metadata: authors: 0: author: Ersado,Lire
count: Azerbaijan
docna: 0: docna: Azerbaijan's household survey data :
            explaining why inequality is so low
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Urban Housing and Land Settlements,Industrial Economics,State Owned Enterprise Reform,Public Sector Administrative & Civil
            Service Reform,Urban Governance and Management,Urban Housing,Poverty Impact Evaluation,Poverty Monitoring & Analysis,Economic Growth,Municipal Management and Reform,Inequality,Poverty Assessment,Poverty Diagnostics,Poverty Lines,Small Area Estimation Poverty Mapping,Democratic Government,Economics and Finance of Public
            Institution Development,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Macroeconomics and Economic Growth,Rural Development,Poverty Reduction
abstracts: cdata!: While the Azerbaijan household income
            and expenditure survey (HIES) data satisfy most empirical
            regularities expected in a typical household survey data,
            the inequality measures based on the data are unusually low.
            For example, for the latest three years for which we have
            data (2002 - 2004), the consumption Gini coefficient (the
            commonly used summary measure of inequality) is in the range
            of 16 - 18 percent. This is among the lowest Gini
            coefficients ever observed in any country, and is extremely
            low even with the standard of countries generally considered
            as most equal in the world. Azerbaijan, a transitional
            economy with a significant natural resource base, is
            unlikely to be the most equal country in the world. The
            objective of this paper is to investigate why inequality
            measures are unusually low in the Azerbaijan household
            survey data. The author presents a methodology for
            diagnosing and identifying the potential sources of low
            inequality in the data, including cluster analysis at the
            primary sampling unit level. The main inference from the
            findings of the cluster analysis is that the observed low
            inequality indices are not due to poor supervision of the
            interviewers and the data collection process. The author
            finds that the main culprits for the observed low inequality
            in the HIES data are (1) the low participation rates of
            wealthy households in the household surveys, and (2) the
            widespread availability of well-targeted public and private transfers.
geo_reg_key: 517208,517198,82689,517191
repnme: repnme: Azerbaijan's household survey data :
            explaining why inequality is so low
display_title: Azerbaijan's household survey
            data : explaining why inequality is so low
listing_relative_url: /research/2006/09/7063026/azerbaijans-household-survey-data-explaining-inequality-so-low
topicv3: Economic Growth m1326241 471,State Owned Enterprises m1326323 721,Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,Land and Housing m1327648 817
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Rural Development,Poverty Reduction
no_of_pages: 19
author_keys: 0: author_key: 457742
model_extractions: WPS4009

# **Azerbaijan’s Household Survey Data: Explaining Why Inequality is So Low**

Lire Ersado<sup>1</sup> Human Development Network Europe and Central Asia Region World Bank

**Abstract**

While the Azerbaijan household income and expenditure survey (HIES) data satisfy most empirical regularities expected in a typical household survey data, the inequality measures based on the data are unusually low. For example, for the latest three years for which we have data (2002-2004), the consumption Gini coefficient—the commonly used summary measure of inequality—is in the range of 16—18 percent. This is among the lowest Gini coefficients ever observed in any country, and is extremely low even with the standard of countries generally considered as most equal in the world. Azerbaijan, a transitional economy with a significant natural resource base, is very unlikely to be the most equal country in the world. The objective of this paper is to investigate why inequality measures are unusually low in the Azerbaijan household survey data. The paper presents a methodology for diagnosing and identifying the potential sources of low inequality in the data, including cluster analysis at the primary sampling unit level. The main inference from the findings of the cluster analysis is that the observed low inequality indices are not due to poor supervision of the interviewers and the data collection process. We find that the main culprits for the observed low inequality in the HIES data are (1) the low participation rates of wealthy households in the household surveys, and (2) the widespread availability of well targeted public and private transfers.

World Bank Policy Research Working Paper 4009, September 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 Send all comments to <u>lersado@worldbank.org. This paper is part of the Azerbaijan</u> Programmatic Poverty Assessment work. I would like to thank Branko Milanovic for insightful comments and suggestions on inequality issues and Christian Petersen for review and useful comments on an earlier draft. Arup Banerji and Aleksandra Posarac have provided overall guidance for Azerbaijan Programmatic Poverty Assessment work.
ii
# **1. Introduction**

Azerbaijan has experienced appreciable economic growth over the last several years. Since 1998, by most macroeconomic indicators, it has enjoyed one of the highest rates of economic growth among the CIS countries. Between 1998 and 2005, average GDP growth rate is in double-digits (Figure 1). Macroeconomic stability, coupled with the recent boom in oil income and prudent management of the windfall, have contributed significantly to the improved economic performance. However, the lion’s share of business and economic activities of the country is concentrated in the capital city.

**Figure 1:** Azerbaijan’s economic growth has been impressive since 1998

<!-- Start of picture text --> Average GDP growth, 1998-2005<br>Uzbekistan<br>Ukraine<br>Tajikistan<br>Russia<br>Mongolia<br>Moldova<br>Kyrgyz Republic<br>Kazakhstan<br>Georgia<br>Belarus<br>Azerbaijan(oil)<br>Azerbaijan<br>Armenia<br>0 5 10 15 20<br>Real GDP growth<br><!-- End of picture text --> Source: Asian Development Bank; IMF; DDP, World Bank.

