rafmacalaba/datause-encoder-data huggingface dataset:
# **2.2 Empirical Bayes estimation**

Empirical Bayes (EB) estimation, also known as Empirical Best estimation, tightens the error distributions by conditioning on all available data in the survey for the purpose of prediction. The ‘observation’ _ea_ (provided that area _a_ is covered by the survey) clearly carries information ̄ about the area random effect _ua_ . In the extreme, for example, where _na_ tends to infinity _ea_ will perfectly reveal _ua_ .

In effect EB estimates are obtained by integrating out area errors _ua_ using probability density _p_ ( _ua|ea_ ), i.e. the probability density of _ua_ conditional on _ea_ observed from the survey. (Non-EB estimates are obtained by using the unconditional density _p_ ( _ua_ ).) The challenge is to work out _p_ ( _ua|ea_ ) along with the marginals _p_ ( _ua_ ) and _p_ ( _εah_ ) without imposing restrictions about their form so that the resulting conditional density _p_ ( _ua|ea_ ) can also take on any form. Currently, the literature on EB estimation avoids this challenge by assuming normally distributed marginals, in which case the conditional distribution will be normal too.

For normally distributed errors it can be shown that _p_ ( _ua|ea_ ) can be written as a function ̄ of _ea_ , the mean of sample errors from domain _a_ . This is not true for general error distributions. However, conditioning on the full vector _ea_ becomes computationally intractable. Therefore we will condition on _e_ ̄ _a_ rather than the full vector _ea_ even in the case of non-normal errors.

A second simplification concerns the known regression residuals for sample households. When conditioning on _ea_ , we ignore that for these households the total error _eah_ = _ua_ + _εah_ is known (since it is one of the components of _ea_ ). Instead we will assume that _εah_ is independent of _ea_ even for sample households. The reason is that in practice sample household _h_ cannot be traced among households in the target domain _a_ (households for which only data on _x_ is available). In most practical settings, the error thus introduced will be negligible. In fact, this error tends to zero as the size of the sample relative to the population size tends to zero.

# **2.3 Distributions matter**

Let _y_ ( _a_ ) and _e_ ( _a_ ) denote vectors of length _Na_ with elements _yah_ and _eah_ for all households from the population. Similarly, _x_ ( _a_ ) will denote a matrix with rows given by _x_<sup>_T_</sup> _ah_<sup>for all</sup><sup>_Na_households.</sup> _ya_ , _ea_ and _xa_ will denote the survey sample analogues. As mentioned above our objective is to estimate: where the function _W_ will generally be non-linear.

Non-normality of the errors _u_ and _ε_ will affect the estimates of _E_ [ _W_ ] via two different channels. Firstly, when the function _W_ is indeed non-linear, the expected value _E_ [ _W_ ] will be a function of the higher moments of the distributions of _u_ and _ε_ . Getting these moments wrong, in other words getting the distributions wrong, is then likely to introduce bias. Secondly, if 6
|**Local**|475|30|
|---|---|---|
|**Subsidiary**|189|12|
|**Not available**|593|38|
|**_Job ad location_**|||
|**Ariana**|410|13|
|**Ben Arous**|300|10|
|**Mannouba**|80|3|
|**Tunis**|2,352|75|

Measuring responses and outcomes For each phone or email response by the employer, the content of the phone call or the email was matched with the corresponding application. Whether a given resume elicited a callback from the employer was first coded. Then, the content of the phone call by the employer was grouped into the following categories: application rejected, employer willing to hire immediately, request for an interview over the phone on the spot, request for an interview in person at a later day, request for additional information, and other.

The main outcome indicator, _Employer Callback_ , was constructed as follows: (1) a value of 1 if the employer called with the possibility of hiring (whether to ask for an interview or additional information) or (2) a value of 0 if the employer did not call back or called to reject the candidate. Two secondary outcome indicators were also measured: (1) a categorical variable on the type of employer answer and (2) the average time required for an employer to respond from the time of application.

# 6. Empirical strategy and main results

Average differences in callback rates: (Positive) gender discrimination Overall, 67 percent of employers advertising jobs did not respond to either applicant within the pair, whereas 15 percent responded to both applicants. Of the employers who responded to one applicant but not the other, 11 percent responded only to the female applicant and 8 percent responded only to the male applicant. At face value, then, it seems that female applicants (25 percent) were, on average, more likely than male applicants (23 percent) to receive a callback. To more systematically analyze the extent to which differences in the callback rates are due to gender, the study team relied on the econometric specification E E E E C E C C , (1) ii ii ii = αα+ ββFFE CCE + εε 24
To be er di tinguish treatmen r al re s, we __ Il fo no and Bonh mme’ (2012) notation: are just vectors o si stimat hereas eddr wsfro the(un nown d fe) ment S. To prepa ethe da for allows’ Algorithm, use estimates from he co al linear tively.

rmutation of =sor A- C). Repeat step 2 through 4m times.

