rafmacalaba/datause-encoder-data huggingface dataset:
- 18 -

- (i) changes in the price of energy to other goods can produce large changes in energy intensity (ratio of energy to output). Any policy which alters the price of energy as a whole is likely to have important effects on the total amount of energy used and hence on the amount of pollution;

- (Ii) there have also been large swings in tho relatlve prices of different fuels coupled wlth large shifts Ln relative fuel use. The exact effects depend crucially on the lags Lnvolved so that to evaluate the full potential of a fuel switching policy it le necesoary to know the speed of adjustment to the pollcy changeg

- (lii) some of the biggest differences in fuel use between countries are assocLated with the domestic availability of the varlous fuels. Any fuel switching policy must allow fully for the price advantage of domestic as opposed to imported fuels.

# Fuel **lisa Out8ide** the O.3.C.D.

The evidence so far presented relates to the most developed sector of the world economy, for which the statistics are the most adequate. However, the focal point of interest is countries at earlier stages of development. Some O.E.C.D. countrles (e.g. Greece and Portugal) in the early 1970s were at a stage of development not very different from many other countries at the present day, but it would be unwise to draw too strongly on their experience which may have been dominated by special factors.

> There _is_ data on a wide range of countries published by the United Nations in "Energy Balances and Electricity Profiles". This covers 48 non O.E.C.D. countries for industry data and 75 non O-E.C.D. countries for power generation data. The time span of years avaLlabl- ^" much shorter so that the evidence on fuel substitution within countries on a consistent basis is rather limited.

Fuel Us, in Industry Outside th O.ELC.D.

Table 2.10 gives the shares of different fuels used by industry for 1986 where all fuels have been converted into energy equivalent units (tetrajoules). An important difference from O.E.C.D. experience is the use of bia-mass which in many cases is very significant and is shown separately.
magnitude. Note also that enterprises owned by females are much less likely to add paid employees.

Employment growth is much more common among enterprises operating in the manufacturing and services sectors, and very uncommon among retail shops. The first two regressions include a dummy variable indicating the enterprise is a retail shop. The third column drops the retail shops from the sample. The results are very similar when the sample is limited to manufacturers.

Our species analysis classified about two-thirds of the own account workers as wage workers and one-third as SME owners. Did a larger percentage of those classified as SME owners add employees? The answer is yes: 13.8 percent of those classified as SME owners added at least one paid employee, compared with 7.8 percent of those classified as wage workers, a difference significant at the .06 level. Not only do we find a difference in rates of growth, but the nearly 14 percent of SME-type enterprises that add employees in less than three years time is much larger than comparable growth rates in the United States suggested by Davis et al (2007).

# **Concluding Remarks**

Are own account workers more like wage workers, or more like owners of larger enterprises? The simplest answer to this question comes from our discriminant analysis. We find that roughly two-thirds of the own account workers should be classified as wage workers rather than entrepreneurs. The more detailed analysis uncovers several interesting patterns. Most significantly, we find that ability, motivation and a competitive attitude are very important in differentiation SME and microenterprise owners. Controlling for these factors, family background and measures of childhood wealth and well being are much less important.

The classification analysis suggests that capital is not the only, and perhaps not even the primary constraint to growth for the majority of the own account workers. The data thus shed light on the recent concern with the lack of dynamism among recipients of microfinance. Microlenders are concerned that few enterprise owners grow large enough to graduate to more formal lending programs. Our data suggest one explanation for this: the majority of the microenterprise owners are more like wage workers than larger
Table A.4: Logit analysis, 1995‐2015, Frequency of ERRs in both the PAD and ICR as dependent variable <u>(Model 1)</u><sup>5</sup>

