rafmacalaba/datause-encoder-data huggingface dataset:
10 The Brazilian National Sector Information System (SNIS) which is a partner in the International Benchmarking Network (IBNET) provides annual information on 27 Brazilian regional water utilities for the period between 1996 and 2004. All surveyed water utilities but one provide water and sewerage services.<sup>11</sup> In Colombia, 228 utilities have been surveyed in 2003 and 2004, but because of missing information for some variables of interest, only 48 utilities can be used at the estimation stage. These 48 utilities provide both water and sewerage services.<sup>12</sup> In Moldova, performance data are available for 41 water and sewerage utilities for each year between 1996 and 2004. Moldovan water utilities provide water and sewerage services, except for two utilities which provide only water services and one utility which stopped providing water services after 2002. For Vietnam, information on 67 provincial water utilities offering only water supply services is available for the period covering the years from 1997 till 2000.<sup>13</sup> Because the sub-sample of utilities providing only water services in Brazil and in Moldova is very small (1 out of 27, and 2 out of 41, respectively), we restrict the analysis to the respectively 26 utilities in Brazil and 39 utilities in Moldova which provide both water and sewerage services. In these two countries and in Colombia, the provision of both water and sewerage services should call for the estimation of a two-output cost function, i.e., the total volume of water produced and the total volume of wastewater collected and treated. However, for the three countries, the coefficient of the wastewater volume was not found significant in the cost function (which would mean that an increase in the amount of wastewater collected and treated does not significantly increase the variable cost of the utility). We believe that the non-significance of this parameter is driven by the high collinearity between the two output variables. High collinearity was also observed for control variables such as network length (water supply network length is highly collinear with sewerage network length), and population to be served (population supplied with water is almost perfectly collinear with population connected to the sewerage system). For that reason, and because estimates of

> 11 See Tupper and Resende (2004) and Seroa da Motta and Moreira (2006) for a description of the institutional and regulatory background of the Brazilian water and sewerage sector. 12 Note that we cannot check if the final sample gathering 48 utilities is representative since information on the water utility size (volume of water produced and number of served customers) is missing for 129 utilities. 13 The original dataset contains data up to 2003. However, because of inconsistency in the data from 2001 onwards, we decided to consider only the 1997-2000 period in the econometric analysis.
Step 2. To develop the water flow and the water level in the river systems of the southwest region, the hydrodynamic module of the MIKE 11 model system (the Southwest Regional Model, SWRM) was used.<sup>14</sup> Inputs for SWRM include the rainfall runoff estimates from the Rainfall-Runoff model, water flow data recorded by river gauges at the upstream end of the river (upstream boundary), water level data from the downstream end of the river (downstream boundary), and the cross-sectional river data. SWRM generated the water flow, water level, and flow velocity of the river systems. These estimates were used for the water flow boundary upstream in the Bay of Bengal model as well as for the river salinity model. Box 1 provides details on SWRM.

Step 3. Salinity of the Bay of Bengal was simulated by the Bay of Bengal (BoB) model based on the MIKE 21FM module of the Danish Hydraulic Institute (DHI). The BoB model domain extends from Chandpur on the lower Meghna River (23° N) in the north to 16° N latitude in the Bay of Bengal in the south. The BoB model used the water flow estimates from SWRM at the upstream boundary. The time-series water level data at the downstream boundary were generated from the DHI Global Tide model. Measured concentrations of salinity along the upstream boundary and constant 32ppt salinity at the downstream boundary were used as salinity inputs. For details on the BoB model, see Box 2.<sup>15</sup> Step 4. A Salinity model (the advection-dispersion module of MIKE 11) is coupled with the hydrodynamic module to assess river water salinity in the southwest coastal zone. Measured concentrations of salinity along the upstream boundary and estimates of the salinity of the Bay of Bengal generated by the BoB model for the downstream boundary were used as inputs.

The BoB model and SWRM were run under the assumption that the polders in the coastal zone were all functioning well and would not be overtopped even in the future.

> 14 The MIKE 11 hydrodynamic module solves an implicit, finite difference scheme to compute unsteady flows in rivers and estuaries. A detailed description of the model is available at http://www.dhigroup.com. 15 The downstream boundary of this model is far from the coast. The maximum depth along the open downstream boundary is more than 2,000m. Salinity at this location does not vary over time and space.

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governments to be responsive. Djankov et al. (2003) find that government ownership in media undermines political and economic freedom. Besley and Prat (2006) develop a model of democratic politics in which media capture is endogenous. The model offers insights into the features of the media market that determine the ability of the government to exercise such capture and hence to influence political outcomes. Our paper is the first to use media data to account for the intensity of and attitudes in reform chatter.

