rafmacalaba/data-use-mentions-extended huggingface dataset:
using these definitions must go beyond income-support measures designed to target the income poor, as they will not reach a large share of this group.

_Figure 11: Overlap of energy and monetary poor using expenditure-based measures (over expenditure, when applicable)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>LIHC 2M M/2 M/2 P10<br>Neither nor (equ)<br>Only energy poor<br>Only monetary poor<br>Energy and monetary poor<br><!-- End of picture text --> _Figure 12: Overlap of energy and monetary poor using expenditure-based measures (over income, when applicable)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>LIHC 2M M/2 M/2 P10<br>Neither nor (equ)<br>Only energy poor<br>Only monetary poor<br>Energy and monetary poor<br><!-- End of picture text --> _Notes: Monetary poverty is the at-risk poverty rate using the income measure reported in the HBS 2021 and, therefore, does not correspond to the official poverty measure. The resulting poverty estimate is slightly above the estimate from the EU-SILC._ _Source: Own estimates based on HBS (2021)._

**In summary, the large differences in the incidence of energy poverty between the alternative expenditure-based measures demonstrate how sensitive these measures are to the underlying definition** ; **the most effective public policies that address energy poverty will, therefore, be highly conditioned by the definition adopted by the government** . The differences in energy poverty incidence rates between the measures confirm the challenge of identifying sound and precise
The fourth lesson is that the IPF can take into account both efficiency and efficacy considerations. This is shaped by the construction of variables. For example, one could use the absolute number of beneficiaries as an input to the SEI to consider policy effectiveness when service expansion is a priority. On the other hand, ‘beneficiaries per dollar spent’ may be more appropriate if the goal is fiscal efficiency. In the case of Panama, where development of water services in rural areas was an important policy goal, the decision was made _not_ to control indicators by project size in order to protect against the possibility of privileging urban projects with greater economies of scale (Marcelo, Mandri‐Perrott, & House, 2015).

The final lesson is that IPF can be used as an opportunity to strengthen data weaknesses. The process of discussing relevant indicators by which to select projects is a catalyst for improving information levels to move towards more complex economic analysis. In Panama, the discussions prompted by the IPF have resulted in improved data project data collection. The IPF, as a process, can be a valuable starting point for improving appraisal by inciting aspirational discussions about the kinds of data that would be most helpful to deciding among projects within a sector.

# **Implementing IPF**

The sequence of implementing IPF, as described in the technical description and pilot applications above, is summarized in Figure 10. With respect to this sequence, the pilot in Panama also gave rise to an important discussion of requirements for implementing IPF and factors of particular interest in further piloting to improve its applicability. These issues are discussed in the following section. Some are process‐oriented and relate to overall infrastructure planning; some are organizational and deal with issues of resources, authority, and capacity; and some are technical, relating to the informational inputs required.

Figure 10. Sequence of the IPF <!-- Start of picture text --> I<br>Criteria • Deliberation and consultation with decision<br>Identification makers and experts
# Table 4: Detailed school funding, infrastructure controls and school fixed effects

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Estimator|log(Payments<br>to teachers)<br>OLS|—log(Out-of-school<br>tutoring)<br>OLS|—log(Payments<br>to teachers)<br>FE|—_log(Out-of-school<br>tutoring)<br>FE|
|Migration|-0.449**|-0.062|-0.099|-0.074|
||(0.210)|(0.170)|(0.214)|(0.195)|
|Log(household income)|0.107|0.186***|0.142|0.123**|
||(0.087)|(0.055)|(0.086)|(0.052)|
|School infrastructure and budget|||||
|Schoolassetindex|-0.000|0.000|||
||(0.000)|(0.000)|||
|Wages (log)|On|-0.019|||
||(0.457)|(0.332)|||
|Teaching materials (log)|-0.062|-0.145|||
||(0.257)|(0.201)|||
|Utilities (log)|-0.244|-0.106|||
||(0.176)|(0.118)|||
|Transports (log)|0.138|0.041|||
||(0.101)|(0
_Tax Reform in Transition_ 3 raising the income threshold at which a positive tax payment must be made. In each case the objective is to obtain better or at least revenues equal to those that would be obtained under the 1992 system. Furthermore, the distributional consequences of the proposed tax reform are examined, taking into account the existing income distribution as reflected in the 1992 household budget survey.

