rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3. CONSUMPTION INSURANCE COEFFICIENTS**

Consumption risk-sharing is tested by model (13). Each test is applied twice, using two different measures of change in consumption: growth rates in total final consumption, and growth rates in household final consumption expenditure. Two different aggregates are considered, where total consumption is averaged across LAC countries and OECD countries. P-values are reported in brackets.

|Countries|||TOTAL|FINALCO|NSUMPTION||||HOUSE|
|---|---|---|---|---|---|---|---|---|---|
||Macroarea:|Latin Amer|ica and Ca|ribbean|Macroarea: O|ECD Count|ries|Macroarea: Lati<br>Carib|n Americ<br>bean|
||−<br>Δ<br><br>_GDP_<br>log|l<br>Δ|)<br>(<br>og<br><sup>_c_</sup>||−<br>Δ<br><br>_GDP_<br>log|)<br>(<br>log<br><sup>_c_</sup><br>Δ||−<br>Δ<br><br>_GDP_<br>log|(<br>log<br><br>_Hc_<br>Δ|
||_id_<br><br>||_a_||_id_<br><br>|_a_||_id_<br><br>|_a_|
|Argentina|1.058|[0.00]|1.283|[0.00]|1.070<br>[0.00]|0.088|[0.62]|1.112<br>[0.00]|1.0|
|Bahamas|0.949|[0.01]|-0.105|[0.86]|0.821<br>[0.05]|1.246|[0.15]|-<br>-|-|
|Belize|1.137|[0.03]|0.175|[0.84]|1.548<br>[0.00]|0.206|[0.80]|1.349<br>[0.13]|1.8|
|Bolivia|0.461|[0.01]|0.545|[0.00]|0.579<br>[0.00]|0.031|[0.84]|0.098<br>[0.66]|0.3|
|Brazil|0.783|[0.00]|1.271|[0.00]|0.522<br>[0.01]|0.661|[0.00]|1.120<br>[0.01]|1.7|
|Chile|1.623|[0.00]|0.728|[0.06]|1.919<br>[0.00]|0.918|[0.02]|1.799<br>[0.00]|1.0|
|Colombia|0.734|[0.00]|0.537|[0.00]|0.803<br>[0.00]|0.675|[0.01]|0.954<br>[0.00]|0.3|
|Costa Rica|1.173|[0.00]|0.748|[0.02]|1.081<br>[0.00]|0.319|[0.03]|0.989<br>[0.00]|0.6|
|Dom. Republic|0.659|[0.15]|0.910|[0.03]|0.502<br>[0.16]|0.786|[0.10]|0.362<br>[0.54|0.5|
|Ecuador|0.947|[0.02]|0.503|[0.14]|0.833<br>[0.00]|0.118|[0.71]|0.840<br>[0.00|0.5|
|Guatemala|0.928|[0.00]|0.552|[0.00]|1.016<br>[0.00]|0.593|[0.00]|0.601<br>[0.00]|0.4|
|Guyana|1.052|[0.08]|1.510|[0.05]|0.892<br>[0.05]|0.602|[0.46]|1.110<br>[0.18]|1.0|
|Haiti|1.210|[0.01]|0.289|[0.60]|1.214<br>[0.00]|0.064|[0.89]|-<br>-|-|
|Jamaica|0.630|[0.05]|-0.297|[0.41]|0.725<br>[0.02]|-0.188|[0.62]|-<br>-|-|
|Mexico|0.982|[0.00]|0.865|[0.00]|0.959<br>[0.00]|0.644|[0.00]|073<br>[0.00]|0.7|
|Nicaragua|0.541|[0.00]|0.084|[0.88]|0.553<br>[0.00]|-0.663|[0.25]|-<br>-|-|
|<br>Panama|0.602|[0.13]|0.008|[0.99]|0.633<br>[0.06]|0.564|[0.39]|0.574<br>[0.29]|0.7|
|Paraguay|0.793|[0.08]|1.526|[0.00]|1.028<br>[0.00]|0.401|[0.37]|0.588<br>[0.21]|1.6|
|Peru|0.482|[0.02]|0.691|[0.04]|0.387<br>[0.03]|-0.450|[0.25]|0.962<br>[0.00]|0.4|
|Suriname|1.428|[0.00]|-0.058|[0.95]|1.470<br>[0.00]|0.435|[0.72]|-<br>-|-|
|Trinidad|1.629|[0.00]|0.308|[0.62]|1.503<br>[0.00]|0.363|[0.58]|2.134<br>[0.00]|-0.2|
|Uruguay|1.287|[0.00]|1.301|[0.00]|1.247<br>[0.00]|0.428|[0.06]|1.815<br>[0.00]|1.1|
|<br>Venezuela|0.602|[0.03]|0.640|[0.15]|0.809<br>[0.00]|0.841|[0.12]|0.673<br>[0.03]|0.2|

Table 6. Correlations between "standard" and new insurance coefficients, recursive and rolling.