Lastly, our paper is linked to the literature on economic reforms and the political business cycle. Fernandez and Rodrik (1991) provide a theory to explain the “status quo bias” against efficiency enhancing reforms. In their model, individual-specific uncertainty about distributional gains creates a bias against reform. Early work on the political business cycle dating back to Nordhaus (1975) shows that macroeconomic cycles could be induced by electoral cycles. Brender and Drazen (2005) provide evidence that political budget cycles are stronger in weaker democracies (citizens in more established democracies do not get fooled by promises made by politicians). Our paper presents evidence on the importance of polity in mediating reform chatter over the business cycle including during crises.

The remainder of the paper is organized as follows. Section 2 presents the data. Section 3 lays out the empirical strategy and main results around the evolution of the intensity of reform chatter. Section 4 presents several extensions around sentiment analysis and the nexus between reform chatter and actual reforms. Section 5 concludes.

# **2. Data**

This section describes the main sources of data used in this paper. The discussion focuses first and foremost on reform chatter given the novel nature of the data, while other, more established, sources are discussed briefly.

# **2.1 Reform chatter**

We make use of text in the form of newspaper articles as our main data source. The data are extracted from Dow Jones FACTIVA via the Dow Jones Data, News and Analytics Platform. Dow Jones FACTIVA is a global repository of newspaper articles covering over one billion articles published in 28 different languages. The full list of languages is listed in Appendix Table A1. Beyond the date of publication and the text, Dow Jones provides several classifiers to navigate searches. These classifiers include language codes, subject codes, country/region codes, industry codes, and publisher information.

The search in each language is conducted so that at least one of the words relating to the economy and at least one relating to reforms appear. For example, in English, the precise query is: _[(“economy” OR “economic”) AND (“reform” OR “reforms”)]_ . This search is performed for each country and each language, over the period from January 1, 1980 to December 31, 2018. The search yields 9.3 million (unique) reform articles. In addition, we obtain the number of total articles 3
Φ , (10) cc ccii ccii AA = rr<sup>cc</sup> where the spillover rate remains a free parameter in this model. rr<sup>cc</sup> In this section, we return to the Ethiopia case as an example to demonstrate how PPI can be used to infer the potential effect of spillovers within a country’s poverty dynamics.

**Estimation Strategy:** PPI offers two distinct methods for analyzing multidimensional poverty. First, the “retrospective” analysis seeks to understand past dynamics by leveraging historical data from an initial survey year, revealing how poverty indicators and policy priorities (budget allocations for an indicator) have evolved over a certain observation period to reach observed values in a final year. This analysis can infer an optimal spillover rate (the spillover rate that best fits the data) and other model parameters (e.g., c and c′ ). Understanding these parameters is essential as spillover rates can vary widely or modestly between dimensions and countries, influencing policy intervention outcomes. αα αα Second, the “prospective” analysis uses historical insights to understand how changes in the spillover rate or the Poverty Space could affect multidimensional poverty dynamics over a future period. This approach guides policymakers on which poverty indicators to prioritize and when, helping to inform effective intervention strategies for future improvements.

In the retrospective analysis, we use national data on poverty indicators from the first survey year (2011) and the last survey year (2019), government expenditure data (per capita in constant USD) from 2011 to 2019, data on the quality of law and monitoring from the World Bank’s World Governance Indicators for 2011 (the quality of law is approximated with the rule of law variable, whereas the quality of monitoring with the control of corruption), and the estimated Poverty Space for 2011. We estimate the model parameters and find the optimal spillover rate by calibrating the model with spillover rates ranging from 0 to 1 (in increments of 0.05) and calculating the goodness of fit. The rate providing the best fit on average is our optimal spillover rate. See Appendix 4 for more details on the data cleaning and calibration procedures for our PPI analysis.

We then conduct a prospective analysis until 2030 using data from the final survey year (2019) and Poverty Space data for the same year. We assume that the rule of law and quality of monitoring remain at the 2019 level and that government expenditure remains at its average value from 2011 to 2019. In 27
# 4.1 The Social Accounting Matrix

The basic dataset consists of a SAM and a set of initial levels and lagged variables. The SAM encompasses 37 accounts including production and retail sectors (8 accounts), educational services (2 accounts), labor production factors and profits (5 accounts), enterprises (1 account), households (4 accounts), government current expenditures, taxes and pensions (12 accounts), government investment expenditures (3 accounts), private investment expenditures (1 account), and the rest of the world (1 account).