The type of analysis and principles enunciated are potentially of wider interest to other EMEs for two reasons. First, the striking similarities in the pattern of income distribution in these economies recently reported by Milanovic (1995) and Hassan and Peters (1996). Second, a comparison of the income tax systems in these economies - as shown in section 2 - reveals that these systems suffer from similar deficiencies as Bulgaria's system, notably, low exemption level for the poor, large number of tax brackets, and high rates. So the analysis is potentially capable of providing useful direction for improving the design of personal income tax not only in Bulgaria, but also in other Eastern European countries contemplating tax reform.

The remainder of the paper comprises three sections. Section 2 reviews the 1992 personal income tax structure and potential and actual revenue performance, utilizing the 1992 income distribution data. This section also compares Bulgaria' s personal income tax system with those in other economies, notably transitional economies. Section 3 presents and discusses the counterfactual analysis of alternative tax structures with equal or better revenue raising potential and re-distributional impact. The analysis also takes into account the existing high tax evasion level. Conclusions and thoughts on broader implications of the analysis for tax reform in other EMEs are given in the final section.

# **_2. The_ Need** **_For Revenue_**

The evolution of Bulgaria's tax revenues and fiscal balance relative to GDP since 1991 are shown in Table 1. The table shows that the country experienced a decline of tax revenue/GDP -- from nearly 38 percent of GDP in 1991 to 28 percent of GDP
not have access to safely managed sanitation (WHO/UNICEF [2017]); hence the scale of the problem is urgent.

Microcredit has been postulated as a promising solution to support households in making the significant outlay necessary to construct an individual household toilet. And indeed, BenYishay et al. [2017] show that demand can be boosted significantly when offering microcredit for toilets that is bundled with the delivery of toilet construction materials to the doorstep of the borrower household.<sup>1</sup> To our knowledge, there is no rigorous evidence on the effectiveness of microfinance loans associated with health investments only by their label.

In this paper, we draw on a cluster randomised controlled trial in rural India to study whether a new microfinance loan product, linked to sanitation investments purely through its label feature, can be effective in increasing household investments in sanitation. The trial, conducted among clients of a leading micro-finance institution (MFI hereon), made available micro-loans for sanitation in randomly selected communities in rural Maharashtra. The loans, offered at a lower interest rate relative to productive business investment loans, were to be used for the construction of a new toilet or the repair or upgrade of an existing toilet. Importantly, the loans were not bundled with a specific toilet or materials, and households were left to their own devices to source materials and arrange construction. Moreover, the implementing MFI did not incentivise or enforce use of this loan product for sanitation investment. Thus, these loans can be considered to be purely labelled loans.

> 1This mirrors the approach generally taken when targeting specific investments, e.g. distributing bed-nets, agricultural seeds, etc., rather than providing households with the cash, either in terms of a grant or a loan (see for example Dupas, 2014; Dizon Ross et al., 2017; Miller and Mobarak, 2013).

2
reveal that about 76 percent of the population lived in the capital, Djibouti city, while 24 percent lived in the regions.

The survey integrates several themes including, among others, recent data on household consumption and living conditions, to monitor and evaluate social development and poverty alleviation policies in the country. The survey also adopted current best practices in several aspects of questionnaire design and the analytical work subsequently done on welfare and poverty measurement. While the adoption of such changes led to a non-comparability with earlier surveys, the DISED used this survey as a new baseline from which indicators of well-being will be monitored in the future.

