rafmacalaba/datause-encoder-data huggingface dataset:
_5_ covered by the contract but under the general jurisdiction of the regulatory mandate. While most of these agencies were supposed to be autonomous and accountable in principle, very few enjoyed these qualities. In most countries, the degree of political control over regulatory decisions continued to be a dominant source of conflict between operators and governments. There are many instances in which the operator considered its main counterpart to be the sector minister or secretary rather than the regulator. And in fact in many cases the formal appeal to regulatory decisions went to the line Minister, wresting autonomy and authority from the regulator. Moreover, while the regulatory agencies were established statutorily, they were rarely. given appropriate resources, training, and instruments to carry on their mandate effectively. Overall, the weakness of these institutions proved to be one of the main determinants of the social and efficiency outcomes of the reforms as discussed later.<sup>9</sup>

# **3.What the Incidence of Renegotiations Reveals**

To identify the importance of the choice of the regulatory regime on the success of reform, we rely on a data base of 954 concession contracts awarded between the mid 1980s and 2000, in the Latin America and Caribbean Region.1<sup>0</sup> The database contains detailed information about the characteristics of these concessions, including general details about the projects (sector, activity, year of award), the award criteria, size and duration of the concession, information with respect to the institutional context and degrees of freedom of the regulator, the type of regulatory framework put in place (price cap, rate of return, no regulation), and other details of the concession contract like arbitration clauses, nationality of operators, among others. In this data base, 56% of the contracts were regulated under a price cap regime, 20% under rate of return regulation. For 24% of the contracts, the regime was a hybrid one.

Table 2 shows how the choice of the two polar regulatory regimes, price caps. vs. rate of return, influences the odds of renegotiation. As mentioned earlier, the majority of the contracts were subject to a price cap-and the hybrid ones were closer to a price cap than a rate of return since they only allowed cost pass-through for a few cost categories. Formally, the choice was consistent with the advice of the international consultants recruited to assist in the preparation of

> 9 For an early discussion of these issues throughout Latin America, see Guasch and Spiller (1999), Berg and Gutierrez (2000), Stern and Holder (1999) or J. Stern (2000). For a more formal one, see Laffont (2003). '° For a full description, see Guasch (2003), and Guasch, Laffont and Straub (2003).
|Manager Controls Income from||-0.003*|0.668***||0.012**|0.719***|
|---|---|---|---|---|---|---|
|Plot||(0.002)|(0.126)||(0.005)|(0.109)|
|Total Days Worked by HH Men||0.098***|0.006||0.045***|0.010|
|||(0.012)|(0.023)||(0.012)|(0.039)|
|Total Days Worked by HH||-0.001|-0.140***||-0.001|-0.046|
|Women||(0.002)|(0.023)||(0.001)|(0.035)|
|Total Days Worked by Men||0.013***|-0.003||0.020***|0.008|
|(non-HH)||(0.003)|(0.006)||(0.004)|(0.015)|
|Total Days Worked by Women||0.000|0.022**||0.002|0.042***|
|(non-HH)||(0.001)|(0.009)||(0.002)|(0.014)|
|All Harvest Sold||0.006***|0.010||0.039***|0.005|
|||(0.002)|(0.008)||(0.008)|(0.028)|
|Plot of Land uses||0.073***|0.004||0.127***|-0.057|
|Urea/Phosphates/NPK/DAP||(0.007)|(0.018)||(0.014)|(0.036)|
|Plot of Land uses||0.069***|0.036**||0.066***|0.135***|
|Pesticides/Fungicides/Herbicides||(0.007)|(0.018)||(0.012)|(0.043)|
|Constant|||-0.408|||-0.644|
||||(0.393)|||(0.516)|
|Observations|13,577|13,577|13,577|4,952|4,952|4,952|

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 42
# **KENYA: INFLATION PROJECTIONS**