The impact of these developments on distribution of wealth and poverty remains largely unclear, however. Although the government of Azerbaijan undertakes a regular annual household survey, the resulting data have been considered by many as unreliable for measuring poverty due to extremely low inequality estimates in the Azeri survey data. The Azerbaijan Household Income and Expenditure Survey (HIES) data, which are the primary source of economic and social information for poverty analysis in the country, exhibit extremely low consumption inequalities. For instance, the measures of inequality such as the Gini coefficient of consumption expenditures—the commonly used summary measure of inequality—are unusually low. Azerbaijan’s per capita consumption Gini coefficients estimable from the HIES data are low—by 1
a large margin—from those in other transition countries and countries with significant natural resource base such as the Russian Federation (see Table 1). For the last three years for which we have data, the Gini coefficients of consumption expenditures were in the range of 16—18 percent. These are among the lowest Gini coefficients ever observed in any country. These are extremely low even with the standard of countries generally considered as most equal in the world such as Denmark, where the Gini coefficient was about 25 percent in 2004. It is very unlikely that Azerbaijan is the most equal country in the world. This oddly low inequality figures has cast serious doubts on the quality and the reliability of the Azerbaijan HIES data.

**Table 1.** Most recent <u>Gini indices of Azerbaijan and neighboring countries</u>

|**Country**|**GINI Coefficient**|**Year**|
|---|---|---|
|**Azerbaijan**|**18.4**|**2002**|
|**Azerbaijan**|**18.2**|**2003**|
|**Azerbaijan**|**16.3**|**2004**|
|Armenia|33.8|2003|
|Estonia|35.8|2003|
|Georgia|40.4|2003|
|Latvia|37.7|2003|
|Lithuania|36.0|2003|
|Macedonia, FYR|39.0|2003|
|Moldova|36.9|2002|
|Poland|34.5|2002|
|Russian Federation|39.9|2002|
|Tajikistan|32.6|2003|
|Turkey|43.6|2003|

Source: Azerbaijan HIES data; WB Poverty Assessment Reports; WB Development Data Platform (DDP).

The objective of this paper is to investigate why inequality figures in the Azerbaijan household survey data are so low to extent of putting the quality and reliability of the data in question. Is it plausible that Azerbaijan has such a low inequality and that the data are merely reflecting the underlying reality in the country? Is the data generating mechanism systematically biased rendering inequality artificially low? Is the variability in the data suppressed due to disproportionately less representation of households in the various income and consumption echelons? Have lack of or poor supervision of the data collection and entry processes caused unnaturally high homogeneity in the data? Is a bad sampling design, which led to unrepresentative sample, to blame for the extremely low inequality? Are respondents, especially in the upper income echelon, concealing their incomes and expenditures, and by so doing depressed income and consumption expenditure disparity in the data? In this paper, we explore the data of the 2002-2004 HIES and seek answers to some or all of the above questions.

The paper puts forward various hypotheses why inequality is low in the Azerbaijan data and empirically tests them. We first employ ANOVA and cluster analysis to investigate if poor supervision of the data collection process had led to increased homogeneity in the resulting data. Secondly, we test the impact of public and private transfers on inequality by calculating 2
consumption inequality with and without various forms of transfers. Thirdly, we examine if inadequate representation of various wealth groups, particularly the poor and the rich households, explains the usually low inequality in the data. This requires the knowledge of the distribution of the non-responding or missing households. Once the missing observations are identified, the next step is how to account for them. We employ the Pareto distribution assumption for accounting for missing consumption per capita of households in the top and bottom wealth brackets (see Section 3). Finally, combination of the effects of transfers and missing households are tested. The results are reported in Section 4.

# **2.  Background**

# **The Azerbaijan Household Survey**

The history of household survey data collection based on international practice is relatively recent in Azerbaijan. The Azerbaijan HIES was first introduced in 2001 and it has been undertaken on a regular annual basis ever since. Several factors are cited as affecting the quality of the Azerbaijan HIES data. To begin with, the current sampling frame, which is based on the 1999 population census, has relatively large primary sampling units (PSU) with, on average, over 16,000 people. Such big primary units are unsuitable for periodic updating and have the potential of putting at risk the selection of a random sample. The survey instrument is deficient for measuring certain aspects of household consumption such as expenditures on durables and does not allow differentiating between emergency medical care expenditures from regular health medical expenditures.

Another peculiar aspect of Azerbaijan household practice is the interviewers’ hiring and work practice. The arrangement of interviewers may affect data collection practice and data quality. In Azerbaijan, one interviewer is permanently assigned to each selected PSUs. There is also a widespread understanding that some or most of the interviewers need further training to improve their ability to conduct the interviews. Moreover, there is unequal hiring practice for interviewers: out of a total 85 interviewers, 65 are regular staff of the SSC, while the rest are on contract basis and with less compensation. Such uneven pay and hiring practices could endanger the consistency and uniformity in the data collection process.

