rafmacalaba/datause-encoder-data huggingface dataset:
|Improved sanitation|0.0376***|0.0365***|0.0373***|
|---|---|---|---|
||(0.0121)|(0.0121)|(0.0121)|
|Improved water|-0.0145|-0.00456|-0.00656|
||(0.0273)|(0.0265)|(0.0269)|
|Water treatment|0.0614*|0.0640*|0.0637*|
||(0.0346)|(0.0344)|(0.0344)|
|Arsenic (WHO)|-0.0711***<br>(0.0136)|||
|Arsenic (BD)||-0.108***<br>(0.0176)||
|Arsenic - medium (11-50 ppb)|||-0.0357**<br>(0.0181)|
|Arsenic - high (51+ ppb)|||-0.119***<br>(0.0193)|
|Observations|7,502|7,502|7,502|
|Standard errors in parentheses||||
|*** p<0.01, ** p<0.05, * p<0.1||||

**Table 4. Correlates of child literacy-numeracy including three-specifications of arsenic exposure (N=7,979 children aged 3-5 years)**

|VARIABLES|(1)<br>WHO|(2)<br>Bangladesh|(3)<br>Risk Levels|
|---|---|---|---|
|Child ever breastfed|-0.0256|-0.0251|-0.0253|
||(0.0231)|(0.0231)|(0.0231)|
|Child is female|0.0157*|0.0157*|0.0157*|
||(0.00814)|(0.00814)|(0.00814)|
|Child's age (log)|0.678***|0.678***|0.678***|
||(0.0344)|(0.0344)|(0.0344)|
|Mother's education (primary/some secondary)|0.117***|0.116***|0.116***|
||(0.00904)|(0.00903)|(0.00904)|
|Mother's education (secondary or higher)|0.235***|0.234***|0.235***|
||(0.0196)|(0.0196)|(0.0196)|
|Urban|0.0287**|0.0283**|0.0284**|
||(0.0120)|(0.0121)|(0.0121)|
|Household size (log)|-0.0472***|-0.0468***|-0.0470***|
||(0.0121)|(0.0121)|(0.0121)|
|Quintile 2|0.0226*|0.0228*|0.0228*|
||(0.0122)|(0.0122)|(0.0122)|
|Quintile 3|0.0401***|0.0403***|0.0403***|
||(0.0131)|(0.0131)|(0.0131)|
|Quintile 4|0.0754***|0.0757***|0.0757***|
||(0.0141)|(0.0142)|(0.0142)|
|Quintile 5|0.109***|0.110***|0.109***|
||(0.0178)|(0.0177)|(0.0177)|

23
4 We term the fourth measure ‘Malthusian’ saving owing to the final term reflecting the immiserating effects of population growth.

# **Data**

All data for the analysis – GDP, gross saving, consumption of fixed capital<sup>3</sup> , and depletion of natural resources (energy, minerals and net forest depletion) – are taken directly from the _World Development Indicators_ (World Bank 2002). Total wealth, employed in the Malthusian saving calculation, is derived using a Perpetual Inventory Model for produced capital stock estimates, present values of mineral and energy rents, and present values of forestry, fishing and agricultural rents, all measured in constant 1995 dollars, providing the basic estimates – these are the same total wealth data employed in Ferreira _et al._ (2003).

As in Ferreira and Vincent (forthcoming) and Ferreira _et al._ (2003), we exclude public expenditures on education from the saving measures – these were shown to perform exceedingly badly in the earlier work. There are a number of plausible reasons for the poor performance: (i) these are gross, rather than net, investment estimates; (ii) private expenditures are excluded; and (iii) expenditures may be a particularly poor proxy for human capital formation, particularly in developing countries – see Pritchett (1996).

We also exclude damages from CO2 emissions. This is partly because the bulk of the damages occur in the longer term, but mostly because damages to other countries (the major effect of emitting CO2) should have no effect on future consumption in the emitting country in the absence of a binding agreement to pay compensation.

# **Methodology of estimation**

One of the key choices to be made in estimating expression (4) is the choice of period over which to calculate changes in consumption. The underlying theory, as expressed in (3), suggests that there is in principle an infinite time horizon. As a practical matter, however, the WDI data on genuine savings are limited to the period 1970-2000, with data for the early 1970s being particularly sparse.

