rafmacalaba/datause-encoder-data huggingface dataset:
manageable. Other approaches to evidence scoping, mapping and synthesis include a number of techniques to reduce the resource intensity of the search process while maximizing yields.

Search techniques adopted in rapid reviews/ evidence assessments might be particularly relevant. This includes for instance only searching English language literature, only covering a limited time period, applying specific search strings if used<sup>16</sup> , or applying a restriction on publication status (Khangura et al., 2012; The Civil Service, 2008). Moreover, focusing predominantly on key repositories of impact evaluations (such as 3ie’s Database of Impact Evaluations<sup>17</sup> , J-PAL, the Cochrane Register of Trials), snowballing and citation tracking (Waddington et al., 2012), use of list serves, key databases and websites relevant to the thematic area under investigation are other techniques which can increase the efficiency of the search. The services of an experienced Information Specialist can be helpful in developing a comprehensive and efficient search strategy.

<mark>The search for systematic reviews should be comprehensive and systematic, but it will be simpler than the search for primary studies and searches typically carried out in systematic reviews (Becker and Oxman, 2011). It should focus primarily on databases of systematic reviews appropriate to the topic, including the Campbell Library, the Cochrane Library, the Environmental Evidence Library, the Database of Abstracts of Reviews of Effects (DARE),</mark> 16 3ie gapmaps share similarities with the evidence maps produced by the Global Evidence Mapping Initiative (Bragge et al., 2011).The focus of these evidence maps is to describe the quality, design and characteristics of research in broad topic areas. In preparing the evidence maps the authors found that using highly sensitive searches with low precision proved unmanageable. Hence, information specialists developed search strings using specific search terms and terms frequently used as keywords in relevant papers and compared this with a highly sensitive search string typically used for systematic reviews (Parkhill et al., 2011). The latter returned significantly more references, but the basic search strategy did not miss any references of significance and the authors conclude ‘results indicate that the EM search method is unlikely to miss the key articles necessary for decision making’ (p. 159). The time and resource savings were significant for the more basic search strategy and the authors suggest the additional resources required for the highly sensitive search strategy not justified for the evidence map.

17 The 3ie database of impact evaluations is based on regular searches of a range of resources, including repositories of impact evaluations, grey literature and academic databases. Researchers use a carefully developed search strategy available in Mishra and Cameron (2013). This strategy can be combined with subject specific keywords and applied to a broader range of databases than those included in the regular searches, including subject specific databases of particular relevance to the thematic focus of the gap map.

12
a control for other characteristics. Section 4 is about the empirical strategy of this paper and explains data and statistical models. This section develops a spatial hedonic model by presenting the basic framework of hedonic analysis and a spatial econometrics model. Section 5 reports the estimation results of the models. Section 6 concludes.

# **2 Background**

Located 90 miles southeast of Mumbai, Pune is the second largest city in the state of Maharashtra and the ninth largest Indian city, with a population of more than three million (Government of India, 2013). Unlike landlocked Mumbai, Pune has been growing outward in a relatively monocentric form, from the old city area to the inner and outer suburbs (Pune Municipal Corporation, 2013). The city is divided into 14 administrative wards; the municipal government (the Pune Municipal Corporation, or PMC) opens its branches in each.<sup>2</sup><sup>_,_3</sup> In addition, the city of Pune consists of 76 electoral wards, from which a pair of male and female municipal councilors (municipal corporators, or _nagarsevak_ in Marathi) is elected through direct election every four years. In tandem with the rapid population growth of the city, slum population increased from 36,725 (7% of the city’s total population) in 1951 to 274,000 (23%) in 1976, and to 1,050,000 (39%) in 2001 (Bapat, 2004). Currently, 5.25 square kilometers of slum area, which accounts for only 2.3% of the total area in the city, accommodate more than 200,000 households (Figure 1) (MASHAL, 2011).