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Pres/Nopre|s Pres/Nopre|s Pres/Nopre|s Pres/Nopre|s Pres/Nopre|s Pres/Nopre|s Pres/Nopres|
|CPIA rating|0.512***|0.387***|0.542***|0.573***|0.513***|0.504***|0.431***|
||(5.24)|(3.86)|(5.50)|(5.12)|(5.21)|(4.33)|(4.24)|
|CPIA scale dummy|‐0.478***|‐0.377**|‐0.487***|‐0.352*|‐0.413**|‐0.250|‐0.365**|
||(‐2.70)|(‐2.11)|(‐2.74)|(‐1.74)|(‐2.31)|(‐1.22)|(‐2.02)|
|Approval fiscal year sequence|‐0.522***|‐0.482***|‐0.516***|‐0.529***|‐0.581***|‐0.530***|‐0.518***|
||(‐7.11)|(‐6.54)|(‐6.96)|(‐6.05)|(‐7.74)|(‐5.86)|(‐6.81)|
|Quadratic form of approval year sequence|0.00893***|0.00832***|0.00882***|0.00899***|0.0101***|0.00919***|0.00903***|
||(6.58)|(6.13)|(6.45)|(5.53)|(7.28)|(5.46)|(6.44)|
|East Asia and Pacific|‐0.165|‐0.149|‐0.189|‐0.131|‐0.199|‐0.170|‐0.191|
||(‐1.08)|(‐0.96)|(‐1.23)|(‐0.74)|(‐1.30)|(‐0.94)|(‐1.23)|
|Africa|‐0.823***|‐0.573***|‐0.884***|‐0.753***|‐0.776***|‐0.555***|‐0.629***|
||(‐5.57)|(‐3.77)|(‐5.92)|(‐4.33)|(‐5.22)|(‐3.07)|(‐4.09)|
|East Europe and Central Asia|‐0.508***|‐0.220|‐0.518***|‐0.444**|‐0.489***|‐0.178|‐0.251|
||(‐3.22)|(‐1.35)|(‐3.28)|(‐2.43)|(‐3.08)|(‐0.93)|(‐1.53)|
|Latin America & Caribbean|‐0.672***|‐0.520***|‐0.677***|‐0.645***|‐0.557***|‐0.441**|‐0.473***|
||(‐4.37)|(‐3.32)|(‐4.39)|(‐3.63)|(‐3.58)|(‐2.42)|(‐3.01)|
|Middle East & North Africa|‐0.280|‐0.132|‐0.345*|‐0.227|‐0.255|‐0.118|‐0.194|
||(‐1.46)|(‐0.68)|(‐1.78)|(‐1.02)|(‐1.32)|(‐0.52)|(‐0.99)|
|Agriculture|2.442***|2.471***|2.442***|2.506***|2.393***|2.505***|2.438***|
||(19.13)|(19.15)|(19.11)|(17.08)|(18.67)|(16.83)|(18.82)|
|Energy & mining|3.015***|2.905***|3.026***|2.997***|3.034***|2.930***|2.933***|
||(20.36)|(19.32)|(20.40)|(17.43)|(20.32)|(16.69)|(19.40)|
|Transport|3.484***|3.304***|3.481***|3.478***|3.527***|3.353***|3.343***|
||(24.11)|(22.59)|(24.05)|(21.00)|(24.18)|(19.76)|(22.59)|
|Water|2.575***|2.515***|2.558***|2.515***|2.540***|2.433***|2.486***|
||(15.48)|(15.01)|(15.36)|(13.13)|(15.20)|(12.56)|(14.78)|
|Urban Development|1.394***|1.330***|1.407***|1.507***|1.386***|1.459***|1.341***|
||(7.24)|(6.86)|(7.29)|(6.86)|(7.16)|(6.56)|(6.89)|
|Log net commit. (m$)||0.315***||||0.283***|0.281***|
|||(7.92)||||(5.87)|(6.71)|
|Project length|||0.0746***|||0.0814***|0.0678***|
||||(3.21)|||(2.98)|(2.87)|
|TTL performance||||0.139**||0.121**||
|||||(2.40)||(2.03)||
|Log (preparation cost, us$)|||||0.312***|0.187***|0.178***|
||||||(5.33)|(2.69)|(2.89)|
|Observations|4020|4020|4020|3065|4006|3055|4006|
|Pseudo R‐squared|0.282|0.296|0.284|0.285|0.288|0.304|0.299|

Note: t statistics in parentheses, * p<0.10, ** p<0.05, *** p<0.01 5 These regressions were also estimated for high CBA projects only. All the coefficients along with their signs were found to be similar to what is reported here.

