rafmacalaba/datause-encoder-data huggingface dataset:
# **The Co-movement of Asset Returns and the Micro-Macro**

# **Focus of Prudential Oversight**

Giovanni Majnoni<sup>1</sup> The World Bank JEL E32, E44, E58, F30, G01, G30

> 1 All opinions expressed in this paper are of the exclusive responsibility of the author and do not compromise the World Bank or its Board of Directors. Comments by Giovanni Barone-Adesi, Stijn Claessens, Mauro Grande, Luc Laeven and Alessandro Rebucci on a previous version of the paper are gratefully acknowledged. The author is responsible for any remaining errors. Contact: Giovanni Majnoni: <u>giovanni.majnoni@bancaditalia.it</u>
# **Table 3:  Correlation of Insurance Payouts with Aggregate Variables**

|Variablename||macro|economi|c variable|s|stock<br>returns|
|---|---|---|---|---|---|---|
|_GDP growth ( percent change, real_|-4.19*||||-5.41**||
|_GDP per capita)_|(2.21)||||(2.54)||
|_Inflation ( percent change, GDP_||0.26|||-1.65||
|_deflator)_||(1.02)|||(1.16)||
|_Change in Treasury bond yield (1-5_|||0.24||-0.02||
|_year maturity)_|||(2.49)||(2.08)||
|_Change in Treasury bond yield (>_||||3.77|3.48||
|_15 year maturity)_||||(8.66)|(9.04)||
|_India SENSEX index_||||||-0.02|
|||||||(0.05)|
|R<sup>2</sup>|0.011|0.000|0.000|0.000|0.015|0.000|
|Number of observations|922|922|871|871|871|657|
|Years ofdata,RHS variable|38|38|30|30|30|23|

Note: Dependent variable is insurance phase payout. Numbers in parentheses are standard errors, which are clustered by year, except for stock returns, which are clustered by time period (i.e., phase interacted with year). ***, **, and * indicate two-sided statistical significance at the 1 percent, 5 percent and 10 percent level respectively. All regressions also include a constant term.

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To conclude generally on pensions and government employment: It seems that **national preferences dominate price and income effects.** Some governments just choose to retire more people, other to employ more of them. Most governments choose retirement when they also face unemployment. The total cost of high unemployment and abundant retirement is usually high. But the generosity of the individual transfers (SOCWAGE) can vary, and so explains the cost of the policy, given the number of beneficiaries.8 The number of beneficiaries is indeed controlled administratively by the design of the pension system - and is, assuming that pensions are usually attractive, the result of an independent policy choice. Specifically, **the problems with high social security outlays in the CEC are largely due to** the excessive number of early retirees, i.e. **to loose eligibility criteria for early retirement** under the current pensions systems.

# **3.3. Expenditure policy choices**

Finally we note (see Table 4) that total government expenditure in the OECD and CEC's tends to be independent of income per capita - as is social expenditure - but to be related to specific policy choices. Unequality, measured by the share of income of the 1 0 percent richest, appears as a key determinant for low government spending in the sample.<sup>9</sup> Egalitarian countries tend to spend more as do countries with a large government payroll or with generous pension benefits

> 7 For the unemployment problem in Europe compared to the United States a similar idea is expressed in CEPR (1995).

> 8 It is important to clarify that pensions are not too high in the sense that they would attract a lot of people into early retirement out of the labour force. 'The incentive effect' of pensions is in fact ruled out by the institutionalized eligibility criteria (under pay-as-you-go systems). Rather, in some countries pensions may be too high because, given the number of pensioners, the replacement rates (SOCWAGE) are too high.

> 9 We could not find the same relation between social spending and unequality. Other elements of total spending play an egalitarian role, possibly government employment. Social spending depending on the way it is targeted and the way it raises the income of the insiders at the expense of the outsiders could also contribute to unequality. Further research would be needed.

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or should not be included in X. This is of course a difficult issue which goes beyond the scope of this paper, but in terms of interpreting the evidence it is useful to make the distinction.'<sup>7</sup> If investment rates are not included, the effect of a variable in X on growth can be interpreted as the effect of that variable on growth by increasing both the rate of investment and the efficiency of investment (see equation (2)). On the other hand, when the rate of investment is included in the regression, the effect of inflation on growth is only due to increased efficiency of investment. Another alternative that can be used to separate the investment and the efficiency channels is to run a regression like (7), but with investment as the dependent variable.

