rafmacalaba/datause-encoder-data huggingface dataset:
# **5. Fiscal rule effectiveness and conditions at the time of adoption**

We have demonstrated that the effects of fiscal rule adoption are not uniform. Depending on the country contexts, rule adoption has a persistent effect on the primary balance in some cases, while it tends to diminish after several years in others. Our next hypothesis is that differences in effectiveness may also stem from the motivations and the conditions present at the time of adoption, independent of a country’s broader institutional context.

We consider conditions that exhibit significant variation over time, complementing the earlier investigation that focused on relatively static factors. We examine both political and economic conditions at the time of adoption, drawing inspiration of studies that have examined the drivers of fiscal rules (IMF 2009; Hallerberg and Scartascini 2015; Elbadawi, Schmidt-Hebbel, and Soto 2015; Badinger and Reuter 2017; Altunbaş and Thornton 2017). Our hypothesis is that, as conditions prompting rule adoption can change over time, the government’s commitment to adhere to the constraints may weaken. Consequently, factors driving adoption may contribute to both successful and unsuccessful outcomes.

## **5.1. State of the economy**

We begin with studying how economic conditions might affect the response to adopting fiscal rules. To measure the state of the economy, we follow as similar approach as Auerbach and Gorodnichenko (2012), Ghassibe ans Zanetti (2022), and Alesina et al. (2024) and consider the following equation: _z_ In this equation, serves as an indicator of the state of the economy, normalized to have zero mean and unit variance at the country level.<sup>20</sup> We employ a weighted average of real GDP growth over the past three years.<sup>21</sup> The weighting function _F(zit)_ ranges between 0 and 1, which can be interpreted as the probability of being in a given state of the economy.

> 20 Our approach slightly differs from Auerbach and Gorodnichenko (2012) and Alesina et al. (2024) as we consider a three-year window, and we account for variations in growth patterns across countries.

> 21 The findings remained consistent when we use real GDP per capita growth or when we use the unweighted average of real GDP growth over the preceding three years. Results are available upon request.

19
shows that most regressors are no longer statistically significant; nonetheless, HIV prevalence continues to present a negative and significant estimated coefficient<sup>16</sup> . The estimates indicate that a 1 percentage point increase in HIV prevalence is associated with 0.5 percentage point reduction in social capital.

Furthermore, we attempt to account for the fact that HIV prevalence might be endogenous to social capital by instrumenting for this variable using national data for male circumcision rates obtained from WHO (2007) and Drain et al. (2006) and a SubSaharan African dummy variable. A number of other papers for African countries, notably Kalemli-Ozcan (2006) and Werker et al. (2006) in regressions for total fertility, school enrolment and economic growth, have used circumcision rates as an instrumental variable for HIV in the light of new medical evidence that male circumcision substantially reduces the risk of HIV transmission.

When using circumcision rates as the only instrumental variable, diagnostics for the first stage regression show that circumcision rates are negatively but insignificantly related to HIV prevalence. One explanation for this is that the relation between circumcision and HIV is not as straightforward as predicted by clinical trials. Beegle and de Walque (2008) quote cases like Ethiopia and Cameroon where the difference in HIV between circumcised and uncircumcised males is very small. Another explanation is that omitted variables might be underestimating the negative effect of circumcision on HIV. We are aware that the use of weak instruments in two stage least squares can be very misleading because it biases the estimated coefficients and the standard errors (Murray, 2006) so we decided to add the geographic variable Sub-Saharan African country to the list of instruments. According to WHO and UNAIDS, SSA is the poorest and the most HIV/AIDS affected region in the world (more then 2/3 of people infected live in SSA, more than 3/4 of all deaths were AIDS related in 2007). Although the results controlling for Scandinavian or Nordic countries are very similar<sup>17</sup> , we prefer to present the latter where the RESET test cannot reject the null of no misspecification at 10% level.

> 16 We also tested the same specification but using government effectiveness or control of corruption instead of rule of law with similar results.

> 17 The estimated coefficient of HIV prevalence is  9% smaller when controlling for Scandinavian instead of Nordic.

16
_M M_ where _gi_ = _ti_ and _zi_ / _ei_ = _zi_ / _ei_ if _i_ is an imported good, and _gi_ = _si_ and _zi_ / _ei_ = _zXi_ / _eXi_ if _i_ is an exported good. Data on protection and subsidies from 1998 are used to estimate this model across a cross-section of agricultural goods. The parameter of interest _a_ is recovered as the inverse of the estimate of the coefficient β1.