A reasonable choice of time horizon would be the mean lifetime of produced capital stocks, roughly 20 years (machinery and equipment lifetimes are typically shorter, 10 years or so, but buildings and infrastructure have lifetimes of several decades). Choosing 20 years would be saying, in effect, that the effects of savings will be felt over the lifetime of the produced capital in which they are presumed to be invested. This is the assumption used below, and testing the estimation for a 10 year time horizon produced less robust estimates overall (in terms of

> 3 Ferreira _et al._ (2003) use estimated figures for consumption of fixed capital derived from the Perpetual Inventory Model used to estimate total stocks of produced capital. Inspection of these figures reveals a fairly large number of anomalous estimates.
measure the exposure of a given firm to the minimum wage as the proportional rise in the wage bill that is needed to bring the wages of all workers up to the new statutory minimum level (Draca, Machin, and van Reenen, 2011). We then implement a difference-in-differences estimation strategy whereby we measure and compare the change in exit rates among high-exposure firms (the treatment group) and low-exposure firms (the control group) before and after the minimum- wage increase. To ensure the validity of the identification strategy, we run several placebo tests in periods prior to the minimumwage increase. We also provide evidence that the estimated effects are unlikely to have been driven by other shocks that affected the Turkish economy.

The paper is organized as follows: Section 2 provides background information on the minimum wage in Turkey, and on the increase that took effect in 2016. Section 3 describes the employee-employerlinked dataset and the national labor force survey that we use for our estimations and reports descriptive statistics. Section 4 presents the identification strategy and provides supportive evidence for its validity. Section 5 reports the main results on the exit of formal firms and on formal employment destruction. It also explores the potential reallocation of workers from exiting firms. Section 6 concludes.

# **2. Background on the minimum wage in Turkey**

## 2.1. Institutional setting

In 1974, Turkey established a nationwide minimum wage, with no variations across regions, industries, or occupations (Gürcihan and Yüncüler, 2016). By law, the national minimum wage must be adjusted at least every two years, although it has been modified every year over the past decade. Following a series of weekly consultations, the new, monthly minimum wage is typically announced for each year in December prior to the year in which it will take effect. The minimum wage can be set either as a single minimum wage that will be implemented for the full year, or for a graduated wage that has one level for the first half of the year, followed by another level for the second half. The minimum-wage-setting consultations are conducted by the Minimum Wage Fixing Commission, a tripartite process of collective bargaining involving the government, the largest labor unions, and the largest employer associations. Decisions are made based on the majority in the voting among members.

As of 2016, Turkey exhibited the highest Kaitz ratio – the ratio of minimum wage to the mean or the median wages – among countries of the Organisation for Economic Co-operation and 6
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Table 6: Comparison of various methods with Näıve2 as the benchmark

|Mdl|5 year fore|cast horizon<br>|10 year for|ecast horizon<br>|
|---|---|---|---|---|
|oe|Median<br>% Better|% signifcant<br>(_p_= 0_._05)|Median<br>% Better|% signifcant<br>(_p_= 0_._05)|
|Lowest sMAPE|184%|88.0%|124%|73.0%|
|VAR3 / VEC3|68%|91.2%|52%|73.5%|
|VAR2 / VEC2|57%|95.2%|43%|73.7%|
|GARCH|121%|85.1%|54%|74.5%|
|ARIMA|69%|91.8%|23%|76.3%|
|HOLT-WINTERS|45%|90.2%|17%|81.7%|
|UCM-RWD|35%|90.5%|1%|79.5%|
|UCM-LLTM|31%|90.8%|0%|77.1%|
|UCM-RWC|-16%|94.2%|-40%|91.2%|

zon and 124 percent higher over the 10-year forecast horizon. The performance of specific forecasting methods over the Näıve2 forecasting model is also improved. Specifically, VAR/VEC, GARCH, ARIMA, and Holt-Winters methods all yield more accurate forecasts than the Näıve2 model over both 5- and 10year forecast horizons, with median accuracy improvement ranging between 17 and 121 percent. As regards Unobserved Components models, both RWD and LLTM methods deliver more accurate forecasts over the 5-year forecast horizon, whereas their forecast accuracy over the 5 year forecast horizon is of the same magnitude as that of the Näıve2 model. Finally, the RWC model yields less accurate forecasts than the Näıve2 model over both 5- and 10-year forecast horizons, with median accuracy decline between 16 and 40 percent. Similar to results reported in Table 5, the differences in predicted forecasts between forecasting methods and the Näıve2 model are mostly statistically significant across all methods, ranging between 85.1 to 95.2 percent of countries over the 5-year forecast horizon, and between 73 and 91.2 percent of countries over the 10-year forecast horizon.