## 1988 1991

<!-- Start of picture text --> 10 -<br>l Inflat. Differ.<br>8 U Target<br>I Sustainable<br>6-<br>CL<br>c , 2-4p<br>1: -2<br>-8 -I<br>-4- a, ~~~~~ ~~ ~ ~ ~c<br>-6<br>1988 1989 1990 1991<br>Fiscal Years<br><!-- End of picture text -->
completely. For the omitted variable bias problem, we control for a large number of variables. These variables are motivated by existing literature on the determinants of women’s employment and are as follows: industry fixed effects to capture segregation of workers by gender across industries; year fixed effects to capture global shocks to women’s employment in the year the firm was surveyed; overall economic development or GDP per capita (constant 2001 prices, taken from WDI); firm’s age (logs) and firm-size (total employees, logs) as proxy measures of various firm attributes; percentage of firm’s output that is exported and percentage of firm owned by foreigners to capture the impact of globalization on women’s employment; two dummy variables indicating if the top manager of the firm is a woman and if the firm has a woman owner, to capture possible discrimination by men vs. women employers against women employees; a dummy variable indicating if the firm provides training, which is particularly important for women who lag behind in technical expertise; a dummy variable indicating if the firm suffered losses due to crime, as women may be particularly sensitive to crime; current state of labor markets proxied by the growth rate of GDP per capita (average over last three years, taken from WDI); ratio of female to male primary enrollment rate and the same for secondary enrollment, to capture the impact of education attainment on job prospects (average over last three years and taken from WDI); supply side effects captured by the proportion of women in the total adult population and fertility rates (taken from WDI); culture, proxied by the percentage of the population that is Catholic, Muslim and Protestant (omitted category is all other religions, data source is La Porta et al. 1999); political empowerment, captured by the proportion of women in the lower house ( _Women in Parliament_ , taken from Inter Parliamentary Union); structural factors affecting job availability for women vs. men, proxied by the percentage share of agriculture and manufacturing in total value added (taken from WDI); burden of care provision on women, proxied by the proportion of the population that has access to 4
input-output table includes the code 562 (Waste management and remediation services). Note that this could mean residential or commercial waste management. Our focus only includes 562112 (Hazardous Waste Collection) and 526221 (Hazardous Waste Treatment and Disposal) from the broader 562 category, because we are more confident that Hazardous Waste Collection serves the tradable sectors rather than local residents.

We also use Mian and Sufi (2014)’s data for several of our regressions. Most notably, we use several pre-crisis county characteristics in 2007 as controls: percentage white, the log of median household income, the log of median home value, percentage of homes occupied by owners, percentage of population with less than high school diplomas, percentage with only high school diplomas, percentage with college degrees, unemployment rate, poverty rate, and percentage urban. We have also included pre-crisis household leverage in 2006 as it serves as a strong proxy for household cuts in consumption (see Mian and Sufi, 2010). Aside from this, we also use their data for the robustness checks we conduct later in the paper, in which we test the influence of credit constraints on the change in supporting sector employment.

Summary statistics for the variables used in our regressions are provided in Table 4.1. Supporting sector employment makes up 3.5% of the total employment in 2007, while tradable employment makes up around 14.5%. On average, a given county lost a staggering 16.1% of tradable employment between 2007 and 2010. In fact, tradable job losses driven by aggregate shocks (i.e. Bartik) for a county between 2007 and 2010 are 18.3% on average.

Most of the variables have full coverage. The only exception is pre-crisis leverage. This variable represent how indebted households in a county were before the crisis. Mian and Sufi (2010) show that pre-crisis household leverage is strongly connected to the decline in 12
<!-- Start of picture text --> Expectation in Autarky<br>1<br>0.9<br>0.8<br>4 07<br>ag00.6<br>5 0.5<br>gyio)<br>B 0.4<br>a 0.3<br>0.2<br>041<br>ie)<br>ie) 041 0.2 0.3 0.4 0.5 0.6 0.7<br>Covariate Shock (Percent Asset Loss)<br><!-- End of picture text --> <!-- Start of picture text --> Expectation in Autarky<br>1<br>0.9<br>0.8<br>4 0.7<br>ag00.6<br>5 0.5<br>gyio)<br>2 0.4<br>a 0.3<br>0.2<br>041<br>ie)<br>ie) 041 0.2 0.3 0.4 0.5 0.6 0.7<br>Covariate Shock (Percent Asset Loss)<br><!-- End of picture text --> <!-- Start of picture text --> Expectation in Autarky<br>1<br>0.9<br>0.8<br>4 3 S 07<br>ag00.6<br>5 0.5<br>gyio)<br>B 0.4<br>a 0.3<br>0.2<br>041<br>ie)<br>ie) 041 0.2 0.3 0.4 0.5 0.6 0.7<br>Covariate Shock (Percent Asset Loss)<br><!-- End of picture text --> <!-- Start of picture text --> Expectation in Autarky<br>1<br>0.9<br>0.8<br>4 3 S 0.7<br>ag00.6<br>5 0.5<br>gyio)<br>2 0.4<br>a 0.3<br>0.2<br>041<br>ie)<br>ie) 041 0.2 0.3 0.4 0.5 0.6 0.7<br>Covariate Shock (Percent Asset Loss)<br><!-- End of picture text -->
# **Table 1 Continued. Mapping** **_:  Banking Sector_**