Using data from EDAM4, it is estimated that an important share of Djibouti’s population is poor and vulnerable. As of 2017, an estimated 21.1 percent of the Djiboutian population lived in extreme poverty, spending less than DJF 117,783 per adult equivalent per year (equivalent to US$2.17 per person per day in 2011, PPP). Rural areas showed significantly higher rates with extreme poverty estimated to be 62.6 percent. The goals of eradicating poverty and boosting shared prosperity will be significantly more difficult to achieve without ensuring affordable broadband access for all, and fully embracing the transformative powers of ICT.

# Provision of digital services in Djibouti

The sluggish development of the ICT sector and limited availability of digital services have differential effects across the income distribution. The subgroup of the population that is most affected by the sectoral environment is expected to be the (mostly poor) rural population of Djibouti. Despite having one of the highest proportions of urban population among the LMI countries in MENA and SSA (with about 85 percent of the population<sup>6</sup> ), mobile broadband coverage in Djibouti in 2018 remains below the regional average (at about 76 percent), and far lower than many MENA or SSA countries which have a smaller share of urban population (Figure 3).<sup>7</sup> This low national coverage rate implies
Finally, more inert sectors also take longer to decarbonize, and we show that higher inertia can be modeled through a higher convexity of the investment cost function. To assess precisely optimal sectoral eorts requires information on sectoral costs and potentials, but also on inertia, an information that is not reported separately by existing studies (IPCC, 2007; McKinsey and Company, 2007).

In policy terms, these results suggest that higher-than-average eorts are justied in highpotential and highly inert sectors such as urban planning (Guivarch and Hallegatte, 2011).

In section 2, we briey introduce the classical approach to the denition of an optimal abatement strategy, with a multi-sector model with evanescent abatement options and sectoral potentials, in the spirit of Nordhaus (1992). In this model, the classical result holds: optimality requires that marginal abatement costs are equal in all sectors. Then, we present in section 3 a slightly dierent model, where abatement options have a permanent impact on emissions. We show that, in this case, marginal abatement eorts should be larger in sectors that will take longer to reach their full abatement potential. We provide an illustrative example based on IPCC abatement costs in section 4 and investigate the role of convexity on investment costs in section 5. Section 6 concludes.

# 2 The classical approach to optimal abatement pathways

Since the seminal article by Nordhaus (1992), optimal abatement trajectories are frequently calculated assuming that a social planner has to choose when to abate GHG emissions. The planner does so by spending on abatement expenditures that reduce emissions at one given point in time; we refer to these abatement expenditures as evanescent abatements. In this framework, the amount of abatement is decided independently at each period, and the cost at one period does not depend on actions undertaken before. In other words, abatement costs exhibit no path dependency. Abatement at time _t_ , _a_ ( _t_ ) , is done at a cost _γ_ ( _a_ ( _t_ )) , where
to 5%. The total (aggregate) value of export revenue that would be lost was around $530 million (of which two-fifths would be accounted for by Bangladesh). Note that these are small numbers—equivalent to only one percent of ODA flows.

Earlier research by Yeats (1995), also using a partial equilibrium framework, estimates that the complete elimination of MFN tariffs by the EU, Japan and the United States would result in total erosion costs for 29 Sub-Saharan African countries of some US$4.2 billion of exports (in present discounted value terms), with the reductions in exports to the EU and Japan on the order of 1-2 percent, and an _increase_ in exports to the

> 34 Also significant were preferences for tobacco. As noted by Alexandraki and Lankes (2004), given that Yamazaki’s dataset ends in 1992, bananas are not covered as the EU banana regime began in 1993.

33
job accessibility in Nairobi, explaining in part why poorer households have such limited job accessibility.

Our findings illustrate the need to assess and enhance access to economic opportunities for disadvantaged workers in African cities, and Nairobi is a case in point. While the poverty headcount ratio in Nairobi declined from 21 percent in 2005/06 to 17 percent in 2015/16, the number of poor slightly increased during the period (KNBS 2018). Despite the overall low poverty rate, nearly 30 percent of residents in informal settlements still remain in poverty (World Bank 2018). More than 20 percent of the poor in Nairobi are unemployed, and about 40 percent of the poor are casual workers. The 2015/16 Kenya Integrated Household Budget Survey (KIHBS) also shows that only 15 percent of the poor use a minibus for commuting, while 70 percent of the poor walk to work. Despite the concentration of poverty, worse living conditions, and limited job accessibility in Nairobi’s informal settlements, it has been difficult for many residents to move out of informal settlements (Nakamura and Karasawa 2018). In such situations, it is critically important to enhance job accessibility to support the poor and/or residents of informal settlements to escape from poverty.