Kormendi and Meguire (1985) include among the X variables the average change in the rate of inflation with respect to the initial year, and concluded that a deceleration of inflation by 2 percent a year could increase the rate of growth by about 1 percentage point (p. 150). When they add investment as a dependent variable the coefficient on inflation declines to about half of its original value, which suggests that high inflation reduces growth by reducing, roughly in equal proportions, both the rate of investment and its efficiency. Nevertheless, this finding is not easy to interpret since it is the change, rather than the level, of inflation what is being used as independent variable. Later on, Grier and Tullock (1989) extend the sample to cover 1951-80 and use five-year average panel data. They conclude that inflation has no effects on growth in OECD countries, but in the rest of the world there is a negative and significant impact of inflation on growth. The coefficient found for non-OECD countries seems to be, however, surprisingly high, since it suggests that an increase of 10 percentage points in inflation may reduce the rate of growth by 1.6 percentage points.

Similar results have been obtained by Fischer (1991) for a sample of 73 countries during the period 1970-85. When investment is included (regression (5)) it is found that a 10 percent inflation rate would reduce growth by 0.5 percentage points, which corresponds to the efficiency channel. In addition, Fischer (1991) finds that 10 percent inflation reduces investment by 1.5 percentage points (regressions (9) to (11)).18 Taken these results together it can be conclude that 10 percent inflation reduces growth by about 0.7 percentage points, of which 0.5 are due to the efficiency

**17See Blomstrom, Lipsey and Zejan (1993) and Barro and Sala-i-Martin (1995, p. 433) for further discussions on this issue.**

**18In addition, regression (5) shows that each percentage point of investment rises growth by 0.11 percentage points.**

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Lastly, in Table 7, we examine the relationship of our main categories of study with domestic violence outcomes. Specifically, we look at the prevalence of emotional, physical, and sexual violence that women reported to have experienced in the last 12 months (perpetrated by her husband).<sup>7</sup> While again, agreement emerges as the category most strongly positively related to well-being outcomes, here we see a change for women taking power vs. being given power. Whereas for women’s reproductive health and children’s health, women taking power is associated with better outcomes than when the husband gives power, here we are potentially observing the repercussions of this power being taken. The coefficients for less severe violence and severe violence are higher, although not significantly different, and the likelihood of the woman having experienced emotional violence is significantly 8 percent higher than the control mean when she takes power versus when she is given power.

This underscores how taking power could potentially come at a cost, as highlighted in a vast literature on how women face higher risk of violence when claiming their rights and going through the process of empowerment (Kim & Motseib, 2009; Diallo & Voia, 2016; Yount & Li, 2009).

7 The DHS domestic violence module is an optional module that is available for some but not all countries. In addition, the population based are all women within the age of 15 and 49 years old and, in our case, it includes only those who are married. In specific, the domestic violence module asks _did your husband do any of the following things to you?_ (in the last 12 months): a) Slap you? b) Twist your arm or pull your hair? c) Push you, shake you, or throw something at you? d) Punch you with his fist or with something that could hurt you? e) Kick you, drag you or beat you up? f) Try to choke you or burn you on purpose? g) Threaten or attack you with a knife, gun, or any other weapon? h) Physically force you to have sexual intercourse with him even when you did not want to? i) Force you to perform any sexual acts you did not want to? j) Say or do something to humiliate you in front of others? k) Threaten to hurt or harm you or someone close to you? l) Insult you or make you feel bad about yourself?.

A yes answer to one or more of items (a) to (c) constitutes evidence of any less severe violence. A yes answer to one or more of items (d) to (g) constitutes evidence of any severe violence. A yes answer to one or more of items (h) and (i) constitutes evidence of sexual violence and a yes answer to one or more of items (j) to (l) constitutes evidence of emotional violence.

