth in Jamaica.
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# **Disability indicators**

In order to determine prevalence or identify a specific ‘functional difficulty status’ group, a threshold needs to be set on the answer scale of functional difficulties. The WG recommends “a lot of difficulty” as the threshold: persons who report “a lot of difficulty” or “unable to do” for at least one domain are considered to have a disability, and persons with ‘no difficulty’ or ‘some difficulty’ to all six questions are deemed as not having a disability.

Different thresholds for the WGSS produce vastly different prevalence estimates (e.g. Bourke et al 2020; Mitra 2018). In addition, if persons with some difficulty are potentially more disadvantaged than persons with no difficulty, this categorization will underestimate the extent of inequalities between persons with and without disability. There is mounting evidence that having ‘some difficulty’ is significantly associated with economic and social deprivations with a gradient from no difficulty to some difficulty to at least a lot of difficulty (Clausen and Barrantes 2020; Mitra 2018). Besides, several censuses under study use yes/no answers to the functional difficulty questions, making the breakdown recommended by the WG impossible. Hence, this study categorizes individuals in three ways. First, for all data sets, it groups individuals into two categories:

- (1) No functional difficulty in all domains;

- (2) Any functional difficulty in at least one domain (answer Yes for data sets with yes/no answers, or reports at least ‘some’ difficulty for graded scales).

Second, following the recommendation of the WG for data sets that have a graded answer scale, we group individuals as follows:

- (i) A lot of difficulty or unable to do in at least one domain;

- (ii) No difficulty or ‘some’ difficulty for all domains

Third, for data sets with graded scales, we partition individuals into three categories:

- (a) No difficulty for all domains;

- (b) Some difficulty in at least one domain but no “a lot of difficulty” or “unable to do”.

- (c) A lot of difficulty or unable to do in at least one domain.

This categorization is more granular and may be able to identify a possible gradient in socioeconomic disadvantage. The second and third categorizations above are both conducted for the subset of 19 data sets that do not have a yes/no answer scale. We use these three categorizations in our analysis and are presented in the tables as panels 1, 2, and 3 respectively. The analysis conducted at the household level categorizes households depending on the functional difficulty status of its members age 15 and older along the three ways of partitioning the population described above.

We use basic proportions to calculate the prevalence of functional difficulties in each country based on the first categorization above for all data sets and based on the second and third categorization for countries with an answer scale. Given that the primary objective of this paper is to make comparisons of development indicators across disability status within a country and not across countries, we do not present age and sex standardized disability prevalence for each 8
scarcity of labor and other resources can be predicted to transfer production from the coastal provinces to the poorer interior provinces. These processes may have already begun; the NBS household surveys show that since 2009, rural household income per capita has grown faster than its urban counterpart and that overall provincial income inequality appears to have leveled off and even slightly declined since 2005 (Fan et al. 2011). There are signs that Chinese society is becoming more sophisticated and better informed and that people’s aspirations are rising. In this situation, the government may selectively introduce stronger policies to diminish various dimensions of inequality as a protection against social disorder or instability.

# <<A>>Concluding Comments

It was inevitable that income inequality would increase significantly as China moved from a centrally planned economy, in which egalitarianism was a cornerstone, to a market-based economy. Material incentives were needed to induce greater effort, saving, investment in physical and human capital, and entrepreneurship. Similarly, economic efficiency was likely to be enhanced by disequalizing processes of cumulative causation. Nevertheless, some of the increase in income inequality was difficult to justify in terms of either efficiency or equity. Much of this unjustified inequality stemmed from the complex institutional framework within which China’s semimarketized economy operated.

26
individuals.

# _III.4 Substitution across goods and inflation inequality_

The other well-known shortcoming of the standard CPI stems from its use of a Laspeyres framework of fixed base-year weights, which amounts to ignoring substitution across goods and hence leads to overstating inflation. This makes the CPI an upper bound for a true cost-of-living index; see e.g., Diewert (1998).

