rafmacalaba/datause-encoder-data huggingface dataset:
of 32.05 USD in the control group. Balances for individuals encouraged to open the account at the branch are not statistically different from those in the control group in either panel.

We thus confirm the prediction of the model that balances increase among individuals that perceive that the transactions costs are lower.

# _4.4 Savings and Other Outcomes_

Table 8 and Online Appendix Table OA5 report the impact of the different treatments on various savings and welfare outcomes. In column 1 of Table 8 we report the willingness to pay elicited at endline for a Sukaliku account with a balance of 1,500 CFA (2.8 USD at the time). Interestingly, individuals encouraged to open a Sukaliku account with an agent are willing to pay 585 CFA more (1.08 USD) for the account, an increase of 13 percent relative to those sent to the branch. In contrast, individuals encouraged to open the account at the branch are not willing to pay more for it than individuals in the control group, consistent with the similar usage of the account in both groups. Column 2 reports whether the individual saves formally, either with Microcred or any other financial institution. The prevalence of saving formally in the control group is 37 percent. The provision of information increases the probability that the individual saves formally by 8.8 percent and being offered a monetary incentive to open a Microcred account increases the probability a further 8.2 percent. In addition, the farther away from the branch (and the agent, as distances are correlated) the individual lives, the lower is the probability that the individual saves formally. While treatments were assigned randomly and as a result we interpret the coefficients associated with treatment dummies as causal impacts, distance to the branch is not randomly assigned and so there may be other factors correlated with distance that may directly contribute to the lack of formal savings. For example, individuals that are farther away may be poorer and have less disposable income than those that live near the branch. We conclude that similar to the take-up of the Sukaliku account, our intervention succeeded in increasing access to finance through formal savings. In column 3 we study whether access to savings enables individuals to respond more easily to unexpected shocks in income. We find that individuals provided with information about the Sukaliku account report being more likely to use savings to deal with a negative income shock. Relatedly, the farther away the individual lives from the branch, the less likely he or she is to use savings to cope with shocks.

Online Appendix Table OA5 reports the impact of opening a Sukaliku account on outcomes such as whether the individual is self-employed, had a business that failed, opened a business, their total monthly expenses and an index of happiness based on questions about 23
**What Is Effective Aid? How Would Donors Allocate It?** Charles Kenny

# **Introduction**

If all donors agreed on aid objectives and the methods to achieve them, we would only need one aid agency. That we have a growing multiplicity suggests that this is not the case.<sup>1</sup> There are many potential goals for aid and little consensus around methods to maximize progress toward any particular goal. There are also considerable unknowns – because aid projects and programs have been too little subject to rigorous monitoring and evaluation and because of the complexity of determinants of success and failure. Having said that, it appears that the quality of institutions is an important factor in determining the results of aid to governments, at least, and that there are a number of reforms to aid practices and distribution that might help to deliver a more significant return to aid resources. We should provide aid where institutions are already strong, where they can be strengthened with the help of donor resources, or where they can be bypassed with limited damage to existing institutional capacity. The importance of institutions to aid outcomes, as well as the fungibility of aid flows, suggests that programmatic aid should be expanded in countries with strong institutions, while project aid should be supported based on its ability to transfer knowledge and test new practices and/or support global public good provision rather than (merely) as a tool of financial resource transfer. The importance of institutions also suggests that we should be cautious in our expectations regarding the results of increased aid flows.

This paper opens with a discussion of various different objectives for aid. It then discusses weaknesses in some of the justifications for assuming that aid will foster development. It looks at what the cross-country aid effectiveness literature and World Bank Operations Evaluation Department (OED) reviews have suggested about effective aid, first in terms of promoting income growth and then for promoting other goals. This review forms the basis for a discussion of recommendations to improve aid effectiveness and a discussion of effective aid allocation.

# **What Are We Trying to Maximize?**

Overseas Development Assistance is presumably designed at least in part to promote development in the recipient country. But this is both inadequate and underspecified. The definition of development is hardly a settled one, and development in the recipient country is not the only goal of ODA. In most of the recent literature on ‘aid effectiveness’ the assumption has been that effective aid is that which increases recipient country GDP per capita growth rates. But that is clearly only one indicator of success. We might be concerned with:

- Income growth in particular places and of particular people.  In particular, we might care most about minimizing inequality or maximizing poverty reduction, perhaps minimizing those living on a dollar a day or two dollars a day, or perhaps

2
households is not trivial. For example, the impact of rising central gas tariffs amount to 2.4 to 5.2 percent of the budgets of the poorest households in small towns.

