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evidence of high repetition rates, as well as poor internal efficiency, can be found in the number of years needed to complete different levels of education. For example, it takes 8.9 years to complete primary school (expected 6), 5.6 years for lower secondary (expected 4), and 7.5 for the baccalaureate degree (expected 3) (World Bank 1995). However, repetition rates are much lower in private religious schools, compared with private secular and public schools.

# **Literature Review**

Research on the distributional impact of public subsidies for education (recurrent expenditure net of costs recovered through fees) originates in the United States (see, for example, Hansen and Weisbrod 1969). These studies conclude that poor families finance the education of children of high-income families. That is, net subsidies for higher education are regressive. Research from household surveys using unit cost data and information on publicly subsidized education in nine African countries concludes that the education subsidy system is progressive, but poorly targeted. In Côte d’Ivoire (as well as in Guinea, Madagascar, South Africa, Tanzania and Uganda), the poorest 20 percent gain about 20 percent of the primary education subsidy, about 10 percent of the secondary education subsidy and a minimal percent of the tertiary level subsidy (Castro-Leal and others 1999). Recent research by Li, Steele and Glewwe (1999) provides similar results. In Côte d’Ivoire (as well as in Nepal, Nicaragua and Vietnam), the richest 20 percent of the population receive more than 30 percent of all public education expenditure.

Very little research has been carried out on private school issues in African countries. A notable exception is Tanzania. Reversing policies of restricting private secondary education, the 2
8 and Phonsaly, in the far north, are poor and predominantly rural. Phonsaly, which borders China, is one of the most ethnically diverse provinces in the country, while Lave Nge and Talieng ethnic groups comprise the majority in Attapeu. Vientiane Capital, in turn, contains the largest urban center in the country, but also peri‐urban and rural areas surrounding the city. The incidence of poverty is lower, and livelihoods tend to be more diverse. Hence, although the survey is not nationally representative, it includes locations that reflect the demographic and socioeconomic diversity of the country.

The clustered random sample includes 600 households from 30 randomly selected enumeration areas (see details in Table 1). In each of the 30 communities the household survey was administered to 20 randomly selected households. Moreover, the sample was stratified to permit an analysis of how outcomes and coping strategies vary across urban and rural settings. Sample weights were calculated and used in the analysis to account for the oversampling in urban areas, and in Attapeu and Phonsaly.

The survey instruments were designed to collect data on household composition and characteristics, housing characteristics and asset ownership, employment and livelihoods, household expenditures and consumption (based on recall of both food and non‐food consumption), shocks (including their frequency and impact on welfare), prevention and mitigation activities, households coping strategies, and government/NGO programs.

# IV. **INCIDENCE, INEQUALITY AND IDIOSYNCRATICITY OF SHOCKS**

Pest infestation and crop/livestock disease are the commonest shocks in Laos, hitting 25 percent of households, see Table 2. Illness is the next commonest shock, hitting 23 percent of households, followed by drought which affected 16 percent of households. Floods and mudslides are also relatively common, as, surprisingly, is death which affected 3 percent of households. Shocks linked to natural and
Policy Research Working Paper 9010

# **Abstract**

This paper uses methods developed by the Commitment to Equity Institute and data from the Household Budget Survey to assess the effects of government taxation and social spending on poverty and inequality in Moldova. The paper presents the first detailed distributional analysis of the tax and expenditure sides of the fiscal system, examining in particular the contribution of different taxes and transfers to poverty and inequality reduction in Moldova, as well as the cost-effectiveness of different taxes and transfers in achieving these poverty and inequality reduction goals. The analysis finds that the tax-benefit system in Moldova is quite pro-poor and has a significant effect on poverty and inequality, with the poverty reduction effect being stronger for lower poverty thresholds. Pensions provide much of the poverty-reducing effect, which is not surprising, given that in an aging society like Moldova, pensions are the main income source for many households. Direct transfers are also quite effective in reducing poverty and are also efficient, providing a relatively high degree of poverty reduction per dollar allocated to these programs, but their overall effect on poverty is muted by their small budgetary allocations.

This paper is a product of the Poverty and Equity 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 acojocaru@worldbank.org, mmatytsin@worldbank.org, and prohnitchi@gmail.com.

