rafmacalaba/datause-encoder-data huggingface dataset:
pesos (US$323) per eligible worker. For comparison, the average daily minimum wage in 2009 was 53 pesos, or US$3.35 (CONASAMI 2015).

The wage subsidy program received 744 applications, of which 339 are approved, going to 396 plants for preserving 309,206 jobs ( _Secretaría de Economía_ 2012). These numbers correspond to 3.8% of employers and 34% of permanent employees in eligible industries.<sup>4</sup> Applications that were not approved did not meet the eligibility requirements and/or firing restrictions of the program.

The total amount of funding disbursed through the program was about 1 billion pesos (US$63 million), corresponding to about US$160,000 per plant on average. Comprehensive information on individual subsidies is not available, but partial data from the Ministry of the Economy on 203 beneficiary plants suggests that amounts ranged from 20,670 pesos (US$1,307) to 50.6 million pesos (US$3.2 million – given to Volkswagen Mexico), with a median of about 1.5 million pesos (US$92,522). As a reference point for the size of this amount, the average size of a loan or line of credit among manufacturing firms in eligible sectors in the 2010 World Bank Enterprise Survey, which covers a representative sample of firms in Mexico, was about 5.4 million pesos (US$340,000), conditional on having a loan or line of credit (47% of firms).

In practice, firms typically received the subsidy many months after they limited layoffs, in part because the process of reviewing applications and disbursing funds took some time. Most funds were approved starting in June 2009 (Galhardi 2009) and some of the amounts were still paid out in later years: out of the total US$63 million, US$53 million were disbursed in 2009, US$1.5 million in 2010 and US$8.5 million in 2011 ( _Secretaría de Economía_ 2012).

# **3. Data and identification strategy**

# **3.1. Data sources**

Data on employment come from IMSS. The data are at the industry level (as per the IMSS industry classification) and include the total number of employees registered with IMSS in each month from January 2000 to November 2013. I use the data for years 2004 through 2013 to estimate the effects of the wage subsidy program and the data for 2000 to 2003 for a falsification exercise.

I also use monthly data on US imports (total aggregated from world) for the period of January 2004 to December 2013, as a control variable. This data come from the US International Trade Commission’s Interactive Tariff and Trade DataWeb and were hand matched to the IMSS industry data using converted Harmonized Tariff Schedule (HTS) codes, at the 4 or 6‐digit level, depending on the industry. Due to differences in the industry classifications, not all industries could be matched, so that US import data are missing for a quarter of the IMSS industries in the study sample.

> 4 Calculations based on data from IMSS for December 2008. IMSS reports number of employers, which may either be plants or firms.

5
ence) shock, which we label “No Demand Shock.” Preserving the interaction between the lockdown and the demand shock, we also study the role of the rest unemployment by requiring that the unemployed and the returning non-essential businesses go through the regular labor market frictions (“No Rest”). Lastly, in terms of policies, we assess the importance of the unemployment insurance and costless firing of workers by working out an alternate case where non-essential entrepreneurs must pay for their wage bills during the lockdown (“No Wage Subsidy”).

**Fig. 14: Shocks, Policies, and Mechanisms**

<!-- Start of picture text --> GDP Unemployment<br>10.0<br>−0.5<br>−1.5<br>2.5<br>−4.0<br>1.5<br>−6.0 1.0<br>0.5<br>−9.5 0.0<br>2 4 6 8 2 4 6 8<br>quarters quarters<br>Consumption Capital Stock<br>2.00<br>0.85<br>−0.5<br>0.00<br>−1.0<br>−2.00<br>−1.5<br>−5.00<br>−7.50<br>2 4 6 8 2 4 6 8<br>quarters quarters<br>benchmark nodem norest nowagesubsidy<br>% change from SS % change from SS<br>% change from SS % change from SS<br><!-- End of picture text --> As for the no demand shock case, Figure 14 shows a slightly slower recovery in _GDP_ with virtually indistinguishable effects on unemployment. Without a compensating preference shock favoring future consumption, standard consumption smoothing incentives imply a smaller fall in consumption and a stronger decline in investment, responses that can be seen in the bottom panels. As a result, the recovery in GDP weakens. The swift recovery in the labor market remains the same thanks to the rest unemployment and also a larger decline in wages.