|**Australian categories**|**Weight**|**Score**|**Weighted**<br>**score**|<br>**The World Bank data**|**Weight**|**Score**|**Weighted**<br>**score**|
|---|---|---|---|---|---|---|---|
|**Expanding the number of banking outlets**|**_0.05_**|||**Expanding the number of banking outlets**|**_0.05_**|||
|One banking outlet with no new banking outlet permitted.|**_0.05_**|**_1_**||Both branches (outlets) and ATMs are restricted|0.05|1||
|Number of banking outlets is limited in number and location.|**_0.05_**|**_0.75_**||Restriction on either branches (outlets) or ATMs|**_0.05_**|**_0.75_**||
|Expansion of banking outlets is subject to non-prudential<br>regulatory approval.|**_0.05_**|**_0.25_**|NA|||||
|No restrictions on banks expanding operations.|**_0.05_**|**_0_**||No restriction on number of outlets or number of ATMs|**_0.05_**|**_0_**||
|**Composition of the board of directors**|**_0.02_**|||**Composition of the board of directors**|**_0.02_**|||
|The score is inversely proportional to the percentage of the<br>board that can comprise foreigners.|**_0.02_**|**_1-b_**||The score is inversely proportionately to the percentage of the board that can<br>comprise foreigners. If b = maximum percentage of the board that can<br>comprise foreigners.|_0.02_|_1-b_||
|**Temporary movement of people**|**_0.01_**|||**Temporary movement of people ( we used information on temporary**<br>**movement ofpeople for accounting services)**|**_0.01_**|||
|No temporary entry of executives, senior managers and/or<br>specialists.|**_0.01_**|**_1_**||No limitation on the duration of stay initially allowed for IPC|0.01|1||
|Temporary entry of executives, senior managers and/or<br>specialists up to 30 days.|**_0.01_**|**_0.75_**||Duration of stay initially allowed for IPC is up to 1 month|0.01|0.75||
|Temporary entry of executives, senior managers and/or<br>specialists up to 60 days.|**_0.01_**|**_0.5_**||Duration of stay initially allowed for IPC is up to 2 month|0.01|0.5||
|Temporary entry of executives, senior managers and/or<br>specialists up to 90 days.|**_0.01_**|**_0.25_**||Duration of stay initially allowed for IPC is up to 3 month|0.01|0.25||
|Temporary entry of executives, senior managers and/or<br>specialists over 90 days.|**_0.01_**|**_0_**||Duration of stay initially allowed for IPC is over 3 month|0.01|0||
|**Total**|1||||1|||

23
There are four parameters calibrated by simulation: the government’s discount factor _β_<sup>_F_</sup> , the parameters associated with the exogenous default cost _d_ 0 and _d_ 1, and the pricing kernel parameter _κH_ . I choose these four parameters to match four targets in the data: (i) an average public debt of 44 _._ 4% of GDP, (ii) an average level of spreads of 267 basis points, (iii) an increase in the spread during high risk premium periods of 296 basis points, which is the average increase in the sovereign spread observed in Mexico during the three high risk premium episodes identified in the data, and (iv) a historical probability of default equal to 2%, which is a rough estimate based on the 2 default episodes observed in Mexico in the last 100 years according to Catao and Mano (2017). The government’s discount factor mainly determine the average debt level, the values of the default cost mainly determine the behavior of spreads, and the pricing kernel parameter is mainly disciplined by the probability of default.