Our research is related to three bodies of literature. First, our work is related to the literature focusing on job accessibility from a perspective of agglomeration economies. Facilitating better matching between firms and workers is, among other channels such as sharing and learning effects, a critical component of agglomeration economies (Duranton and Puga 2004). A body of empirical studies estimated the effects of city size and density for productivity, though most of them focus on developed countries (see Combes and Gobillon 2015 for a review). Limited job accessibility could, however, restrict such benefits of agglomeration economies. In view of cities as labor markets (Bertaud 2014), some studies assess accessibility, measured as the number of opportunities an individual can access within a given amount of time, for Buenos Aires
Using variance estimates from the above equations, we will provide measures of vulnerability according to the different sources of vulnerability. First, we are concerned with vulnerability induced by structural (or permanent) poverty, that is the fraction of vulnerable households whose expected mean consumption ln _y_ ˆ _ij_ is already below the poverty line ln _z_ . Second, we will measure vulnerability induced by risk, that is the fraction of vulnerable households whose expected mean consumption ln _y_ ˆ _ij_ lies above the poverty line ln _z_ . As in Chaudhuri _et al._ (2002), a household is considered as vulnerable if the estimated vulnerability index is greater than the vulnerability threshold of 0.29.

# **_Identification Issues_**

A problem associated with the estimation of equations (1) and (9) is that idiosyncratic and covariate observable shocks are potentially endogenous for at least three reasons. First, since the shocks are self-reported by the households in the questionnaire, it might be reported with errors. Hence, it is possible that households with a certain level of consumption or welfare consider an event to be a shock, while others with a different level of consumption or welfare may not. Second, if consumption levels influence the likelihood of exposure to the shock then reverse causality may arise. For instance, health shock has not the same probability of occurring depending on household consumption/income level. This problem is most likely to happen with idiosyncratic shocks. Community shocks are less likely to be influenced by household consumption or income. Third, shocks may be correlated to the error term because of unobserved heterogeneity. Unobserved factors may indeed influence both exposure to shocks and consumption/income. For example, richer households may better irrigate their lands. If irrigation is not observed, the estimated impact of drought on consumption declines may be upwardly biased.

These sources of estimation bias are difficult to take into account with cross-sectional data. However, as proposed in Datt and Hoogeveen (2003), a potential solution is the use of instrumental variables (IV
0.135)|(0.032)|(0.024)|
|Rural|0.003|-0.013|-0.059**|-0.007|
||(0.002)|(0.115)|(0.029)|(0.018)|
|Oil prices|0.004***<br>(0.000)||||
|Diplomatic exchange||-0.759***<br>(0.248)|||
|Confict|||0.258***<br>(0.054)||
|Illegal status||||-0.552***<br>(0.087)|
|Legal status||||0.169***<br>(0.056)|
|Observations|10,636|1,517|1,453|6,108|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=2.|283; Illegal mig|.=1.739; Legal|mig.=2.441||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Data comes from the 2018 ELMPS survey. Our analysis is restricted to individuals who were also interviewed in 2012. Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in
We examine these dynamics in Ho Chi Minh City, using high‐resolution local‐scale flood maps designed specifically for HCMC (Lasage et al. 2014) and proxy for poverty using the spatial location of potential slums from the Platform for Urban Management and Analysis (PUMA) data set (World Bank 2015). The PUMA data set also has information on locations of urban expansion from 2000 to 2012. We therefore examine exposure to flooding in all three locations – urban areas as a whole, potential slum locations, and areas of urban expansion. The results we present below are for all districts in HCMC; results for only districts with slum areas are similar and thus not reported.