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Figure 1: Annual CPI inflation and its major components, 1999:1-2009:3 <!-- Start of picture text --> (Annual change contributions, %)<br>70<br>Nonfood: All other items<br>60<br>Nonfood: Construction, house rents, energy<br>50 Food: All other items<br>Food: Cereals<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Figure 2: Annual food inflation and its major components, 1999:1-2009:3 <!-- Start of picture text --> (Annual change contributions, %)<br>100<br>Cereals Pulses<br>Spices Tubers<br>80<br>Food taken away from home Others<br>60<br>40<br>20<br>0<br>-20<br>-40<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> As food accounts for 57 percent of total household consumption expenditure, adequate rainfall and good crop harvest are associated with low food and CPI inflation. This link seems to have been absent 2004/05-2007/08, since food inflation continued to accelerate despite good weather and an agricultural production boom. Annual food inflation, measured in simple growth rates, rose from 18.2 percent in June 2007 to a peak of 91.7 percent in July 2008. At the same time overall inflation rose from 15.1 7
affords. Insurance is realized as a subsidy to patients when they seek care and the flip side of that subsidy is a payment to providers. But providers respond to the financial incentives created by how those payments are structured and therefore the value of the insurance is critically tied to the nature of these responses. The lack of non-price mechanisms combined with the difficulties of structuring price incentives for appropriate care imply that health insurance could prove to be a very expensive tool for improving financial protection that actually _lowers_ the quality of health care in LMICs. Ultimately, it is the prices in the crises that matter.

# How Are Health Insurance Schemes Structured?

We start by mapping the overall coverage of health insurance across LMICs using data from the Demographic and Health Surveys, which are nationally representative health-related surveys that target women aged 15-49 and their children under the age of five in LMICs, with a traditional focus on maternal and child health. Questions on health insurance coverage were sporadically included in the late 1990s for some countries like Bolivia, Jordan, Peru, and <mark>Türkiye,</mark> and a standard question (v481) was introduced in 2003. As of April 2022, of 426 surveys ever fielded, 100 surveys in 62 countries have health insurance data available. We compiled all the health insurance questions to create a single database of just under 5 million observations, representative of countries with a total population of about 3.5 billion people; for additional details, see Appendix A available with this paper at the JEP website. When combined with OECD health insurance data (Scheil-Adlung 2014), our DHS-based data allow us to present a unified picture of health insurance coverage and its correlates across multiple countries, adding to previous studies that have focused on single or small groups of countries (for example, Amu et al. 2022; Barasa et al. 2021; Wang, Temsah, and Mallick 2014; National Population Commission NPC/Nigeria and ICF International 2014).

For LMICs where data was available from either the Demographic and Health Surveys or the OECD, close to half of the total population now reports that they have health insurance (Table 1). Coverage is nearuniversal for several countries in East and Central Asia and Latin America and middling in India and some Sub-Saharan African countries (Namibia, Sudan, Rwanda, Ghana). Other countries in Sub-Saharan Africa and South Asia are still below 5 percent coverage. Interestingly, most of the growth in coverage, when it occurred, is very recent. For instance, health insurance coverage in Rwanda increased from 41 percent in 2005 to 83 percent by 2019, from 56 percent in 1998 to 88 percent in 2013 in <mark>Türkiye</mark> and from 40 percent in 2012 to 61 percent in 2017 in Indonesia. In a historical context, this expansion is quite rapid as European 4
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

# **ANNEXES**

## **Further Comparative Data on Real Exchange Rate Indices**

To facilitate comparison of REER indices built with CPIs versus GDP deflators, we provide Figure 11 and Figure 12 below.

### **Figure 10 Comparison of BREER Indices**

#### **Comparison of Bilateral RERs**

<!-- Start of picture text --> 120<br>Bi RER-GDP Defl - France<br>Bi-RER-PPP w/France<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>Years<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Indices 1995=100<br><!-- End of picture text -->

**Figure 11: :Comparison of MREER Indices.**

**Com parison 0f Multilateral REERs**

<!-- Start of picture text --> 140<br>Multi-RER-PPP w/12 C ountries<br>Multi RER-GD P D efl - 12 C ountries<br>130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>Ye a rs<br>Indices: 1995=100<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text -->

**The BREER-MREER deviation is similar for both measures.** Do the two sets of indices differ in terms of the deviations between bilateral versus multilateral measures of

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Policy Research Working Paper 9298