_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
currency devaluation, Azerbaijan’s local currency, the Manat, remained stable. However, what was felt in the economy and in particular in sectors such as trade and construction was the drop in oil prices. Sectors that remained more or less unaffected were those which are mainly independent from international markets such as agriculture. Thus, the vulnerability of the Azerbaijani economy mainly stems from the lack of diversification which will remain a challenge for the future (Hübner and Jainzik (2009)).

With respect to the banking sector, indeed most banks had to stop lending at some point and the quality of the loan portfolio deteriorated from 2.2 to 3.2 percent of loans being delinquent by more than 90 days in the first quarter of 2009 (Central Bank Azerbaijan (2009)). With respect to deposits, crisis-effects were visible as business clients increasingly withdrew their savings to keep their businesses going and households attempted to convert their savings from Manat to USD due to their fear that the Manat would devaluate. The Central Bank provided comprehensive stabilization measures in the third quarter of 2008 which included, among others, an emergency facility for liquidity support and a decrease in the refinancing interest rate from 12 to 3 percent (Hübner and Jainzik (2009)). And while the low financial intermediation would otherwise be considered unsatisfactory, it helped the banking sector to remain somewhat immune against the effects of the crisis as the sector as a whole is only integrated into global financial markets in a limited way. Nevertheless, those few banks which are globally connected directly felt the effects of the turmoil in financial markets. Apart from that, all Azeri banks had to deal with an increase in their customers’ risks when the economic crisis hit the country.

# **3 Institutional background, data and methodology**

# **3.1 The bank**

The data we use for the empirical analysis was generated using the Management Information System (MIS) of AccessBank Azerbaijan.<sup>**9**</sup> The mission of AccessBank is to provide financial services at European standards to micro and small businesses and low and medium income families while also offering products for larger enterprises.<sup>10</sup> AccessBank was founded by international financial institutions, which are still shareholders today, in October 2002 under the name Micro Finance Bank of Azerbaijan (MFBA). Until today,

> 9 To get a better understanding of the bank’s business, the different loan types and borrower groups as well as the crisis events, we conducted informal interviews with the bank’s management.

> 10 See http://www.accessbank.az/en/index.html or AccessBank (2008) for detailed information on the bank and its business activity.

8
FIGURE 7.2 <!-- Start of picture text --> 57<br><!-- End of picture text --> <!-- Start of picture text --> GNP Deflator Deviations from the Base Run.<br>for Six Blocks. After (+/-) 10 Percent Chanees<br>in the United States Taraet Monetary Base in MULTIMOD<br>US PGNP CR PGNP<br>0.0011<br>0.004<br>0.004<br>-001~~~~~~~I:<br>0.000<br>i.~~~~~~~~~~~~~~~~~~~~~~~~-.0 4<br>-0.001<br>o Ie ooI 4 P'n *n n<br>JA PGNP LIPGNP<br>owe 0.06. ~~~~~~~~~~~~~~~~~~~~~0.4000 -eJ<br>0.0? @000* .n<br>0.063 0.0?3<br>0.00. 0.004-<br>0.04 £~~~~~~~~~~~~~~~~~~~~-.0<br>-0.004<br>o0.eaw- o0._004<br>0.003 -e.ooe<br>011 0.0 00* 103 11 1<br>-0.002.<br>-0002000 O. *O?: ~~~~~~~~~~~~~~~~~~~~0.003<br>0.004: e-0.00. 0w ~~~~~~~~~~~~~~~~~~~~0.0034 e "o v w.0<br>0.04 -0-<br>0.003, 000 008 l0o 00 000 124oeot00<br>0.002~ ~ ~~~~~~~~~~~~~~0<br>-0.0010-.01<br>-002<br>-*.406 -0.007 ~~~~~~~~~~~~~~~~~~~~~~-0.00*~~~~~~~~~~~~~~~~~~~~-0.004<br>19113 IN4 in~~~~~~0In 0 I 0ol3 0004.00 0006 06<br>Ki ot PO11<br><!-- End of picture text -->
US$300/month or less, but with a long right tail stretching into monthly profits that number in the thousands (Figure 8). The average monthly profit reported by the surveyed firms is US$860, which amounts to 30 percent of total monthly revenue. This figure lines up reasonably well with the average reported annual (2010) profit of US$12,396 which works out to just over US$1,000 per month. It is also consistent with the entrepreneurs’ expected profits for the entire 2011 (approximately US$1,090), a somewhat curious result given the general lack of optimism about enterprise growth prospects expressed by entrepreneurs during the focus group sessions (most entrepreneurs complained about growing costs, higher taxes, and increased levels of competition).