30
|

2 = ee ee 3 Siexles 98 eo Be a a gE,° [7 a a a eS ror ee = oe s E oe ee a as & z wis ies ee a a in ae EF ce ene | re Me NS gg cD) tas Ae ee ee Sen Ge oe - ir we 4 “a &§ tn oe i E F: . cin @2aeen oe ote * _— jaDo| 28 aifieeea oefe SeeaPe PeAe aPe CaeaOo ~~ oP.2aeo Bee FF = 2) So i. 2 Cros Soe S a 3 » (whe ue oo _e. i. oo € S 7 2 & 2 we me oe NESE OURE Se Ieee S22 222 Bee eee Hee ~~& o EH = = -— ee oe Soeneo ema RRS Boer BEE ee: ESE ee Be Se Ee See See ne Sane o . s 5 a a i a Eee oo ene ves BE E i) 5 Z Areea hEee Ole EeeGea oran a Bee. SauAE s S a a ee Bee HES Be ES #2 BEES Ee at Sra . oo eee Bee Bo See EEE See Bessie Hee ESSE See 3 3 =. ee a of ae 4 f a a an es Ep EES be eee ae . a gee i ae oe eee a ae 2 - TEE a oe a ae a oe ar ae See Eee ag2 5 §); 2. a 2 g ERE 2. 398 8 i a & Seo E Bs. ~ #88 Be 8g = of = 5 me oS£BaSSESS Se38 2ZG2e ee &sigacdez **SE** Be QeSaasOpa BeaawegBS **s** aisegi **e** kBBee **e** Beabsseses|BaPo EE= aSSa poes52%8
**Table 2.1   Singapore’s GDP Distribution by Sectors, 1960-2002 (%)**

|Industry|1960|1970|1980|1990|1995|2000|2002|
|---|---|---|---|---|---|---|---|
|Agriculture & Mining|3.9|2.7|1.5|0.4|0.2|0.1|0.1|
|Manufacturing|11.7|20.2|28.1|28.0|26.3|25.9|27.4|
|Utilities|2.4|2.6|2.1|1.9|1.6|1.7|1.8|
|Construction|3.5|6.8|6.2|5.4|7.0|6.0|5.6|
|Commerce|33.0|27.4|20.9|16.3|17.3|19.1|15.5|
|Transport & Communication|13.6|10.7|13.5|12.5|12.4|11.1|11.9|
|Financial & Business Services|14.4|16.7|18.9|25.5|25.5|25.3|25.3|
|Other Services|17.6|12.9|8.7|9.9|9.8|10.9|12.4|
|Total|100%|100%|100%|100%|100%|100%|100%|

_Notes_ : 1. Figures may not add up to 100 due to rounding.

2. Total GDP excludes owner-occupied dwellings and calculations for taxes and duties on imports and imputed bank service charge. _Sources_ : Calculated from MTI (1990); Department of Statistics, _Yearbook of Statistics Singapore_ , various years; Ministry of Trade and Industry, _Economic Survey of Singapore_ , various years.

**Table 2.2  Electronics Manufacturing Industry Growth in Singapore, 1960-2001**

|Year|Output<br>$mn|No. Workers|Value Added<br>$mn|Fixed Asset<br>$mn|Val.Add/<br>Labor<br>$’000|Val.Add/<br>Output<br>%|Capital/<br>Labor<br>$’000|
|---|---|---|---|---|---|---|---|
|1960|17.1|1,252|7.9|na|6.3|46.2|na|
|1970|212.9|11,251|99.1|na|8.8|46.5|na|
|1980|5,344.0|71,727|1,668.9|585.1|23.3|31.2|8.2|
|1990|27,878.1|122,797|7,716.6|3,757.3|62.8|27.7|30.6|
|2001|62,201.5|98,012|11,615.3|15,911.0|118.5|18.7|162.3|
||||Average Per<br>|Annum Growth|Rate (%)<br>|||
|1960-1970|32.4|26.9|32.1||3.4||n.a.|
|1970-1980|34.2|18.1|29.3||10.2||n.a.|
|1980-1990|18.0|5.5|16.5||10.4||14.1|
|1990-2001|7.6|-2.0|3.8||5.9||16.4|
|||Percentage of To<br>|tal Manufacturing<br>|||||
|1960|3.7|4.6|5.6|na||||
|1970|5.5|9.3|9.1|na||||
|1980|16.9|25.1|19.6|7.8||||
|1990|39.1|34.9|35.7|20.8||||
|2001|45.0|28.4|36.4|33.1||||

_Sources_ : Economic Development Board, Report on the Census for Industrial Production, various years; Economic Development Board, Report on the Census of Manufacturing Activities 2000.

10
diversification/specialization) by dominance. We do not include transfer and other income in order to improve the clarity of the presentation. A summary of the analysis can be found in Table 5.