## (Insert Figure 1 here)

Based on the Maharashtra Slum Areas (Improvement, Clearance, and Redevelopment) Act (henceforth, the Slum Act), government agencies have identified 477 slum pockets in Pune. The Slum Act defines a slum based on its poor living environment and as any area that “is or may be a source of danger to the health, safety or convenience of the public of that area or of its neighborhood, by reason of the area having inadequate or no basic amenities, or being insanitary, squalid, overcrowded or otherwise” (Government of Maharashtra, 1971). In its planning document, the Pune Municipal Corporation describes slums in the following three categories (Pune Municipal Corporation, 2013): The first type of slums are those located in the core city area. Poor people settled in these areas back in the 18th century, and these settlements later turned to slums. The second type of slums exist in today’s inner suburb areas. As the city grew in size, migrants settled in the areas outside the core city area in order to take advantage of good employment opportunities and available vacant land. Extraordinarily huge slum agglomerations have developed in the Parvati Hill and Yerwada 5
Table 4. Impacts of PEP Migratory Status on Socioeconomic Well-being

||Well-being<br>(Index)|Consumption<br>per Capita<br>(log)|Labor<br>Income (log)|Employed|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|1[PEP_i_ = 1]|1.284**|0.481**|0.221**|0.261*|
||(0.473)|(0.181)|(0.107)|(0.248)|
|q-values|[0.017]|[0.017]|[0.026]|[0.080]|
|Obs. left|415|476|255|401|
|Obs. right|735|1,036|569|710|
|Observations|3,424|3,801|1,819|3,424|
|Mean values (Non-RAMV refugees)|0.000|1.373|0.351|0.561|
|Outcome Level|Individual|Household|Individual|Individual|

_Notes:_ Dependent variables: (i) Well-being (Index) is constructed using the outcome variables of columns (ii) to (iv) using the methodology of Kling et al. (2007); (ii) Annual consumption (log) is the logarithm of annual consumption per capita in million COP; (iii) Labor Income (log) is the logarithm of the monthly labor income that includes wage, extra pay, and revenue from independent work in million COP; and (iv) Employed is a dummy [=1] if reported as employed and has a wage (includes independents and family workers). All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets. _∗∗∗_ significant at the 1%, _∗∗_ significant at the 5%,<sup>_∗_</sup> significant at the 10%.

48
(1983) was skeptical that managers would perform their best knowing that doing so would only ratchet up the target for future periods. Mallon also argued that the experience with similar incentive schemes in US military procurement contracts had been disappointing because “efficient firms faced with the prospect of sharing profits over estimate costs while inefficient firms permitted to share losses underestimate costs” (Mallon, 1983, paraphrasing McCall, 1970, p. 844). Mallon placed more faith in competition as a tool for forcing managers to reveal information and proposed that any SOE activities that could be hived off into competitive markets should be. In practice, only a handful of developing countries adopted Jones’ suggestion of awarding bonuses to managers based on performance, the best documented examples being Korea and Pakistan. There was some introduction of competition but only in industries that were not natural monopolies.

Both qualitative and quantitative evidence suggest information asymmetry is a serious problem and that bonuses, competition and other devices may alleviate it somewhat. Anecdotes about soft targets abound in the literature on performance contracts. And in interviews managers often reported that bonuses gave them an added incentive to improve performance as well as a tool with which to motivate staff, although less so where the contracts were so short that ratcheting up quickly discouraged effort. Shirley and Xu’s first study of 12 utility SOEs failed to find to find any significant relationship between firm performance and contract features designed to reduce the managers’ information advantage, but that was probably because the sample size was small.<sup>6</sup> In their second study of 628 Chinese manufacturing firms, however, Shirley and Xu found that productivity was higher if the firm’s performance was judged primarily by profits rather than by more complex measures that the managers might more easily manipulate. They also found that productivity increased if a significant portion of wages depended on firm performance, if the managers were selected through a process of competitive bidding and if the firm operated in a more competitive industry.<sup>7</sup>

> 6 Shirley and Xu (1997) hypothesized that government officials could counter the managers’ advantage by insisting on contracts with a few key financial measures, not changing the measures often and rewarding managers who performed well with bonuses or non-financial awards.  They found no apparent correlation between firm performance and these features.

