rafmacalaba/data-use-mentions-extended huggingface dataset:
are weak while others may delay economic recovery. So far, however, no empirical analysis has attempted to shed light on these questions.

# **2.  Data and Empirical Methodology**

We are interested in examining whether the quality of the institutional framework has an impact on the efficacy of policies for financial crisis resolution. Specifically, we want to explain whether a country’s (weak) institutional framework can render accommodative policies of crisis resolution ineffective meaning that the adoption of these policies will eventually result in higher fiscal outlays and higher economic output losses compared to situations where a country is equipped with a better institutional framework. We try answering these questions by using a country-level database. In this section, we first describe the data (for a detailed description of the variables and sources see Appendix Table 1) and then the methodologies we apply.

For data on fiscal outlays, we use Honohan and Klingebiel (2003), and update the data for recent crises using Kane and Klingebiel (2002), Caprio and Klingebiel (2003), and IMF (2003). Data are collected for 36 banking crisis episodes from 1977 to the present. The fiscal outlay figure includes both fiscal and quasi-fiscal outlays for financial system restructuring, including the recapitalization costs for banks, bailout costs related to the government covering obligations due to depositors and creditors, and debt relief schemes for bank borrowers. We scale the fiscal outlay estimates by gross domestic product (GDP). The variable, FISCAL OUTLAY, thus represents the fiscal outlay estimate of financial distress as a percentage of GDP. We use this as a measure of the 15
used to estimate the change in the average income for each household category, while the variance of this income is assumed fixed.

In this paper, we instead utilize the actual distribution of income within our 12 household categories, based on the 1994 Family Income and Expenditure Survey (FIES) of 24,797 Filipino households, without imposing a fixed functional form. The 12 household categories are obtained by grouping households by region (urban-rural), the education of the household head and this individual’s occupation. Changes in average household income are derived for each household category from the CGE model and then applied to all corresponding households in the FIES to compute changes in household welfare and poverty.

# **1. Background**

## **1.1  Agricultural Stagnation**

The agricultural sector employs about 35 percent of the Philippine labor force and accounts for roughly 20 percent of GDP. If linkages with agricultural-related sectors, including food processing and the farm supply industry, are added to this total, the farm and food-related industry contributes 40 percent of GDP and employs two-thirds of the labor force (David 1997). This sector has been characterized by low rates of productivity increase and correspondingly low growth rates in the last two decades. Growth decelerated from an annual average of 6.7 percent in the 1970s to 1.1 percent in the first half of the 1980s. Although the second half of the 1980s saw some recovery, agriculture again lost steam in the 1990s with an annual growth rate of just 2 percent.

The Green Revolution was the main driving force behind the high growth in the 1970s. However, because of an inherent policy bias against agriculture, coupled with the collapse in 4
the duration of the PEN 2008-2011 (although at the time of the Study, around 39 million USD of new funds were approved for the upcoming five years).<sup>38</sup> The national and international NGOs that work on HIV/AIDS in Colombia do not provide funding. Rather, they contribute to the country response by supporting DTSs in the provision of prevention and promotion services, family support, and information campaigns.

# **_(b) HIV/AIDS Budgets_**

Regarding budget data, since the 2003 reform, HIV/AIDS funds for preventive actions have been part of broader SRH Programs. Thus, ETSs do not report HIV/AIDS budgets in a differentiated way. The EPSs do not contain specific budgets for HIV/AIDS.

As a result, the only available data on HIV/AIDS budgets are those included in the PEN 2008-2011. However, informants interviewed for the Study confirmed that the budget included in the PEN was intended to provide some initial ball-park financial figures for the strategies and interventions described. The PEN budget does not include real assignations to specific expending units with set milestones and compromises that require evaluation, and that could be used to hold the funds ́ recipients accountable.

The PEN budget does not include expenditures on direct HIV/AIDS care or ARVs treatments—nor does it include specific budget lines for priority populations. It does however establish tentative budgets for each of the four axes (Promotion and prevention; Integral care; Support and social protection; and Follow-up and evaluation of the response) that conforms to the Plan, and to the 81 actions it includes that are clustered around 11 types of activity.