. Th empirical distribution of the full set of sis herefore an estimate of the distrib on of Table 2 shows the results of Simulation 3. We present results using 6 different combinations of parameters which determine the correlation between treatment effects and omitted variables and the independence of treatment effects from potential outcomes. The moment and deconvolution estimation methods always yield the same average effect, but the differences are particularly pronounced in the respective estimates of the standard deviation of treatment effects. As we would expect, both the deconvolution and moment methods perform relatively better when their assumptions are satisfied, which is the case for deconvolution in column one only and for the moment-based approach in column two only. In our simulation setup, the moment approach yields standard deviation estimates, which are closer to the truth when an omitted variable is negatively correlated with treatment effects, regardless of whether the covariance satisfies the moment or deconvolution assumptions. In contrast, the deconvolution standard deviation estimates are a better approximation of the truth when the omitted variable is positively correlated with treatment effects.

vect rc mplete repli atio softhev ctor houldbe sedi theal_ rit m since it is mean zero by constru tion replication whe lication or with Arellano and homme (2012) ea th pro edur 2000 times and discar the first 500 iterations. In practice, the lar t number of iteratio s that omp ting and time constraints allow is desirable.
Table 34

|Dependent variable: Nee|d of fiscal|adjustment|based on E|scolano et|al.(2014)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.341***|0.367***|0.369***|0.375***|0.606***|0.787***|-0.042|0.403***|
|**Economic factors**<br>structural as % GDP|(0.037)|(0.045)|(0.041)|(0.047)|(0.094)|(0.186)|(0.124)|(0.140)|
|GDP growth (t-1)||-26.612***<br>(3.497)||-25.740***<br>(3.720)|-24.202***<br>(3.774)|-25.364***<br>(3.834)|-24.277**<br>(3.759)|*-25.700***<br>(3.727)|
|Inflation (t-1)||-12.074***||-10.558***|-9.418**|-10.984***|-9.698**|-10.616***|
|||(3.597)||(3.754)|(3.732)|(3.769)|(3.837)|(3.770)|
|U.S. Interest rate||-0.146*||-0.199**|-0.305***|-0.271**|-0.298***|-0.197**|
|||(0.084)||(0.100)|(0.108)|(0.105)|(0.106)|(0.100)|
|**Political factors**|||||||||
|Gini|||0.108***|0.094***|0.077**|0.173***|0.083**|0.095***|
||||(0.033)|(0.036)|(0.036)|(0.050)|(0.036)|(0.037)|
|Rule of law|||0.144|0.196|0.128|0.198|-0.875*|0.192|
||||(0.259)|(0.332)|(0.336)|(0.334)|(0.453)|(0.332)|
|Margin of Majority|||1.266|1.561|1.705|1.702|1.720|2.033|
||||(1.043)|(1.118)|(1.118)|(1.135)|(1.112)|(2.476)|
|Election's year|||0.229|0.406*|0.446*|0.431*|0.435*|0.408*|
||||(0.211)|(0.231)|(0.233)|(0.232)|(0.233)|(0.231)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.383***<br>(0.114)||||
|Gini * Deviation||||||-0.010**<br>(0.004)|||
|Rule of law * Deviation|||||||0.107***<br>(0.031)||
|Majority * Deviation||||||||-0.047<br>(0.220)|
|Observations|1,065|1,053|957|947|947|947|947|947|
|Number of ifscode|60|60|53|53|53|53|53|53|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_p<br>Standard errors in parent<br>*** p<0.01, ** p<0.05, *|0.161<br>heses<br>p<0.1|0.307|0.193|0.320|0.341|0.329|0.342|0.320|