> 7 Although these features significantly improved performance, the gain was not enough to make contracting advantageous.  This result occurred because Shirley and Xu (1998) estimated that a basic contract, without

22
The waiting time function for buses was derived from the relationship between average waiting time and number of buses as provided in Meignan et al. (2007). In the base case, the parameter constant, ����, is calibrated to match the base data on waiting time for bus by origin and destination, (�, �). Think of a scenario where there is an increase in bus supply. On the one hand, this will potentially encourage people to switch to bus from all other modes as the waiting time for bus improves. This will reduce aggregate traffic for all other modes and hence it will reduce congestion. On the other hand, additional buses running on roads will take more space and frequent stops will disrupt the traffic flow, which will increase congestion on the roads. The net effect depends on how many buses are running on roads, relative to the switch in ridership to bus from the other modes. Equation (26) implies that an additional bus can potentially create congestion on the road for the other modes. Equation in (25) is the most commonly used congestion function. The value of the exponent, ��, can typically range from 1.2 to 4. We are using the value of 3.5 suggested in Arnott (2013). Detailed discussions on the data and key assumptions are presented in Appendix A. The calibration results are summarized in Tables 1-4.

**TABLE 1:** Base data except transportation

||**Municipal**<br>**Beirut**|**Greater**<br>**Beirut**|
|---|---|---|
|_Residents_|445,184|997,422|
|_%Workers_|41.18|44.01|
|_Jobs_|198,839|423,489|
|**Production**|||
|_i_<sup>_A_</sup>|19,877.33|11,925.34|
|_i_<sup>_w_</sup>|4363.2|3452.035|
||0.30|0.30|
|/<br><br><sup>_X_</sup><br>|0.55|0.73|
|_i_<br>_i_<br>|||
|**Consumers**|||
|_i_<sup></sup>|0.29|0.27|
|_i_<br>_M_ (LBP)|3,636,000|2,876,696|
|1|2|2|
|1 <br>|||
|~~~~|0.7539|0.7539|
|**Real estate**|||
|��� (LBP/Sq.Meter)|503,640|73,350|
|_Bi_<br>_R_<br> (LBP)|426,750|227,400|
|_Hi_<br>_R_<br> (LBP)|175,800|83,550|

17
18 approach (Pesaran and Shin, 1999, and Pesaran, Shin and Smith, 1999) because it is robust to the order of integration and cointegration of the regressors, hence the pre-testing procedures may be avoided. This approach also has the advantage that the lags in each of the regressors are allowed to be different, and the endogeneity problem can be eliminated by appropriate selection of the lag length (Pesaran and Shin, 1999).

|**_Estimated L_**|**_Ta_**<br>**_ong-Run Coeffic_**<br>**_1991:01_**<br>|**_ble 7_**<br>**_ients for the Pri_**<br> **_– 2002:01_**<br>|**_mary Balance_**<br>||
|---|---|---|---|---|
||AIC|RBSC|SBC|HQC|
|Debt to GDP ratio|.14*<br>(.05)|.15*<br>(.05)|.14**<br>(.07)|.12***<br>(.07)|
|Output (in logs)|18.3*<br>(5.6)|20.8*<br>(5.6)|21.2*<br>(6.9)|18.0*<br>(6.5)|
|REER (in logs)|-7.6*<br>(1.9)|-7.6*<br>(1.8)|-8.96*<br>(2.71)|-9.8***<br>(2.57)|
|Real interest rate|-.01***<br>(.004)|-.01**<br>(.003)|-.01**<br>(.008)|-.01*<br>(.004)|
|Sovereign spreads (in logs)|.30<br>(.65)|.44<br>(.65)|.37<br>(.89)|.01<br>(.84)|