The following tables—which were obtained from the PEN—show the budget allocations for the four years of the Plan (2008-2011) classified by PEN axis and type of activity:

> 38 Personal communication by Dr. Ricardo Luque, advisor to the Dirección de Promoción y Prevención del Ministerio de Salud y Protección Social.

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landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology 102, 99–111. https://doi.org/10.1016/j.enggeo.2008.03.014

- Gómez, H., Kavzoglu, T., 2005. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology 78, 11–27. https://doi.org/10.1016/j.enggeo.2004.10.004

- Inselberg, A., 2009. Parallel coordinates: Interactive visualisation for high dimensions, in: Liere, R., Adriaansen, T., Zudilova-Seinstra, E. (Eds.), Trends in Interactive Visualization: State-of-the-Art Survey. Springer London, London, pp. 49–78. https://doi.org/10.1007/978-1-84800-269-2_3

- Islam, M.A., Murshed, S., Kabir, S.M., Farazi, A.H., Gazi, M.Y., Jahan, I. and Akhter, S.H., 2017. Utilization of open source spatial data for landslide susceptibility mapping at Chittagong District of Bangladesh—an appraisal for disaster risk reduction and mitigation approach. International Journal of Geosciences, 8(04), p.577.

- Jarvis, A., Reuter, H., Nelson, A., Guevara, E., 2008. Hole-filled SRTM for the globe Version 4 [WWW Document]. CGIAR-CSI SRTM 90m Database. URL http://srtm.csi.cgiar.org (accessed 12.30.18).

- Khan, M.J.U., Islam, A.K.M.S., Bala, S.K., Islam, G.M.T., 2020. Changes in climate extremes over Bangladesh at 1.5 °C, 2 °C, and 4 °C of global warming with high-resolution regional climate
data are representative at the wave level, so it is possible to compare the two waves in each country to assess the impact of seasonality.

In the absence of widespread irrigation systems, cereals are harvested only once a year from October to December; the agricultural lean season, which coincides with the rainy season, therefore runs from June to September while the pastoral lean season starts earlier and takes place between April and June. For Burkina Faso, Mali, Niger, and Senegal, the bulk of the interviews in the first wave took place between October 2018 and December 2018, although interviewing began in Burkina Faso and Senegal in September 2018 (see Annex 1). The second wave for these four countries was carried out between April 2019 and July 2019. Thus, for Burkina Faso, Mali, Niger, and Senegal, the first wave roughly corresponds with the harvest and immediate post-harvest (non-lean) season, especially as the 2018 rains arrived on time and resulted in above average biomass production (Action Contre La Faim, 2018). By contrast, the second wave captures the 2019 pastoral lean season and the first part of the agricultural lean season.<sup>9</sup> Since the timing of the EHCVM is similar in Burkina Faso, Mali, Niger, and Senegal, results from these countries can be pooled as well as presented separately. Annex 2 provides more detail on the timing of seasons in the Sahel.

The cross-wave comparisons for Chad will not be pooled with the other countries, given the timing of the EHCVM data collection there. In Chad, the first wave of the EHCVM data was collected between June and September 2018, which corresponds to the lean season of the 2017/2018 agricultural year, while the second wave was collected between January and April 2019. This differs too much from the data collection schedule in Burkina Faso, Mali, Niger, and Senegal for the data from all five countries to be combined when comparing the waves. However, Chad is retained for Section 3 to provide
|Interaction: 2016*Textile and Apparel||-0.0908***<br>(0.00952)||
|---|---|---|---|
|Interaction: 2005*Female|||-0.569***<br>(0.0130)|
|Interaction: 2010*Female|||-0.0408***<br>(0.0113)|
|Interaction: 2016*Female|||-0.0739***<br>(0.00843)|
|Constant|6.811***|6.810***|6.781***|
||(0.0118)|(0.0118)|(0.0119)|
|N|159316|159316|159316|
|F-test|2684.25|2471.24|2555.56|
|AdjR-square|0.3572|0.3582|0.366|

Note: The grand mean effects of the industries are calculated; additional controls include age squared, weekly hours worked, and industry dummies. Standard errors are in parentheses. Statistically significant coefficients at the 10,5, and 1 percent level are indicated by one, two, and three asterisks respectively. Source: Author's estimates using data from Bangladesh LFS.