The economy enters the most protracted recession when we do not allow rest unemployment. In this case, despite the temporary nature of the lockdown shock, returning non-essential businesses and unemployed workers must go through the labor market fric29
not only are the Nordestinos more than five times more likely to fall below the "foodonly" or indigent poverty line compared to _Paulistas_ they are also 25 percent more likely to do so when education, skills, and other individual characteristics are taking into account.

Poor states are catching up with rich states in Brazil. The Northeast is catching up with the richer regions in Brazil and has on a per-capita GDP basis been growing faster than Brazil as a whole over the last ten years.<sup>2</sup> Figure 2.1 plots the ratio of per-capita GDP of the Northeast region relative to that of Brazil during 1989-98. Since 1995 growth in the Northeast has been faster than the Brazil average. Macroeconomic stabilization in the aftermath of the inflation-beating Real Plan of 1994, trade liberalization at the beginning of the 1990s, as well as a pronounced investment effort in the Northeast all had a positive impact on growth in the Northeast.

**Figure 2.1: Per-capita GDP in Northeast relative to Brazil (1989-98)**

<!-- Start of picture text --> GDP pc NE/ GDP pc Brazil<br>0.48<br>0.47<br>0.46<br>0.45 z x \<br>0.44<br>0.43<br>0.42<br>0.41<br>0.4<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998<br><!-- End of picture text --> _Source:_ Carrizosa, Fiess, and Vemer (2001) based on data from _Contas Regionais do Brasil._

> According to the PNAD 1999, 33.5 million Brazilians have a history of migration between states during any time in their life (Table 2.1). The largest share of these lifetime migrants came from the SE (35 percent) followed by the NE region (32 percent). Migration between different states in the same region appears to be of particular importance, and 28 percent of the migration in the NE is intra-regional migration, which is the lowest in Brazil. For example, about one-half of the migration observed in the SE occurred within the SE. The respective figures for the South, North, NE and Center regions are 42 percent, 35 percent, 28 percent, and 31 percent respectively.

> 2 Estimating geometric growth rate from recently released GDP data from _Contas Regionais do Brasil_ (IBGE), 1985-1998, Carrizosa, Fiess, and Vemer (2001) find that during 1985 - 97 per-capita GDP in the Northeast increased by 3.7 percent while per-capita GDP in Brazil increased by 3.0 percent.

4
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public program p in a country: (a) improves with an increase in its income; (b) improves (or does not worsen) if an increased proportion of the country's resources are spent on that public program; and (c) depends on other country-specific factors. The outcomes could, for example, be indicators of health status (e.g., child mortality) or education attainment (e.g., proportion of children completing primary education) at any given point of time, which can be influenced, among other factors, by public spending.

Taking logs, equation (1) can be written in linear form as: Given data the above model can be estimated to examine, among other things, the link between public spending on program p and the associated outcome of that program.

## _3.1 Modeling the efficacy ofpublic spending: The governancefactor_

In estimating the relationship between public spending and a development outcome as specified in equation (2), a researcher typically takes the information on spending from public budget documents. But what if only a fraction of that spending is actually undertaken and the remainder disappears (as in the Ugandan example given above). To allow for such a possibility, let us suppose that only a fraction y (.) of public resources are actually spent for productive purposes. Following Pritchett (1996), ,f, the coefficient on public spending on program p in equation (2) can be written as:

