rafmacalaba/datause-encoder-data huggingface dataset:
supported the private sector and promoted a more open economy, with some efforts at restructuring the public sector.<sup>32</sup> Significant steps were taken in trade and industrial policy, though (unlike China) agriculture has been neglected.<sup>33</sup> The evidence from India’s National Sample Surveys suggests that economic growth has been poverty reducing, including in the reform period. However, a number of factors appear to have dampened the impact on poverty. The rise in inequality is one factor, as noted by a number of observers.<sup>34</sup> Underlying this rise in inequality—and dulling the impact of growth on poverty— one finds signs of geographic and sectoral divergence in India’s growth process (Datt and Ravallion, 2002, 2009).<sup>35</sup> One aspect of this is the urban-rural composition of growth. As in China (and most developing countries) absolute poverty measures are higher in the rural sector, though the urban-rural gap is not as large as found in China. (The ratio of mean consumption in urban areas of India to rural areas is about 1.3, which is about half the ratio of mean income in urban China to that in rural China; see Ravallion and Chen, 2007, and Datt and Ravallion, 2009.) India has also seen divergence over time between urban mean consumption and the rural mean, which has contributed to rising overall inequality. Additionally, inequality has risen within both urban and rural areas since the early 1990s (Datt and Ravallion, 2009).<sup>36</sup> Like China, past research has pointed to the importance of rural economic growth to national poverty reduction in India, although there are signs that the process of economic growth is changing, making urban economic growth more pro-poor (Datt and Ravallion, 2009). There is evidence of a much stronger linkage from urban economic growth to rural poverty reduction in the early 1990s. While the attribution to economic reforms cannot be proven, these findings are at least consistent with the view that the reforms have fostered a process of growth in India’s urban economy that has brought larger benefits to the rural poor.

> 32 For an overview of the reforms see Ahluwalia (2002).

> 33 Policy reforms in other areas (lower industrial protection and exchange rate depreciation) have brought indirect benefits to agriculture, notably through improved terms of trade, higher demand for farm products through the urban income effect, and some growth in agricultural exports. However, at the same time, the reform period saw a decline in public investment in key areas for agriculture, notably rural infrastructure.

> 34 See Ravallion (2000), Deaton and Dreze (2002) and Sen and Hiamnshu (2004).

> 35 This appears to proceed the reforms period starting in 1991. Bandyopadhyay (2004) finds evidence of “twin peaks” in India’s growth process over 1965-97, whereby the divergence is between two “convergence clubs”, one with low income (50% of the national mean) and one with high income (125%).

> 36 This is only true within urban areas if one corrects for changes in survey design, as discussed in Datt and Ravallion (2009).

18
# 11. **References**

Andres, L. A., Briceno, B., Chase, C., & Echenique, J. A. (2014). _Sanitation and externalities: evidence from early childhood health in rural India_ (No. 6737). The World Bank.

Arnold BF, Null C, Luby SP, _et al_ . (2013) Cluster-randomized controlled trials of individual and combined water, sanitation, hygiene and nutritional interventions in rural Bangladesh and Kenya: the WASH Benefits study design and rationale. BMJ Open 2013; 3(8):e003476. doi:10.1136/bmjopen-2013-003476 Baqui AH, Black RE, Yunus M, Hoque AR, Chowdhury HR, _et al_ . (1991) Methodological issues in diarrhoeal diseases epidemiology: definition of diarrhoeal episodes. Int J Epidemiol 20: 1057– 1063.

Bartram J, Cairncross S (2010). Hygiene, sanitation and water: Forgotten foundation of heatlh. PLoS Med 7(11): e1000367. Doi:10.371/journal.pmed.1000367 Biran, A., Schmidt, W. P., Varadharajan, K. S., Rajaraman, D., Kumar, R., Greenland, K., Gopalan, B., Aunger, R. & Curtis, V. (2014). Effect of a behaviour-change intervention on handwashing with soap in India (SuperAmma): a cluster-randomised trial. The Lancet Global Health, 2(3), e145-e154.

Bruhn, M., & McKenzie, D. (2009). In pursuit of balance: Randomization in practice in development field experiments. American economic journal: applied economics, 200-232.

Cairncross, S., J. Bartram, O. Cumming, and C. Brocklehurst (2010). “Hygiene, sanitation, and water: what needs to be done?,” PLoS Med Nov 16;7(11).

Cameron, L., Shah, M., & Olivia, S. (2013). Impact evaluation of a large-scale rural sanitation project in Indonesia.

Clasen, T., Boisson, S., Routray, P., Torondel, B., Bell, M., Cumming, O., Ensink, J., Freeman, M., Jenkins, M., Odagiri, M., Ray, S., Sinha, A., Suar, M. & Schmidt, W.P. (2014). Effectiveness of a rural sanitation programme on diarrhoea, soil-transmitted helminth infection, and child malnutrition in Odisha, India: a cluster-randomised trial. The Lancet Global Health, 2(11), e645-e653.