# 6. Conclusions

Recent developing country research finds evidence that trade shocks affect local labor market outcomes including earnings, informal and formal employment levels, and skill premium. These impacts vary depending on the type of shock being considered. On one hand, evidence shows that rising exports to industrial countries reduced poverty and spurred reallocation of labor from informal to formal jobs in several developing countries like India, Vietnam, and China (McCaig, 2011; McCaig and Pavcnik, 2018; Artuc et al., 2019; and Erten and Leight, 2017). On the other hand, trade liberalization measures slowed
: as we increase the number of years in each period, perhaps increasing the accuracy with which we characterize volatility, we reduce the number of periods we can use in our fixed effects estimation, reducing its efficiency. Nonetheless, we are able to create 4 time periods while still measuring volatility over a relatively long period of time7See King and Levine (1993a) for a detailed description of the calculation of these measures.

8The POLITY III dataset includes ten different variables which aim to measure the level of institutional democracy and autocracy across countries over time. The results presented here use the variable _XRCOMP,_ the extent to which executives are chosen through competitive elections. Our results are robust to using alternative measures from this dataset: The _POLIY_ variable was never statistically significant in any of the estimations possibly because, for many countries, these variables do not change much over time. Therefore, it is difficult to identify their impact in a fixed-effects estimation. The complete data can be accessed at http://garnetl.acns.fsu.edu/ phensel/intlpoli.html.

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<sup>2</sup> While most of this empirical evidence is based on the use data for advanced economies, it allows us not only to keep the discussion limited to the scope of the paper, but also to contextualize our results based on a broader set of both develop and developing countries.

GVCs expansion and the consequent change in the nature of trade can help to

> 2For a literature review of the range of factors that could be responsible for the global synchronization of inflation see for instance Ha et al. (2019).

5
as shown in Figure C1(c) and the share of BTF in total transport expenditure also fell as seen in Figure C1(d).

In Table A2, we present the average treatment effects of the scheme on household expenditures, as estimated using equation (7). Across columns 1 to 5, it is evident that households in and Tamil Nadu less on com- Punjab spend considerably transportation pared to their counterparts in neighboring states during the post-treatment periods relative to the pre-period. Specifically, following the scheme implementation, expenditure on BTF as a proportion of total transport expenses for treated households decreased by an average of 6.6 percentage points compared to the control group (column 5).

A concern might be that differences in COVID regulation stringency measures across treatment and control groups could drive changes in household expenditures. Although we have controlled for COVID-19 cases in our main specification, as a robustness check, we control for the share of the state had either recom- additionally days government mended or required closing public transport in a month and the share of days the state government had either recommended or required individuals not to leave the house in a month. We present the results in Appendix Table D6. Estimated coefficients remain robust to adding these controls with magnitudes and standard errors similar to those in the baseline Table A2. Finally, the significance levels of the baseline estimates remain robust when the standard errors are clustered at the district level (see Appendix Table D7).