The SAM dataset was derived so as to (i) reflect characteristics of a typical labor-exporting MENA country, and (ii) represent an equilibrium dataset, including a balanced government budget, a zero balance of payments (BoP), and zero net foreign borrowing. Nevertheless, the net factor service account of the BoP includes private and foreign interest payments reflecting past foreign borrowing. The characteristics of the SAM data can be summarized as follows. On the output side, agriculture and the informal sector account for respectively 12 and 35 percent of total output at producer prices. In contrast, private urban formal production accounts for almost 46 percent of total output. On the demand side, private current expenditures account for 64 percent of GDP, whereas government current expenditures and wages account for 11 percent of GDP. At the same time, total investment expenditures represent 22 percent of GDP, implying that our “prototype” labor-exporting MENA country is running a trade surplus equivalent to 2.8 percent of GDP.

Looking at the BoP, total net remittances to households amount to 1.2 percent of GDP. Together with the trade balance surplus, this is financing a deficit on the net factor services account amounting to 4 percent of GDP. This includes private foreign interest payments (-1.5 percent of GDP) and government foreign interest payments (-2.5 percent of GDP). The trade balance is dominated by non-agricultural imports and exports, with agricultural exports accounting for only 8 percent of total export earnings, and non-agricultural imports accounting for 92 percent of total import expenditures. The level of trade openness, measured by the ratio of the sum of imports and exports to GDP, amounts to a moderate 38 percent.

Looking at the government budget, indirect taxes in the form of production and retail level taxes (excluding payroll taxes) account for 84 percent of total government revenues. Household tax revenues, amounting to 13 percent of total government income, represents the largest revenue item among direct tax items, whereas enterprise taxes account for only 4 percent of revenues.

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# **ABSTRACT**

Vietnam’s young private sector is growing fast. Crucial to this growth has been a policy environment that increasingly recognizes the importance of private entrepreneurship— particularly its potential to help address the country1s pressing need for significantly increased wage employment creation. Expanding the benefits of private sector growth beyond urban centers out into the rural areas where most Vietnamese live—and where poverty and underemployment are heaviest—will require significantly increased information flows on what is working and what is not.

This paper presents an objective picture of Vietnam’s emerging private sector, two years after initial implementation of the country’s much praised Enterprise Law. The country’s private companies are significantly better off than they were just a couple years earlier, when regional economic recession and stagnation on domestic policy reforms had brought development of the formal private sector to a near standstill. At the same time, the sector1s small base means that its impressive rates of job creation still fall far short of matching the booming growth of the overall work force.

Information for this paper is based on data collected from Vietnam’s General Office of Statistics, a small number of individual company case studies, and a national firm-level survey designed and implemented by the authors. Research for the paper revealed significant gaps in available private sector data and flaws in current data gathering methodologies—calling into question the ability of policy makers and advisors to understand rapid, ongoing economic developments and make appropriate and timely policy decisions. The authors hope that this paper can serve as a starting point and an impetus for more targeted research aimed at identifying and addressing specifc obstacles to sustainable and broad based job and wealth creation.
**Table 1: Incidence of Poverty and Food Stamps in Jamaica**

||_November_<br>_1989a_|_November_<br>_1990_|_November_<br>_1991_|_November_<br>_1992_|
|---|---|---|---|---|
|Percent Poor|26.9|27.9|38.9|34.2|
|Poverty Gap|8.8|7.9|13.7|10.6|
|Poverty Severity|3.9|2.9|6.6|4.4|
||_Percent Households Re_|_ceiving Food S_|_tamps_||
|Poorest Quintile|33.7|29.3|42.6|45.0|
|2|26|20.4|27.6|36.6|
|3|17|14.2|20.3|27.1|
|4|8.7|9.2|11.7|16.3|
|Top Quintile|4.0|2.7|4.0|6.1|
|Jamaica|14.9|12.8|17.8|20.0|

a Quintile data for 1989 refers to the month of July of the Jamaican Survey of Living Conditions.

_Source:_ Jamaica: A Strategy for Growth and Poverty Reduction Country Economic Memorandum, 1994, World Bank; and Jamaica Survey of Living Conditions Report 1992, The Planning Institute of Jamaica and The Statistical Institute of Jamaica.

# **3 The Jamaican Food Stamp Program (FSP)**<sup>**2**</sup>

In order to mitigate the adverse short-run impacts of macro-economic stabilization measures on the poor, the Government of Jamaica (GOJ) implemented the FSP in 19843.

