rafmacalaba/datause-encoder-data huggingface dataset:
Dakar as predictors.<sup>13</sup> The humidity and temperature data for Dakar are collected by Visual Crossing (2024). We run the model separately for each monitor as well as combining the data from both monitors. We also run the model separately for hourly observations and for daily observations. We implement the calibrations at both the hourly and daily levels.

Table 1: Calibration Coefficients comparing Regulatory and nearby Purple Air monitors (Hourly)

||Monitor a|t Bel-Air|Monitor a|t Pikine|Bel-air &|Pikine|
|---|---|---|---|---|---|---|
||(1)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(2)<br>Count<br>>2.5|(3)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(4)<br>Count<br>>2.5|(5)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(6)<br>Count<br>>2.5|
|Mass Concentration PM2.5|2.378||0.762||1.588||
|Count concentration >2.5||1.486||1.780||1.715|
|Humidity|-0.606|-0.674|-1.766|-1.564|-1.178|-0.860|
|Temperature|-1.072|-0.504|-2.423|-1.228|-1.583|0.819|
|Constant|66.63|72.85|232.1|171.2|142.5|57.96|
|Out-of-Sample _R_<sup>2</sup>|0.615|0.799|0.365|0.515|0.449|0.565|

**Note** : Lasso linear model with 5-fold cross-validation. Mass PM2.5 ( _μg/m_<sup>3</sup> ) refers to the mass concentration of fine particulates with a diameter of fewer than 2.5 microns. Count (particles/100ml) refers to the count of all particles greater than or equal to 2.5 micrometers in diameter. Data is aggregated hourly before calibration.

To calibrate the low-cost monitor data to the local regulatory-grade monitors, we use the model that combines the data from the two locations and use the PA PM2.5 measure as the main predictor. Table 1 displays the coefficients and out of sample R<sup>2</sup> for each calibration conducted at the hourly level.<sup>14</sup> We apply the following calibration equation corresponding to the fifth column of Table 1 to generate a local calibration: Local Calibration PM2 _._ 5 = (Unadjusted PM2 _._ 5 _×_ 1 _._ 59) _−_ (Humidity _×_ 1 _._ 18) _−_ (Temperature _×_ 1 _._ 58)+142 _._ 2 (3) In the absence of data from regulatory-grade monitors, satellite data may provide an alternative for calibrating the data from low-cost monitors. Therefore, we also calibrate the Purple Air data with the satellite data using a similar calibration procedure.Using the MERRA-2 satellite data for the calibration, there is only a single value for Dakar. Therefore, we use a Dakar-wide average of the Purple Air monitors to generate the satellite calibration model: Satellite Calibration PM2 _._ 5 = (Unadjusted PM2 _._ 5 _×_ 1 _._ 66) _−_ (Humidity _×_ 1 _._ 3) _−_ (Temperature _×_ 4 _._ 84)+249 _._ 6

> 13Additionally, we test including the number of particles larger than 2.5 micrometers as a predictor instead of the PM2.5 value, because the number of larger particles has a very high correlation with the regulatory-grade monitor values.

> 14In a similar manner, we generate a daily local calibration using the same data aggregated daily (Table A2).

12
# **Table 1: GDP per capita (USD) and Personal Remittance Receipts as share of GDP200009 and 2010-18 averages**