**Table 5. Impact of Gas Price Increase**

(By expenditure quintile and location in percent of total household consumption)

||Scenario 1: Impac<br>|t byEnergyPr<br>|oduct<br>|Scen<br>|ario 2: Impact|byEnergyPr<br>|oduct<br>|
|---|---|---|---|---|---|---|---|
||Central Gas<br>LPG|Electricity|Central Heat|Central Gas|LPG|Electricity|Central Heat|
|Magnitude of price increase/<br>Quintile|37.5%<br>37.5%<br>(1)<br>(2)|30%<br>(3)|37.5%<br>(4)|100.0%<br>(5)|100.0%<br>(6)|80%<br>(7)|100.0%<br>(8)|
|**_A. All households _**<sup>**_1_**</sup>||||||||
|Poorest|0.3<br>0.1|1.4|0.0|0.7|0.3|3.2|0.1|
|Second|0.6<br>0.1|1.3|0.1|1.3|0.3|2.9|0.3|
|Third|0.5<br>0.1|1.3|0.3|1.2|0.3|2.9|0.7|
|Fourth|0.7<br>0.2|1.2|0.5|1.4|0.3|2.7|1.1|
|Richest|0.6<br>0.1|0.9|0.6|1.3|0.3|2.1|1.2|
|**_B. Connected households with positi_**|**_ve expenditures _**<sup>**_2_**</sup>|||||||
|Poorest|2.1<br>3.7|1.7|...|4.5|8.2|3.9|...|
|Second|2.3<br>2.8|1.5|...|5.1|6.2|3.4|...|
|Third|2.1<br>2.3|1.5|...|4.7|5.0|3.3|...|
|Fourth|2.1<br>2.0|1.3|...|4.7|4.4|3.0|...|
|Richest|1.5<br>1.4|1.2|...|3.4|3.1|2.7|...|
|Of which:||||||||
|**_B1. Large Cities_**||||||||
|Poorest|1.8<br>...|2.3|...|3.9|...|5.2|...|
|Second|1.2<br>...|2.1|4.7|2.7|...|4.8|10.4|
|Third|1.0<br>...|2.2|5.3|2.2|...|4.9|11.8|
|Fourth|1.0<br>...|1.9|5.2|2.1|...|4.3|11.6|
|Richest|0.7<br>...|1.5|3.6|1.6|...|3.5|7.9|
|**_B2. Small towns_**||||||||
|Poorest|2.4<br>4.3|2.1|...|5.2|9.6|4.7|...|
|Second|2.7<br>3.9|1.8|...|6.1|8.7|4.1|...|
|Third|2.8<br>3.4|1.7|...|6.3|7.6|3.8|...|
|Fourth|3.6<br>2.9|1.5|...|7.9|6.3|3.4|...|
|Richest|3.5<br>2.0|1.3|...|7.8|4.5|2.9|...|
|**_B3. Rural areas_**||||||||
|Poorest|1.6<br>3.2|1.6|...|3.5|7.2|3.7|...|
|Second|2.6<br>2.6|1.3|...|5.8|5.7|3.0|...|
|Third|2.7<br>2.1|1.3|...|6.0|4.6|2.9|...|
|Fourth|3.3<br>1.9|1.1|...|7.4|4.1|2.5|...|
|Richest|2.5<br>1.4|0.9|...|5.5|3.0|2.0|...|

Source: HBS and Bank staff calculations.

Note: "..." indicate insufficient observations. Based on a geometric mean of the relative price change. 1Unconnected households as well as households that are connected but have missing expenditure data are assumed to have zero expenditures on energy.

2Includes only households that are connected and are reporting expenditure information. Implicitly, the calculations include households that are connected but have missing expenditure data, and these households are assumed to have expenditure levels equal to average expenditures by quintile and location.