<u>ARG BOL BRA CHL COL CRI DOM ECU GTM GUY HND HTI JAM MEX NIC PER PRY</u> Recursive <u>0.92 0.15 0.17 0.57 0.78 0.75 0.61 0.98 0.32 0.58 0.42 1.00</u> -0.20 0.98 -0.76 -0.83 <u>0.17 Rolling 0.83</u> -0.37 0.38 0.99 0.26 0.83 <u>0.51 0.77 0.65 0.59 0.68 1.00 0.17 0.90 0.99 0.79</u> -0.16 26
reasons the survey was not conducted. The PNAD is a nationally representative household survey using a consistent sampling methodology.

We present an analysis of how the gender differential has evolved over time, through the turbulent period of 1988 to 1998 during which Brazil undertook its program of stabilization and economic liberalization. We do this by applying the Juhn, Murphy and Pierce decomposition analysis (henceforth JMP) that expands the Blinder-Oaxaca decomposition to take account of distributional changes over time.<sup>5</sup> As far as we know, this is the first time that this methodology has been applied to data for a developing country to assess changes in the scale and determinants of gender wage discrimination.

The paper is structured as follows. Following this introduction, section II reviews the existing literature on the gender wage differential in Brazil, section III outlines the decomposition methodology used and describes the data source, section IV presents our empirical findings and section V concludes.

# **II. Gender discrimination in Brazil: a review of existing literature**

The first studies on gender and the pay gap in Brazil date back to the mid-1980s.<sup>6</sup> Camargo and Serrano used data from the Labor Ministry’s survey of the formal labor market (Relação Anual de Informações Sociais – RAIS) for 1976 to estimate gender wage gaps for 22 sectors of Brazilian manufacturing. Their results suggested that education was the most important variable in the determination of wages for female 3
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|**Year**|**Event**|**Location**|**Fatalities**|**Income Group**|
|---|---|---|---|---|
|2010|Earthquake|Haiti|222,570|LIC|
|2004|Earthquake,|Sri Lanka, Indonesia, Thailand,|220,000|LIC, LMIC,|
||tsunami|India, Bangladesh, Myanmar,||UMIC|
|||Maldives, Malaysia|||
|2008|Cyclone Nargis,|Myanmar|140,000|LIC|
||storm surge||||
|1991|Tropical cyclone,<br>storm surge|Bangladesh|139,000|LIC|
|2005|Earthquake|Pakistan, India, Afghanistan|88,000|LIC, LMIC|
|2008|Earthquake|China|84,000|UMIC|
|2003|Heat wave,|France, Germany, Italy, Portugal,|70,000|UMIC, HIC|
||drought|Romania, Spain, United Kingdom|||
|2010|Heat wave|Russia|56,000|HIC|
|1990|Earthquake|Iran|40,000|UMIC|
|2003|Earthquake|Iran|26,200|UMIC|

**Table 1.** Disasters in terms of fatalities (1980-2012). Only very few of the deadliest disasters have occurred in highincome countries. Low-income countries (LIC) have experienced the highest human losses. _Source: MunichRE, NatCatSERVICE, Wirtz_ (2013)

|**Year**|**Event**|**Location**|**Losses (US$ m)**|**Income Group**|
|---|---|---|---|---|
|2011|Earthquake,<br>tsunami|Japan|210,000|HIC|
|2005|Hurricane Katrina,<br>storm surge|USA|125,000|HIC|
|1995|Earthquake|Japan|100,000|HIC|
|2008|Earthquake|China|85,000|UMIC|
|2012|Hurricane Sandy,|USA, Canada, Bahamas, Cuba,|65,000|HIC*|
||storm surge|Dominican Rep., Haiti, Jamaica,<br>Puerto Rico|||
|1994|Earthquake|USA|44,000|HIC|
|2011|Floods|Thailand|43,000|UMIC|
|2008|Hurricane Ike|USA, Cuba, Haiti, Dominican<br>Rep., Turks and Caicos Islands,<br>Bahamas|38,000|HIC*|
|1998|Floods|China|30,700|UMIC|
|2010|Earthquake,<br>tsunami|Chile|30,000|HIC|