Finally, there is belief among those who are involved in the collection and use of the HIES data that the surveys do not adequately represent all income groups of the country. It has been widely acknowledged that rich households tend to decline participation in the surveys.

# **Empirical regularities in the HIES data**

The Azerbaijan HIES data satisfy most empirical regularities expected in a typical household survey data. The data have a lot of the characteristics typically found in a good household survey data. In terms of the composition of household consumption, expenditure on food as a share of total household income consistently declines from poorest deciles to the richest deciles. While the poorest tend to spend nearly over 90 percent of their total income on food consumption, the richest spend, on average, a little over 40 percent of their total income on food. The Engel’s Law, 3
which is accepted as a basic principle of income and consumption, is strongly evident in the Azeri household data for all three years considered in this analysis. As expected, rural areas spend a bigger share of their total expenditures on food. Households with significantly higher human capital such as educational attainment have higher income generating opportunity, other things being equal. Per capita income, expenditures and wage earning for households with household heads having higher level educational attainments are consistently higher than those corresponding to less educated household heads. These observations and empirical regularities hold true across time and geographic locations (see Ersado, 2006 for details).

**Table 2.** <u>Consumption share of income decreases with income</u>

||||||**INCOME**|**DECILES**|||||
|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|
|**2002**|||||||||||
|Income (2003<br>prices)|95,205|125,080|141,808|155,152|167,062|178,533|191,894|208,677|235,342|332,082|
|Total consumption<br>(% income)|144|112|102|96|93|89|87|86|83|80|
|Food(% income)|93|72|65|60|58|55|53|51|50|44|
|**2003**|||||||||||
|Income (2003<br>prices)|90,468|120,808|138,132|152,568|166,420|181,555|199,594|221,262|252,612|355,712|
|Total consumption<br>(% income)|161|121|110|105|99|94|90|85|82|73|
|Food(% income)|107|81|73|67|62|58|54|51|47|40|
|**2004**|||||||||||
|Income (2003<br>prices)|109,357|134,651|149,794|163,564|175,837|188,862|202,804|220,803|254,302|365,693|
|Total consumption<br>(% income)|132|110|105|100|99|97|93|91|88|79|
|Food(% income)|91|76|71|67|65|62|59|55|52|42|

Source: Azerbaijan HIES data.

Another good aspect of the Azerbaijan data is that the survey made a reasonable capture of the Gross National Income (GNI). For instance, the average consumption per capita in the 2002 survey is $386 annually vs. the 2002 GDI per capita of $742. The capture ratio is thus 52 percent, which is slightly below the average 57 percent capture ratio for Eastern Europe and former Soviet Union countries. But the capture ratio of the Azerbaijani survey is much better than Armenia’s (34 percent) and is similar to Georgia’s (54 percent).

However, as discussed above, the extremely low inequality in the data is implausible for Azerbaijan. The ratio of per capita consumption of the top 5 percent of people compared to the bottom 5 percent (also known as ventile ratio) is extremely low. The ventile ratios are respectively, 4.2, 4.1 and 3.5 in 2002, 2003 and 2004 (Table 3). The corresponding ratio for the top and bottom 10 percent of the people was only 3.2, 3.1 and 2.8 respectively for 2002, 2003 and 2004. These values are significantly smaller than in comparable countries. For example, in 2002 consumption ventile ratio was 7 in Armenia, 22 in Georgia, 10 in Russia, and 7 in Ukraine (see Milanovic, 2005). It is not obvious whether this low value in Azerbaijan is due to an unrealistically high income of the bottom, or due to an unrealistically low income of the top.

4
**Table** **<u>3.</u>** Azerbaijan has extremely low top-bottom consumption ratio

||**Consumption (A**<br>|**zeri manats)**<br>||
|---|---|---|---|
||**2002**|**2003**|**2004**|
|bottom 5% (2003 prices)|81,826|87,207|102,157|
|Top 5% (2003 prices)|345,022|353,932|361,873|
|**95/5 ratio**|**4.2**|**4.1**|**3.5**|
|bottom 10%(2003 prices)|91,293|96,641|110,952|
|Top 10%(2003 prices)|299,803|308,527|315,816|
|**90/10 ratio**|**3.3**|**3.2**|**2.8**|

Source: Azerbaijan HIES data.

The unusually low level of inequality is not just limited to the national aggregate data; it equally applies at the regional level. For example, the average per capita consumption in Baku is only 7 percent higher than the country-wide average (Table 4) in 2002. In 2004, it was about 12 percent higher than the national average. The average per capita consumption in the poorest region in 2002 and 2003 (i.e., Nakhchivan) is only about 12 percent lower than the national average. The regional Ginis are, similar to the national-wide Gini, uniformly low. Baku’s inequality of close to only 20 percent would make it one of the most (perhaps the most) equal city in the world.