- 63 -
# **Table A8. Survey Methods’ Differential Effects on Employment and Work Status by Exposure to Social Norms Related to Housework**

||(1)<br>Employed|(2)<br>Working|
|---|---|---|
|Average time spent on housework (hours)|-0.044|-0.001|
||(0.048)|(0.036)|
|Female|-0.268***|-0.207**|
||(0.088)|(0.080)|
|LOA|-0.032|0.000|
||(0.086)|(0.074)|
|ESR|-0.041|-0.005|
||(0.089)|(0.066)|
|LOA x Female|0.069|-0.027|
||(0.123)|(0.114)|
|ESR x Female|-0.013|-0.064|
||(0.124)|(0.110)|
|Average time spent on housework x Female|-0.026|-0.074|
||(0.072)|(0.065)|
|LOA x Average time spent on housework|0.031|0.000|
||(0.069)|(0.060)|
|ESR x Average time spent on housework|0.015|-0.018|
||(0.074)|(0.052)|
|LOA x Average time spent on housework x Female|-0.005|0.095|
||(0.099)|(0.092)|
|ESR x Average time spent on housework x Female|0.050|0.080|
||(0.100)|(0.087)|
|_Treatment Effects_|||
|Effects of LOA on men exposed to less housework|-0.032|0.000|
|Effects of LOA on men exposed to more housework|-0.001|0.000|
|<br>Effects of LOA on women exposed to less housework|0.037|-0.027|
|Effects of LOA on women exposed to more housework|0.063*|0.069*|
|Effects of ESR on men exposed to less housework|-0.041|-0.005|
|Effects of ESR on men exposed to more housework|-0.025|-0.022|
|<br>Effects of ESR on women exposed to less housework|-0.054|-0.068|
|Effects of ESR on women exposed to more housework|0.011|-0.006|
|Observations|2,480|2,480|
|Outcome(Control Group)Mean|0.571|0.670|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by exposure to social norms for housework. This exposure measure is estimated as the average time in hours that other women in the EA devoted to housework the previous day. This variable is estimated using the Time Use module. We present estimated coefficients from a model that includes a triple interaction between the treatment, sex, and the exposure measure. We control for a vector of variables ( ) that includes sex, the exposure measure, double interactions, and the list of control variables selected using a double-LASSO procedure for each outcome. Under iihss _Treatment Effects_ we report the XX total effects of the LOA and ESR for each subsample relative to the group of individuals with similar exposure in the control group (see Figure 3 for a graphic representation of these total effects). The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level are presented in parentheses. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1.

45
26 TABLE 4 (monthly data) log (Ml) - a + b (INF)

# ARGENTINA

|Period|a|b|Dl|D3|Ad<br>D4|justed R<br>Squared|D.W.|
|---|---|---|---|---|---|---|---|
|1982.12-1989.04|-3.4|-0.023|<br>0.081|-0.15|<br>-0.26|<br>0.73|0.46|
||(-66.4)|(-8.3)|(1.6)|(-3.2)|(-4.8)|(52.0)||

Dl - 1 for 85.07-87.03 D2 - 1 for 87.11-88.09 D3 - 1 for 88.09-89.04 BRAZIL

|Period|a|b|Dl|D2|D3|Adjusted R<br>Squared|<br> <br>D.W.|
|---|---|---|---|---|---|---|---|
|1980.01-1989.06|-0.28|<br>-0.026|<br>0.59|<br>-0.04||0.64|0.36|
||(-7.2)|(-7.2)|(9.5)|(-0.6)||(67.3)||

Dl - 1 for 86.03-87.06 D2 - 1 for 87.07-88.06 D3 - 1 for 86.03-89.06
macroeconomic level. See, for example, Kiyotaki and Moore (1997), Bernanke and Gertler (1989), Caballero and Hamour (1994, 1996, 1998), and Caballero and Krishnamurty (2001). Others, such as Acemoglu et al. (2003), point to the relevance of institutional development in the control of crises and management of shocks.

Notwithstanding these contributions, the extent to which domestic structural characteristics can account for the relative instability of aggregate output remains an open question. This paper contributes to this literature by examining how certain domestic structural characteristics influence the impact that external shocks may have on aggregate output. The broad issue under study here is whether a country’s vulnerability to shocks is not purely random but linked to structural characteristics driven at least partially by policy and related specifically to trade and financial openness, financial depth, and labor and firm flexibility.