# **4.2 Comparisons across developing country groups**

Tables 7 - 10 compare effectiveness of the best performing method (based on sMAPE criterion) across developing countries based on their regional, income, generation capacity and energy intensity characteristics. Table 7 reports the average sMAPE and MdRAE measures of forecast accuracy across regions over 13
year dummies; _α, β, γ, δ, η_ = Parameters to be estimated; _ε_ = Error term; _i_ = individuals; _c_ = countries and _y_ = years. We estimate the life satisfaction equation cross-section on a pooled sample of world citizens as described below: A wide range of reduced specifications will be considered as well as alternative estimations of the standard error. We use the robust Huber-White sandwich estimator and regional clusters for a robust estimation of the standard error. As shown below, the dependent variable is categorical and all estimations are made with an order logit model.

As a measure of subjective well-being ( _H_ ), we use _Life satisfaction_ . The question asked is: “ _All things considered, how satisfied are you with your life as a whole these days?_ ” Answers include a ten steps ladder where ‘1’ stands for “ _Dissatisfied_ ” and ‘10’ stands for “ _Satisfied_ ”. This is a common question used in happiness research and validation studies conducted by psychologists and social scientists show that answers to such questions are reliable (Lepper 1998, Sandvik et al. 1993, Fordyce 1988, Inglehart 1990, Saris and Scherpenzel 1996).

_Income_ ( _X_ ) is measured as self-positioning in a ten-steps income scale where the income brackets have been measured in local currency in each country.<sup>10</sup> This is not self-declared income but the positioning of individuals into income brackets. In some sense, this is a more accurate indicator than self-reported income which is known to be underreported in household surveys worldwide. That is because people are not asked to tell how much they earn but simply to say to which income bracket they belong to.

For cross-country comparability purposes, the income variable has been further transformed into mid-class values, real terms, USD and Purchasing Power Parity (PPP). In the World and European Values surveys, each country uses a ten steps income scale where each step is reported in local currency. For each country/year, we first calculated the midclass values in local currencies. For the lower class, we used the average between zero and the lower bound of the second class. For the upper class, we used the lower bound inflated by 20%. This is evidently a normative choice based on the notion that the distribution of incomes in the top decile is typically right-skewed, with most of the observations concentrated near the lower bound. The top class has a relatively small number of observations and changing the inflation factor from 20% to, say, 30% has a very marginal impact on results. However, the upper class contains outliers and if we had used higher inflation factors it is as if we were trying to better represent these outliers rather than the median value of the top class.

Mid-class values were then transformed into constant, USD and PPP values using the IMF GDP and PPP data published in the IMF economic outlook report. The IMF GDP data are reported in nominal values (local currency and USD), constant values with base 2000 (local currency) and PPP values (constant USD equivalent) providing all ingredients

> 10This variable is the only income variable present in the database.