Clasen TF, Bostoen K, Schmidt WP, Boisson S, Fung ICH, Jenkins MW, Scott B, Sugden S, Cairncross S. (2010) Interventions to improve disposal of human excreta for preventing diarrhoea. Cochrane Database of Systematic Reviews 2010, Issue 6. Art. No.: CD007180. DOI: 10.1002/14651858.CD007180.pub2.

Clasen T, Schmidt WP, Rabie T, Roberts I and Cairncross S (2007) Interventions to improve water quality for preventing diarrhoea: systematic review and meta-analysis. BMJ 334(7597): 782.

Coombes, Y and Paynter, N (2010). Scaling Up Handwashing Tanzania: A Handwashing Behavior Change Journey. The World Bank Water and Sanitation Program, Washington, D.C.

Coville, A., & Orozco, V. (2014). Moving from efficacy to effectiveness: using behavioural economics to improve the impact of WASH interventions.Waterlines, 33(1), 26-34.

32
- 37 -

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- Stedinger, Jery, Richard Vogel, Efi Foufoula0Georgiou. 1993. Frequency analysis of extreme events, in _Handbook of Hydrology_ , eds. by David Maidment, pp. 18.1-18.66, McGraw-Hill, Inc., New York.
# Figure 11 Japan MCY, **M1Y,** QMY and DCPY overtime

<!-- Start of picture text -->
1.4<br>1.2<br>EL<br>0.6<br>@><br>1-952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989<br><!-- End of picture text -->

**year**

<!-- Start of picture text -->
| 0. MCY -- - -Q..MY --- -^--...-<br><!-- End of picture text -->

DCPY
<!-- Start of picture text --> All other firms ROIC Stars<br>ol LO,<br>g<br>ray<br>2g<br>=<br>a<br>0 1 2 3 0 1 z 3<br>OPEXMKUP PT_14...<br>Histogram of OPEX Markups<br>———_ Kernel weighted local polynomial plot of ROIC on OPEX Markups<br><!-- End of picture text -->
As noted earlier, this rule is certainty equivalent: the same interest rate rule would be optimal in a world with no uncertainty about aggregate demand shocks. But suppose now that the central bank does not know for sure the values of 'Yl and **72;** all that is known is that these parameters are drawn from independent, normal distributions with means 71 and **Y2** and variances _oa_ and a **2,** respectively. In this case, it can be shown (see Martin (1999)) that the optimal instrument rule becomes This equation shows that as uncertainty about the parameters in the transmission process of policy shocks increases (that is, as a **2** rises), the optimal instrument response to movements in current inflation becomes smaller.<sup>5 5</sup> The fundamental reason for this result is the following. As shown earlier (equation (8)) the per-period loss function can be decomposed into the sum of the squared expected deviation of each variable from its target (or the squared bias), and the conditional variance of that variable. With additive uncertainty, the variance is independent of the policy rule, and so policy decisions aim only at minimizing expected deviations in inflation. By contrast, under uncertainty about the parameters of the model, the variance of (future) inflation depends on the level of nominal interest rates; in particular, large movements in the policy instrument in response to deviations between actual and targeted inflation tend to reduce _bias,_ as implied by the first term in (8)-at the cost, however, of increasing the _variance_ of inflation, the second term in (8). The central bank will therefore internalize this effect by choosing a lower optimal level of interest rates.<sup>5 6</sup> A similar result would hold in a more general, linear-quadratic setting in which the central bank pursues several policy objectives simultaneously.

By and large, model-based simulation studies, for instance by Ha (2000), Martin and Salmon (1999), and Sack (2000), have confirmed the practical importance of the Brainard effect. The optimal interest rate rule calls for more gradual adjustment in the presence of parameter uncertainty. In particular, Ha (2000) examined, using a numerical model for New Zealand, the impact of uncertainty about the transmission lag of monetary policy (that

> 5 5 Note that as **U2** tends to zero, (31) becomes identical to (30).

56It is important to note, however, that Brainard himself qualified this result by showing that it does not necessarily hold when the _covariance_ between parameters (the policy multiplier, in his example) and the additive disturbance is sufficiently negative.