I use total public debt data from IMF datasets. While this data includes domestically held and long-term debt, in the model all domestic bonds mature in one period and are held by foreigners.<sup>30</sup> Since the model is not rich enough to consider many debt instruments, there is a trade-off when choosing the debt instrument that disciplines the model.<sup>31</sup> While there is no perfect way to solve this trade-off, I choose to target total public debt because it is the most general instrument that approximates the impatient behavior of the government. In the appendix, I present an alternative calibration in which the government’s discount factor is calibrated to match debt service.

# **5.2 Key Statistics: Model vs Data**

Now, I report long-run moments in the data and in the model simulations.<sup>32</sup> Table 4 shows that model simulations match the calibration targets. Table 5 shows that the model also does a good job in mimicking the behavior of reserves and debt. In particular, model simulations rationalize a reserves to GDP ratio of 6 _._ 5%, which corresponds to 75% of the average level of reserves observed in Mexico from 1994 to 2017 (8 _._ 7% of GDP). Moreover, the model matches a high and positive

> 30Total public debt data also includes debt denominated in domestic and foreign currency, while the model implicitly assumes that the real exchange is equal to one so there is no distinction between domestic and foreign currency. 31In a work in progress, I am enhancing the baseline model by adding long-term debt as in Bianchi, Hatchondo, and Martinez (2018).

> 32Moments in the model are computed by generating 1,000 simulations samples of 500 periods each and taking the last 50 observations of samples in which the last default was observed at least 25 periods before the beginning of the sample.

26
## 2.4 Closing conditions

Aggregate demand is the sum of private and public consumption and is dened as: We assume that the cost of opening new vacancies are socially shared.

Assets are in zero net supply, which implies the equilibrium condition: Net foreign assets are determined by: ̃ ̃ where the last term in brackets represents the trade balance: _TBt_ = _QtNx,tρx,tyx,t − N_<sup>_∗_</sup> _x,t_<sup>_ρ_ ̃</sup><sup>_∗_</sup> _x,t_<sup>_y_ ̃</sup> _x,t_<sup>_∗_.</sup>

# 3 Calibration

We calibrate the model using quarterly data from the U.S. and Brazilian economies. We believe that Brazil is an illustrative example of an emerging country which, starting form high level of informality in the late 1990s, has adopted a set of policy initiatives to facilitate the move to formality.<sup>12</sup> A rst program, called SIMPLES, was launched in 1996 and was followed by a second one, the SUPERSIMPLES program, in 2006. Since, in Brazil there is a strong correlation between size of company and prevalence of informality, these programs aimed at reducing the costs of formalization through a simplication and a reduction of tax rates and tax regulations for Brazilian micro rms with no more than ve paid employees.<sup>13</sup> Since the SUPERSIMPLES came into force in July 2007, some 9 million businesses have joined this system of taxation and the formal rate has increased by 11 percentage points (see Fajnzylber et al. (2011)).<sup>14</sup> In this section we discuss the calibration strategy. Broadly speaking, we choose some parameter values from the literature, while other parameters are set so to match macroeconomic series observed for the United States and Brazil. We assume that the two countries are asymmetric, hence some parameters describing labor and goods markets may dier across countries. Table 1 summarizes the asymmetric calibration.

> 12See ILO (2014) for a discussion and an evaluation of other programs launched in emerging countries to move to formalization.

> 13The SIMPLES program combined six dierent federal taxes and social contributions into a single monthly-based rate. The two reforms also reduced the tax burden considerably.

> 14While Fajnzylber et al. (2011) nd very large eects of the SIMPLES program on formality rates, Monteiro and Assunção (2012) nd positive and signicant eects on formalization rates only among rms in the retailer sector. For a reconciliation of these two studies, see Piza (2016).

14
ˆ **Figure 7:** Evolution of _EDθa_ ˆ and H, 1990-2019 (where _θ_ = 1 and _a_ = 70).