**Table 4.** <u>Similar to the national inequality, regional inequalities are extremely low</u>

|||2002<br>|||2003<br>|||2004<br>||
|---|---|---|---|---|---|---|---|---|---|
||Gini|Consumption<br>(% of<br>national<br>average)|Income<br>(% of<br>national<br>average)|Gini|Consumption<br>(% of<br>national<br>average)|Income<br>(% of<br>national<br>average)|Gini|Consumption<br>(% of<br>national<br>average)|Income<br>(% of<br>national<br>average)|
|Azerbaijan|18.4|100|100|18.2|100|100|16.3|100|100|
|Baku|21.4|107|114|20.0|107|115|17.9|112|117|
|Non-Baku urban|17.4|95|100|18.5|94|101|15.7|94|98|
|Rural|16.8|99|92|16.7|100|91|14.9|98|92|
|Nakhchivan|14.9|88|89|16.7|87|89|15.5|95|92|
|Absheron-Guba|19.4|97|105|19.3|97|103|15.3|98|99|
|Mughan_Salyan|15.9|96|94|15.5|95|94|15.5|89|93|
|Ganja-Gazakh|16.7|97|92|18.7|99|92|15.8|97|94|
|Sheki-Zagatala|14|102|93|14.7|99|93|12|99|95|
|Lankaran_Astara|15|99|94|13.8|101|93|14.5|101|94|
|Shirvan|17.7|95|96|17.4|96|96|16.6|91|93|
|Qarabagh-Mil|18.2|103|95|18.1|102|96|14.9|96|93|

Source: Azerbaijan HIES data.

# **Did poor supervision lead to low inequality?**

To investigate whether poor supervision of the data collection process led to low inequality, an ANOVA and cluster analysis techniques on the inequality estimates at the primary sampling unit level were employed. As each interviewer is permanently assigned to a certain primary sampling 5
unit, these analyses are tantamount to analyzing the effect of the interviewers on inequality measures. The main findings of this analysis are summarized below.<sup>2</sup> The main inference from the findings of the ANOVA and cluster analysis is that the observed low inequality indices are not likely due to interviewers’ work practice, nor do they reflect poor supervision of the data collection process. The results of the ANOVA and cluster analysis of the three years data were compared across years and interviewers to see if any pattern emerges by cluster of interviewers. The interviewers were very consistent for all three years 2002, 2003 and 2004 examined. Gini coefficients were uniformly low for all interviewers and for all years. The interviewers appear to be a reasonably homogeneous group in terms of enumeration level inequality. Furthermore, interviewers in the extreme cases (i.e., reporting relatively small or large Ginis) did not remain extreme in all years. For example, only two interviewers, each in 2003 and 2004, had a Gini of above 30 percent and these were not the same. And three interviewers had a Gini that was below 10 percent, two interviewers in 2003 and a different interviewer in 2004. The overall implication that interviewers not no meaningful contribution towards the observed low inequality holds true unless one is to argue that all interviewers through out the country are colluding, a very improbable proposition. Colluding is not practical and physically impossible for interviewers who are geographically isolated and dispersed through out the country.

# **3. Potential Sources of Low Inequality —Hypotheses**

The observed low inequality in the HIES data could be indicative of various underlying conditions. Some of these possible conditions and associated hypotheses are presented below. In the following sections, we test the hypotheses that are testable with the available information in the HIES data. It is anticipated that these will likely help us judge the quality of the HIES data for using to analyze poverty in Azerbaijan as well as identify the potential sources of low inequality in the data.

First, the reason for low inequality in Azerbaijan could be that inequality is in fact low and the low Gini is just reflecting the underlying reality in the country. For anyone with the understanding of the economic structure of Azerbaijan and regional distribution of welfare, such explanation is implausible. The observed Gini coefficients are too low to be credible for Azerbaijan. It is implausible to think that Azerbaijan is the most equal country in the world, and Baku the most equal city. However, certain government and donor programs for the poor and vulnerable households could provide, albeit not sufficient, explanation for the apparently low inequality in the Azeri data. This led us to our first hypothesis.

- **Hypothesis 1:** The government’s social protection programs, the involvement of donors in support of these programs, and family support networks have played an important role in reducing welfare disparity among the population. Well targeted social transfers to the poor and the vulnerable groups, such as the displaced population, lifted the consumption levels of these groups without affecting the welfare of the people in the upper

> 2 For more details, see Ersado (2006).

6
consumption brackets. Evidence abound in the HIES data that transfers from government in the form of child allowance, pensions and other social assistance programs are important components of household consumption (see next section). Transfers from family members abroad and within the country are also important sources of consumption in Azerbaijan. International donor supports are significant, particularly for the displaced population. The outcome of such measures would undoubtedly lead to reduction in inequality, while at the same time alleviating poverty. It is unlikely this explains away all the observed extremely low inequality in Azerbaijan, but it could be a contributing factor.

Secondly, the data generating mechanism may be systematically biased and does not represent well the various segments of the Azeri population. This scenario gives rise to the following three hypotheses:

- **Hypothesis 2:** The inequality in the data is suppressed due to the fact that the HIES data have fewer representatives of households in the upper income echelon. This situation could arise, for instance, if some households in the richest category, decline participation in the survey. From communications with Azeri officials with the knowledge of the HIES data, there is high likelihood and clear evidence that the rich households tend to decline responding to survey questionnaires.