8
|P111665|2011|2013|7/2/2012|4 Closed|Brazil|
|---|---|---|---|---|---|
|P111665|2011|2013|2/11/2013|5 Closed|Brazil|
|P111665|2011|2014|7/10/2013|6 Closed|Brazil|
|P111667|2009|2009|8/13/2008|1 Closed|Haiti|
|P111667|2009|2009|2/20/2009|2 Closed|Haiti|
|P111667|2009|2010|7/29/2009|3 Closed|Haiti|
|P111667|2009|2010|11/17/2009|4 Closed|Haiti|
|P111667|2009|2010|5/7/2010|5 Closed|Haiti|
|P111667|2009|2011|12/2/2010|6 Closed|Haiti|
|P111667|2009|2011|6/29/2011|7 Closed|Haiti|
|P111667|2009|2012|12/28/2011|8 Closed|Haiti|
|P111667|2009|2012|6/29/2012|9 Closed|Haiti|
|P111669|2011|2011||0 Closed|Sao Tome and Principe|
|P111669|2011|2012|9/22/2011|1 Closed|Sao Tome and Principe|
|P111669|2011|2012|5/28/2012|2 Closed|Sao Tome and Principe|
|P111669|2011|2013|1/25/2013|3 Closed|Sao Tome and Principe|
|P111669|2011|2014|8/7/2013|4 Closed|Sao Tome and Principe|
|P111669|2011|2014|5/24/2014|5 Closed|Sao Tome and Principe|
|P111669|2011|2015|12/9/2014|6 Closed|Sao Tome and Principe|
|P111669|2011|2015|6/19/2015|7 Closed|Sao Tome and Principe|
|P111669|2011|2016|12/11/2015|8 Closed|Sao Tome and Principe|
|P111669|2011|2016|6/17/2016<br>|9 Closed|Sao Tome and Principe|
|P111669|2011|2017|6/14/2017|10 Closed|Sao Tome and Principe|
|P111669|2011|2021|7/23/2020|11 Closed|Sao Tome and Principe|
|P111679|2009|2009||0 Closed|Central African Repub|
|P111679|2009|2010|12/29/2009|1 Closed|Central African Repub|
|P111679|2009|2010|5/28/2010|2 Closed|Central African Repub|
|P111679|2009|2011|3/23/2011|3 Closed|Central African Repub|
|P111679|2009|2012|10/10/2011|4 Closed|Central African Repub|
|P111679|2009|2012|6/3/2012|5 Closed|Central African Repub|
|P111679|2009|2013|12/26/2012|6 Closed|Central African Repub|
|P111679|2009|2014|7/8/2013|7 Closed|Central African Repub|
|P111679|2009|2014|2/4/2014|8 Closed|Central African Repub|
|P111679|2009|2015|8/18/2014|9 Closed|Central African Repub|
|P111679|2009|2015|2/25/2015|10 Closed|Central African Repub|
|P111679|2009|2020|7/25/2019|11 Closed|Central African Repub|
|P111697|2009|2011|6/21/2011|1 Closed|Nigeria|
|P111697|2009|2013|4/27/2013|2 Closed|Nigeria|
|P111741|2010|2010|12/23/2009|1 Closed|West Bank and Gaza|
|P111741|2010|2010|6/14/2010|2 Closed|West Bank and Gaza|
|P111741|2010|2011|1/14/2011|3 Closed|West Bank and Gaza|
|P111741|2010|2012|9/30/2011|4 Closed|West Bank and Gaza|
|P111741|2010|2012|4/27/2012|5 Closed|West Bank and Gaza|
|P111741|2010|2013|12/26/2012|6 Closed|West Bank and Gaza|
|P111741|2010|2014|1/9/2014|7 Closed|West Bank and Gaza|
|P111757|2010|2010||0 Closed|Yemen,Republic of|
|P111757|2010|2011|1/11/2011|5 Closed|Yemen,Republic of|
<!-- Start of picture text --> (a) Governance (b) Gender Parity<br>Part. S. Prov. —____—_@+—___}—<br>Health ———_@__ +<br>Account. e+ Education 4» |.<br>Trans. +e Women 4, ==<br>Efficiency e Gov. —+_____@ —__+—<br>Intra Sp<br>Ccapture Inter i<br>-0.4 -0.2 0.0 0.2 0.4 -0.4 -0.2 0.0 0.2 0.4<br>Estimate Estimate<br><!-- End of picture text -->
Table 53