# _What is the fiscal cost of protecting the poorest households?_

Under the same two scenarios for natural gas price increase, the aggregate increase in expenditures on gas and energy products is estimated. The upper bound on the aggregate increase is calculated by assuming that consumption levels are unchanged (no substitution). The estimates reflect the effective cost of protecting all or selected 13
APPENDIX A: TECHNOLOGY AND THE STRUCTURE OF RAILROAD COSTS 31 ...

|A. I|Fixed and Variable Costs .31|
|---|---|
|A.2|Sunk Costs .33|
|A.3|Economies of Scale and Scope .34|
|A.4|Economies of Size vs. Economies of Scale and Density .36|
|A.5|Empirical Evidence on Scale Economies .37|

|APPENDIX B: RAIL COSTS, PROFITABILITY AND INFRASTRUCTURE CHANGES|40|
|---|---|
|B. I<br>Railroad Costs and Infrastructure Variables .....................|40|
|B.2<br>Low Density Lines and Profitability ..........................|41|
|REFERENCES<br>..............................................|43|
P P P i 0<sup>+ αα</sup> 1<sup>P+ αα</sup> 2<sup>(G</sup> i 3<sup>(P</sup> i 4 i<sup>= αα</sup> 5 i<sup>P</sup> 6<sup>P</sup> ii−1i 7 G<sup>GGPP/PPG</sup> 8<sup>GGPP)</sup> i<sup>+ αα</sup> 9 (P<sup>VV/C</sup> P P<sup>CCPP)+</sup> P i + 10 i ............ _....... (3)_ +αα UUPP + αα PPGGCCPP + αα GGCCGGSS + αα CCPP+ αα GGCCEESS + αα /GGP PP) αα GG+ ωω The expected sign of (α1, α2, α3, α4, α5, α7, and α10) > 0, whereas the coefficient of α6 and α8 are < 0.

Where PINFRA is the physical infrastructure index, SINFRA is the social infrastructure index; FINFRA is the financial infrastructure index, GSDP is the gross state domestic product, PGSDP is the per capita GSDP, UR is the urbanization rate, INV is the investment as a ratio of GSDP, CEXP is the capital expenditure of states, PDEN is the population density (per sq.km.), Debt is the internal debt as a ratio of GDP, CRE is the per capita bank credit, SMNF is manufacturing GDP, SSER is services sector GDP and SCST is the percentage of SCST population. Dummy (D) is the dummy variable used to capture political factors (whether both state and central governments are from the same party or part of the same coalition government). D takes the value of 1 if the center and State governments belong to the same ruling or coalition party, while it is 0 in other cases. Lag levels of dependent variables are included to capture the dynamic aspects of the data.

The above three equations (Equations 1, 2, and 3) are estimated separately using the dynamic panel model (GMM System proposed by Arellano and Bond, 1998). Given that our dependent variable (infrastructure index) is dynamic and depends on its past values, a Dynamic model (System GMM) is appropriate and it has advantages over fixed, random, or GMM difference estimators. The literature suggests that there exists two-way causality between infrastructure and economic growth. For instance, studies such as World Bank, 1994; Straub, 2008; Calderon, 2009 empirically prove infrastructure positively affects economic growth. On the other hand, studies by Munnell, 1992; Gramlich, 1994 find that economic growth stimulates infrastructure development. Therefore, there is the possibility of a two-way dynamic relationship between the two. To avoid the potential endogeneity, our study applies dynamic panel estimates (GMM-system) proposed by Arelleno and Bond (1998), which considers the endogeneity problem and removes the weak instrumental problem of the GMM model. In addition, the GMM system not only controls potential endogeneity and heterogeneity but also improves the efficiency of estimates by including original levels instruments with the difference (Roodman, 2009). Further, unlike other estimators, the system GMM does not require any assumption about distribution (Verbeek, 2000). More importantly, when the cross15
are linked to changes of goods prices through the production technology and the functioning of the factor markets. Different production technologies are approximated by different factor and intermediate inputs intensities across sectors, as shown in Table 6, and factor markets function so as to mimic short-term adjustment possibilities. Capital is sector specific, and the farm and non-farm sectors constitute two segmented markets for skilled and unskilled labor.