- **Hypothesis 3:** Households in the lower income echelon are inadequately represented in the data. Several factors could lead to survey data unrepresentative of the poorest households such as their location. The poorest households tend to live in inaccessible locations and in areas with poor infrastructures such as roads. In such conditions, lack of or poor supervision of the interviewers and data collection process, may lead to replacement of the poor households with better off  ones in more accessible location or to many missing households in the data, most of which are poor.  Also it is not implausible to think the poor may decline participation in the survey.

- **Hypothesis 4:** The data do not fully represent both the upper and lower income echelons. In the scenario where households in the upper and lower welfare echelon are “truncated”, the resulting data would have unnaturally high homogeneity and therefore low inequality.

Thirdly, various forms of transfers and deficiency in the data generating mechanism could lead to the observed low inequality:

- **Hypothesis 5:** Combination of factors stated as conditions for hypotheses 1 through 4 are responsible for extremely low inequality in Azerbaijan.

Finally respondents, although they participated in the survey, were less forthcoming on the questions about their wealth. This is a common problem in most household surveys through out the developing world; Azerbaijan is not an exception. This led us to our final hypothesis:

- **Hypothesis 6:** Respondents, especially those participating households in the upper income echelon, were concealing their incomes and expenditures, thus depressing income and consumption disparity in the data.

7
# **4. Hypotheses Tests and Results**

Hypotheses 1 through 5 are testable using the available information in the surveys. The last hypothesis does not yield itself to statistical testing. The null hypothesis for each of the testable hypotheses is that _measures to correct for factors hypothesized to lower inequality will not lead to significant increase in inequality._ The alternative is _these measures will significantly increase inequality in the Azerbaijan data_ . As there is no established lower or upper limit to the level of inequality, a _significant_ level of increase would be one that makes the Gini coefficient “reasonable” for Azerbaijan and comparable to that of other countries in the region and with similar economic structure.

# **_Hypothesis 1_**

We test **hypothesis 1** by calculating consumption inequality with and without various forms of transfers such as social transfers (including pensions, state benefits and certain privileges), and transfers from family and friends abroad and within country. As can be seen in Table 5, over 90 percent of all households report receiving some kind of transfer income. Over half of urban and over 55 percent of rural households were reported to receive pension income. Transfers from friends and family members reached about 73 percent of Azerbaijan households, with nearly 80 percent of rural households reporting receipt of transfer income from friends and family members. Transfers also constituted a large share of household income. For example, in 2004 total transfer income accounted for a non-trivial 29 percent of total household income per capita nationwide.

**Table 5.** Most Azeri households report receipt of transfer income in 2004

||**_Azerbaijan_**|**_Baku_**|**_Non-Baku Urban_**|**_Rural_**|
|---|---|---|---|---|
||**Sha**|**re of househ**|**olds receiving transfers**||
|All transfer|91.9|84.3|91.3|96.3|
|Social transfer|78.4|66.8|77.7|84.9|
|_of whichpensions_|52.7|45.6|54.8|55.2|
|Transfers from family/friends|73.0|64.1|69.2|79.9|
||**Shar**<br>|**e of transfer**<br>|**s in the household incom**<br>|**e**<br>|
|Total transfer|29.1|23.7|29.0|32.5|
|Social transfer|13.3|11.2|13.0|14.8|
|_of whichpensions_|10.9|9.5|11.0|11.7|
|Transfers from family/friends|15.8|12.5|16.1|17.7|

Source: Azerbaijan 2004 HIES data.

Inequality increases substantially after subtracting transfers from household per capita consumption. Table 6 presents Gini coefficients without transfers over time and by area of residence. This holds true nationally as well as at regional levels. Nationally the inequality increases by over 73 percent in 2002, by over 81 percent in 2003 and by over 94 percent in 2004 if no transfers were available for consumption (Table 6). If only social transfers were subtracted from consumption, the Gini coefficient increases by about 50, 53 and 67 percent in 2002, 2003 and 2004, respectively. The findings suggest that social transfers had welfare equalizing effect, which is reflected in lower inequality with transfers than without transfers. There are evidences 8
elsewhere that a sharp increase in the level of government cash transfers to individuals can lead to decline in inequality (Keane and Prasad, 2001). Keane and Prasad (2001) show that despite growing labor earnings inequality following transition to market economy, the rise in transfer expenditures in Poland in the early years of transition mitigated the increase in overall income inequality. Transfers from families and friends have equally important effect on inequality. For instance, without transfers from family members and friends networks, the Gini coefficient would increase by 59, 67 and 67 percent respectively in 2002, 2003 and 2004.

There is important policy implication from the findings under hypothesis 1. The fact that inequality increases significantly without transfers implies that transfer incomes (particularly those from government sources) are reasonably targeted well. As equalizing effect of increased from 2002 to 2004 further suggests that the efficiency of targeting improved over time. As a result, the incomes of households in the lower wealth echelons were lifted more so than the incomes of the rich.

**Table** **<u>6.</u>** Transfers have welfare equalizing effect

|**Gini coefficient**||**2002**<br>|||**2003**<br>|||**2004**<br>||
|---|---|---|---|---|---|---|---|---|---|
||without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>friends|
|Azerbaijan|31.8|27.5|28.1|32.9|27.9|29|31.8|27.2|27.2|
|Baku|33.4|30.3|30.8|33.8|29.1|30.8|29.5|27.5|25.7|
|Non-Baku urban|31.6|26.6|27.5|34.2|28.7|29.8|31.2|27.2|26.8|
|Rural|30.5|25.9|26.7|31|26.1|27.1|31.8|25.8|26.8|

Source: Azerbaijan HIES data.