# **G.2 Evidence from the Delhi Primary Survey**

We next utilize the sample from the Delhi Primary survey data to examine changes in monthly transportation expenditure, including bus expenses, at the _individual_ level.<sup>31</sup> Among non-users, 64% spent |1 _∼_ 1,000 per month on transportation and the remaining spent more than |1,000. Of users, both new and continuous, 55% did not spend any money on transportation, including buses, per month in the period after
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 48 3. With the generated census vector **y** _d_<sup>(</sup><sup>_l_),calculate the indicator</sup><sup>_δ_</sup> _d_<sup>(</sup><sup>_l_)</sup> = _hd_ ( **y** _d_<sup>(</sup><sup>_l_)).</sup> 4. Repeat step 3 for _l_ = 1 _, . . . , L_ . The MC approximation of the EB predictor of _δd_ is obtained by averaging the indicators over the _L_ simulated censuses: The MSEs of the Census EB estimators may be estimated through a slight modification of the bootstrap procedure described for EB. The difference between the EB and Census EB estimators lies in the fact that the units from the survey cannot be identified in the census. Therefore, in each bootstrap repetition, we cannot generate census vectors **y** _d_<sup>_∗_(</sup><sup>_b_)</sup> , _d_ = 1 _, . . . , D_ , and then take the sample part **y** _s_<sup>(</sup><sup>_b_)</sup> from them. For the Census EB predictors, in step 2, we generate the bootstrap census vector **y** _d_<sup>(</sup><sup>_b_),</sup> _d_ = 1 _, . . . , D_ , using the values of the auxiliary variables from the census, **X** _d_ , _d_ = 1 _, . . . , D_ . In step 3, the true values of the bootstrap indicators are obtained from the bootstrap census vectors, _δd_<sup>_∗_(</sup><sup>_b_)</
not only the efficient economic cost but also the costs arising from the inefficiencies of the service provider. The _efficient economic cost_ , meanwhile, covers all the economic resources required for efficient service delivery. These include operation and maintenance expenses and also all capital costs such as depreciation and return on capital. Estimating cost-reflective tariffs is a data-intensive endeavor.

For our purposes, cost-reflective tariffs are taken from a novel data set provided by Andres et al. (2019). The alternative approach followed by Andres et al., despite its assumptions and simplifications, improves on the existing estimates. In a nutshell, this approach entails answering the following question: “What is the long-run incremental cost of providing water and sanitation services for a given company?” It is based on a simple, utility-wide, bottom-up model, drawing

> 4 See, for instance, Abramovsky and Phillips (2015) for a description of different microsimulation models for taxes and benefits using household survey data.

4
To create this resource, a variety of instruments can be employed. On the demand side, a set of information-gathering tools are briefly assessed and the most promising one -- the household energy survey -- is delineated in more detail. For this type of survey, key considerations are presented concerning overall design, sample selection, questionnaire design, logistics and fieldwork, and data processing. On the supply side, techniques for understanding the dynamics of comnmercial and traditional fuel supply wil be outlined in an upcoming Energy Department paper.

' This paper was prepared by Josef Leitmann (Energy Planner, ESMAP), with the assistance of Zouhair Souissi (Summer Intern, World Bank). Advice from the field was provided by Samir Amous, Doug Barnes, Michel Matly, Gordon McGranahan and Azedine Ouerghi.
and _Quality of supervision_ jointly convey the evaluated quality of project implementation and monitoring by the World Bank. Importantly, both of these variables capture the services provided by the World Bank to ensure quality at entry of the project and effective implementation through appropriate supervision rather than the public investment management of the implementing country’s authorities. Given that the objective of the PIQ index is to quantify country-specific public investment management quality, we control for these World Bank inputs.