||GDP PPPper capita|2000-09|2010-18|
|---|---|---|---|
||2018|average|average|
|**High remittance recipients**||||
|Albania<br>|13,364|15.8|11.1|
|Armenia|10,343|15.2|16.0|
|Bosnia and Herzegovina|14,624|19.6|10.9|
|Georgia|12,005|7.6|10.6|
|Kosovo|11,348|18.9|15.2|
|Kyrgyz Republic|3,885|10.7|29.5|
|Moldova|7,272|25.6|20.6|
|Montenegro|20,690|6.4|11.3|
|Serbia|17,435|8.6|8.4|
|Tajikistan|3,450|25.4|35.2|
|Ukraine|9,249|3.1|7.4|
|Uzbekistan|8,556|7.3|8.7|
|**median**|10,846|13.0|11.2|
|**Fuel exporters**||||
|Azerbaijan|18,044|2.8|2.5|
|Kazakhstan|27,880|0.2|0.2|
|Russian Federation|27,588|0.3|0.4|
|Turkmenistan|19,304|0.2|0.1|
|**Median**|23,446|0.3|0.3|
|**Other transition economies**||||
|Belarus|19,995|0.8|1.7|
|North Macedonia|16,359|3.4|3.4|
|Bulgaria|21,960|4.9|3.0|
|Croatia|27,580|3.5|3.9|
|Czech Republic|39,744|0.7|1.1|
|Estonia|35,974|1.2|2.0|
|Hungary|31,103|0.7|2.6|
|Latvia|30,305|2.8|4.8|
|Lithuania|35,461|2.1|3.7|
|Poland|31,337|1.6|1.4|
|Romania|28,206|0.5|2.0|
|Slovak Republic|33,736|1.2|2.1|
|Slovenia|38,049|0.8|0.8|
|**Median**|31,103|1.2|2.1|

# Source: World Development Indicators

Note: Annual data on remittance receipts begins for Kosovo in 2004, Montenegro and Serbia in 2007, Tajikistan in 2002, Turkmenistan and Uzbekistan in 2006.

9
In terms of impact of migration, one must first decide where the impact occurs – on the migrant while abroad or on return, on the household left behind or on the community. Second, the outcome of interest needs to be collected properly. For example, if the topic of interest is poverty, the survey must collect consumption or income data from the household. Another important issue is the identification strategy, as unobservable factors affecting migration decisions are also likely to be correlated with the outcome of interest. Ideally, one would rely on an experimental design in which the treatment is randomly assigned, and before and after information are collected. However, given the nature of migration, this is hardly ever the case.<sup>7</sup> Sasin and McKenzie (2007) discuss these issues extensively.

The information collected will depend on a number of factors, including the length of the questionnaire, the capacity and training of fieldworkers, but, most importantly, whether information is gathered directly from the migrant or through a proxy respondent. Use of a proxy might severely constrain the ability to ask questions in depth. However, a minimum set of questions can easily be asked about the emigrants, including their basic demographic characteristics, education level, occupation abroad, country (and location) of current residence, the year of first (and last) migration and remittance behavior. Other questions may be asked about legal status, marital status, the basic demographic composition of the household abroad, frequency and nature of contact with the household, and occupation prior to migrating. Further questions that relate to the specific objectives of the survey can also be added. An attempt could be made to collect

> 7, An exception is work by McKenzie et al. (2006), who take advantage of the random allocation of New Zealand visas to Tongan residents.