# _i=y(.)*fp_ (3)

where /3p is the productivity of public capital that is created from the spending on program p. Let us further assume that y(.), which measures the efficacy of public spending, is a function of the state of governance, Gi , in each country i. Therefore, Yi = XO,i + _ql,i Gi_ (4) Gi could indicate the level of corruption or the institutional capacity in country _i._ Substituting from equations (3) and (4), and rearranging terms, equation (2) can be written as: 6
|||||**Table 5.**|**Additional Tarif**|**f on Exports**|**by Origin**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**BTA**|**FU**|**BTA**|**DU**||**BT**|**DE**||**BTA**|**FR**|**BTA**|**DR**|
||||||**Tari**|**ff**|**Export R**|**ebate**|||||
||All<br>manufacturing<br>|Energy-<br>intensive<br>manufacturing|All<br>manufacturing<br>|Energy-<br>intensive<br>manufacturing|All<br>manufacturing<br>|Energy-<br>intensive<br>manufacturing|All<br>manufacturing<br><br>|Energy-<br>intensive<br>manufacturing<br>|All<br>manufacturing<br>|Energy-<br>intensive<br>manufacturing|All<br>manufacturing|Energy-<br>intensive<br>manufacturing|
|Rest of High Income|5.1%|8.7%|3.2%|5.8%|3.3%|6.1%|4.0%|6.8%|1.1%|9.2%|0.7%|5.9%|
|Brazil|5.8%|9.9%|4.4%|7.1%|4.5%|7.4%|4.3%|6.7%|2.9%|10.4%|2.1%|7.2%|
|China|26.1%|42.7%|3.1%|6.2%|3.3%|6.5%|3.7%|6.8%|0.5%|44.6%|0.6%|6.3%|
|India|20.3%|28.5%|3.5%|6.8%|3.6%|7.0%|7.1%|9.8%|1.1%|29.8%|1.0%|6.9%|
|Russia|35.3%|40.0%|5.2%|6.5%|5.4%|6.7%|3.5%|5.1%|6.7%|42.0%|4.2%|6.6%|
|Rest of East Asia|9.5%|14.8%|3.1%|5.7%|3.2%|6.0%|3.8%|7.0%|1.1%|15.6%|0.7%|5.7%|
|Rest of South Asia|10.3%|39.2%|3.0%|5.7%|3.1%|5.9%|3.8%|6.4%|0.2%|41.0%|0.3%|5.8%|
|Rest of ECA|23.5%|39.7%|4.1%|6.7%|4.2%|6.9%|3.7%|5.5%|2.2%|42.3%|2.2%|6.8%|
|Middle East and North Africa|16.6%|30.4%|3.2%|6.3%|3.3%|6.5%|4.0%|6.3%|0.7%|32.0%|0.7%|6.4%|
|Sub-Saharan Africa|10.9%|19.0%|3.9%|6.6%|4.1%|6.8%|3.7%|6.4%|3.0%|20.1%|1.9%|6.7%|
|Rest of LAC|6.6%|11.8%|4.2%|7.6%|4.4%|8.0%|4.7%|7.7%|1.8%|12.3%|1.3%|7.8%|

_Notes:_ **_BTAFU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in imports;_ **_BTADU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in domestic production;_ **_BTADE:_** _Industrial countries alone reduce emissions by 17% with import tariffs on all merchandise imports and rebates on all merchandise exports based on carbon content in domestic production;_ **_BTAFR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in imports;_ **_BTADR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in domestic production_ 28
2002 to 2006 and household assets in 2004-2006. The main variables used in our estimation are age, schooling level, schooling level of the spouse, an indicator for the birth of a child in the current year, ages of all children, number of years the respondent worked in the formal sector, number of years the respondent worked in the informal sector, labor sector choice, labor sector choice of the spouse, annual earnings and private household wealth.<sup>36</sup> We merge the household survey data with the administrative data on pension savings accumulations.

To arrive at our estimation sample, we impose the following sample restrictions: (i) Our model incorporate the rules of the AFP pension system. We excluded from the estimation sample workers who reported only making contributions to a pension system other than AFP. We do, however, incorporate those workers who worked before 1980 and accumulated some pension rights under the previous pension system and then switched to the AFP system. In the model, the value of these rights is captured through the value of their Recognition Bond (“bono de reconocimiento”), which we add to the funds accumulated in the AFP account upon retirement.<sup>37</sup> (ii) Incorporating marriage decisions is not feasible given the model’s complexity. To limit the impact of this abstraction, we set the initial age in our model to 35, an age at which most people’s marital status has been determined. We use in estimation individuals who are 35 or older in 2004, the initial year of our sample. We excluded respondents who reported getting married after the age of 35.

(iii) We excluded households with missing information on key variables and with inconsistencies across survey rounds with respect to age, education and civil status (2,502 respondents).

The final sample contains 6109 households, some consisting of a single person and some of a couple, for a total of 5482 women and 4843 men.