**Figure 8: Distribution of monthly profits**

<!-- Start of picture text --> 15<br>10<br>5<br>0<br> 10 30 100 300 850 3,000 10,000 30,000<br>Log of self-reported monthly profits (x-scale in US$)<br>Percent<br><!-- End of picture text --> _Source_ : Authors’ calculations with EMES data.

# **3. How do we model firm profitability?**

The emphasis of this paper is on modeling profitability rather than productivity because the former is the main determinant of firm survival. From the firm owner’s perspective, decisions are normally made based on their impact on (expected) profitability rather than productivity, which most small firm owners do not observe directly. Moreover, while profitability and productivity are obviously linked, more productive firms are not necessarily more profitable, and vice versa. Katayama et al (2009) note that “success ultimately depends upon profits rather than efficiency or product quality,” a view echoed by Foster et al (2008) who show that “selection [for survival] is on profitability, not productivity ... productivity is only one of several possible idiosyncratic factors that determine profits.” Finally, as mentioned in the previous section, measuring productivity without detailed information on quantities and prices at the firm level is fraught with difficulties while profits present a direct and robust alternative.

We model profitability as a function of observable firm, owner, and location/sector characteristics. Similar to other studies, profits of firm _i_ depend on a set of owner characteristics _Xi_ such as age, gender, education, and the first principal factor of entrepreneurial ability components in Figure 2; firm characteristics _Zi_ such as weekly hours spent on the business by the owner, access to formal sources of financing, whether the location is owned or rented, and the number of employees; and location and sector dummies _Si_ : 10
For the policy rate we adopt two approaches. First, we construct a composite policy rate series in the manner of Patra and Kapur (2013).<sup>24</sup> This is based on which series was used by the RBI at the time for monetary policy purposes, as detailed in Table 6. In the second approach, we use bank rate for the period 1997-2001 and repo rate from 2002<sup>25</sup> (See Figure 3B). The exchange rate is calculated as quarter-overquarter percent change in the nominal exchange rate with respect to US dollar. The budget deficit and market borrowing variables are highly seasonal at quarterly frequency, so we adjust them for seasonality and express as a percent of seasonally adjusted nominal GDP.<sup>26</sup>

**Table 6: Effective policy rates**

<!-- Start of picture text --> Duration<br>Instrument<br>Month Quarter (cal. year)<br>Effective Bank rate Jan1996 - Feb2002 1996q1 - 2002q1<br>Policy Reverse repo rate Mar2002 - June2006 2002q2 - 2006q2<br>rate Repo rate July2006 - Nov2008 2006q3 - 2008q4<br>Reverse repo rate Dec2008 - May2010 2009q1 - 2010q2<br>Repo rate June2010 - present 2010q3 – present<br> Source: Based on information in Patra and Kapur (2013).<br>Figure 3: Policy rates<br>Figure 3A: Bank rate, the repo rate, and the Figure 3B: Composite policy rate series<br>reverse repo rate<br>14 Bank rate 14 Policy rate 1 Policy rate2<br>12 Repo Rate 12<br>Reverse Repo Rate<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>Source: RBI, CEIC Source: Authors’ Calculations<br>1996q2 1997q4 1999q2 2000q4 2002q2 2003q4 2005q2 2006q4 2008q2 2009q4 2011q2 2012q4 2014q2 2015q4 2017q2 2018q4 1996q2 1997q4 1999q2 2000q4 2002q2 2003q4 2005q2 2006q4 2008q2 2009q4 2011q2 2012q4 2014q2 2015q4 2017q2 2018q4<br><!-- End of picture text --> Note: The first composite policy rate series is based on the policy rates at different points in time, as per Table 6. In the second composite policy rate series, bank rate is used for the period 1996-2001 and repo rate from 2002. The correlation between the two policy rate series is 0.93.

> 24 Bank rate, the repo rate, and the reverse repo rate are available as monthly averages and end of month values. The results are insensitive to the two series. In the results reported here, we have used monthly averages. 25 The two series are highly correlated, as is evident in Figure 4.

> 26 We use both X-11 and X-13 ARIMA SEATS to seasonally adjust these series. The choice of filter does not seem to matter in the results.