**Table 5. Summary of stochastic dominance rankings, by country/dataset**

||**By income source**|**By income**<br>**diversification/**<br>**specialization**<br>**(total hh income)**|**By income**<br>**diversification/**<br>**specialization**<br>**(pc exp)**|
|---|---|---|---|
|Malawi 2011|1. Non ag wage—crop<br>2. Self employ<br>3. agr wage<br>4. livestock|1. Non ag wage<br>2. Self employ<br>3. Farm—Diverse<br>4. Agr wage|1. Non ag wage—Self<br>employ<br>2. Farm (low lev)—<br>Diverse (high lev)<br>3. Agwage|
|Tanzania 2009|1. Crop<br>2. Non ag wage— Self<br>employ|1. Non ag wage—self<br>employ<br>2. Diverse|1. Non ag wage—Self<br>employ<br>2. Farm—Diverse|
||3. Livestock—agr wage|3. Farm<br>4. Agr wage|3. Ag wage|
|Tanzania 2010|1. Crop<br>2. Non ag wage|1. Diverse—self<br>employ—farm—|1. Non ag wage—Self<br>employ|
||3. Self employ<br>4. Livestock<br>5. Agr wage|non ag wage<br>2. Agr wage|2. Farm—Diverse<br>3. Ag wage|
|Uganda 2010|1. Crop<br>2. Non ag wage<br>3. Self employ|1. Diverse—non ag<br>wage—self employ<br>2. Farm|1. Non ag wage—Self<br>employ<br>2. Farm—Diverse|
||4. Livestock—agwage|3. Agr wage|3. Agwage|
|Uganda 2011|1. Non ag wage<br>2. Crop—self employ|1. Non ag wage—<br>Diverse—self|1. Non ag wage—Self<br>employ|
||3. Ag wage—livestock|employ<br>2. Farm|2. Diverse<br>3. Farm|
|||3. Agr wage|4. Agwage|
|Niger 2011|1. Non ag wage—crop<br>2. Self employ<br>3. Livestock—agr wage|1. Non ag wage<br>2. Diverse—self<br>employ—ag wage<br>3. Farm|1. Non ag wage—Self<br>employ—Ag wage<br>2. Farm—Diverse|

Turning first to sources of income, Figure 20 provides graphs of the cumulative income distribution in each country, and in Table 5 we find the ranking. A consistent, though somewhat surprising, pattern emerges across the four countries. Crop and non-agricultural wage income tend to stochastically dominant the other sources of income, followed by self-employment, with livestock and agricultural wage employment associated with lower levels of income. The consistent strong showing of crop income is surprising until one considers that almost all households, rich and poor, have crop income. What becomes important is the relative mix of income-generating strategies.

19
**Figure 4: Tax Evasion Risk and Excess Interaction**

<!-- Start of picture text --> a. Risk Score b. Tax Rate<br>8 .4<br>7 .35<br>6 .3<br>5 .25<br>4 .2<br>-.1 0 .1 .2 .3 -.1 0 .1 .2 .3<br>Excess interaction share Excess interaction share<br>95% CI Fitted 95% CI fitted<br>Risk score Tax rate<br><!-- End of picture text --> <!-- Start of picture text --> c. Log Initial Unit Price (Rel. to Internal Prices) d. Initial Hypothetical Tax Revenue Losses<br>.8<br>0<br>.6<br>-.2<br>.4<br>-.4<br>.2<br>-.6<br>0<br>-.8<br>-.1 0 .1 .2 .3 -.1 0 .1 .2 .3<br>Excess interaction share Excess interaction share<br>95% CI fitted 95% CI fitted<br>e. High Potential Tax Yield Dummy<br>.8<br>.6<br>.4<br>.2<br>0<br>-.1 0 .1 .2 .3<br>Excess interaction share<br>95% CI fitted<br>Log potential revenue loss<br>Log (unit price/median unit price)<br>High potential yield declaration (probability)<br><!-- End of picture text --> _Notes_ : The graphs, generated using STATA 17, show weighted local polynomial plots (using the Epanechnikov kernel function) of a selected number of declaration characteristics capturing tax evasion risk on calibrated excess interaction shares for the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention). The sample used is the regression sample used to generate baseline results (see e.g. Table 3). _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated using calibration methods (as explained in Section 4). Initial hypothetical tax revenue losses refer to the tax revenue losses estimated based on internal reference prices. "high potential tax yield" declarations are those for which the hypothetical tax yield if the declaration was valued using external reference prices exceeds 20,000 USD. CI stands for confidence interval.