18
# Table 3a: Detailed input counts for industries, 2000

|||Org|anized se|ctor|Uno|rganized s|ector|
|---|---|---|---|---|---|---|---|
|NIC|IndustryDescription|Mean|SD|Median|Mean|SD|Median|
|15|Food products and beverages|1.76|1.55|1.00|2.58|1.58|2.00|
|16|Tobacco products|2.09|1.39|2.00|1.51|0.86|1.00|
|17|Textiles|1.88|1.50|1.00|1.61|0.96|1.00|
|18|Wearing apparel; dressing and dyeing<br>of fur|3.11|2.01|4.00|2.87|1.62|3.00|
|19|Leather; luggage, handbags, saddlery,<br>harness and footwear|2.87|1.94|3.00|3.41|1.40|4.00|
|20|Wood and wood products, except<br>furniture; straw and plating|1.86|1.51|1.00|1.52|0.93|1.00|
|21|Paper and paper products|2.80|1.55|3.00|2.31|1.24|2.00|
|22|Publishing, printing and reproduction of<br>recorded media|2.88|1.61|3.00|2.54|1.36|2.00|
|23|Coke, refined petroleum and nuclear<br>fuel|1.62|1.27|1.00|1.75|0.91|1.00|
|24|Chemicals and chemical products|3.88|1.65|5.00|3.37|1.42|3.00|
|25|Rubber and plastic products|2.86|1.68|3.00|2.09|1.28|2.00|
|26|Other non-metallic mineral products|2.54|1.59|2.00|2.07|1.14|2.00|
|27|Basic metals|2.23|1.57|2.00|2.01|1.29|2.00|
|28|Fabricated metal products, except<br>machinery and equipments|2.23|1.63|2.00|2.09|1.29|2.00|
|29|Machinery and equipment, n.e.c.|3.56|1.69|4.00|2.72|1.50|3.00|
|30|Office, accounting and computing<br>machinery|3.12|2.04|4.00|3.88|1.58|5.00|
|31|Electrical machinery and apparatus,<br>n.e.c.|3.83|1.56|5.00|3.33|1.50|3.00|
|32|Radio, television, and communication<br>equipment and apparatus|3.43|1.85|4.00|3.81|1.32|4.00|
|33|Medical, precision and optical<br>instruments, watches and clocks|3.50|1.68|4.00|2.74|1.24|2.00|
|34|Motor vehicles, trailers and semi-<br>trailers|2.97|1.81|3.00|2.58|1.50|2.00|
|35|Other transport equipment|2.74|1.86|3.00|2.33|1.46|2.00|
|36|Furniture, manufacturing n.e.c.|2.73|1.92|3.00|2.44|1.43|2.00|
||Traditional|2.19|1.67|2.00|2.33|1.47|2.00|
||Modern|3.26|1.78|4.00|2.68|1.50|2.00|

Notes: See Table 1. "n.e.c." stands for Not Elsewhere Classified. Appendix Tables 2a and 2b provide details on industry sizes. "Modern" industries are comprised of the following (NIC 98 2-digit): 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35.
- 31 -

- **S. Grants to** NOOs are 28 percent of bilateral aid. It is estimated that approximately a **third of** NGO projects involve health (S70 million). It is assumed that about a third of **the health** activities are of direct benefit to mothers.

6. In the absence of clear statistical categories, these assumptions cannot be considered official estimates. If water, sanitation, and transport expenditures are added, the total estimate would be enhanced.

Since detailed comprehensive statistics are not available, SM expenditures can only be estimated on the basis of discussions with SIDA staff and a review of the documents noted.

<!-- Start of picture text --> Estimated Financial Flows to Developing Countries<br>S millions /_<br>1985 1986 1987 1988<br>Total Health Sector<br>(multilateral, bilateral,<br>non-governmental) 123 136 135 182<br>Safe Motherhood<br>activities 40 45 46 47<br><!-- End of picture text -->

- **_/I_** This table uses an exchange rate of SEK6=S1.00

Based on information provided by SIDA 1989. Bilateral Development Assistance 1988-89, Stockholm. SIDA, Swedish Development Assistance, Appropriations 1988/89. Government of Sweden, Budgetaret 1989/90, Utrikes-departementet, Lettera C. Interternationellt utvecklingssamarbete, Riksmotet 1988/89. SIDA health sector totals 1958-1986 Swedish International Development Authority (SIDA) Birger Jarlsgatan 61 S 105 25 Stockholm
Dolls, Fuest, Peichl (2012) and Dolls, Fuest, Peichl, Wittneben (2018) analyze the capacity of European tax benefit systems to act as an automatic stabilizer in times of income changes and how this capacity has been affected by policy changes.

# 2. **Assessing vertical redistribution**

## **2.1 Measuring redistribution**

Redistribution is typically measured using the Reynolds-Smolensky index of redistribution as the difference between the Gini coefficients of market income and disposable income: RS��Gini<sup>������</sup> �Gini<sup>����������</sup> � Figure 1, which is based on the OECD Income Distribution and Poverty Database<sup>3</sup> , shows the Reynolds-Smolensky index of redistribution for a selection of countries. The European Union’s average level of redistribution equals 21 Gini points. This is almost twice as large as the redistribution occurring in the United States, amounting to 11 Gini points.