_*The vast majority of economic losses were incurred in the USA, i.e. HIC_

**Table 2.** Disasters in terms of absolute economic losses (in US$ m, original values, 1980-2012). The costliest disasters have almost exclusively occurred in high-income countries (HIC). _Source: MunichRE, NatCatSERVICE, Wirtz_ (2013)

Tables 1 and 2 present the most harmful natural catastrophes in terms of fatalities, as well as absolute economic losses between 1980 and 2012. Two features are particularly striking: The vast majority of human fatalities as a result of natural catastrophes are incurred in low-income or lower-middle-income countries (Table 1). On the contrary, the highest absolute economic losses from natural catastrophes are incurred in high-income economies (Table 2).

4
**Table 6: Tax Evasion Risk and Excess Interaction During Delegated Randomization**

|Du|ring delegated r|andomization|of inspector ass|ignment|||
|---|---|---|---|---|---|---|
|**Dependent variable:**|**Excess**<br>**interaction**<br>**share**|**Tax rate**|**Risk score**|**Log initial**<br>**weight**|**Log initial**<br>**unit price**<br>**(relative to**<br>**internal**<br>**prices)**|**Initial hyp.**<br>**tax**<br>**revenue**<br>**losses**|
||**A. With**|**held from ra**|**ndomization**||||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Withheld from randomization (WFR)|0.064**<br>(0.026)|0.088***<br>(0.010)|1.173***<br>(0.222)|0.160<br>(0.117)|-0.197***<br>(0.061)|0.199***<br>(0.061)|
|Observations|17,736|17,738|17,169|17,738|17,728|17,728|
|R-squared|0.153|0.026|0.011|0.001|0.011|0.012|
|**B. Excess**|**interaction - **|**delegated ra**|**ndomized dec**|**larations only**|||
|||(7)|(8)|(9)|(10)|(11)|
|Random excess interaction share||0.083|-0.024|-0.302|0.076|-0.078|
|||(0.173)|(2.316)|(1.251)|(0.169)|(0.158)|
|Observations||16,461|15,925|16,461|16,454|16,454|
|R-squared||0.000|0.000|0.000|0.000|0.000|
||**C.**|**Excess inte**|**raction**||||
|||(12)|(13)|(14)|(15)|(16)|
|Excess interaction share||0.307***|4.212**|0.392|-0.840**|0.836**|
|||(0.084)|(1.360)|(0.844)|(0.311)|(0.312)|
|Observations||17,736|17,167|17,736|17,726|17,726|
|R-squared||0.008|0.004|0.000|0.005|0.006|
||**D. **|**Combined m**|**easures**||||
|||(17)|(18)|(19)|(20)|(21)|
|Withheld from randomization (WFR)||0.079***|1.015***|0.177|-0.110***|0.114***|
|||(0.009)|(0.234)|(0.139)|(0.032)|(0.033)|
|Excess interaction share||0.093|1.196|0.147|0.032|-0.032|
|||(0.094)|(1.609)|(1.251)|(0.104)|(0.098)|
|WFR*Excess interaction share||0.051|1.169|-0.386|-1.286***|1.262***|
|||(0.067)|(2.000)|(1.531)|(0.327)|(0.325)|
|Observations||17,736|17,167|17,736|17,726|17,726|
|R-squared||0.027|0.011|0.001|0.015|0.016|

_Notes:_ Standard errors clustered two-way by inspector and by broker presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. WFR stands for withheld from randomization. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated using calibration methods (as explained in Section 4). _Random excess interaction share_ is the excess interaction share calculated using only the set of declarations that were not withheld from randomization. "Observations" refers to the number of non-singleton observations. OLS estimation is used. The sample covers the period November 18, 2017 to November 17, 2018 (i.e. the period of the delegated randomization intervention).