Concern about the endogeneity of the regressor compels using the appropriate methodology. Quite clearly, import penetration (inverse of _z_<sup>_M_</sup> ) is influenced by protection and the export-tooutput ratio (inverse of _z_<sup>_X_</sup> ) is influenced by subsidies. Further, which sectors are capable of organizing politically is also influenced in part by their expectation of the degree to which they will benefit from tariffs and subsidies. Thus, the regressor _Ii_ . _zi_ / _ei_ is a nonlinear transformation (here product) of two endogenous variables, and is therefore itself endogenous. In order to estimate the coefficient β1 consistently we construct exogenous variables capable of instrumenting the component endogenous variables, and then use the method of Kelejian to instrument the composite regressor.<sup>21</sup> The Kelejian method simply requires us to use not only the instruments linearly, but their higher order terms (quadratic, cross-products) as well.

Constructing instruments for the agricultural data is a nontrivial task due to paucity of data on variables that are truly exogenous. Testing the exogeneity of instruments is thus necessary. Even if instruments pass the test of exogeneity, it is prudent to investigate whether the instruments are “weakly” related to the endogenous regressor. If so, then it seriously affects inferences that may be made from small samples. An important literature has grown around this subject in recent years in response to strong concerns about the endogeneity problem in natural experiments.<sup>22</sup> These studies and others indicate that the first-stage _F_ -statistic may be used to assess whether a weak instrument problem exists. If a weak instrument problem exists, the popularly used two-stage least squares (2SLS) estimator can suffer a possibly intolerable level of

> 21 Kelejian (1971).

> 22 See e.g. Stock and Staiger (1997) and Stock and Yogo (2005).

11
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# **1 Introduction**

Understanding the mechanisms by which firm-level revenue shocks impact wage setting is important for explaining the drivers of wage differentials and wage premia across firms and workers, how firms adjust their wage policies to absorb shocks, as well as the role played by wage rigidities in the international transmission of business cycles. Naturally, appreciation is growing in a number of fields—macroeconomics, labor, industrial organization, as well as international trade—for careful empirical work seeking to improve our understanding of these mechanisms.<sup>1</sup> However, research has faced two important challenges. First, it has been difficult to quantify in a systematic but precise way the unexpected component of demand shocks at the firm-level, while distinguishing it from anticipated changes in market conditions. This distinction is important for establishing a causal link between changes in demand and changes in wages. Second, studying the mechanisms by which these unexpected demand shocks impact wage setting requires detailed information on the components of worker compensation.

In this paper, we estimate the effects of unexpected demand shocks on the components of worker compensation. We also explore if and how the wage effects of such shocks are related to managerial skill. We propose a new methodology to identify the unexpected component of demand shocks at the firm-level, exploiting forecast errors in the GDP growth of export markets. In each destination, the unexpected component of demand shocks is measured as the difference between the GDP growth actually observed and recent forecasts of the International Monetary Fund. We then aggregate these shocks at the firm-year level, weighting by the initial share of destinations in firms’ total sales. Since firms initially served different destinations to a varying degree, they were differentially exposed to these unexpected demand shocks across markets. We show that these unexpected shocks tend to be uncorrelated with initial firm attributes, and account for firm fixed effects throughout the estimation.

The empirical analysis draws on an unusually rich collection of data sets for the population of private sector firms operating in Portugal during 2006-2018. We combine a yearly firm census with information on export transactions and employer-employee panel data. We also use an auxiliary data set from a management survey for 2016, covering a subset of these firms, in order to establish a correlation between managerial skill and observable management practices. The employer-employee data allow us to distinguish between effects on each of the various components of worker compensation, including base wages, overtime pay, and other regular and

> 1Recent contributions to this literature, reviewed in more detail below, include Card, Cardoso, Heining, and Kline (2018), Kline, Petkova, Williams, and Zidar (2019), Aghion, Akcigit, Hyytinen, and Toivanen (2018), Grigsby, Hurst, and Yildirmaz (2021), and Fr ́ıas, Kaplan, Verhoogen, and Alfaro-Serrano (2024).

1
From space it is possible to observe the surface of the earth and measure the light that is emitted at different wavelengths. Vegetation indexes such as the NDVI translate visible red and near infrared radiation into a decimal number between -1 and 1 which describes the greenness of a specified geographical area. In order to use NDVI as a proxy for drought, it is common to calculate the anomaly, i.e. the deviation from a long-run average for a specific time of the year. Figure A8 shows how NDVI is calculated as the ratio between near infrared radiation and visible red radiation; a higher index value is related to a greener land surface.