The Gini difference is a measure of the redistributive effectiveness of taxes and transfers at a given point in time: the larger the difference, the larger the impact of taxes and transfers on reducing inequality in market incomes. Market incomes include gross labor incomes and earnings from employment and self-employment (both permanent and temporary jobs), capital income, incomes from property as well as private pensions. Disposable incomes equal market income minus direct taxes and social insurance contributions (for both employees and self-employed), plus any additional transfers (such as pensions, means tested benefits and non-means tested benefits). Direct taxes include for example personal income taxes, taxes on capital dividends and interests, as well as property taxes.<sup>4</sup> Incomes are observed at the household level and weighted with the modified OECD equivalence scale. The Gini coefficients for market income, market income plus pensions, and disposable income for the EU-28 countries have been calculated using the microsimulation model EUROMOD (see Annex B for a description of the model) and are reported in Figure 2.

> 3 Available at http://stats.oecd.org/Index.aspx?DataSetCode=IDD.

> 4 As is common in the literature, due to data limitations, indirect taxes (e.g. consumption taxes and VAT) are not part of the analysis.

4
The rest of the paper is organized as follows. Section 2 provides information on Azerbaijan’s economy and how it was affected by the financial crisis. Section 3 describes the institutional background as well as the data and methodology while section 4 reports the empirical results. Section 5 concludes.

# **2 Azerbaijan’s economy and the financial crisis**

Azerbaijan’s economy is highly dependent on the oil and gas sector. The high real growth rates of 12 percent on average since 1998 with a peak in 2007 of 25 percent would not have been possible without the oil sector which accounts for about 60 percent of GDP and 95 percent of all exports (Economist Intelligence Unit (2009b)). Furthermore, growth is concentrated in urban areas and in the extractive sectors leading to high disparities within the country. Thus, while the average per capita income (in PPP) was at 11,413 USD in 2008 – corresponding de facto to a middle income country – an estimated 20 percent of the whole population still lives in poverty (Economist Intelligence Unit (2009b)). The omnipresent corruption and state interventions in the economy also hinder the future development of the country.<sup>7</sup> The banking sector in Azerbaijan has shown high growth rates over the last years, starting from a relatively low level. Total sector assets grew by 78 percent to 8 billion USD in 2007 while the total loan portfolio increased by 102 percent to 5.4 billion USD (Central Bank Azerbaijan (2008)). Yet, financial intermediation measured as total banking assets over GDP was with 27 percent at year end 2007 still low.<sup>8</sup> The sector remains highly concentrated with the only state-owned bank (International Bank of Azerbaijan) accounting for 39 percent of total banking assets in April 2009. The microfinance sector is targeted by 12 banks and 20 non-banks and even though the high number of banks may lead to the impression that the country is over-banked, access to credit for micro and small enterprises remains one of the main impediments for further growth especially in rural areas (IFC (2009)).

The global financial and economic crisis affected Azerbaijan’s economy from the fourth quarter of 2008 onwards and growth slowed down to 9% in 2009. Compared to other CIS countries such as Georgia, the Ukraine or Russia the overall macroeconomic and financial situation seems rather comfortable due to the high growth rates and the constant inflow of hard currency during the years _before_ the crisis. While other CIS countries suffered from high

> 7 In 2008, Azerbaijan has ranked 158th out of 180 countries on the list of Transparency International (Transparency International (2008)).

> 8 For means of comparison, Georgia and Russia have financial intermediation ratios of 42 and 52 percent, respectively.

7
more sensitive to health provision than socio-economic conditions (White, 2004). The latter is for example shown by Omariba, Beaujot and Rajulton (2007) using the 1998 Kenyan DHS: While demographic factors are more important in explaining infant (under 12 months) mortality, socioeconomic, socio-cultural and hygienic factors are more important in explaining child (under five) mortality.