_<u>Table 5: Factor price changes due to full trade liberalization</u>_

||<br>Δ**P**|Δ(**P/CPI)**|
|---|---|---|
|**_Non-Farm Segment:_**|||
|**Skilled Labor**|-0.3|2.1|
|**Unskilled Labor**|0.6|2.9|
|**Capital**|-0.6|1.8|
|**Sk/Unsk wage gap**||-0.9|
|**_Farm Segment:_**|||
|**Skilled Labor**|-4.0|-1.6|
|**Unskilled Labor**|-6.3|-3.9|
|**Capital**|2.7|5.1|
|**Sk/Unsk wage gap**||2.5|
|**_Price indexes:_**|||
|**Food price index**|-3.6||
|**Non food price index**<br>**CPI**|-1.5<br>-2.4||

Sources: Author calculations from model results. Notes: The first column, Δ **P** , represents the percent variation of the price of each factor with respect to the initial levels, and Δ **(S/CPI)** is the percent variation of the price deflated by the Consumer Price Index.

In the farm segment (which corresponds to the macro-sector _Agriculture_ in the previous tables), capital (including land) has a positive real price change and skilled and unskilled labor experience wage reductions. The expanding agricultural sectors – shaded in Table 6 – are those which use capital relatively more intensively than _Basic Grains_ , the contracting sector. The combined _Coffee_ , _Other Agricultural Products_ and _Livestock_ sectors, the largest output gainers, use almost 70% of the total farm capital value added. Unskilled labor in _Basic Grains_ contracts relative to skilled labor.

24
<!-- Start of picture text --> 80<br>3. "The process of<br>developing new rules or policies<br>is usually such that affected 70<br>businesses are informed."<br>65<br>This is true<br>60<br>(1) always 71<br>(2) mostly E] s<br>(3) frequently L<br>(4) sometimes 50<br>(5) seldom E<br>(6) never Ej 45<br>40<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text --> 3. "The process of developing new rules or policies is usually such that affected businesses are informed."

**Percentage of firms who ticked (4),** (5), **or (6) for question 3**

Marked differences between regions are revealed by this question. 75 percent of all entrepreneurs in Africa, the CEE and the CIS believe that affected businessmen are not informed about upcoming changes in rules and policies. By comparison only around 40 percent of businessmen in Asia and the developing countries feel ill informed.

# _Question 4: Participation_

This question on the predictably of rules and policies is closely linked to question 3. Negative reaction to surprises in new rules and policies are less likely if entrepreneurs can participate in the process of developing new rules and can voice their concerns either directly or through their business association.

<!-- Start of picture text --> 4. "In case of go<br>important changes in laws<br>or policies affecting my B5<br>business operation the 80<br>government takes into<br>account concerns voiced 7<br>either by me or by my 70<br>business association."<br>65<br>This is true 60<br>(1) always 55 |l<br>(2) mostly<br>(3) frequently<br>(4) sometimes j 45<br>(5) seldom I 40 F<br>(6) never World LDC DC SSEA MNA CEE LAC SSA CIS<br>Percentage of firms who ticked (4), (5), or (6) for question 4<br><!-- End of picture text -->

12
Households with better educated members have significantly higher probabilities of working in the non-farm sector, while a higher level of land ownership is associated with a higher probability of working in the farm sector. Households in the Dhaka district, with younger and older heads, and non-Muslim heads also have a higher probability ofjoining the non-farm sector. The probit was then used to construct estimates of the expected values of the error terms in (4.1)(4.2) conditional on X and W, which are used to test for correlations between eNF and v, and between eF and v.

We derive identifying restrictions from a life-cycle model of consumption smoothing through participation in the rural non-farm sector." We postulate that as a farm household ages, and the children reach adulthood, some of the children take over the farmn (leaving the parents supported by transfers and some non-farm activity, which may have been there all along, but was secondary), while other children split away to take up non-farm activities (possibly new ones, or possibly pre-existing secondary activities). Thus, at any one time, we will see a U-shaped relationship between probability of being in the non-farm sector and age of the household head; the middle-aged heads (with children not yet at earning ages) will be more likely to still be in farming. By this process of shifting between farm and non farm sectors according to stage of the life cycle, combined with intra-family (inter-household) transfers, we assume that consumption within each sector can be smoothed over the life-cycle. Thus, while stage of the life cycle is an important determinant of mobility across sectors within the rural economy, consumption within sectors is successfiully protected from life-cycle effects. We tried two versions of these life-cycle identifying restrictions. In the first, the vector W in the selection model included all variables in X plus the age and age squared of the spouse. In the second we deleted age and age squared of the head from X, but retained them in W.