# **Abstract**

This paper uses a panel of firms from the Mexican Economic Censuses and analyzes at the microeconomic level how labor markets adapt to the adoption of information and communication technologies. The paper studies the effects of the adoption of information and communication technologies over the labor structure of the firm and wages. Thus, it assesses whether increasing the use of information and communication technologies leads to an increasing demand for skilled relative to low-skilled labor, and, thus, analyzes its effects on the wage gap between the two groups. The results of this analysis show that there is indeed an effect of the adoption of information and communication technologies over the demand for higher-skilled workers. However, for the manufacturing and services sectors, instead of increasing the wage gap between skilled and unskilled workers, the wage gap decreases. The results for the manufacturing sector appear to be driven by an increasing sophistication of blue-collar workers due to the organizational adjustments derived from the adoption of information and communication technologies.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at liacovone@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 9683

# **Abstract**

The movement of workers from the farm sector to a more productive nonfarm sector has failed to generate significant gains in labor productivity in recent decades in many developing countries. This paper offers a new perspective on the barriers to growth-enhancing structural transformation, combining structural modeling with enterprise census data from Ghana. The paper argues that subnational differences in the intersectoral productivity gap between the nonfarm informal and formal sectors constrain the productivity gain from structural transformation. In Ghana, intersectoral productivity gaps among the richer regions are on average three times larger than among the poorer regions. The disparity in regional intersectoral productivity gaps is modeled as reflecting the disparity in the regional misallocation of labor between the informal and formal sectors. Misallocation is identified as the output wedge between the informal and formal sectors. Simulations suggest that a more productive nonfarm informal sector reduces the disparity in regional intersectoral productivity gaps and, in turn, increases national productivity and the contribution of structural transformation to national productivity. For example, a 90-percent reduction in the disparity in regional intersectoral productivity gaps raises Ghana’s national aggregate productivity by 11.9 percent and the contribution of structural transformation to productivity by 19.7 percent.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at saumik.paul@newcastle.ac.uk and draju2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Our purpose in this paper is not to advocate a particular position, but rather to shed some new light on the measurement issues involved and to explore the interrelationships between the various measures and the approaches. We present measures of fairness, catastrophe in health spending and impoverishment, relate them to the previous literature, and compare them with one another. We illustrate the various measures empirically using data on out-of-pocket payments for health care in Vietnam. This is not an uninteresting case study. In 1998, around 80% of health spending in Vietnam was paid out-of-pocket. Unsurprisingly, in the World Bank's recent _Voices of the Poor_ consultative exercise (Narayan et al. 2000), payments for health care came across as a major concern of poor people in Vietnam. Three key changes occurred in Vietnam during the 1990s which make the study of Vietnam and the period chosen additionally interesting (World Bank et al. 2001). First, user fees in the public sector rose. The increase was especially pronounced for hospital care, where fees appear to have risen by over 1000% in real terms between 1993 and 1998, but were also noticeable in commune health centers even though these were still supposed to be free in 1998. Second, there was a large rise in fees for private clinics and doctors. These apparently rose by nearly 600% over the period 1993-98. Third, expenditures on drugs actuallyfell over the period 199398, due to a 30% fall in the real price of medicines during the period in question. The latter seems to have been due in part to deregulation of the pharmaceutical sector and in part to increased donor assistance in drug supplies. Fourth, social health insurance was introduced in 1993 (World Bank et al. 2001). Initially, this was on a compulsory basis for formal sector workers and civil servants. However, more recently the scheme has been opened up to others on a voluntary basis-including the family members of insureds. By 1998, 12% of the Vietnamese population was covered by social insurance, a little over half of these being covered on a voluntary basis. Compulsory social insurance covers some of the costs of both inpatient and outpatient care, and also pays for drugs used in inpatient treatment. The voluntary scheme has two levels of coverage, the less generous (and less expensive) of which covers only inpatient care, while the higher-priced more generous package includes outpatient care and some drug costs. Most voluntary enrollees have opted for the less costly package. Insurance coverage is most common among the higher income groups.

It is important to be clear what we are _not_ doing in this paper. Any assessment ol the fairness of a health care system requires looking not just at what people pay for health services _but also at how much they use services_ (van Doorslaer, Wagstaff, and Rutten 1993). Health care payments and health service utilization are, in other words, both key "focal" variables whose distributions have to be examined in any assessment of the fairness of a health care system. For each focal variable there is a distribution that is _2_