We find that a relatively high percentage of the potential slum areas are exposed to floods, ranging from 68.9% (for a 10‐year return period) up to 83.3% (for a 1000‐year return period). When considering all urban areas of HCMC, exposure to flooding is lower: 63% (for a 10‐year return period) up to 68.3% (for a 1000‐year return period). A sea level rise of 30 cm increases the extent of flooded areas the most in slum areas and for a low‐probability but recurrent flood (10‐year flood). For a 10‐year flood and looking only within slum areas, we find an increase in exposure of 15 percentage‐points due to sea level rise, compared to a difference of 5.7 percentage‐points when looking at the entire urban area of HCMC. These results, as presented in Figure 1, suggest slum areas to be more exposed to floods than non‐slum areas.<sup>4</sup> Due to cognitive biases, it can be hypothesized that flood risk from frequent events (like the 10‐year return period event) are more likely to be remembered than a rare event (like the 1000‐year return period event) and thus more
potential of index insurance to foster smallholder farmers’ productive investments, however, they also show that effects are more uncertain than suggested by the pooling model. In fact, the pooling model is misleading because it considers that impacts are homogeneous across studies (thus understating the uncertainty around treatment effects).

We then use estimates from the Bayesian hierarchical model to gauge the external validity of index insurance experiments. We report evidence on the conventional pooling factor, a metric which emphasizes the percentage of total variation in treatment effects stemming from sampling variation (the higher this metric, the more suggestive it is of external validity). Averaged across all outcomes and studies, we find moderate pooling of information: 52% of the observed variation in treatment effects stems from sampling variation. We obtain similar results using two complementary metrics: the brute force pooling metric and the generalized pooling factor. This suggests moderate external validity of index insurance experiments. We note however that with few experiments the pooling factor cannot be precisely estimated: the

> 3The risk index is defined as _Rik_ = ∑ _rmkdimk_ / ∑ _dimk_ , where _rmk_ is the coefficient of variation of the yield of crop _m_ in study _k_ , and _dimk_ is the amount of land devoted by household _i_ to the cultivation of crop _m_ in study _k_ . It captures the riskiness of crop portfolios.

> 4 To derive the coefficient of variation _rmk_ of the risk index, we complement survey data from the original studies with time series on agricultural yields (see Section 3.1 below for more details).

> 5For reference, we compare the study-specific estimates of the ITT (intent-to-treat effect) and of the ATET (average treatment effect on the treated). If anything, we see larger heterogeneity in the ATET estimates (Figure A3).

3
used the opportunity to pass power and resources to the subnational levels where they held some of the executive offices. Also, the Liberal Party, which had favored decentralization since the nineteenth century, had a plurality of delegates-about one-third-many more than the traditionally centrist Conservative party (Buenahora 1997). Congress decided to live with decentralization because it gave local administrations the power to execute spending, and these local entities were usually allied with their congressmen, acting as their machines in many cases (Archer and Shugart 1997; Willis, Garman, and Haggard 1997).

**Table 1.** Growth, Inflation, **and** Fiscal Balances in Colombia, 1987-97

||(percent)<br>|||
|---|---|---|---|
|_Indicator_|_1987-90_|_1991-94_|_1995-97a_|
|Real growth (annual average)|4.5|4.3|3.5|
|Inflation (annual average)|15|9|7|
|Overall fiscal balance as a percentage<br>of GDP||||
|Central|-1.0|-1.1|-4.2|
|Department|-0.2|-0.3|-0.2|
|Municipal|-0.1|-0.3|-0 .1|
|Primary balance as a percentage of GDP||||
|Central|0.1|0.3|-2.2|
|Departrnent|-0.1|-0.1|0.1|
|Municipal|0.0|0.0|0.2|

a. 1995 only for department and municipal balances. _Sources:_ World Bank database; Central Bank of Colombia, table 4.

As the system operated in the late 1990s, strengths emerged, such as increased political participation, and so did weaknesses, such as mismatched spending authority and excessive borrowing by some