48
14

-

- ERPij log of the mean effective rate of protection.<sup>Given that</sup> Chilean protection was essentially uniform during<sup>the sample</sup> period, variation in this protection measure is<sup>due only to</sup> Colombia. For Chile, we set this variable<sup>to 0. Colombian</sup> figures are averages of effective protection measures<sup>for</sup> 1979, 1984 and 1985 reported in Cubillos and Torres<sup>(1987).</sup>

Hence, for example, one possible regression explaining<sup>the kth employment</sup> percentile is:

- (7) EMPkijt - OllnQt + 621n(M/Q)ijt + 031n(X/Q)±jt + 64TUR1<sup>i+p</sup> 5<sup>TURijlnQijt</sup> + 06 TURijln(M/Q)ijt + 0 7TURijln(X/Q)ijt + **A.** j + pjt + jt

Here- lnQ proxies total market size, while<sup>ln(X/Q) and ln(M/Q) proxy exposure</sup> to international markets. (When interpreting coefficients on these latter variables, it must be kept in mind that<sup>the regression has already controlled</sup> for total output.) The average turnover<sup>rate, TUR, is used as a measure of</sup> the extent, and thus the ease, of entry and<sup>exit into an industry over time.</sup> High turnover rates are consistent with<sup>low sunk costs of entry, and hence</sup> should reflect the potential for competitive pressures f;om domestic rivals. Also, as discussed in section II, the sensitlvity of size distributions to demand shift should depend upon the ease<sup>of entry and exi.. We therefore</sup> interact our turnover variable with the trade<sup>variables in the regression</sup> equations. Finally, to control for<sup>the industry-specific</sup> technology effects and country-specific macro conditions, represented by A and **_j._** respectively, industry and time dummies are included. 14Equation 7 can, of course, also be estimated using productivity percentiles, PRDk, as dependent variables.

As seen in Table 1, there are fairly<sup>significant and persistent cross-</sup> country differences in trade exposure<sup>and average plant size, but plant size</sup>
# **APPENDICES**

## **Appendix 1: Further Details on US Farm Price Support and Food Aid Policy Evolution**

US commodity price stabilization and food aid policies experienced dramatic changes during the period Nathan Nunn and Nancy Qian (2014, hereafter NQ) study. We demonstrate that results of the NQ strategy do not correspond to the periods in which the true policy regime was closest to the one they describe, indicating that a source of variation other than US commodity price stabilization and associated food aid policy is likely driving their results. Furthermore, the type of policy regime they have in mind effectively ended in the mid-1990s, calling into question the current policy relevance of their results given dramatic changes in the way aid is distributed in recent decades.

## _A. Changes in Commodity Price Support and Surplus Disposal Policies_

The mechanism NQ describe to generate the relationship between wheat production and quantity of aid is the following: “The USDA accumulates wheat in high production years as part of its price stabilization policies. The accumulated wheat is stored and then shipped as food aid to poor countries.” The US long had a policy of agricultural commodity price supports that indeed created a link between aggregate annual production and government procurement of wheat that was subsequently used as food aid; indeed, surplus disposal was an explicit policy objective of the main US food aid program launched in 1954 (Barrett and Maxwell, 2005). However, US commodity price support and food aid procurement policies changed dramatically during the period of the NQ data, and the true form of policy has an important bearing on the interpretation of NQ’s IV strategy.

32
**Figure 4. Macroeconomic indicators**

<!-- Start of picture text --> (in percent)<br>Employment (payroll) Industrial production<br>1.5 1.0<br>1.0 0.5<br>0.5 0.0<br>-0.5<br>0.0<br>-1.0<br>-0.5<br>-1.5<br>-1.0<br>-2.0<br>-1.5<br>-2.5<br>-2.0 -3.0<br>-2.5 -3.5<br>-3.0 -4.0<br>Jun-06 Dec-06 Jun-07 Dec-07 Jun-08 Dec-08 Aug-06 Feb-07 Aug-07 Feb-08 Aug-08 Feb-09<br>Export Overall macroeconomic factor<br>1.0 1.5<br>0.5 1.0<br>0.5<br>0.0<br>0.0<br>-0.5<br>-0.5<br>-1.0 -1.0<br>-1.5 -1.5<br>-2.0<br>-2.0<br>-2.5<br>-2.5<br>-3.0<br>-3.0 -3.5<br>-3.5 -4.0<br>Aug-06 Feb-07 Aug-07 Feb-08 Aug-08 Feb-09 Jun-06 Dec-06 Jun-07 Dec-07 Jun-08 Dec-08<br><!-- End of picture text --> Source: Bloomberg, Datastream, ILO, UK Statistical Office Note: The overall macroeconomic index is derived as the predicted common factor in a factor analysis of twelve variables (transformed into Y-o-Y growth in all cases): US index of industrial production, Germany index of industrial production (incl. construction), UK index of production (all industries), Japan index of production (all industries), US total exports, Germany exports f.o.b., UK exports, Japan exports, US nonfarm total employed, Germany persons engaged in economic activity, UK all persons in employment, Japan employed persons.