For analytical purposes we can distinguish two aspects of output vulnerability to external shocks. The first is the frequency and strength of the shocks affecting the country. The second one is the effect that a shock of a given size and frequency can have on the country’s output. This paper is dedicated to the second aspect of vulnerability. For this purpose and working with a panel sample of 90 countries and annual observations for the period 1974-2000, the paper applies an econometric methodology that isolates and standardizes the shocks, estimates their impact on GDP, and examines how this impact depends on the domestic conditions outlined above.<sup>2</sup> From an empirical perspective, the relevant question for the paper is whether the differential impact of a _given_ external shock is related to country characteristics. Controlling for the size of the shocks is not an easy task. Most of the recent literature on this issue has relied on either indirect evidence from difference-in-difference estimation (see, for example, Braun and Larrain 2005, Caballero, Cowan, Engel, and Micco 2005, and Raddatz 2005) or calibrated macroeconomic models developed to match certain moments of developing countries’ economic performance (for a survey, see Arellano and Mendoza 2004). This paper takes a different approach and directly estimates the output

> 2 An analogy can illustrate these ideas: how vulnerable people are to disease depends on the seriousness of the disease itself (first aspect) and how well prepared they are to bear a given disease (second aspect).  In order to analyze the second aspect, it would be necessary to examine how people facing the same disease (in type and strength) react. This would shed light on why some people suffer so much from an attack of, say, the flu, while others remain unscathed.

3
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the two variables does not imply anything about tle structure of causation between the variables. The above facts imply that any simple assertion derived from Maithusian (or NeoMalthusian) theories that population growth will everywhere and always worsen living standards must be wrong in at least some country and historical episodes. On the other hand an assertion that population is not a problem for economic growth anywhere seems also certain to be incorrect, in spite of the small average effect. This has led to the "revisionist<sup>t</sup> ' (neo-Malthusian) view, which seems to have emerged as the new consensus in some circles, that population growth is harmful to econonic progress only under certain conditionsl. This, for instmace, is the conclusion reached by Cassen (1994) in his recent survey of the literature: What are the "new conclusions" to be drawn from all this? It seems fairly clear that population growth is not the overwhelming affliction that some have claimed and cerainly not the pjime cause of difficulties of development; but it also should not be regarded with equanimity. The degree of hindrance to the improvement of individua **l** iving standards that rapid population growth is likely to afford is hard to establish. It has not been helped by economists' models, which give the same result whether a country has a hundred or a hundred million people; whether the population is growing fast or slowly; whether the country is well-off or poor; and whether or not it has good government with sensible economic policies, equitable income distrbution, well functioning markets and institutions and efficient agriculture with scope for expansion.

.However these "revisionist" hypotheses about differences in the impact of population across types of countries, although attractive and plausible, have yet to be adequately explored or attract any empirical support.

The question is: if additional policy effort were successful in reducing the national ferdlity rate, would the result be higher lifetime output per person? If the answer is not 10 This is the view reflected in the 1984 World Development Report, (see also Birdsall, 1988) as well as the National Research Council report (NRC, 1986).
This procedure produces the four “synthetic” climate projections mentioned above (i.e., WET, DRY, COOL, and HOT). This method retains many aspects of the time series properties of historical climate variability (i.e., the historical baseline, which remains constant across all scenarios) and overlays future climate changes. It is important to point out that this procedure has implications for variability. For months with predicted increases in temperature and/or precipitation, peaks will be accentuated. Predicted temperature almost never declines; however, some months experience drying trends in nearly all GCMs and one GCM predicts an overall drying trend (see Table 1). When the GCM predicts drying, historical dry periods are deepened and tend to be extended. Based on these climate scenarios, we next assess the implications of climate change for crop production.

# _Crop and water balance models_

We use a generic crop model called CLICROP to simulate the impact of the baseline and climate change scenarios on rain-fed and irrigated crop yields and on irrigation water demand (Strzepek et al., forthcoming). CLICROP was specifically designed to capture climate change impacts since it models water stress from both insufficient and excess water supply (measured daily). Yield reduction due to retarded root growth resulting from excess water is known as “waterlogging”. Water-logging reduces yields via oxygen loss and root growth hindrance (see Sieben, 1964). The inclusion of water-logging and crop-specific parameters is an extension over simpler models, such as the FAO’s CROPWAT. Moreover, CLICROP’s daily time scale allows it to capture the shorter but higher intensity rainfall expected in Eastern Africa (Solomon et al., 2007).

The effects of the atmosphere (i.e., temperature and precipitation) are modeled indirectly in CLICROP via evapotranspiration (Allen et al., 1998) and infiltration to the soil layers (based on soil properties). Soil composition is considered at each site and is used to calculate soil moisture in each soil layer, including the moisture allowed to percolate into deep soil layers. Water balances and the upward flow of soil water are then measured. As rain falls on the given soil, certain amounts are allowed to run off, infiltrate and percolate through deep layers (in addition to the demands of evapotranspiration). Crops are then allowed to draw what water that they can from the soil layers. Crop yields are estimated using the approach proposed by Allen et al. (1998) with the additional possibility that yields are reduced when excess water results in submersion.