Standard error in (). *Significant at the .01 level; ** Significant at the .05 level; *** Significant at the .10 level Table 7 shows that, in the long run, output is positively correlated with the primary balance. However, Table 8 shows that, in the short run, the correlation is negative, implying that fiscal expansions are associated with primary balance reductions, and the primary balance increases during output contractions, verifying the pro-cyclical nature of fiscal balances. Another interesting result depicted in Table 7 is the positive and significant relationship between the primary balance and the public debt ratio. This fact may be interpreted as the result of a fiscally responsible sovereign that adjusts its primary to compensate changes in the debt ratio.

Finally, in this section we estimate the cyclical component of the primary balance by regressing this variable on the long-run components of each of the explanatory variables used in the previous exercise. The residual of such regression is the part of the primary balance explained by the transitory or cyclical components of each of the explanatory variables. Hence, we interpret this residual as the cyclical component of the primary balance (Figure 16). In general, we observe that this component fluctuates between plus or minus 1 percent of GDP, with the most recent levels close to lower bound. That is, at the end of 2003, the economic slowdown and other transitory fluctuations of variables affecting the primary balance had a negative impact of close to one percent of GDP, compared to the positive impact of more than one percent of GDP in early 2000. Given that the observed primary balance improved by .5 percent of GDP during the period, the structural balance improved by close to 1.5 percent of GDP.
# **2. Results**

# **2.1 Effect of Economic Growth on the Rise of the Middle Class**

Table 1 shows predictions of the country-specific effects that changes in the natural logarithm of GDP per capita between 1970 and 2010 had on the income shares of the 3<sup>rd</sup> and 4<sup>th</sup> quintiles in ASEAN countries. To generate these numbers, one first needs an estimate of the causal effect that GDP per capita has on the income share of the 3<sup>rd</sup> and 4<sup>th</sup> quintiles. Brueckner et al. (2015) provide such an estimate. For a panel of 104 countries spanning the period 1970-2010, they use oil price shocks and trade-weighted world income as an instrument for GDP per capita. Their panel fixed effects estimates show that exogenous increases in GDP per capita have a significant positive effect on the income share of the 3<sup>rd</sup> and 4<sup>th</sup> quintiles.<sup>2</sup> The relevant equation is: (1) Inequalityct = ec + ft + αln(GDP p.c.)ct + εit From the estimates provided in Brueckner et al. (2015), we can compute the effect that changes in the natural logarithm of GDP per capita between 1970 and 2010 had on the income shares of the 3<sup>rd</sup> and 4<sup>th</sup> quintiles of ASEAN countries. Specifically, this is done by multiplying the coefficients reported in columns (4) and (5) of Table 1 in Brueckner et al. (2015) with the changes of ASEAN countries' natural logarithms of GDP per capita between 1970 and 2010.<sup>3</sup> Data on GDP per capita are from the Penn World Table (Heston et al., 2012).

From Table 1, one can see that economic growth of ASEAN countries between 1970 and 2010 had a substantial impact on the share of income accruing to the middle class. In column (1) a measure of the middle class is the income share of the 3<sup>rd</sup> quintile. In column (2) a measure of the upper middle class is the income share of the 4<sup>th</sup> quintile. For the average (median) ASEAN country, growth in GDP per capita between 1970 and 2010 increased the income share of the 3<sup>rd</sup> quintile by around 5.7 (6.2) percentage points. The effect on the 4<sup>th</sup> quintile is slightly larger, around 7 percentage points.

> 2 Brueckner et al. (2015) do not find a significant non-linear effect of national income, i.e. [ln(GDP p.c.)]<sup>2</sup> , on the income shares.

> 3 This follows from totally differentiating equation (1): Δinequalityct = αΔln(GDP p.c.)ct.