The food stamp program was targeted to two main categories of people who were considered at nutritional risk: (i) pregnant and lactating women and children under 5 years of age; and (ii) the poor, elderly and handicapped. The continuing devaluation of the Jamaican dollar coupled with inflation prompted GOJ to expand the eligibility category in September 1990, to include poor single member households with incomes below J$3,000 per annum and families with two or more members with an income below J$7,200.

> 2 This section is based on Grosh (1992) which provides a detailed description of the operation of the programn. See also Andersen (1993).

> 3 In addition, a school feeding program was also introduced.

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think that if this is the case, then sanitation externalities depend on both the individual’s _Sjk_ and his neighbor’s access _Sj′k_ , or _Rk_ ( _Sjk, Sj′k_ ). In order to separate the direct benefit from the external benefit, we assume that a household’s access has no effect on the village ratio, _∂S_<sup>_<u>∂R</u>_</sup> _jk_<sup>_<u>k</u>≈_07,resultingin</sup><sup>_Rk_(</sup><sup>_Sj′k_).</sup> With this in mind we can rewrite our hypothesis from Equation (1). In Equation (2) we assume that a child’s health improves when he gains access to sanitation (the direct benefit). Furthermore, in Equation (3) we assume that as the proportion of neighbors with access to sanitation increases there are improvements to a child’s health (externality). And the size of the phenomenon grows at higher rates of access that could be described as a ‘positive concave externality effect’.

# **5 Empirical Implementation**

We test the hypothesis presented in Equations (2) and (3) for different types (in terms of quality) of access to sanitation infrastructure, namely access to improved sanitation facilities, access to fixed-point defecation, and open defecation, based on the theory that as quality of sanitation improves so does health. These levels are defined from the ‘Sanitation Ladder’ (Figure 1). The rungs on the ladder represent an improvement in sanitation infrastructure, which correlates with gains in health outcomes. We define access to sanitation according to the definitions used by the JMP<sup>8</sup> , which tracks country progress towards achieving the MDGs related to water and sanitation. The JMP uses two key characteristics to classify access to sanitation: (a) degree to which infrastructure safely isolates excreta

> 7If one thinks of the share that one household has over the ratio of access of a village, an irrelevant share for the analysis would be 5 percent. For this we need a minimum of 20 households in a village. In the data that we are using 90 percent of the households surveyed are in villages with at least 20 households.

> 8World Health Organization/United Nations Children’s Fund Joint Monitoring Programme for Water Supply and Sanitation website http://www.wssinfo.org

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In its final form the applied model to estimate hidden costs, H is expressed as: H = R* - R where R* is the expected revenue and R is the actual revenue. H can be expressed alternatively as a function of tariffs below cost recovery, unaccounted losses and poor collections, by substituting for these variables: H = Qe (Tc – Te) + Qe Tc (lm – ln) / (1 – lm) + Qe Te (1 – Rct) where, Qe = end user consumption Tc = average cost-recovery price Te = weighted average end user tariff lm = total loss rate ln = normative loss rate Rct = collection rate See Annex 1 for a full derivation.

Note that tariffs set below cost-recovery levels as a recognized policy should be considered an explicit subsidy and therefore should be deducted from the overall calculation. In this case the correct measure of hidden costs would be as follows: H = R* - R – T where T is the amount of capital or other transfer. No attempt has been made to estimate T in this paper and hidden costs may in some cases be overestimated.

# **Data Used in the Hidden Costs Calculator**

Hidden costs estimates in the energy sector span 22 ECA countries. However due to decentralization of water service provision, coverage in this paper is limited to the 16 countries with available data. Data collection has focused on 2000 to 2003 using internationally known sources or existing data collection instruments where possible. Since there are no external datasets of desired financial performance (e.g., average cost-recovery price), supplemental data have been applied (from existing World Bank Group publications and reports; from World Bank Group and International Monetary Fund sector specialists). The paper presents information reported in external datasets or by utilities that have been checked for inconsistencies and outliers but no special audit has been conducted. Specific assumptions that may be used in place of sector specific data, including normative loss rates and average cost recovery prices, are presented in Annex 1. All data presented in this paper are in 2001 constant US dollars to eliminate trends or fluctuations that result from currency conversions.

# **Key Findings:   Hidden Costs in the ECA Region are Significant**

Throughout the region during this period, the power sector’s share of hidden costs has been the largest. The water sector and gas sector contributions are of similar magnitude to each other but their combined impact is still less that that of the power sector. Tariffs set below the cost-recovery rates is the single most significant factor in the energy sector—power and gas while unaccounted losses are the main factor in the water sector.

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