40
<!-- Start of picture text --> Figure 4<br>Share of the Unofficial Economy in CEE and FSU Countries, 1989 and 1994<br>Uzbekistan<br>*Poand S 9 994<br>*Romania |j 1989<br>*Czech Republic<br>*Hungary<br>Lithuania<br>*Bulgaria<br>Kazakhstan i<br>Moldova<br>Azerbaijan<br>Georgiar<br>F'SiJ average<br>*CEE average;<br>0 10 20 30 40 50 60 70<br>CEE countries<br>Note: 1994 estimates are based on the Conservative Elasticity Scenario<br><!-- End of picture text -->
adjustments and represent transactions.<sup>3</sup> The information on other financial and macroeconomic variables was obtained from the International Financial Statistics (IFS) and Global Financial Stability Report (GFSR). Our analysis focuses on the following variables discussed in section 4: banking sector domestic deposits, banking sector foreign liabilities, real GDP, inflation, deposit rate, EONIA rate, trade openness, and credit-to-GDP ratio.

Since information on bank credit is obtained from the ECB, we only observe countries starting from the beginning of the euro, or from the moment they joined the monetary union, turning our panel unbalanced. Table A1 lists the year in which each country is first observed in our data set. For advanced EU countries, the sample period starts in 2003:Q1, but for most emerging European countries, the sample period starts in 2004:Q1. For some of these countries (Croatia, Estonia, Latvia, and Slovenia), bank credit data availability is a greater constraint and the sample only starts in the late-2000s. The sample ends in 2014:Q1.<sup>4</sup> For convenience in exposition, we define the pre-crisis period as the period 2003:Q1-2007:Q4 and the post-crisis period as the period 2008:Q1-2014:Q1.

Figure 1 plots the growth of credit of different maturities in advanced countries and emerging countries. Credit expanded in both advanced countries and emerging countries, but it grew much faster in emerging countries than in advanced countries. An interesting feature is that all three maturity groups expanded at roughly the same rate in advanced countries, but long-term credit expended much faster than short-term credit in emerging countries. Credit stopped growing at the end of 2008, and declined in 2009-2010. While credit was on average growing faster in

> 3 Specifically, growth rates are corrected for reclassifications of instruments and other breaks in the series, for valuation changes, and for loan write-offs and write-downs. See ECB (2012) for more details.

> 4 For euro area countries, information by maturity on the total amount of loans to non-financial institutions only covers loans within the euro area, and for other euro area countries, it covers domestic loans. In the case of euro area countries, most of loans to non-financial firms are domestic loans.

9
<!-- Start of picture text --> bred 57.64<br>8<br>[s)<br>£<br>c<br>Ss<br>eg<br>=<br>3<br>s<br>2<br>3 24.45<br>8g<br>c 15.72<br>8 13.54<br>®<br>a<br>6.99<br>5.24<br>oO<br>None Other More Less Safety Less<br>comfort crowding harassment<br><!-- End of picture text --> <!-- Start of picture text --> Assigned to<br>reserved space<br>x T1 (low level<br>users reserved<br>space)<br>Assigned to<br>reserved space<br>xT2<br>Assigned to<br>reserved space<br>levelx T3 users(high fom——_— crc<br>reserved space)<br>-.03 -.02 -01 0 01<br>ofEffectphysicalon probabilityharassment<br>P-value, all effects equal: .68.<br><!-- End of picture text -->
increase the demand for women workers. Countering these positive effects, maternity leave may depress demand for female workers if part of the costs of maternity leave directly or indirectly falls on employers. The direct costs may include employer’s monetary contribution (if any) to maternity leave benefits, while the indirect costs involve short-term work disruption and loss of skills due to the increased absence during childbirth. A negative supply side effect could be that longer maternity leave may delay women’s return to work. The net impact of these competing effects of maternity leave is essentially an empirical issue. Our analysis reveals that the positive effects dominate. We summarize this in the following hypothesis.

_Hypothesis 1_ : Higher duration of maternity leave is associated with a higher share of female workers in a firm.

# 2.2 _Other determinants of female employment_

There are several other determinants of maternity leave highlighted in the literature. If these determinants happen to vary systematically with maternity leave, our results for the impact of maternity leave on female workers may suffer from the omitted variable bias problem. Thus, as a first defense against the problem, we draw on the exitsting literatue to control for several determinants of female employment. The motivation for the controls is as follows.