NDVI data is obtained from the MODIS Terra satellite. It has been orbiting Earth daily since 2000, and here we employ a pre-processed product made publicly available by NASA that has a temporal resolution of one month and a spatial resolution of 0.05 degrees (3 arc minutes or around 5.8 km at the equator). It is later aggregated to 0.25 degrees in order to match the resolution of the rainfall data and reduce the number of observations. In the end we have a data frame with 1440 x 720 observations over 180 months for every location.

Like NDVI, land surface temperature is measured from space globally using the MODIS Terra satellite, and again, the product in use has a spatial resolution of 0.05 degrees. Year-onyear changes in both daytime and night time temperatures are included in the model (see Table A2 in the Annex). On average, it is expected that day time temperatures affect greenness negatively since hotter means drier in most parts of the world. Night time temperatures are likely to affect greenness positively, however, since cold also becomes a serious constraint for plant growth when moving away from the equator.

While greenness is best seen from above, rainfall is harder to measure using satellites. This study uses data from the Tropical Rainfall Measuring Mission (TRMM) which to our 31
**Table 1: Summary Statistics for Values of Trade Facilitation Indicators**

|**Category**<br>|**Indexed inputs**|**Source **|**Mean**|**Std.**<br>**Dev.**|**Min**|**Min.**<br>**Importer**|**Max**|**Max.**<br>**Importer**|
|---|---|---|---|---|---|---|---|---|
|**Port Efficiency**<br>|Ports Facilities|GCR|.636|.189|.261|Bolivia|1.000|Singapore|
||Air Transport|GCR|.710|.166|.229|Slovak<br>Republic|1.000|Singapore|
|_Aggregate Index_|||.673|.169|.345|Bolivia|1.000|Singapore|
|**Customs Environment**|Hidden Import Barriers|GCR|.702|.167|.368|Paraguay|1.000|Finland|
||Bribery|GCR|.689|.175|.343|Bangladesh|1.000|Iceland|
|_Aggregate Index_|||.695|.163|.384|Paraguay|0.979|Finland|
|**Regulatory**<br>|Transparency of Government||||||||
|**Environment**<br>|Policies|WCY|.619|.205|.089|Argentina|1.000|Finland|
||Control of Corruption|KKZ|.746|.140|.530|South Africa|1.000|Finland|
|_Aggregate Index_|||.689|.139|.353|Venezuela|1.000|Finland|
|**Service sector**<br>**infrastructure**<br>|Speed and Costs of Internet Access|GCR|.629|.162|.348|Vietnam|1.000|Finland|
||Effect on Internet on Business|GCR|.719|.102|.481|Greece|1.000|Finland|
|_Aggregate Index_|||.674|.121|.482|Mauritius|1.000|Finland|

Source: Authors’ calculation based on Global Competitiveness Report 2001-2002, Kaufmann, Kraay and Zoido-Lobaton (2002) and World Competitiveness Yearbook 2002.

28
``` ` ́` ́ .saedataimport,varlist(xvarp_hhsize_hhzvarplinepline2rdefrdef2rentdefrdef_rentroom2rdef_ren
>tall2)area(localityid_n)uniqid(hhid)datain(`censo ́)dataout(`matadata ́)
Savingdatavariablesintomatamatrixfile(38) 12345 ......................................
```

# **6.2 Model**

In this section the command for the modeling stage of the analysis is presented. The syntax of this is as follows: ``` saemodellmm/povmapdepvarindepvar[if][in][awpwfw],area(varnamenumeric) varest(string)[zvar(varlist)yhat(varlist)yhat2(varlist)alfatest(string)vce(string)] ```

- `area():` The `area` option is necessary and specifies at which level the clustering is done, it indicates at which level the _ηc_ is obtained. The only constraint is that the variable must be numeric and should match across datasets, although it is recommended it follows a hierarchical structure similar to the one proposed by Zhao (2006).

- The hierarchical id should be of the same length for all observations for example: AAMMEEE.<sup>31</sup>

- `varest():` The `varest` option allows the user to select between _H3_ or _ELL_ methods for obtaining the variance of the decomposed first stage residuals. The selection has repercussions on the options available afterwards. For example, if the the user selects _H3,_ parameters must be obtained via bootstrapping.

The following are optional. In the case of homoskedasticity, the `zvar, yhat,` and `yhat` options should not be specified.