Consistently with treating farm vs non-farm sector choice as endogenous, we also estimated the consumption equations with and without the occupations dummies; since there are no obvious instrumental variables, dropping the occupation dummies is the only option if one ' On life-cycle/demographic effects on participation in non-farm activities see Kimhi (1996). Strictly identification is possible without exclusion restrictions, given the nonlinearity in W.

23
may motivate corrupt tax officers to work harder in order to detect evasion – ostensibly an especially likely outcome if an incentive/bonus system is put in place that would mimic or compete with the bribery system already in place (Chand & Moene, 1999). This has possible unanticipated consequences for the behavior of corrupt tax officials: the presence of a bonus system strengthens the position of the tax official in negotiating a bribe, and potentially enables them to extract a larger bribe than otherwise would be the case (e.g. to compensate for the forgone bonus).

Corruption in a country even beyond tax administration has a pervasive negative impact on tax effort. Bird and Martinez-Vaseuqz (2008) argue more legitimate and responsive state is an essential precondition for a more adequate level of tax effort in developing countries and also high-income countries. Besley and Persson (2014) provide empirical evidence of a strong negative correlation between fiscal capacity and corruption.

Thus, recent literature is still contradictory on whether taxpayers view corruption as an obstacle to doing business. According to Torgler and Schneider (2009), in countries where corruption is systemic and the government budget lacks transparency and accountability, the obligation of paying taxes cannot be assumed to be an accepted social norm – i.e. tax morale is lower. They also claim that there might be a crowding-out effect of morality among the tax administrators when there are a great number of corrupt colleagues.

This paper attempts to evaluate competing ideas in the literature about firms’ experience of corruption in tax administration, using the empirical data on the prevalence and nature of firms’ interactions with tax authorities in 28 ECA countries. Special emphasis is given to examination of taxation-related determinants of corruption prevalence (frequency and magnitude of bribery), as well as the effect of the interaction with tax authorities on perception of tax and overall corruption and reasons behind the situation where firms do not view corruption as an obstacle.

# **<mark>3. DATA AND HYPOTHESES</mark>**

The paper uses data collected through the EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS).<sup>3</sup> The paper uses the results of the most recent wave of the survey; for consistency purposes, the regression analysis presented in this paper excludes

> 3 The BEEPS is a joint initiative of the European Bank for Reconstruction and Development and the World Bank. The BEEPS has been carried out in five rounds: in 1999, 2002, 2005, 2008, and 2012/13 and covers virtually all former Eastern Bloc countries of Central and Eastern Europe and the former Soviet Union (except Turkmenistan), as well as Turkey. The latest BEEPS in the Russian Federation was conducted in 2011/12.

3 | P a g e
14

**Figure 2c: Percentage of Firms Ranking the Constraint as Major or Severe by GDP/capita— constraints rising with income: labor regulations and shortage of skills**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>labor regulations<br>20 Poly. (skilled worker)<br>Poly. (labor regulations)<br>10<br>0<br>Burundi (100) DRC (120) Ethiopia(160) Malawi (160) Guinea‐B(180) Eritrea(220) Uganda (280) Gambia(290) Madagascar(290) Mozambique(310 Tanzania(340) Mali(380) Burkina Zambia(490) Benin(510) Kenya(530) Mauritania(560) Senegal(710) Lesotho(960) Angola(1350) Cape Vde(1870) Swaziland(2280) Namibia(2990) South Afr(4960) Botswana(5180) Mauritius(5260)<br><!-- End of picture text --> Figures 2a-2c suggest an approach to classifying constraints and the way in which firm’s perceptions of their difficulties are likely to vary with income level. At the low end of the scale, manufacturing firms are most likely to be concerned about the most basic constraints to doing business. Is there a reliable power supply? Can finance and premises be secured? Can the firm plan ahead, or does macroeconomic instability make this impossible? In some countries, individual constraints can be at a level where they can be considered as truly binding. Electricity tariffs in Uganda would have to increase to almost $ 0.29 per kWh if the consumer were to bear the full costs of electricity including the expensive thermal generation used in attempts to plug capacity gaps. The cost of load shedding to the economy is significant, and expensive back-up generation has impacted the competitiveness of industrial production. The cost of un-served energy has been estimated at about US$0.39 per kWh excluding multiplier effects<sup>4</sup> . Not surprisingly, 87% of Ugandan forms considered electricity as a major or severe constraint in 2006. These constraints do not die away completely as the business climate

> 4 Power Planning Associates, 2007.