rafmacalaba/datause-encoder-data huggingface dataset:
hypothesis. Using the data set constructed for this paper, the results obtained by Farrell and Fuchs (1986) can be replicated and confirmed.<sup>14</sup> However, this section will argue that the findings of Farrel and Fuchs (1986) are not sufficient to reject the causality from schooling to smoking because the decision to quit smoking is also strongly influenced by educational attainment and generally occurs when schooling is definitive.

Figure 6 illustrates that it is between age 30 and 35—between 1945 and 1955, when the information about the dangers of smoking started to diffuse—that the difference in smoking prevalence between college graduates and other individuals born between 1915 and 1920 started to emerge. This suggests that the decision to stop smoking—and the timing of this decision—is very important for explaining the differences in smoking prevalence across education categories and motivates a more formal analysis of the incidence of smoking cessation which combines the probability to quit with the age at quitting.

Following Meyer (1990) and Finkelstein and Poterba (2004), I use a semiparametric proportional hazard model using Cox’s partial likelihood technique (Cox 1972, 1975) to perform a survival analysis. Using the same data set, described previously, of smoking histories derived from subsequent National Health Interview Surveys, the sample is restricted to individuals who had smoked in their life and were still smoking at age 25, who are then observed from age 25 onward when their schooling level can be considered definitive.<sup>15</sup> The duration measure is the length of time between entry under observation at age 25 and exit for individuals who continued smoking until the date of the survey or the date of smoking cessation for former smokers at the date of interview. The failure event is smoking cessation.<sup>16</sup> The hazard for individual _i_ at time _t_ is assumed to be:

- (1) _hi(t) = ho(t) exp (xi’β)_

where _ho(t)_ is the baseline hazard at time _t_ , which is left unestimated, _xi_ is the vector of explanatory variables for individual _i_ and _β_ is the vector of parameters to be estimated. The implicit assumption behind this proportional hazard model is that the baseline hazard has the same shape for everybody and that covariates like education, gender, race, and age would affect it proportionally.

It is possible, however, to allow for the baseline hazard to take a different shape for different groups of individuals, by stratifying the estimation. In that case, the hazard for individual _i_ at time _t_ takes the form:

> 14 Results available on request.

> 15 Ninety-three percent of individuals who have smoked in their life started at age 25 or younger.

> 16 I abstract from temporary interruptions in smoking that are shorter than one year (see note 5).

9
rights over their capital and output. This has a number of negative consequences: First, informal producers are poorly protected by the police and the judicial courts from crimes committed against their property. Second, since they lack the capacity to enter into legally binding contractual obligations, their access to capital markets, for financial, insurance, and corporative purposes, is seriously impaired. Lastly, they find obstacles to use other public services such as social welfare, skill training programs, and government-sponsored credit facilities.

The state, as the institution that both monitors the regulatory and enforcement systems and administers public services, plays a crucial role in the formation of informal economies. If state officials, or interest groups related to them, profit in some way from the presence of the informal sector, they will create an environment that makes informality attractive or simply unavoidable. To the extent that excessive regulations are created to benefit particular interest groups and not society in general, the presence of the informal sector is a result of the failure of political institutions to protect and promote an efficient market economy.

This paper attempts to organize the current information on the determinants of the informal sector and its effect on economic growth through, first, a simple endogenous growth model and, second, empirical evidence from Latin America. The next section describes the main determinants and features of the informal sector.<sup>3</sup> Then, the paper models the presence of the informal sector in the economy and its relationship to output growth within the framework of the endogenous growth literature,<sup>4</sup> specifically, the work in which government's provision of public goods and services is considered explicitly, as