In this regard, the Boskin report identified two types of substitution bias. The first, estimated to raise measured inflation by 0.25 percentage point annually, is _lower-level_ substitution bias and occurs when consumers substitute between similar items within a category (e.g., substituting between pippin and gala apples). The second type, estimated to boost inflation by 0.15 percentage point annually, is called _upper-level_ substitution bias and occurs when consumers substitute between items from different categories (computers for television sets, for example) in response to price changes.

From the perspective of inflation inequality, the overstatement of inflation implied by the substitution bias is a concern if it differs between rich and poor households. This is not entirely implausible; for example, if the rich enjoy broader upper-level (i.e., inter-category) substitution possibilities than the poor (because their consumption basket includes a lower share of hard-toreplace items such as food, say) then the CPI would exaggerate the inflation rate faced by the rich more than that faced by the poor. In a similar fashion, lower-level (i.e., intra-category) substitution could broaden the gap between inflation of poor and rich because the latter might have access to a wider variety of within-category substitutes. Under these conditions, our finding above of anti-rich inflation could be partly an artifact of the use of a Laspeyres index.

An indirect way to assess this issue is to compute a Paasche index, whose weights are given by the current consumption basket, rather than that of the initial period, thereby capturing the effects of substitution across goods on households’ consumption patterns. Following the notation in (1) and (2), we have: However, the Paasche index raises the opposite problem of that posed by the Laspeyres index, namely that it tends to understate inflation by overstating the degree of substitution. The ideal Fisher index, given by the geometric average of the Laspeyres and Paasche indices, has been shown to provide a much better alternative, and is in fact viewed by many as the best practical approximation of a true cost-of-living index (see Diewert 1998).

9
# **Graph (13a): Scaled Residuals - 3 Lags - Open Economy VAR**

<!-- Start of picture text --> 0.2 r rm2 r rgdp<br>0.1<br>0.0 0.0<br>-0.1<br>-0.2<br>1980 1990 2000 2010 1980 1990 2000 2010<br>10<br>r INFL r Interestrt<br>2<br>0 0<br>-2<br>1980 1990 2000 2010 1980 1990 2000 2010<br>0.5<br>r DISC r Dexrtend<br>2.5<br>0.0 0.0<br>-2.5<br>1980 1990 2000 2010 1980 1990 2000 2010<br><!-- End of picture text -->

# **Graph (13b): Long Run Stability Test of - 3 Lags - Open Economy VAR**

<!-- Start of picture text -->
1.00 Roots of companion matrix<br>0.75<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>-0.75<br>-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0<br><!-- End of picture text -->

75
(b) an indirect effect reflecting greater investment in physical capital that is induced by having more productive workers.

Under standard assumptions from the macro development accounting literature (that are detailed in Appendix A5), projected future per capita GDP will be approximately 1/x times higher in the “complete education and full health” scenario than in the “status quo” scenario for a country where the value of the HCI is x . For example, a country such as Morocco with an HCI value of 0.5 could in the long run have future GDP per capita in this scenario of complete education and full health that is approximately 1/0.5 or two times higher than in the status quo scenario. What this means in terms of average annual growth rates of course depends on how “long” the long run is. For example, under the assumption it takes 50 years for these scenarios to materialize, then a doubling of future per capita income relative to the status quo corresponds to roughly 1.4 percentage points of additional growth per year.

# **6.  Conclusions and Caveats**

Like all cross‐country benchmarking exercises, the HCI has limitations. Components of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Data on test scores come from different international testing programs that need to be converted into common units, and the age of test takers and the subjects covered vary across testing programs. Moreover, test scores may not accurately reflect the quality of the whole education system in a country, to the extent that tests‐takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly‐changing world. Data on enrollment rates needed to estimate expected school years often have many gaps and are reported with significant lags. Socio‐emotional skills are not explicitly captured. Child and adult survival rates are imprecisely estimated in countries where vital registries are incomplete or non‐existent.

One objective of the HCI is to call attention to these data shortcomings, and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers, and not a finely‐graduated measurement of small differences between countries. Naturally, since the HCI captures outcomes, it is not a checklist of policy actions, and right type and scale of interventions to build human capital will be different in different 8
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> *The figures for MEP, ION?, &nd ****Cban.e rate** are perCentage deviation from the baseine. *The **figures** for sbort-tern interest rate &re **change** in the percentage imil.