# **Table 7.** Percent increase in the Gini coefficients after transfers are removed

|**Gini coefficient**||**2002**<br>|||**2003**<br>|||**2004**<br>||
|---|---|---|---|---|---|---|---|---|---|
||without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>friends|
|Azerbaijan|73|50|53|81|53|59|95|67|67|
|Baku|56|52|44|69|45|72|65|38|43|
|Non-Baku urban|82|44|75|85|55|90|99|47|71|
|Rural|81|55|79|86|56|82|114|55|80|

Source: Azerbaijan HIES data.

# **_Possible Reasons for Missing Observations_**

Testing hypotheses 2, 3 and 4 are relatively more complicated than hypothesis 1. It requires knowledge of the number of missing observations, preferably at the lower level of geographic aggregation. Fortunately the sampling framework for Azerbaijan HIES established identical 9
sample size for all primary sampling units (PSUs). In all surveys, an interviewer is assigned to each PSU and required to collect data from 104 sampled households per year. As pointed out in the introduction, there are 85 interviewers throughout Azerbaijan, leading to a total 8840 observations if all selected households were interviewed and their data retained. The fact that we have interviewer identification numbers in the data allows us to count the number of missing households (non responding households or households deemed to have unusable data) at the interviewer (i.e., PSU) level: _Nmisp = 104 - Nobsp_ Where Nmis _p_ denotes the number of missing households at PSU _p_ , and Nobs _p_ denotes the number of usable observations (households) available at PSU _p_ . If the part of the population that is left out is different than the part that is observed, there will be differences between the survey results and what is actually true in the population. Table 8 below shows the distribution of missing households at the regional level. The percentage of missing households is higher in urban areas than rural. There were more than twice as many missing households in 2002 than either 2003 or 2004, suggesting some improvement in response rate over time.

**Table** **<u>8.</u>** Distribution of missing households in Azerbaijan HIES data

|||**2002**<br>|||**2003**<br>|||**2004**<br>||
|---|---|---|---|---|---|---|---|---|---|
||Total<br>Sample|Missing|%<br>Missing|Total<br>Sample|Missing|%<br>Missing|Total<br>Sample|Missing|%<br>Missing|
|Azerbaijan|8840|683|7.7|8840|316|3.6|8736|311|3.6|
|Baku|2392|200|8.4|2392|159|6.6|2392|133|5.6|
|Non-Baku urban|2808|250|8.9|2704|84|3.1|2288|91|4.0|
|Rural|3640|233|6.4|3744|73|1.9|4160|87|2.1|
|Nakhchivan|520|49|9.4|520|23|4.4|520|10|1.9|
|Absheron-Guba|1040|152|14.6|1040|52|5.0|1040|53|5.1|
|Mughan_Salyan|728|52|7.1|728|10|1.4|728|12|1.6|
|Ganja-Gazakh|1144|63|5.5|1144|24|2.1|1144|32|2.8|
|Sheki-Zagatala|624|37|5.9|624|6|1.0|624|20|3.2|
|Lankaran_Astara|832|36|4.3|832|17|2.0|832|12|1.4|
|Shirvan|624|20|3.2|624|8|1.3|624|16|2.6|
|Qarabagh-Mil|1144|74|6.5|1144|17|1.5|1144|23|2.0|

Source: Azerbaijan HIES data.

Non-response or missing observations at the household level occur when a household or person selected for the survey does not participate in the survey or does participate but does not provide complete information. There can be many reasons for missing observations or non-response in survey data. A listed housing unit chosen for the sample may be found unoccupied when an interviewer visits the housing unit. Even if it is occupied, several adverse events may prevent data collection. Although a housing unit is occupied, its residents may be away from home during the entire survey period. Health conditions, whether permanent, such as hearing impairment or blindness, or temporary, such as an episode of a severe acute illness, may preclude an individual from responding as well. More importantly, a non-response also may occur when a person at home during the survey refuses to participate as an individual or as a representative of the entire unit.

From communications with Azeri officials with the knowledge of the HIES data, the predominant reason for non-response was refusal of the rich households to participate in the 10
surveys. There is clear evidence that the rich households tend to decline responding to survey questions. Azerbaijan State Statistical Committee (SSC) informed us that most other causes of missing observations have been addressed by replacing originally selected households that could not be interviewed at the time of interview, through a random draw from the census list. A cursory examination of the proportion of the missing observations and income shows positive association. A two-way scatter plots of the share of missing households and mean consumption expenditures at the economic regions level show positive correlation between wealth and refusal to participate in the survey in 2004 (see Figure 2). There are other empirical evidences in the literature that high-income households are less likely to participate because of a high opportunity cost of their time or concerns about intrusion in their affairs (Atkinson, 1987; Korinek, et al. 2005). Groves and Couper (1998) found that higher socio-economic status tended to be associated with lower response rate in the US surveys.