-2-
**Table 7: Mining and Assessment of Public Services: the interplay of decentralization and corruption**

||(1-LPM)|(2-LPM)|(3-LPM)|(4-LPM)|(5-LPM)|(6-OLS)|(7-LPM)|
|---|---|---|---|---|---|---|---|
|_Dependent Variables_<sup>_1_</sup>_:_|Living standards|Water & Sanitation|Jobs|Health|Education|Public<br>Services|Optimism|
|Active 50 km|-0.033***|-0.039***|-0.047***|0.000|0.003|-0.032***|-0.064***|
||(0.009)|(0.009)|(0.008)|(0.009)|(0.009)|(0.008)|(0.011)|
|Inactive 50 km|-0.016***|-0.043***|-0.012**|-0.023***|-0.028***|-0.036***|-0.032***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.006)|
|Bribe|-0.089***|-0.014|-0.027**|-0.001|-0.040***|-0.051***|0.003|
||(0.012)|(0.012)|(0.011)|(0.012)|(0.012)|(0.010)|(0.014)|
|Decentralization<sup>4</sup>|-0.065***|-0.068***|-0.232***|-0.279***|-0.317***|-0.234***|-0.024|
||(0.016)|(0.017)|(0.016)|(0.018)|(0.018)|(0.015)|(0.018)|
|_Active 50 km * Decentralization_|0.600***|0.384***|0.298***|0.466***|0.646***|0.717***|0.180*|
||(0.090)|(0.081)|(0.073)|(0.082)|(0.081)|(0.075)|(0.093)|
|_Inactive 50 km * Decentralization_|0.168***|0.525***|0.053|0.511***|0.751***|0.503***|0.050|
||(0.044)|(0.044)|(0.040)|(0.045)|(0.044)|(0.038)|(0.050)|
|_Active 50 km * Decentralization * Bribe_|-2.817***|-1.130***|-0.678**|-2.553***|-2.972***|-3.244***|-0.487|
||(0.364)|(0.331)|(0.294)|(0.333)|(0.331)|(0.324)|(0.373)|
|_Inactive 50 km * Decentralization * Bribe_|0.870***|-0.895***|0.123|-0.702***|-1.298***|-0.087|-0.362**|
||(0.172)|(0.150)|(0.128)|(0.151)|(0.148)|(0.139)|(0.161)|
|Natural Resources Rents (ln)|-0.006**|-0.030***|0.008***|-0.036***|-0.052***|-0.027***|0.068***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Control of Corruption|0.066***|0.059***|0.036***|0.017***|-0.000|0.024***|0.089***|
||(0.004)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.004)|
|Constant|1.482***|1.361***|1.402***|1.504***|1.294***|1.711***|1.542***|
||(0.124)|(0.120)|(0.112)|(0.121)|(0.120)|(0.107)|(0.131)|
|R-squared|0.04|0.06|0.02|0.04|0.04|0.06|0.06|
|Observations|101,349|118,081|117,455|118,786|118,392|97,765|68,527|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

# **_Notes:_**

1: Significance level: * p<0.10, ** p<0.05, ***p<0.01. Robust standard errors are in parenthesis.

2: Specifications (1) to (5) refer to the respondents' view on how the government is handling the improvement of living standards of the poor and other public services such as in health, water and sanitation. The dependent variable in specification (6) is a composite indicator constructed through polychoric correlation and which combines all the respondents' views on various types of public services and policies. The sample size is reduced due to inconsistent missing patterns across the different assessment variables. The outcome variable in specification (7) refers to the expected living standards of the respondent in the near future (12 months after the survey collection). Decentralization is measured by the 25
- 44 -

Keuning, Steven and Erik Thorbecke. 1989. "The Impact of Budget Retrenchment on Income Distribution in Indonesia: A Social Accounting Matrix Application", Paper prepared for the OECD Development Center, Paris.

Mulijanto. 1989. "Consumer Price Index as an Indicator to Assess Inflation: The case of Indonesia". Institute of Social Studies, The Hague.

Papanek, Gustav F. 1988. "Changes in Real Wages: Employment, Poverty, Competitiveness". Research Paper No.2, Development Studies Project, BAPPENAS/BPS, Jakarta.

Rao, V.V. Bhanoji. 1984. "Poverty in Indonesia, 1970-1980: Trends, Associated Characteristics and Research Issues." Mimeo, World Bank Resident Mission, Jakarta.

_ 1986. "Indonesia: Poverty Trends, 1980-84". World Bank Resident Mission, Jakarta.

Ravallion, Martin. 1988a. "Poverty in East Java: Sectoral, Regional and Seasonal Profiles". In H. Dick, et al. (eds.), The Development of East Java Under the New Order, The Research School of Pacific Studies, The Australian National University.