_Agreement type_ reflects the type of legal instrument (signed agreement) associated with the project. It includes the following categories: CARB (Carbon Initiative), GEF (Global Environment Fund), IBRD (International Bank for Reconstruction and Development lending), IDA (International Development Association lending), MONT (Montreal Protocol), RETF (Recipient Executed Trust Fund), and SPF (Special Fund). Most projects in the dataset (86 percent) are IBRD and IDA lending projects. The sector fixed effects control for twelve sectors defined using data from the World Bank Operations Portal and based on the dominant sector of operation in each project.<sup>12</sup> We use Equation 2.1 to build three versions of the public investment quality index: a time-invariant measure (PIQ-F) and two time-variant measures (PIQ-D and PIQ-Q). To build the first measure, we start by estimating Equation 2.1 using all available data. We then recover the regression’s residuals and build the index by computing country averages of the residuals. As mentioned, the idea of the index is to obtain a measure of average country-specific project quality which is orthogonal to project characteristics and World Bank inputs. This procedure is similar to estimating the model with country fixed effects and then using the country fixed effects (which measure country-specific unobserved heterogeneity) as our index of public investment quality. We prefer to use OLS and then average the residuals because this approach is simpler from a computational point of view and reduces potential multicollinearity problems. In any case, fixed effects estimations
how they can serve as a tool for field experiments and source of valuable empirical data. We emphasize the design and logistical challenges associated with applying the conjoint method in the field. In Section 3 we use data from our recent conjoint survey conducted in Sri Lanka to evaluate WSS preferences for poor and non-poor households. Through the conjoint survey data, we can determine what service attributes affect WSS demand and whether these attributes vary across poor and non-poor households. We find that households care about service quality (not just price) and the households have diverse preferences in terms of 3
These arguments have clearly not exhausted the theoretical possibilities. For example, I have said nothing about the possibility of the poor forming a coalition with a subset of the non-poor so as to protect the poor from cuts.

However, the above discussion provides adequate warning against presuming that the poor will necessarily bear the brunt of cuts in situations in which the non-poor hold the balance of power over the budget. The need for specific actions to protect the poor must then be deemed an empirical question.

The rest of this paper addresses that question using data for Argentina. Evidence will be drawn from very different sources. In the next section, time series evidence on aggregate budget allocations across types of spending will be studied. After that, the paper turns to evidence for just one anti-poverty program. Although the data will be very different, in both the aim will be to examine how the composition of spending varies with total spending i.e., to estimate empirical models motivated by equation (4).

# **3. Social spending in Argentina during fiscal expansions and contractions**

While methods of measurement differ, it is widely agreed that in the mid-1990s less than 30% of the population is poor by Argentinean standards (World Bank, 1999). The level of "social spending" averaged 56% of total government spending in the period 1980-97 (Government of Argentina, 1999). Less than half of this went to "social services" (education, health, water and sewerage, housing and urban development, social assistance, and labor programs); the remainder can be labeled "social insurance" (pensions, public health insurance, unemployment insurance). Spending on social services is believed to be pro-poor, in that the poorest x% of households receive more than x% of spending, but this is not so for social insurance (Gasparini, 1999; Llach and Montoya, 1999). Table 1 reproduces recent estimates of the incidence of public spending on social insurance as distinct from the social services
Megginson and Netter’s survey, tend to focus on simple comparisons of pre- and postreform situations using narrow measures of financial and operating performance. As both reform and performance are multi-dimensional, it is important to examine the details of how each reform impacted on variables of socio-economic. The ability to do this is restricted by over-identification and a shortage of degrees of freedom. This makes econometric analysis challenging.

# **2.1 Framework for the study of electricity sector reforms**

Electricity sector reforms are multi-dimensional activities with interacting factors and a variety of impacts. The process generally involves a set of concrete steps or measures based on a specific model of reform. At one level, these measures involve structural and organisational changes to the industry, and at another level there is a requirement for appropriate institutional arrangements such as legislation and new agencies.

In addition, sector endowments and characteristics such as size, resource mix, historical development, define the initial market structure and starting point and can influence the reform path and outcome. Market structure is then influenced by various measures, such as unbundling of vertically integrated enterprises, as well as institutional factors such as regulatory authorities and legislation.

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and de Soto, 1989). See Thomas (1992) and Portes (1994) for excellent overviews. Our approach in this paper equates informality and noncompliance with societal norms such as tax obligations, labor protections, census enumerations, business guild participation, _etc._ In line with both Thomas' and Portes' characterizations, we are concemed with unregulated/umnonitored activities that are ostensibly legal, not those that are truly illegal (criminal).

^ See Besley (1995) for an excellent overview.

See Thomas (1992) and Portes (1994). Exceptions include Esfahani and Salehi-Isfahani (1989), Rauch (1991), Loayza (1995), and Banerji and Jain (1996).

4 For the remainder of the paper we will use "informal" exclusively to characterize the production and distribution of goods processes.