|Dependent variable: F|iscal adjus|tment when|needed b|ased on Esc|olano et al.|(2014)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.002|-0.005|0.002|-0.005|0.005|0.016|-0.022**|-0.026**|
|(% of GDP)|(0.005)|(0.006)|(0.006)|(0.006)|(0.010)|(0.020)|(0.010)|(0.010)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.494***||-1.145**|-1.056**|-1.106**|-1.103**|-1.180**|
|||(0.482)||(0.488)|(0.468)|(0.495)|(0.474)|(0.518)|
|Inflation (t-1)||-0.186**||0.427|0.419|0.446|0.426|0.350|
|||(0.082)||(0.291)|(0.286)|(0.290)|(0.264)|(0.286)|
|U.S. Interest rate||-0.025||-0.053**|-0.054**|-0.056***|-0.056***|-0.056**|
|||(0.016)||(0.021)|(0.021)|(0.021)|(0.021)|(0.021)|
|**Political factors**|||||||||
|Gini|||0.008|0.008*|0.008*|0.022*|0.008*|0.007*|
||||(0.005)|(0.004)|(0.004)|(0.013)|(0.004)|(0.004)|
|Rule of law|||0.028|0.104**|0.093**|0.102**|-0.018|0.103**|
||||(0.037)|(0.044)|(0.046)|(0.043)|(0.098)|(0.043)|
|Margin of Majority|||-0.075|-0.001|0.009|0.019|-0.008|-0.918***|
||||(0.118)|(0.126)|(0.130)|(0.129)|(0.134)|(0.323)|
|Election's year|||-0.011|-0.017|-0.016|-0.018|-0.016|-0.012|
||||(0.031)|(0.031)|(0.031)|(0.031)|(0.032)|(0.031)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.018<br>(0.011)||||
|Gini * Rigidity||||||-0.001<br>(0.000)|||
|Rule of law * Rigidity|||||||0.004<br>(0.003)||
|Majority * Rigidity||||||||0.038***<br>(0.014)|
|Constant|0.122|0.470**|-0.237|-0.188|-0.170|-0.739|0.281|0.364|
||(0.142)|(0.189)|(0.388)|(0.312)|(0.309)|(0.578)|(0.416)|(0.354)|
|Observations|638|624|558|546|546|546|546|546|
|R-squared|0.000|0.032|0.006|0.046|0.052|0.049|0.052|0.056|
|Number of ifscode|68|68|61|61|61|61|61|61|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard error<br>***p<0.01,**p<0.05|s clustere<br>,*p<0.1|d by country|in parenth|eses|||||

- 82 -
<!-- Start of picture text --> A . Households Reporting No Variation in SR Soil Quality B . Households Reporting No Variaton in SR Soil Type<br>9 0<br>3 3<br>fo} fo}<br>3° 5 °<br>aa<br>2 z<br>S x o x<br>x x<br>o u n<br>oO Oo<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Number of Plots Cultivated Number of Cultivated Plots<br>Excludes households with >10 plots for lack of sufficient observations. Excludes households with>10 cultivated plots for lack of sufficient observations<br><!-- End of picture text --> <!-- Start of picture text --> C. Households Reporting No Variaton in SR Soil Texture<br>re y<br>3<br>3°<br>s3 e<br>3<br>3<br>8<br>Oot<br>2<br>a<br>o<br>1 2 3 4 5 6 7 8 9 10<br>Number of Cultivated Plots<br>Excludes households with >10 cultivated plots for lack of sufficient observations<br><!-- End of picture text --> <!-- Start of picture text --> D . Households Reporting No Variaton in SR Soil Color<br>a<br>3<br>Lo<br>Ooan<br>Sa<br>[o}<br>=<br>et<br>Es<br>nN<br>fo}<br>1 2 3 4 5 ; 6 7 8 9 10<br>; Number of Cultivated Plots 7 ;<br>Excludes households with > 10 cultivated plots for lack of sufficient observations<br><!-- End of picture text -->
**oI** ICY RFSEARCH WORKING PAPER 2549

# Summary **findings**

Luttmer uses instrumental variable methods and the decomposition of income into transitory and persistent components to distinguish underlying income inequality and changes in poverty from the effects attributable to measurement error or transitory shocks. He applies this methodology to household-level panel data for Russia and Poland in the mid-1990s. Luttmer finds that:

* Accounting for noise in the data reduces inequality (as measured by the Gini coefficient) by 10-45 percent.

* Individuals in both countries face much economic insecurity. The median absolute annual change in income

or spending is about 50 percent in Russia and about 20 percent in Poland. But roughly half of these fluctuations reflect measurement error or transitory shocks, so underlying levels of income and spending are much more stable than the data suggest.

_*_ The apparent high levels of economic mobility are driven largely by transitory events and noisy data. After transitory shocks are accounted for, about 80 percent of the poor in both Russia and Poland remain in poverty for at least one year. So there is a real risk of an entrenched underclass emerging in these transition economies.

This paper-a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Central Asia Region-is part of a larger effort in the region to analyze poverty and inequality in Europe and Central Asia. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Cecile Wodon, room J7-268, telephone 202-473-2542, fax 202-473-8466, email address cwodon@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at luttmer@uchicago.edu. February 2001. (38 pages) _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 coPyntries they represent._ Produced by the Policy Research Dissemination Center