For this examination, the effects of CO2 fertilization are not considered in our analysis. As a result, we may overestimate yield losses caused by climate change. Recent free-air carbon enrichment (FACE) studies contradict the results of earlier closed-laboratory experiments that suggested the presence of strong positive productivity effects for major crops due to higher CO2 concentration levels (Long et al, 2006). At the same time, however, the validity of FACE results has been questioned (Tubiello et al, 2007). The debate remains unresolved at present.

5
19

# **FIGURE 7**

<!-- Start of picture text --> impact of Indexed R.paymente<br>on Subsidies<br>40-<br>130-<br>20-<br>1990 2002: end of loans repayments<br>-- initial subsidies -- revised subsi dies<br><!-- End of picture text --> <!-- Start of picture text --> Savings Achieved through<br>Indexatlon of Repayments<br>30 2<br>25<br>-1.6<br>20.- 11 .= 1.4<br>20 ~~~~~large savings could be achieved1.<br>immediately ifrepayments were Indexed 1.2 ;<br>(for example 1% of GDP in 1991) CD<br>-0.8 *R<br>°05-- hs!]1 E0 -0.6<br>5 ~~~~~~~~~~~~~~~~~0.4<br>.0.2<br>1990 0<br>savings In Billion - svings in % of GDP u<br><!-- End of picture text -->
One limitation of the EIS database is that it only covers registered firms and workers. In Turkey, about a fifth of wage workers are informally employed. This means that workers drop out of the registry when they either lose their job, or when they accept jobs that are “off the books” in the same firms or in another firm. Workers who transition into informal employment or unemployment are no longer observed in the database. Similarly, firms are no longer observed in the database if they shut down or, alternatively, if they no longer employ registered workers.

# 3.2. The Turkish Household Labor Force Survey

To assess the impacts of the minimum wage on informal employment and total employment, we complement our causal analysis (which relies on the EIS registry) with data from a nationally representative survey. This allows us to gain insights into whether workers who lost their formal jobs due to firm destruction following the raising of the minimum wage remained formally employed, transitioned into informal employment, or became unemployed. We use data from the Turkish Household Labor Force Survey (HLFS), a nationally representative household survey conducted annually from 2000 to 2018. The survey collects detailed information on labor-market outcomes of the Turkish population, including representative samples of those in formal and informal employment, and those who are unemployed or out of the labor force. The survey distinguishes the formality status of each worker by the response given to a question on whether employed individuals are registered with social security institutions – the definition of formality also used by the EIS registry. In addition, the survey records the wages of both formal and informal workers, allowing one to look at potential “lighthouse” effects of the minimum wage on the wage of informal workers who are outside the scope of formal labor regulations.

# 3.3. Descriptive statistics

Table 1 reports summary statistics from the EIS data on the composition and characteristics in the population of registered firms in Turkey. Microenterprises and small firms represent the large majority of formal firms in Turkey. Half of all formal firms are microenterprises with no more than 2 formal employees each; 72 percent of formal firms have no more than 5 employees, and 84 percent have no more than 10 employees. These statistics highlight the importance of having data that cover firms of all sizes with no minimum-employment threshold when assessing the impact of the minimum wage on firms in developing economies.

10
||Effect<br>size|Added<br>years of<br>equivalent<br>schooling|Return<br>to<br>impact<br>(%)|Program<br>cost per<br>individual,<br>2011 PPP|EYOS per<br>$100|Net Present<br>Value of<br>increased<br>income, PPP<br>current<br>international $|Benefit‐<br>cost ratio|References|
|---|---|---|---|---|---|---|---|---|
|Linking school cmte to<br>localgovt,Indonesia|0.17|1.07|4.1|0.6|186.6|$2,190|3809.7|Pradhan et al.<br>(2014)|
|Electing school cmte &<br>linking to local govt,<br>Indonesia|0.22|1.40|7.9|2.0|71.9|$4,186|2142.1|Pradhan et al.<br>(2014)|
|Remedial education,<br>India|0.14|0.90|5.0|10.7|8.4|$1,533|142.6|Banerjee et al.<br>(2007)|
|Camera monitoring,<br>India|0.17|1.11|6.2|17.7|6.2|$1,780|99.5|Duflo, Hanna, and<br>Ryan(2012)|
|Individually‐paced<br>computer assisted<br>learning,India(Yr 2)|0.48|3.09|17.3|72.7|4.2|$5,275|71.6|Banerjee et al.<br>(2007)|

Source: Data from J‐PAL (2014). Calculations by authors.

27