4
developing countries,<sup>4</sup> the impact of the latter in terms of individual incomes has received little attention. The goal of this paper is precisely to characterize this impact and to examine potentially mitigating policies. More specifically, we assess the effects of aggregate volatility and extreme output fluctuations on key microeconomic dimensions such as inequality, poverty, labor income and school enrollment, and investigate the channels and mitigating factors underlying these effects.

While the literature have examined the (bi-directional) link between growth and poverty,<sup>5</sup> the conventional view that recessions have deleterious effects in terms of social exclusion and human capital losses, particularly persistent and regressive in less developed countries, has received relatively little attention. A few empirical studies on the subject are worth noting. Breen and Garcia-Peñalosa (2005) show that output volatility and income inequality (as captured by the Gini coefficient and the income share of the top quintile) are positively correlated across countries.<sup>6</sup> Agénor (2004), based on a small 19-country panel, finds an asymmetric effect of real output growth per capita on poverty: deeper recessions tend to raise poverty, whereas faster recoveries do not reduce it in the same degree.<sup>7</sup> Evidence on a positive correlation between output volatility and inequality is also reported by Gavin and Hausmann (1998) and Laursen and Mahajan (2004): in the latter, inflation, financial depth, and government social security expenditures are considered as the suggested channels; in the former, it is the lack of safety nets, that forces low-income children drop out of school in bad times not to return in good times, thereby perpetuating income inequality. While these pieces are often either too fragmentary, based on a limited country sample or subject to methodologically shortcomings –and have therefore failed to make a convincing case in one

> 4 The literature on output volatility is too vast to summarize here. On the incidence of terms of trade shocks, see Easterly et al. (1997), Fernández Arias et al. (2001), Barro and Sala-i-Martín (2004), Loayza et al. (2005) and Arora and Vamvakidis (2005). On the role of openness on output volatility, see Easterly et al. (2000), Kose et al. (2003), Bekaert et al. (2004) and Cavallo (2005). On the mitigating role of exchange rate flexibility, see Broda (2004), and Edwards and Levy Yeyati (2005) and Ramcharan (2007). Raddatz (2007) provides an encompassing analysis. Of particular interest for our purposes is a recent line of research that looks into the effects of extreme output drops (see, e.g., Becker and Mauro, 2006).

> 5 See. i.a., Kraay (2006) and López and Serven (2007) for the impact of poverty on growth, and the latter for the feedback of poverty and inequality on growth.

> 6 Checchi and Garcia-Peñalosa (2004) also explore the effect of output volatility and economic inequality through the channel of human capital.

> 7 The same asymmetry is found by De Janvry and Sadoulet (2000) for a panel of 12 Latin American countries between 1970 and 1994.

3
# WPS4018

## FORGONE EARNINGS FROM SMOKING:

## EVIDENCE FOR A DEVELOPING COUNTRY

_Michael Lokshin and Kathleen Beegle_<sup>_*_</sup> _Development Research Group, World Bank_

### ABSTRACT

This paper estimates the economic losses related to the negative effect of smoking on wages in a context of a developing country. Using data from the 2005 Albania Living Standards Monitoring Survey, we jointly estimate a system of three equations: the smoking decision and two separate wage equations for smokers and non-smokers. The results show that, after controlling for observed characteristics and taking into account unobserved heterogeneity in personal characteristics, smoking has a substantial negative impact on wages. On average smokers’ wages are 20 percent lower than the wages of similar non-smokers, providing strong evidence for the potential policy relevance of tobacco control initiatives for developing countries.

### World Bank Policy Research Working Paper 4018, October 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

***** Address for correspondence: _Michael M. Lokshin,_ e-mail: mlokshin@worldbank.org; _Kathleen Beegle_ , e-mail: kbeegle@worldbank.org, phone 202-458-9894. Both at Development Economics Research Group, The World Bank, 1818 H Street NW, Washington, DC 20433,

We thank Martin Ravallion, Jed Friedman, Jishnu Das, and Gero Carletto for useful and constructive comments.

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