> , 1988b. "Income Effects on Undernutrition". Economic Development and Cultural Change, forthcoming.

> ,_ 1989a. "Do Price Increases for Staple Foods Help or Hurt the Rural Poor?", PPR Working Paper 167, The World Bank (Oxford Economic Papers, forthcoming).

> _1989b. "Consumption Norms and Measurement Errors in Household Expenditure Surveys", Mimeo.
commodities that are substitutes for housing services (e.g., recreation vehicles, boats, etc.) or complements (e.g., household furnishings and equipment). This reduces the demand and income elasticity of housing.

Figure 1 provides supporting evidence that housing investment and consumption reflect stages of economic development. It plots housing investment and national per capita GDP for all countries spanning 1960-2011. There are two inflection points – the first one is observed when housing investment takes off around national development levels of about $3,000 (2005 USD) and the second one, when housing investment slows down at development levels around $36,000 (2005 USD). Consider Africa, where cities are growing rapidly but the region’s development is only at around $ 992 (2005 USD). Urbanization is likely to be messy in the near future as there is still some way to go before housing investments take off.

<!-- Start of picture text --> Figure 1: Housing investment and stages of economic development – An S-<br>shaped curve<br><!-- End of picture text --> Source: World Bank staff estimates based on dataset of housing investment; data from national accounts and World Development indicators. Lowess graph shown here.

# **2.2 Magnitude of housing investment increases with economic development**

Consistent with the S-shaped trajectory, we find that the share of housing investment in GDP has been steadily increasing for developing countries across different stages of income. Between 2001 and 2011, housing investment in low income countries averaged 4.56 percent of GDP (up from 2.51 percent between 1960 and 1971) and 9.12 percent in upper middle income countries 5
Table 5: Effects on women’s salaried activity outcomes

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Brigadiere has done salaried<br>work 1h last 30 days|Brigadiere salaried work<br>duration in hours per month|Brigadiere total monthly<br>salary income|Brigadiere total monthly<br>salary income (ihs)|
|**Full sample**|||||
|ITT estimate|0.005|11.613|2993.193**|1.102**|
||(0.057)|(9.091)|(1167.416)|(0.494)|
|Observations|1966|1967|1967|1967|
|_R_<sup>2</sup>|0.106|0.221|0.212|0.139|
|Control mean|0.329|91.317|6494.465|3.480|
|**Have child aged 0-2**|||||
|ITT estimate|0.031|17.549*|3267.278***|1.343***|
||(0.062)|(9.386)|(1093.277)|(0.489)|
|Observations|1126|1127|1127|1127|
|_R_<sup>2</sup>|0.096|0.237|0.219|0.139|
|Control mean|0.328|96.645|6452.145|3.460|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

Table 6: Effects on women’s agricultural activity outcomes

||(1)<br>Brigadiere has worked on agr<br>act 1h last 30 days|(2)<br>Brigadiere agr act work duration<br>in hours per month|(3)<br>Brigadiere total monthly<br>agr act income|(4)<br>Brigadiere total monthly<br>agr act income (ihs)|
|---|---|---|---|---|
|**Full sample**|||||
|ITT estimate|-0.023|9.165|106.979|0.266|
||(0.044)|(7.298)|(321.352)|(0.194)|
|Observations|1966|1967|1967|1967|
|_R_<sup>2</sup>|0.106|0.367|0.433|0.144|
|Control mean|0.260|77.202|2581.367|1.201|
|**Have child aged 0-2**|||||
|ITT estimate|-0.036|8.954|59.204|0.228|
||(0.045)|(8.056)|(292.838)|(0.178)|
|Observations|1126|1127|1127|1127|
|_R_<sup>2</sup>|0.134|0.349|0.433|0.143|
|Control mean|0.272|79.889|2733.261|1.282|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