**Table 3. Correlation matrix of macroeconomic indicators**

||Employment Growth|Industrial Growth|Export Growth|
|---|---|---|---|
|Employment Growth|1|||
|Industrial Growth|0.91|1||
|Export Growth|0.86|0.92|1|
|Source: Bloomberg, Data|stream, ILO, UK Statistical O|ffice||

The overall macroeconomic index is derived as the predicted common factor in a factor analysis of 12 variables (transformed into year-on-year growth in all cases): the U.S. index of industrial production, the German index of industrial production (including construction), the U.K. index of production (all industries), the Japanese index of production (all industries), U.S. total exports, German exports f.o.b., U.K. exports, Japanese exports, U.S. nonfarm employment, Germans engaged in economic activity, U.K. employment, and Japanese employment. One factor explains 64 percent of the common variance.

10
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# **Box 4.1. Methodology for calculating the benefits of ICT**

Affordability significantly affects access to telecommunications services. As the price of broadband service rises, the number of fixed broadband subscribers per 100 inhabitants drops (see figure).

# **Relation between broadband penetration and broadband affordability, world**

<!-- Start of picture text --> 10<br>y = 16.928x -1.092<br>1 R2 = 0.7842<br>0.1<br>0.01<br>0.001<br>0.10 1.00 10.00 100.00 1000.00 10000.00<br>Fixed broadband basket as % of GNI per capita, 2009<br>bscribers per<br>100 inhabitants, 2008<br>Fixed broadband su<br><!-- End of picture text --> The cost of Internet access largely depends on the wholesale price paid for international Internet connectivity. At present, African countries rely heavily on satellite connections for Internet access. But fiber-optic cables can lower the cost of Internet access if countries allow Internet service providers open access to the cables. For example, in Kenya, connectivity to a fiber-optic cable spurred a 75 percent drop in international bandwidth prices.

Assuming Kenya’s wholesale cost reduction was applicable to other countries and that international wholesale prices accounted for half of the Internet service provider’s cost structure, the reduction in retail prices is assumed to be 37.5 percent. The potential savings for consumers in African countries, once they have open access to undersea fiber-optic networks, can then be estimated. The revised broadband tariff is used to estimate the number of new broadband subscriptions based on the equation shown in the figure. Based on these assumptions, it is estimated that a 37.5 percent reduction in retail broadband prices would result in a _consumer_ savings of $159 million for existing subscribers. The lower broadband prices would trigger new subscriptions estimated at around 2.7 million (compared with 833,000 in 2008). These new subscriptions would generate an additional $800 million of new revenue.

Certain assumptions in the model should be noted. The model assumes a standard broadband tariff, even though there are a number of packages that differ according to speed. It assumes a scenario similar to Kenya’s in terms of the degree of the price reduction, and that half of the wholesale price reduction will be passed on through retail prices. It also assumes that there is a lone relationship between broadband pricing and take-up, even though other variables such as education and infrastructure availability will also have an impact. Finally, the model shows the one-off effect of a 37.5 percent reduction in retail tariffs. The timing of the full reduction is likely to spread over several years in some countries.

_Source:_ AICD **.** _Note:_ ICT = information communication and technology; GNI = gross national income.