Several studies have documented differences across industries in the share of female and male workers (see Borrowman and Klasen 2020, Olivetti and Petrongolo 2014, World Bank 2011, Goldin 2006). Factors such as “brain vs. brawn” content of jobs, culture and norms that view some industries and jobs as more “female oriented” than others, flexibility of working hours and so forth, are some of the underlying factors for the industry-wise segregation of men and women. Job 7
# 7. References

- ‐

- Alderman, H., Behrman, J. R., Grantham McGregor, S., Lopez Boo, F., & Urzua, S. (2014). Economic perspectives on integrating early child stimulation with nutritional interventions. _Annals of the New York Academy of Sciences_ , _1308_ (1), 129-138.

- Akseer, N., Kamali, M., Arifeen, S. E., Malik, A., Bhatti, Z., Thacker, N., ... & Bhutta, Z. A. (2017). Progress in maternal and child health: how has South Asia fared?. _bmj_ , _357_ , j1608.

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

- Chambers, R., & Von Medeazza, G. (2013). Sanitation and stunting in India. _Economic & Political Weekly_ , _48_ (25), 15.

- Clasen, T., Schmidt, W. P., Rabie, T., Roberts, I., & Cairncross, S. (2007). Interventions to improve water quality for preventing diarrhoea: systematic review and meta-analysis. _Bmj_ , _334_ (7597), 782.

- Clasen, T., Boisson, S., Routray, P., Torondel, B., Bell, M., Cumming, O., ... & Ray, S. (2014). Effectiveness of a rural sanitation programme on diarrhoea, soil-transmitted helminth infection, and child malnutrition in Odisha, India: a cluster-randomised trial. _The Lancet Global Health_ , _2_ (11), e645-e653.

- Dangour, A. D., Watson, L., Cumming, O., Boisson, S., Che, Y., Velleman, Y., ... & Uauy, R. (2011). Interventions to improve water quality and supply, sanitation and hygiene practices, and their effects on the nutritional status of children. _Cochrane Database Syst Rev_ , _3_ .

- Dreyfuss, M. L., Stoltzfus, R. J., Shrestha, J. B., Pradhan, E. K., LeClerq, S. C., Khatry, S. K., ... & West, K. P. (2000). Hookworms, malaria and vitamin A deficiency contribute to anemia and iron deficiency among pregnant women in the plains of Nepal. _The Journal of nutrition_ , _130_ (10), 2527-2536.

- Fewtrell, L., Kaufmann, R. B., Kay, D., Enanoria, W., Haller, L., & Colford Jr, J. M. (2005). Water, sanitation, and hygiene interventions to reduce diarrhoea in less developed countries: a systematic review and meta-analysis. _The Lancet infectious diseases_ , _5_ (1), 42-52.

- Field, E., Glennerster, R., & Hussam, R. (2011). Throwing the baby out with the drinking water: Unintended consequences of arsenic mitigation efforts in Bangladesh. _Work. Pap., Dep. Econ, Harvard Univ., Cambridge, MA_ .

Freeman, M. C., Garn, J. V., Sclar, G. D., Boisson, S., Medlicott, K., Alexander, K. T., ... & Rehfuess, E. A. (2017). The impact of sanitation on infectious disease and nutritional status: A systematic review and meta-analysis. _International journal of hygiene and environmental health_ , _220_ (6), 928-949.

- George, C. M., Oldja, L., Biswas, S., Perin, J., Lee, G. O., Kosek, M., ... & Azmi, I. J. (2015). Geophagy is associated with environmental enteropathy and stunting in children in rural Bangladesh. _The American journal of tropical medicine and hygiene_ , _92_ (6), 1117-1124.

- Gruber, J. S., Ercumen, A., & Colford Jr, J. M. (2014). Coliform bacteria as indicators of diarrheal risk in household drinking water: systematic review and meta-analysis. _PloS one_ , _9_ (9), e107429.

22
# • **Abstract**

Program administrators are often faced with the difficult problem of allocating scarce resources among regions in a country when interventions are aimed at addressing multiple objectives. One main concern is the tradeoff between poverty reduction and improvement of environmental quality. To provide a framework for analysis, this paper develops a model of optimal budget allocation that allows for variations in three factors: administrators' valuation of objectives; their willingness to accept tradeoffs among objectives and regional allotments; and regional administrative costs. The results from an application of this model using information for Lao PDR show that simple poverty indicators alone do not provide consistent guidelines for policy. However, when different poverty indicators are embedded in an optimizing model that incorporates preferences and costs, the resulting provincial allocations are very similar. This suggests that adoption of a formal analytical approach to resource allocation can help promote the harmonization of regional policy guidelines.

1