37
**Table 8: Potential Changes in Sovereign Bond Spreads and Reduction in External Public Debt from Higher Data Transparency**

||**Change in**<br>**if data transpar**<br>**t**|**Spreads (bps)**<br>**ency improves**<br>**o:**|**Reduc**<br>**if data tra**<br>**Top qua**|**tion in externa**<br>**nsparency imp**<br>**rtile of middle**|**l debt**<br>**roved to:**<br>**income**|<br> <br>**Reduct**<br>**if data tra**<br>**Top quartile**|**ion in external**<br>**nsparency impr**<br>**of upper middl**|**debt**<br>**oved to:**<br>**e income**|
|---|---|---|---|---|---|---|---|---|
|**Country**|**Top quartile of**<br>**middle income**|**Top quartile of**<br>**upper middle**<br>**income**<br>**_(US_**|**_$ million)_**|**_(% GDP)_**|**_(% Export)_**|**_(US$ million)_**|**_(% GDP)_**|**_(% Export)_**|
|**Angola**|-10.16|-11.08|67.41|0.06|0.16|73.61|0.06|0.18|
|**Cameroon**|-4.64|-5.52|3.52|0.01|0.05|4.19|0.01|0.06|
|**Cote d'Ivoire**|-3.94|-4.66|23.04|0.06|0.15|27.30|0.07|0.18|
|**Ethiopia**|-2.22|-2.91|2.23|0.003|0.03|2.94|0.004|0.04|
|**Gabon**|-13.61|-14.49|30.81|0.21|0.42|32.85|0.22|0.45|
|**Ghana**|-2.85|-3.66|25.08|0.04|0.11|32.28|0.05|0.14|
|**Kenya**|-6.20|-7.01|38.35|0.05|0.32|43.47|0.06|0.36|
|**Mozambique**|-4.71|-7.06|3.43|0.03|0.06|5.16|0.04|0.09|
|**Namibia**|-4.09|-4.61|5.19|0.04|0.11|5.86|0.04|0.12|
|**Nigeria**|-2.59|-3.43|20.50|0.01|1.29|27.18|0.01|1.71|
|**Senegal**|-0.43|-1.18|1.86|0.01|0.04|5.13|0.02|0.10|
|**South Africa**|-0.04|-0.58|0.71|0.0002|0.001|9.80|0.003|0.01|
|**Zambia**|-10.36|-11.86|31.48|0.12|0.31|36.11|0.14|0.35|
|**SSA**|-4.12|-4.87|342.74|0.02|0.14|405.38|0.02|0.16|

Note: The change in spreads is calculated as the impact of data transparency on sovereign spreads while holding constant the level of institutional quality and external debt. The reduction in public external debt is computed by multiplying the change in spreads by the outstanding stock of Eurobonds.

_Source: World Bank, Bloomberg and PRS Group_ 32
While East Asia leads the pack, LAC has the lowest access rate — typically less than 10 percent of the homes have access to internet in LAC countries.

Table 2 also shows the percentage of households with access to electricity, an indicator which is only available for developing countries. Latin America’s median access rate equals 87 percent, short of the 90 percent of other middle-income countries, and far behind the 98 percent observed in the successful East Asian economies. Across countries in the region, Venezuela, Chile and Costa Rica exhibit access rates on par with East Asia’s. At the other end, less than two-thirds of the population enjoys access to electricity in Bolivia and Honduras.

Access to transport is approximated by the widely-used rural access index (RAI), which measures the percentage of the rural population living within a short distance (2 km) of an all-season passable road. Table 2 shows that Latin America trails East Asia as well as other middle-income economies along this dimension. Among LAC countries, access to transport is particularly poor in Nicaragua, where it reaches just over one-fourth of the population.

Regarding access to safe water, we report the percentage of population with access to safe water, including treated surface water and untreated but uncontaminated water such as from springs, sanitary wells, and protected boreholes. Latin America ranks below the group of other middle-income countries. At just under 80 percent, its access rate is still far from the almost-universal coverage observed among successful East Asian economies. Uruguay is the only Latin American country to have reached universal access. In Bolivia and Nicaragua, less than half of the population enjoys access to safe water.

Finally, regarding access to improved sanitation, Latin America has caught up with the norm of middle-income countries, reaching a median access rate of 93 percent. Both East Asia’s successful economies as well as industrial countries enjoy universal access. In Latin America, a number of countries (notably Ecuador and Paraguay) have shown major progress since 1990, although at present few countries show access rates comparable to those of East Asia.