- <u>`zvar():`</u> The `zvar` option is necessary for specifying the alpha model, the user must place the independent variables of the alpha model under the option.

- `yhat():` The `yhat` option is also a part of the alpha model. Variables listed here will be interacted ˆ

- with the predicted _y_ = _Xβ_ from the OLS model.

- `yhat2():` The `yhat2` option is also a part of the alpha model. Variables listed here will be interacted with the predicted _y_ ˆ<sup>2</sup> = ( _Xβ_ )<sup>2</sup> from the OLS model.

- `alfatest():` The `alfatest` option may be run in any stage, but is useful for selecting a proper first stage. It requests the command to output the dependent variable of the alpha model for users to model for heteroskedasticity.

- `vce():` The `vce` option allows users to replicate the variance covariance matrix from the _OLS_ in the PovMap 2.5 software. The default option is the variance covariance matrix from the PovMap software, `vce(ell` ), the user may specify robust or clustered variance covariance matrix to replicate the results from the `regress` command.<sup>32</sup>

> 31In the case this were done for a specific country, AA stands for the highest aggregation level, MM stands for the second highest aggregation level, and EEE, stands for the lowest aggregation level.

> 32The variance covariance matrix presented by the PovMap software is not standard in the literature. The variance covariance

15
covariates. Results (Table 3, column 2) show that the effect of internal supervision does reduce to insignificance when instructional quality is controlled, a result consistent with the hypothesis that the effect of internal supervision does indeed work through instructional quality to predict achievement.

Table 3: Instructional Quality as a Predictor of Total Achievement

|Predictors<br>Wi|thout Int|. Super|vision|With Int.|superv|ision|
|---|---|---|---|---|---|---|
||Coeff|SE|t|Coeff|SE|t|
|(a) School/Classroom|Level||||||
|Inercept|-40.59|11.54|-3.52|-44.27|11.84|-3.74|
|Infrastructure|25.15|15.64|<br>1.61|30.32|16.06|1.89|
|Remoteness|6.65|2.74|<br>2.43|6.62|2.73|2.43|
|Preservice Education|<br>20.09|12.22|<br>1.64|24.75|12.67|1.95|
|Internal Supervision||||6.73|5.03|1.34|
|Textbooks|5.78|2.83|<br>2.04|4.89|2.90|1.68|
|Instructional|||||||
|Quality|1.72|1.01|<br>1.71|1.27|1.06|1.20|
|(b) Student Level|||||||
|GPA|25.55|1.15|22.17|25.59|1.15|22.21|
|Repetition|-22.82|3.77|-6.05|-22.77|3.77|-6.04|
|SES|4.83|3.22|<br>1.50|4.82|3.22|1.50|
|Time to School|-4.56|2.32|-1.96|-4.54|2.32|-1.96|
|Sex|-2.09|2.51|-0.83|-2.10|2.51|-0.83|
|Dialect|15.28|8.31|<br>1.84|16.69|8.37|1.99|
|Breakfast|12.34|4.49|2.75|12.27|4.49|2.73|

Variances

|Between class|2644|2627|
|---|---|---|
|% Explained|30.1|30.1|
|Within class|3073|3073|
|% Explained|25.0|25.0|

30
refinements to non-experimental methods can lead to a better replication of the experimental results.

In summary, these papers demonstrate that more accurate non-experimental estimates can be achieved if the treatment and non-experimental control groups are: i) compared over a common support (eg. the distribution of the likelihood of receiving the treatment is similar in both groups), ii) located in the same labour markets, and iii) administered the same questionnaire (eg. data is collected from both groups in an identical manner). A significant improvement can further be achieved if data is collected from both the preand post-treatment periods and a ‘difference-in-differences’ estimator is used to control for unobserved differences between the treatment and control groups by differencing out individual fixed effects which are correlated with both the outcome and the likelihood of being treated. Nonetheless, even with these refinements, Smith and Todd (2005, p.305) conclude, “Our analysis demonstrates that while propensity score matching is a potentially useful econometric tool, it does not represent a general solution to the evaluation problem.” Recall that PINZMS collects data for a sample of non-applicants to the lottery selected from either the same villages that the migrants had been living in prior to migrating or in the same villages that unsuccessful ballots were found in and administers them an identical questionnaire to the one given to other non-migrants in our sample (eg. the experimental control group). Thus, these individuals serve as a perfect non-experimental control group on which to test alternative methodologies for estimating the gains from