53
Table 12 GDP Growth Regressions: Sensitivity of Estimation<sup>to Choice of Deflatorsa</sup> (Dependent Variable: Per Capita GDP Growth)

||1987 US Dollars|1985 International|Prices|
|---|---|---|---|
|Independent Variables|OLS<br>**IVb**|OLS|IV|
|Constant|0.1I1***<br>-0.040<br>(0.020)<br>(0.035)|0.169***<br>(0.022)|0.126***<br>(0.025)|
|Dummy for 1974-83|-0.019***<br>-0.012***<br>(0.003)<br>(0.004)|-0.016***<br>(0.003)|-0.012***<br>(0.004)|
|Dummy for 1984-93|-0.021 ***<br>-0.013***<br>(0.003)<br>(0.004)|-0.016***<br>(0.003)|-0.012***<br>(0.004)|
|Initial Per Capita GDP|<br>.0.009***<br>-0.003*|-0.020***|-0.018***|
||(0.002)<br>(0.002)|(0.003)|(0.003)|
|Initial Schooling|0.003<br>0.001|0.005*|0.002|
||(0.003)<br>(0.003)|(0.003)|(0.003)|
|Population Growth|-0.411 *<br>-0.146|-0.240|-0.116|
||(0.227)<br>(0.181)|(0.200)|(0.196)|
|Investment/GDP|0.014***<br>0.011**<br>(0.004)<br>(0.005)|0.010***<br>(0.003)|0.008**<br>(0.003)|
|Govemment Size|-0.009**<br>-0.059***|-0.019***|-0.035***|
||(0.004)<br>(0.012)|(0.004)|(0.009)|
|Quality of Bureaucracy|0.015<br>0.011|0.013|0.013|
|(O worst, I best)|(0.009)<br>(0.010)|(0.008)|(0.009)|
|<br>Policy Distortion|-0.006***<br>-0.008* **|-0.006***|_-0.005**_|
||(0.001)<br>(0.002)|(0.001)|(0.002)|
|Terms of Trade Changes|-0.020<br>0.013|-0.027|0.058|
||(0.042)<br>(0.041)|(0.037)|(0.048)|
|Latin America Dummy|-0.017***<br>-0.030***<br>(0.004)<br>(0.005)|-0.016***<br>(0.003)|-0.020***<br>(0.004)|
|Sub-Saharan Africa Dummy|.-0.031 ***<br>-0.029***|-0.027***|-0.026***|
||(0.005)<br>(0.005)|(0.005)|(0.005)|
|Socialist Dummy|l<br>-0.013***<br>-0.010*|-0.012*|-0.014*|
||l_____ _<br>_(0.005)_<br>(0.006)|(0.007)|(0.008)|
|Number of Observations|231<br>217|231|217|
|R-squared'|0.5602<br>0.5417|0.6161|0.562|

*** significant at 1% level ** significant at 5% level * significant at 10% level

' Standard errors, corrected for heteroscedasticity, are in parentheses.

- **b** For all IV regressions in Table 12, the instruments for the following variables<sup>are the previous five year lag of itself</sup> and regional and decade dummy variables: policy distortion; and investment/GDP.<sup>The instrument for the</sup> govemment size variable is the prediction form the govemment size instrumental<sup>variable regression reported in</sup> Table 11. All other variables in the regression were treated as exogenous.

'The R-squared is not an appropriate measure of goodness of fit with<sup>instrumental variable regressions.</sup> 53
lower than otherwise. However, to the extent that lower efficiency of public investment translates into lower growth and revenues, aid requirements will increase. Thus, the net effect of an increase in efficiency on foreign assistance is in general ambiguous.

In the foregoing presentation, we have assumed that the PIP incorporates estimates of projected spending on _total_ wage payments at constant prices, N_SAL<sup>PIP</sup> . In principle, these estimates should be derived from the projected path of an “effective” average wage (at constant prices) on new hires, N_RWG<sup>PIP</sup> , PIP multiplied by the increase in public sector employees, N_∆LEG . Thus, instead of (13), total wage payments at current prices would be given by From the point of view of fiscal accounting, decomposing the projected wage bill of the public sector into movements in an effective wage and changes in employment may not matter too much, given that both variables are treated as predetermined in our approach. However, from the perspective of the labor market, this decomposition could be quite important. The reason is that hiring by the public sector affects the composition of labor supply in the economy, and could therefore have significant implications for the private sector. A sharp increase in the demand for educated labor by the government, for instance, could drive wages up in the private sector. In turn, this may translate into higher prices and lower output. Similarly, higher wages on new hires in the public sector could have an adverse “signaling effect” on wages in the private sector. Thus, higher public investment could generate negative externalities, which could mitigate the positive supply-side effects accounted for in SPAHD macro models.

Finally, it should also be noted that our methodology may involve some element of “double counting” when it comes to evaluating maintenance spending. Indeed, SPAHD models calculate maintenance requirements endogenously, by 10