11
Another external factor standing behind the surge is the structural change in international financial markets and this is believed to be caused by permanent rather than transitory factors (that is, the change is due to technological and institutional developments that are most likely nonreversible). Also, the larger share of equity investment in the current surge of inflows suggests that foreign investors now have a longer investment horizon than in the late 1970s. As a result, there is little chance of seeing a reversal of flows in the short run because of a temporary negative development, which most likely would be the case with hot money. In sum, variables external to developing countries are unlikely to change substantially in the short or medium term, thus a generalized reversal of capital inflows would not be induced.

In addition to the two papers already discussed, Chuhan and others (1993) tries to measure the importance of domestic factors, as well as external factors, using monthly data on portfolio flows (bonds plus equity) from U.S. sources as the dependent variable. The sample includes 18 developing (recipient) countries in East Asia and Latin America during 1988-92. The authors conclude that domestic factors are at least as important as external factors in attracting flows to Latin American countries, and three to four times more important for East Asian countries. Yet their conclusions have limited policy implications because the explanatory variables used as proxies for domestic factors-for example, the stock priceearnings ratios and the secondary market price of a country's external debt-are not controlled by policymakers. Furthermore, as demonstrated in Fernandez-Arias (1994), the domestic explanatory variables used by Chuhan and others (1993) are not independent of international interest rates.

Although external factors may explain a large part of the recent surge in capital inflows, the three papers mentioned above have limited implications because they fail to include in their samples countries that have not received substantial inflows in recent years, possibly inducing a sample-bias problem. Indeed, as mentioned earlier, the uneven distribution of private flows among regions and among developing countries within those regions-as well as the recent developments in Mexico, Turkey, and Venezuela-is suggestive of the role played by domestic factors in attracting flows to developing countries. Furthermore, the recipient countries began receiving the inflows in different periods, with some starting as early as 1988 7
The results for the first intervention tend to support the habit formation theory. Even though treated participants slightly regain weight after incentives are removed, we observe a lasting positive effect on weight loss if compared to the baseline weight. The development of a behavioral automaticity that operates against the general tendency of weight regain is best able to explain the results. In turn, our results argue against monetary rewards crowding out intrinsic motivation in the present study population. It may well be that motivational effects are also present, but they seem not to be large enough to notably oppose beneficial effect of developed habits.

Our findings are in line with those obtained by Charness and Gneezy (2009). They show that financial incentives to exercise have lasting positive effects, arguing that there is scope for monetary intervention in health‐related habit formation. While previous experimental studies on financial incentives for weight loss, such as Volpp et al. (2008), do not find evidence of a backfire effect either, we are the first to report a positive effect of monetary rewards for weight loss after the intervention has ended.

# **4.2. Effect of monetary incentives to maintain a previously achieved target weight** _Validity of the randomization procedure_

The upper panel of Table 5 presents descriptive statistics for the study population used in the analysis of the second intervention (Column 1) and each experimental group (Columns 2‐4). Except the indicator for the clinic in Bad Mergentheim, all variables appear to be balanced between the different experimental groups. Most importantly, body weight at the start of the medical rehabilitation stay, at the start of the first intervention and after 4 months is uncorrelated with treatment (see middle panel of Table 5). The average participant in the second intervention lost about 7.3 percent of the original weight during the weight‐loss phase. About 59 percent of the participants achieved their individually assigned target weight after 4 months.

In the lower panel of Table 5, we show the average attrition rates in our sample. Attrition among participants of the second intervention is lower than among participants of the first intervention (see Table 2). Yet, we do not observe any structural attrition pattern for the second intervention, i.e., the treatment groups are not significantly more likely to comply than the control group. For this reason, we abstain from an extensive discussion of the sensitivity of the results for the second intervention with respect to sample attrition (see Section 3).

# _Body Weight After 10 Months (Period of Intervention II)_

While members of the control group had reduced their body weight by 7.7 percent during the weight‐loss phase (Table 5), they significantly regained about 2.8 percent during the weight‐ maintenance phase (see Table 6, Column 1). The treatment groups, which similarly reduced their body weight in the first four months, in contrast, did not significantly regain weight during the 17