Tables 1 and 2 present summary statistics for the estimation sample and for all EPS respondents, imposing the age restriction only. Table 1 shows demographic and labor force participation information whereas table 2 describes the distribution of earnings, non-pension 36We construct a wealth measure that also includes the reported value of equity in major household assets, such as the home and car.

> 37We obtained a dataset on the recognition bond values from the Superintendence of Pensions, which we linked to the survey data.

21
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41

- Teranavithorn, Supee and Gael Raballand. 2009. _Transport Prices and Costs in Africa ̧_ World Bank.

- Tullock, G. 1959. “Some problems of majority voting,” _Journal of Political Economy_ , 67: 571-79.

- van de Walle, Nicolas.  2001. _African Economies and the Politics of Permanent Crisis, 1979-1999._ New York: Cambridge University Press.

- Vicente, Pedro C. 2010. “Is Vote-buying Effective? Evidence from a field experiment in West Africa,” Working Paper, Trinity College, Dublin

Vicente, Pedro C. and Leonard Wantchekon. 2009 “Clientelism and Vote Buying: Lessons from Field Experiments in African Elections”. Oxford Review of Economic Policy Wantchekon, Leonard. 2009. “Can Informed Public Deliberation Overcome Clientelism? Experimental evidence from Benin,” Working Paper, Department of Politics, New York University

- Wantchekon, Leonard. 2003. “Clientelism and Voting Behavior: Evidence from a Field Experiment in Benin” _World Politics_ 55: 399-422

- World Bank.  2003. _World Development Report 2004: Making Services Work for Poor People_ , Washington DC

- World Bank. 2007. _Uganda: Public Expenditure Review_ , Report number 40161-UG, Washington, DC

- Yanagizawa-Drott, David. 2010. “Propaganda and Conflict: Theory and Evidence from the Rwandan Genocide,” Working Paper, Kennedy School of Government, Harvard University

- Young, Daniel J. 2009. “Support you can count on? Ethnicity, partisanship, and retrospective voting in Africa,” Afrobarometer Working Paper No. 115.
# **1. Background**

Despite the significant economic progress that Tanzania has experienced over the years and considerable investments in water supply infrastructure through both government and donor funding, a significant proportion of its population remains without proper access to improved drinking water. While one of the agreed Millennium Development Goals (MDGs) is to halve the proportion of people that do not have access to water services by 2015, Tanzania only increased its access to improved drinking water from 54 percent to 56 percent (JMP, 2015). The country now faces an even more difficult task of meeting the Sustainable Development Goals (SDGs) to provide universal coverage of safe water by 2030.

Despite its efforts through the two phases of the Water Sector Development Program (WSDP), one persistent problem that has adversely affected the country’s effort in increasing access to improved water services is the prevailing high levels of non-functionality or failures of its current water infrastructures and in particular, water points. Technology constraints also play an important role (de Bont et al., 2019). This situation is not unique to Tanzania. Water point failures have been documented by a number of studies across the Sub-Saharan African region. In countries such as Nigeria (Andres et al., 2018) and Ghana (Fisher et al., 2015), water points tend to fail frequently, particularly during the early years after construction. The situation is similar in Mozambique (Jansz, 2011), Uganda (Nekesa & Kulanyi, 2012) and Ethiopia (Alexander et al., 2015; Schweitzer et al., 2015). However, evidence indicates that the problem of water point failures may be relatively more serious in Tanzania with some estimates putting the figure as high as 44 percent (Banks & Furey, 2016) The importance of access to water supply cannot be overstated. Apart from meeting the human necessity of water, it has immediate health impacts (Hyland and Russ, 2019) which in turn affects other developmental outcomes including education attainment and even long run poverty outcomes (Zhang and Xu, 2016; Mangyo, 2008; Alderman et al., 2001). Evidence indicates that lack of functioning infrastructure will undermine economic growth (Agenor, 2010; Barbier, 2004). Moreover, evidence also indicates that infrastructure—including water and sanitation—is likely to offset moderate macroeconomic shortcomings during the initial stages of economic development (Moller and Wacker, 2017; Gibson and Rioja, 2017; Amann et al., 2016).