57
**The result of this paper is a synthetic dataset that contains all variables in the EU-SILC plus energy spending shares and an energy poverty indicator, which we can ultimately use to overlap energy with monetary poverty, or to describe the energy poor population in Bulgaria.** The resulting dataset from our data fusion exercise is a synthetic version of the EU-SILC, which has information on incomerelated variables I, households' access to social protection programs, imputed expenditure-related information C (the energy spending share and energy poverty indicator), a set of overlapping variables, and all other variables included in the EU-SILC. In the last step, we use the generated dataset to analyze if the energy and monetary-poor overlap. We also explore energy spending shares by income quintiles. Future research can use the generated dataset to conduct detailed studies about the energy poor population in Bulgaria.<sup>14</sup>

# Data Description and Preparation

Data and Target Variable Description

**In this paper, we use two different data sources: the 2019 HBS and the 2020 EU-SILC with reference income from 2019**<sup>**15**</sup> . We focus on the pre-COVID year (2019) because we want to make sure that our data is representative of spending patterns in Bulgaria and not affected by idiosyncratic crisis-related behavior induced by the COVID-19 pandemic.<sup>16</sup> For this reason, we rely on the HBS from 2019. The HBS is a survey conducted every year and collects data on expenditure patterns by households in Bulgaria. The survey consists of 2,952 households and is a quarterly survey, meaning that the information is representative of each quarter of the year.

**The HBS relies on a two-stage random sample design of households and uses expenditure diaries.** In the first stage of the sampling design, census enumeration areas are selected randomly, while in the

14 For a first study of this type, see Robayo-Abril and Rude (forthcoming b).

> 15 In EU-SILC, <mark>the reference period of income refers to</mark> the calendar year before the year in which the survey took place.

16 Examples could be increased expenditure on medical expenses, for example, or decreased expenditure on restaurant visits given the strict stay-at-home policies.

14
The Mexican Pension Annuity Market custody and investment administration, inflation and duration matching rules, and credit risk limits.

**Admitted assets** ⎯ According to the RIRT, assets admitted in representation of liabilities must belong to the following list (CNSF 2006a):

- Securities registered in the SIC of the Mexican Stock Exchange.

- Investments in equity and fixed income mutual funds with portfolios that include foreign securities issued by Central Banks or any other level of government by countries of the European Union, or countries belonging to the Technical Committee of the International Organization of Securities Commissions (IOSCO), as well as securities that are registered, authorized or regulated for general public sale by the Banking and Securities National Commission (CNBV) or the equivalent organizations of the above mentioned countries.

- Securities denominated in national currency issued by international financial institutions in which Mexico is a member.

- Private equity investment funds in Equity Investment Societies (Sociedades de Inversión de Capitales, SINCAS), as well as in trusts whose purpose is to capitalize firms.

It is important to notice that investment rules also include the following requirements:

**Investment limits** ⎯ Investment limits comprise both limits, by type of instrument and by issuer, as summarized in the next two tables.

**<u>Table 9: Investment Limits by Type of Security for Technical Provisions</u>**

|**Investment type**|**Limit (in percent)**|
|---|---|
|(1) Papers issued or backed by the federal government|100|
|(2) Papers issued or backed by credit institutions or international<br>financial bodies|60|
|(3) Papers issued by SINCAS|1|
|(4) Papers issued or backed by institutions different from those in (1),<br>(2), and (3)|30|
|(5) Papers issued or backed by institutions different from those in (1),|5|
|(2), and (3) exchange at the Mexican Stock Exchange||
|(6) Discount and rediscount operations|5|
|(7) Credit with pledge bonds or papers guarantee|5|
|(8) Credit with mortgage guarantee|5|
|(9) Real state properties|25|
|(10) Repurchasing|30|
|(11)Foreigndebt papers|2|

**Source** : RIRT and CNSF (2006b).

- 15 -
# **_7.  Empirical Results_**

The time series properties of the data as well as different diagnostic tests are conducted. The series are tested for stationarity using the ADF, order of integration, co-integration between the variables, significance of the variables, and weak exogeneity were also undertaken for a closed and open economies formulations. (Full details on the econometric procedures are available in Annex VIII.)

## **_Long-run Modeling—Closed Economy Formulation_**

The estimation process is then followed by a set of tests for weak exogeneity and stationarity. Table (7d & e) shows the results of various hypothesis tests for statistical significance of beta coefficients (elasticities) in addition to the weak exogeneity tests of alphas.

The test statistics show that betas for the constant and inflation—individually and jointly—are not statistically different from zero at a 5 percent significance level. This indicates that the inflation rate does not enter the long run equation for real broad money balances.

However, when the joint statistical significance for betas of constant and inflation is tested together with the alpha coefficient for real money, the test fails to reject that these three coefficients are jointly different from zero at a 5 percent significance level with χ2 (2) = 10.602. This provides ample evidence that the model converges towards equilibrium.

The individual tests for betas of all other variables reject the null hypothesis that they are excluded from the long run—co-integrating—relationship at a 5 percent significance level for real income and a 1 percent significance level for the interest rate and the discount rate.

The joint test for statistical significance of betas strongly rejects the null hypothesis at all significance levels as well giving χ2 (5) = 32.104. Therefore, the three variables (real income, opportunity cost, and real consumption) have a long run relationship to real money demand in Egypt.

Further tests of statistical significance of alphas were conducted to determine the feedback effects of the co-integrating relationship. The money demand own alpha was negative and statistically significant at a 5 percent level, hence a long run relation can be identified.

24
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