**Figure 2** . Rate of refusal is higher in the richer regions

<!-- Start of picture text --> Baku<br>Absheron-Guba<br>Sheki-Zagatala<br>Shirvan<br>Ganja-Gazakh<br>Qarabagh-MilNakhchivan<br>Mughan-Salyan Lankaran-Astara<br>190000 200000 210000 220000 230000 240000<br>Mean per capita consumption<br>Source: Azerbaijan HIES, 2004.<br>.06<br>.05<br>.04<br>.03<br>Proportion of missing households<br>.02<br><!-- End of picture text --> With this backdrop, for the purpose of hypothesis 2, we assume that all missing households came from the upper wealth echelon due to their unwillingness to participate in the survey. The assumption that all missing households are due to either lack of participation of the rich is quite plausible for reasons stated in the last paragraph. But we also make an opposite assumption that all missing households came from the bottom wealth echelon for reasons stated under hypothesis 3. Moreover, we embrace the possibility that the missing households came from both upper and lower end of the wealth ladder. This scenario is explored for testing hypothesis 4.

11
<!-- Start of picture text --> 3.0<br>2.5 —"<br>— ke?<br>2.0 =r!<br>15<br>1.0<br>0.0 eee<br>0 2 3 4 5<br><!-- End of picture text -->
The values of consumption aggregates for households who did not participate in the survey were simulated using the _Pareto distribution_ assumption. Let _xm_ be equal to the highest consumption per capita in the data (i.e., the consumption per capita for missing households is equal to or greater than the highest consumption per capita in the existing HIES data). Also assume that k (shape parameter) is equal to 2. We test hypothesis 2 by calculating consumption inequality after accounting for upper truncation in the distribution as described above. Similarly Hypothesis 3 can be tested by calculating consumption inequality after accounting for lower truncation in the distribution. For hypothesis 3, consumption per capita for missing households, which are now assumed to be the poorest, was assumed to be equal to or less than the lowest consumption per capita in the existing HIES data.

The likelihood that exclusion of households in the lower wealth echelon had led to lower inequality is very low. Table 9 presents the Gini coefficients and Figure 3 presents percent changes in them under hypotheses 2 and 3. The Gini coefficient does not exhibit appreciable increase with the assumption that the missing households were in the bottom wealth distribution. On other hand, the assumption of upper truncation (i.e., the missing households belong to the wealthiest upper echelon) appears to contribute to apparent low inequality. For example, accounting for exclusion of rich households in the data will lead to over 120 percent increase in Gini coefficient in 2002 (from 0.184 to 0.412). The impact of upper truncation is even higher in non-Baku urban areas, where the Gini coefficient would increase by close 150 percent (0. 172 to 0.432) in 2002 (Figure 4). The Gini coefficients would also increase appreciably in 2003 and 2004 after accounting for the missing households. Note that the effect of lower truncation is extremely low compared to the assumption of upper truncation.

Therefore, the most likely factor for the observed low inequality in the Azerbaijan HIES data appears to be the under-representation of households in the higher income brackets. This is likely so due to inadequate participation of the richer households in the surveys. However, the inequality figures after accounting for top truncation are still too low to be credible for Azerbaijan, particularly for 2003 and 2004. This suggests the most credible source of lower inequality may be the combination of both lack of participation of rich households and the transfers. This possibility is tested under hypothesis 5 below.

**Table** **<u>9.</u>** Lack of participation of rich households <u>partly explains low inequality in Azerbaijan</u>

|**Gini Coefficients**|**200**<br>|**2**<br>|**200**<br>|**3**<br>|**20**<br>|**04 **<br>|
|---|---|---|---|---|---|---|
||**Top**<br>**Truncation **|**Bottom**<br>**Truncation **|**Top**<br>**Truncation **|**Bottom**<br>**Truncation **|**Top**<br>**Truncation **|**Bottom**<br>**Truncation **|
|Azerbaijan|41.2|18.4|28|20.4|32.4|18.4|
|Baku|43.2|23.2|34.8|23.9|37.7|20.9|
|Non-Baku urban|43.3|26.5|26.9|20.3|33.3|17.9|
|Rural|38.2|22.9|22.8|18.1|26.6|16.5|

Source: Azerbaijan HIES data.

13
<!-- Start of picture text --> Figure 4. Top truncation is the more likely source of low inequality<br>Percent increase in Gini coefficient<br>2002 2003 2004<br>160<br>Top<br>truncation<br>140<br>120<br>100<br>80<br>60<br>Bottom<br>truncation<br>40<br>20<br>0<br>Source: Azerbaijan HIES data.<br>Azerbaijan Baku Non-Baku Rural Azerbaijan Baku Non-Baku Rural<br><!-- End of picture text -->

# **_Hypothesis 4_**

The Gini coefficients under hypothesis 4 are not large enough for Azerbaijan, compared to its comparators in Table 1, particularly for 2003 and 2004, to warrant that the missing households were from both the top and the bottom of the wealth distribution. Under hypothesis 4 we assumed that there was truncation both in the top and bottom wealth groups. Here we substituted the consumption per capita of half of the missing households following hypothesis 2 and the remaining half were replaced following hypothesis 3. The resulting Gini coefficients are reported in Table 10. The findings thus reinforce the conclusions under hypotheses 2 and 3 that the likely source of low inequality is upper truncation rather than lower truncation or mixed truncation.