In this paper, we utilize information from the Tanzania Water Point Mapping (WPM) compiled in November 2016 to identify the reasons for the failure of water points in the country. Using a variety of statistical methods, we seek to understand the impact of the following factors on water points failure: (i) the age, (ii) technology type; (iii) geographic patterns; (iv) hydrological characteristics; (v) type of promoter; and (vi) day to day management type of these points. The results indicate that water points 2
Table A7. Average pollution intensity by sector in SUST-RUS 2006 data (thousands tons of emission per trillion RUB of output)

|||**Particula**|**Sulphur**|**Carbon**<br>**monoxid**|**Nitrogen**|**Hydrocar**|**Volatile**<br>**organic**|
|---|---|---|---|---|---|---|---|
|||**te matter**|**dioxide**|**e**|**oxide**|**bons**|**compone**<br>**nts**|
|||**PM**|**SO2**|**CO**|**NOx**|**CnHm**|**VOC**|
|A|Agriculture, hunting and forestry|22,56|4,27|26,00|5,20|4,38|1,99|
|CA_col|Mining of coal and lignite; extraction of peat|85,85|19,13|55,54|17,71|1 099,66|1,42|
|CA_gas|Extraction of natural gas; service activities incidental to gas<br>extraction, excluding surveying|18,27|5,74|172,04|5,55|70,20|45,04|
|CA_oil|Extraction of crude petroleum; service activities incidental to<br>oil extraction, excluding surveying|223,13|76,91|2 044,28|67,66|464,59|900,12|
|CB|Mining and quarrying, except of energy producing materials|321,72|236,69|547,46|79,21|4,43|12,11|
|DA|Manufacture of food products, beverages and tobacco|14,99|12,10|36,58|9,78|0,81|6,19|
|DB|Manufacture of textiles and textile products|24,59|16,91|44,57|15,37|0,77|3,84|
|DC|Manufacture of leather and leather products|60,78|79,45|2,61|7,82||1,30|
|DD|Manufacture of wood and wood products|102,05|23,41|324,76|32,42|0,60|18,61|
|DE|Manufacture of pulp, paper and paper products; publishing<br>and printing|119,41|120,43|100,45|58,68|0,77|12,30|
|DF|Manufacture of coke, refined petroleum products and nuclear|14,03|68,39|70,30|16,59|33,72|169,47|

100
- We are able to benchmark the TEs against other, mostly non-transition countries in 2004-05.  In particular, we have BEEPS data from developed and cohesion Europe (Germany, Spain, Greece, Ireland, Portugal), developed Asia (South Korea) and even emerging Asia (Vietnam, coming from a different transition path).<sup>1</sup>

The benchmarking BEEPS surveys have not been used much by researchers to date, but play a central role in our analysis, because together with the time series dimension of the BEEPS data, they enable us to analyze the convergence process. Where our approach differs from most previous studies using the BEEPS data is that our focus is not on direct measurement of institutions, something which is notoriously difficult in the best of circumstances.<sup>2</sup> Rather, we look primarily at the function of, and convergence in, markets and firm behavior that are shaped by particular institutions. We focus in particular on competition and market structure, finance and the structure of lending to firms, and how firms respond to the economic environment by restructuring. Analyzing restructuring activity through the convergence prism is particularly interesting in the transition context, since it provides an example where we expect successful convergence to be associated with a high initial level of restructuring – possibly appearing after an initial low level of restructuring and then “take-off” early in the transition period – as firms make major investments to adjust to the new market economy environment, followed by a decline over time. Our analysis is thus closest in spirit to the “structural convergence” literature pioneered by Kuznets and Chenery. Unlike this literature, however, which operates at the sectoral level, and the growth and convergence literature, which operates at the macro level, we are drilling down to the level of firms.

The paper is organized as follows. Section II describes the BEEPS surveys and samples and introduces the country classification we use. Section III sets the scene by presenting the stylized facts of growth and convergence as they have been analyzed in the literature

> 1 The “Cohesion” group includes Greece, Ireland, Portugal and Spain, the countries which in the late 1990s were recipients of the EU cohesion funds.

> 2 Thus Blanchard (2007) makes this point for an application that is relatively straightforward compared to ours: measurement of labor market institutions in the OECD countries.

7