- 19 -
15

**<u>Table 5: Econometric results</u>**

|||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|---|
|||**Ln**<br>**Telephone**<br>**Subscribers/**<br>**1000 people**|**Ln Price of**<br>**Local**<br>**Telephone**<br>**Call (2000 US**<br>**cents-3min)**|**Ln Monthly**<br>**Residential**<br>**Subscription**<br>**Fee (2000**<br>**US$)**|**Ln Monthly**<br>**Business**<br>**Subscription**<br>**Fee (2000**<br>**US$)**|**Ln Phone**<br>**Faults**<br>**(reported**<br>**faults/100**<br>**mainlines)**|**Ln Mainlines**<br>**per**<br>**Employee**|
|**(1)**|Privatization|0.439***|0.905***|0.716***|0.648***|-0.472**|0.029|
|||[0.088]|[0.138]|[0.164]|[0.157]|[0.207]|[0.090]|
|**(2)**|IRA|0.047|0.562***|0.121|0.123|0.446**|-0.102|
|||[0.096]|[0.149]|[0.173]|[0.167]|[0.224]|[0.102]|
|**(3)**|IRA * Privatization|0.014|-0.253***|-0.015|-0.075|0.078|0.031|
|||[0.037]|[0.059]|[0.066]|[0.063]|[0.086]|[0.040]|
|**(4)**|Privatization *<br>Developed|-0.526***|-0.064|-0.548***|-0.309***|-0.423**|-0.285***|
|||[0.067]|[0.103]|[0.121]|[0.116]|[0.202]|[0.067]|
|**(5)**|IRA * Developed|0.014|-0.334***|-0.264**|-0.114|-0.306|-0.253***|
|||[0.076]|[0.112]|[0.131]|[0.126]|[0.221]|[0.076]|
|**(6)**|Corruption Index<br>(0=low 1=high)|0.687***|0.435***|-0.034|0.046|-0.202|0.566***|
|||[0.058]|[0.130]|[0.151]|[0.145]|[0.199]|[0.057]|
|**(7)**|Privatization *<br>Corruption|-0.216**|-1.115***|-0.524***|-0.455**|0.150|-0.082|
|||[0.098]|[0.175]|[0.187]|[0.181]|[0.277]|[0.116]|
|**(8)**|IRA * Corruption|-0.280**|0.144|0.079|0.200|-1.339***|-0.584***|
|||[0.122]|[0.198]|[0.225]|[0.217]|[0.323]|[0.142]|
|**(9)**|Investment Risk Index<br>(0=low 1=high)|0.508***|0.340**|0.016|0.232|-1.670***|-0.015|
|||[0.079]|[0.147]|[0.160]|[0.156]|[0.212]|[0.078]|
|**(10)**|Privatization *<br>Investment Risk|-0.186*|-0.746***|-0.371*|-0.348*|0.672**|0.390***|
|||[0.106]|[0.183]|[0.198]|[0.189]|[0.281]|[0.117]|
|**(11)**|IRA * Investment Risk|0.088|-0.702***|0.146|-0.145|0.655**|0.861***|
|||[0.105]|[0.180]|[0.189]|[0.186]|[0.280]|[0.116]|
|**(12)**|Ln GDPpc (2000 US$)|2.173***|-0.619***|-1.516***|-0.725***|0.386*|2.383***|
|||[0.050]|[0.161]|[0.174]|[0.167]|[0.219]|[0.059]|
|**(13)**|Ln Population|1.433***|-0.313|-4.696***|-2.367***|-0.821|1.148***|
|||[0.242]|[0.425]|[0.477]|[0.474]|[0.635]|[0.262]|
|**(14)**|Time Trend|0.091***|-0.050***|0.065***|0.010|-0.080***|0.049***|
|||[0.005]|[0.009]|[0.010]|[0.010]|[0.013]|[0.005]|
||Constant|-215.877***|112.275***|-43.055***|19.473|169.524***|-131.740***|
|||[7.601]|[14.207]|[14.152]|[14.849]|[19.470]|[8.147]|
||Observations|1597|1322|1412|1393|926|1436|
||R-squared|0.99|0.85|0.81|0.84|0.95|0.96|
||R-squared within|0.94|0.36|0.19|0.13|0.43|0.87|
||Number of countries<br>in sample|126|118|123|123|115|125|

- Standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%.

- R-squared is significant at the 1% level in all regressions.

- Developing countries are those in which 2001 GNI per capita was equal or less than $9,205.

- All regressions include country fixed effects.

- Estimation technique: GLS.