According to Charmarbagwala et al. (2005), a third important determinant of child health and nutrition outcomes appears to be the availability of clean drinking water and sanitation. By preventing infections and diarrhea these two factors lead to better nutritional outcomes for a given nutrition supply and so reduce mortality. While this logic certainly applies, the difficulties of operationalizing access to safe water in (DHS-based) empirical studies may explain why the results on clean water and sanitation in the reviewed studies tend to be mixed. Ssewanyana and Younger (2007), for example do not find significant effects of variables related to the quality of drinking water and of sanitation on infant mortality. Similarly, Lavy et al. (1996) only find the expected positive effect on child health only for rural areas. Brockerhoff and Hewett (2000) do find a positive effect of piped water access, but their regression does neither include income variables nor an urban dummy (only a dummy for the largest city).

While the above child, maternal, and certain household-level proximate as well as socio-economic explanatory variables are common to most studies (albeit operationalized in different ways), most studies tend to focus on a particular set of determinants or context factors. The studies can be grouped into those that focus on health infrastructure and vaccination (often in terms of rural-urban disparities), differential mortality between socioeconomic (and/or ethnic or racial) groups, as well as the impact of HIV/AIDS, respectively.

Unfortunately, estimating the effects of the provision of health services on child mortality is not a straightforward exercise. One of the main reasons is the lack of data that would allow linking (individual) child mortality data, one the one hand, with the availability and quality of health services, on the other. Only few DHS surveys, for example, provide information on health infrastructure through a specially designed community module. Brockerhoff and Derose (1996) examine the impact of preventive health care on child survival determinants in five east African countries (Kenya, Madagascar, Malawi, Tanzania, and Zambia) using available DHS since 1992. For that purpose, they estimate child mortality models for three time intervals (0, 1-11, 12-23 months) including variables on the use of preventive health care, immunization coverage (individual and cluster info) and fertility regulation by mother. They conclude that preventive health measures are a key determinant of 10
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12
Page 5 The WDTs report that the debt stocks were highly concentrated, with some 15 of the 44 Sub-Saharan countries accounting for 97 percent of the total. C6te d'Ivoire and Nigeria each accounted for nearly $7 billion, or a combined 60 percent; Sudan, Co%igo and Gabon accounted for another $4.6 billion, or 21 percent. An additional 13% represented the outstandings of Kenya, Mozambique, Zaire, Came'roon, Zimbabwe and Niger; while Liberia, Madagascar, Ethiopia and Tanzania, each with stocks of over $100 million, accounted for an aggregate 3 percent of the total.

Zambia is also considered to be a major debtor country, but most of its commercial bank debt stocks are classified as short-term debt, consisting in large part of non-rescheduled principal and interest arrears on short-term and long-term debt.

In addition, the commercial banks are believed to account for a major portion of Africa's estimated $13 billion in short-term debt stocks (section 5 below).

# 2. Long-Term Debt Flows

After averaging over $2.5 billion per year between 1980-1982, net flows of long term commercial debt declined sharply as of 1983, as both banks and borrowing countries began to feel the pinch of a global economic contraction and a growing international debt crisis. In the period 1985-1988 flows remained sluggish despite the improvement in the performance of both some Sub-Saharan African countries and the global economy.

# Disbursements

From a peak level of some $4 billion per year in 1980-82, the WDTs report that commercial bank long-term loan disbursements to Sub-Saharan Africa totalled $14.5 billion from 1983-88, or $2.4 billion per year (Table 5).

in addition to the overstatement resulting from C6te d'Tvoire statistics, the relatively high level of disbursements in recent years do not reflect a general maintenance of lending activities in the region. Disbursements were heavily concentrated in a few countries, primarily the oil exporters and those with a more vibrant private sector. During 1985-88, with COte d'Ivoire arguably in the lead, Kenya, Gabon, Nigeria, Cameroon, Congo and Zimbabwe each generated disbursements in the $100-200 million range. During this same period some 26 African countries are shown to have received no long-term commercial bank disbursements, Commercial banks received an average $1.5 billion per year in principal repayments for the period 1980-82, rising to an average $2.1 billion per year in 1983-88, for a total of $12.8 billion during this more recent period (Table 6).