12
# **1 Introduction**

Over recent decades, the escalating climate crisis has become increasingly apparent. This crisis is characterized by extreme conditions such as a continuous rise in Earth’s temperature and extended periods of both rainfall and drought. Greenhouse gas emissions that cause climate change are part of a broader process of environmental degradation, generating unhealthy levels of air pollutants, including particulate matter. The repercussions of these hazards —extreme temperature, extreme rainfall, and hazardous air— on health outcomes, including disease spread and long-term outcomes, have been examined for some countries in the scientific literature, although their health impacts are often studied in isolation from one another.

Climate change will likely increase the frequency and severity of extreme weather events and, consequently, their impact on health and human capital. The latest report from the Intergovernmental Panel on Climate Change (IPCC) highlights that even under the 2°C scenario, there will be a definitive rise in average temperatures across all regions (IPCC, 2022). This warming trend is accompanied by projected changes in temperature and precipitation patterns, resulting in an amplified risk of either drought or intense precipitation events (Arias et al., 2021). This escalating health burden caused by climate change has become a growing concern for health agencies and policymakers worldwide. As extreme conditions persist and intensify, it is crucial for researchers and policy makers to produce regional evidence of weather impacts on health and to assess the effectiveness of adaptation actions.

Recent estimates show that nearly 1,000 children under five die every day as a result of diarrhea caused by a lack of access to clean water and sanitation (World Health Organization, 2014). Providing water and sanitation, with associated sustained behavior change, is a critical arm of health policy. However, public health studies quantifying the role of sanitation in mitigating climate-related disease are scarce and often mixed (Howard et al., 2016; Carlton et al., 2014).<sup>1</sup> In this study, we quantify the potential impact of climate change on a selected health outcome, diarrhea incidence in children, and assess the effectiveness of a sanitation policy, increasing toilet ownership, in mitigating the impact of weather shocks and adapting to climate change.

> 1For the specific case of Cambodia, Howard et al. (2016) suggested that poor water quality and sanitation were responsible for the statistically significant impact of floods on diarrheal disease in 2 of 16 provinces in Cambodia. Similarly, Carlton et al. (2014) found that rainfall was associated with diarrhea incidence in Ecuador, with drinking water treatments decreasing the impact of rainfall but sanitation and hygiene having no impact.

1
Table E.1: Model Fit: Sample and Simulated Moments

|Moments|Sample|Simulated|
|---|---|---|
|Panel (a): Child Care Choices|||
|Percent using Child Care Centers|||
|Married mothers, college, working full time|65.22|62.20|
|Married mothers, college, working part time|43.48|42.80|
|Married mothers, college, not working|6.87|8.03|
|Married mothers, less than college, working full time|44.00|51.29|
|Married mothers, less than college, working part time|25.53|27.40|
|Married mothers, less than college, not working|6.52|5.15|
|Single mothers, working full time|61.02|62.39|
|Single mothers, not working|15.87|19.29|
|Percent using Non-relative Care|||
|Married mothers, college, working full time|11.30|7.27|
|Married mothers, college, working part-time|26.09|4.25|
|Married mothers, college, not working|2.29|0.72|
|Married mothers, less than college|2.61|2.08|
|Single mothers|4.29|3.40|
|Percent using Relative Care|||
|Married mothers, college, working|21.12|28.74|
|Married mothers, college, not working|2.29|3.85|
|Married mothers, less than college, working|53.28|41.30|
|Married mothers, less than college, not working|3.26|7.31|
|Single mothers, working|33.77|33.67|
|Single mothers, not working|9.52|16.16|
|Panel (b): Child’s Cognitive Score|||
|Average Score|||
|Married and single mothers, college|13.10|13.17|
|Married and single mothers, high school|12.53|12.58|
|Married and single mothers, incomplete high school|12.13|12.15|
|All mothers, working|12.92|12.79|
|All mothers, not working|12.68|12.64|
|Married mothers, college|13.20|13.17|
|Married mothers, less than college|12.60|12.61|
|Married mothers, not working|12.77|12.73|
|Married mothers, high school, not working|12.59|12.58|
|Single mothers|12.38|12.35|
|Single mothers, not working|12.21|12.30|
|Single mothers, high school, not working|12.16|12.25|
|Average Score by care choice|||
|All mothers, choosing High-quality centers|13.25|13.27|
|All mothers, choosing Low-quality centers|12.84|12.62|
|All mothers, choosing Non-relative care|12.69|12.71|
|All mothers, choosing Relative care|12.54|12.69|

71