14
**Table 10.** There is little <u>evidence of exclusion of poor households</u>

|||**Gini Coefficients**<br>||
|---|---|---|---|
||**2002**|**2003**|**2004**|
|Azerbaijan|36.6|24.8|26.8|
|Baku|36.3|27.9|27.5|
|Non-Baku urban|38.0|25.5|28.4|
|Rural|35.8|22.2|24.9|

Source: Azerbaijan HIES data.

# **_Hypothesis 5_**

Under the assumptions for hypothesis 5, the Gini coefficient for Azerbaijan increase more appreciably than under any other hypotheses. Here we made the assumption that the extremely low inequality in Azerbaijan HIES data arose from lack of and/or inadequate participation of rich households and significant amount of transfers among Azeri households. Table 11 presents the Gini coefficients after removing transfers and replacing the consumption per capita for missing households following the Pareto distribution assumption under hypotheses 2 and 3. After accounting for upper truncation and transfers, the Gini coefficient more than double in most cases (see Table 12). After removing all transfer incomes and accounting for refusal of rich households, the Gini coefficient in 2002 increases to over 200 percent for Azerbaijan as the whole. Inequalities in 2003 and 2004 similarly climb by about 145 and 194 percent respectively.

The overall findings here suggest that the main culprit for extremely low inequality in the Azerbaijan HIES data is not only refusal of rich households to participate in the survey, but also significant infusion of transfers to the Azeri household consumption. Transfers from both government and private sources are important sources of income. Furthermore the transfers disproportionately benefited those households in the lower wealth category than those in the upper income groups.

**Table 11.** The most likely reasons for low inequality are lack of participation of the rich and <u>significant transfers from government and family networks</u>

|||**2002**<br>||**Gi**<br>|**2003**<br>**ni Coefficie**<br>|**nts**<br>||**2004**<br>||
|---|---|---|---|---|---|---|---|---|---|
||without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|
|Azerbaijan|55.8|46.0|49.4|44.5|33.3|37.4|47.9|38.0|40.3|
|Baku|57.5|47.7|51.4|51.3|39.8|44.9|50.7|42.9|44.0|
|Non-Baku urban|58.0|48.0|51.8|45.7|32.3|37.8|49.7|39.0|41.7|
|Rural|52.8|43.2|46.2|37.4|27.8|30.7|42.6|32.2|35.1|

Source: Azerbaijan HIES data.

15
**Table 12.** Percent increase in the <u>Gini coefficients after transfers are removed</u>

|||**2002**<br>|||**2003**<br>|||**2004**<br>||
|---|---|---|---|---|---|---|---|---|---|
||without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|
|Azerbaijan|203|150|168|145|83|106|194|133|147|
|Baku|169|139|140|157|99|151|183|114|146|
|Non-Baku urban|233|159|230|147|75|141|216|111|165|
|Rural|214|159|210|124|67|106|186|93|136|

Source: Azerbaijan HIES data.

# **5. Conclusions**

The Azerbaijan Household Income and Expenditure Survey (HIES) data show extremely low inequality measures, which wrongly suggest that Azerbaijan is one of the most or perhaps the most equal country in the world. Such oddly low inequality figures have cast serious doubts on the quality and the reliability of the HIES data for poverty analysis. This paper analyzed the HIES data to find out the main culprits for low inequality. The results of this analysis are summarized in Table 13.

The study finds two main sources of low inequality in the data. First, Azerbaijan HIES data are somewhat unrepresentative of living conditions of the population because of the fact that the richer households are disproportionately less willing to participate in the surveys. While this would have no impact on the aggregate number of poor households, it would affect the FGT poverty measures (i.e., headcount ratio, poverty gap ratio and poverty severity). The missing households add to the pool of the non-poor, so including them would lower the headcount poverty rates. Secondly, transfers have inequality reducing effect and there is significant amount of transfer incomes going into the hands of Azeri households. The finding suggest that households in the lower wealth echelon are benefiting more from transfers than those in the richer category, which in turn implies government and other transfer programs are relatively well targeted. Inequality in the HIES data would increase significantly—more than two-fold—if the effects of top truncation and transfers were accounted for (see Table 13).

**Table 13.** <u>Inequality in Azerbaijan HIES data under various hypotheses</u>

||**2002**|**2003**|**2004**|
|---|---|---|---|
|_Base case_|_18.4 _|_18.2 _|_16.3 _|
|Hypothesis1: transfereffect|31.8|32.9|31.8|
|Hypothesis 2: top truncation|41.2|28.0|32.4|
|Hypothesis 3: bottom truncation|18.4|20.4|18.4|
|Hypothesis4: bottomand top truncations|36.6|24.8|26.8|
|Hypothesis 5: transferand bottomtruncation|36.1|35.3|31.9|
|Hypothesis 6: transfer and toptruncation|55.8|44.5|47.9|

Source: Azerbaijan 2002-2004 HIES data.

16