<!-- Start of picture text --> 60 50<br>50<br>40<br>40<br>30<br>% 30 %<br>20<br>20<br>10<br>10<br>0 0<br>Year<br>Income Deprivation Lifespan Deprivation (70)<br>Headcount ratio ED (70)<br>Lifespan Deprivation share in ED (right axis)<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Reading: in 1990, a newborn expected to be deprived of 58% of its life: 17 percentage points lost because of lifespan deprivation and 41 because of income deprivation. Lifespan deprivation accounted for 30% of expected deprivation in 1990.

wrong ranking whenever life expectancy and headcount diverge (which is the case we focus on). The main interest of robust comparisons is to measure the extent to which PALE allows to improve on a ranking based on the headcount only: in all these situations, irrespective of the value given to _θ_ , the ranking under PALE contradicts the poverty ranking.

# **Inter country comparisons**

To what extent does _PALEθ_ help in robustly ranking countries, as compared to a simple headcount? Figure 8 reports the proportion of all country-pairs comparisons whose ranking based on life expectancy and headcount ratio differs. There are 23% of them.<sup>25</sup> The share of these ambiguous cases for which _PALEθ_ provides a robust answer is equal to 37 percent, independently of the value given to _θ_ . In other words, 37% of these “ambiguous” cases are wrongly classified by the headcount ratio. Note also that the share of ambiguous comparisons that our index unambiguously solves strongly increases over time, owing to the falling incidence of absolute poverty in many countries.<sup>26</sup>

> 25These are the only situations in which PALE can offer a different ranking than the headcount. 26The falling incidence of absolute poverty implies that differences in H across countries in a given year become, on average, smaller over time. This explains why the share of ambiguous comparisons that our index unambiguously solves increases over time. This is easy to see when assuming that the differences in LE across countries in a given year remain constant over time. Indeed, a smaller difference in H can be “over-compensated” by a smaller difference in LE.

19
8 the rents they were expecting to get when they signed the contract, or that they saw a favorable environment to secure more favorable terms through renegotiation demands.

A review of the evidence on the efficiency gains achieved as a result of the restructuring is thus a necessary step to be able to assess the contribution of the adoption of price cap regimes to the degree of renegotiation in the region. 15

**Table 4: Who initiated the renegotiation conditioned on regulatory regime? (% of total requests)**

||BothGovernmentandOperator|Government|Operator|
|---|---|---|---|
|Allsectors||||
|PriceCaps|11%|6%|83%|
|RateofReturn|39%|34%|26%|
|HybridRegime|30%|26%|44%|

Source: Guasch (2003)

# **4. The efficiency payoffs from the adoption of price caps and other reforms**

A plethora of studies evaluating different aspects of the impact of the reform program in infrastructure in Latin America have been recently completed (e.g. Birsdall and Nellis (2002) and Ugaz and Waddams (2003)). Practically all of them show significant performance gains, improved quality of service and increases in coverage (but not as large as expected). For example, in their study of the regulation of the electricity sector in Latin America and of the telecommunications sector, Fischer and Serra (2000, 2002) argue that the privatization-cumregulation of these Latin American sectors has been, in general terms, successful: privatised firms have sharply increased their efficiency and coverage.

Most of these studies, however, shy away from the economic efficiency concept associated with the adoption of a price cap regime. The evidence on this measure of efficiency is much more scarce and does not cover all sectors nor all countries equally well. We thus need to rely on highly diverse types of studies to document the efficiency gains. Among the studies on

> **15** For Argentina, see Chisari, o., A. Estache and C. Romero (1999), Delfino, J.A. and A. Casarin (2003) or Ennis and Pinto (2003); for Bolivia, see Barja and Urquiolo (2003); for Brazil, Mueller (2001); for Chile, Bitran, E., A. Estache, J. L. Guasch and P. Serra (1999), Engel, E, R. Fisher and A. Galetovic (2000), Paredes (2003) or Rudnick and Raineri (1997): and for Peru , Torero, M and A. Pasco-Font (2003) for evidence on the effects on prices, quality and investments but this is less directly associated to the adoption of the price cap regime.
<!-- Start of picture text --> Men Women<br>8 | 8<br>6 | 6<br>224 | 4<br>0SF@Sot& ESS& $ & ESF2 0 Sege SFSF FS$ SF& SE@& FSSL<br>oe ee oS s & Ft oe és<br>¢ by es g ¥ &<br>ME Last 12 months MM Last7 days<br><!-- End of picture text -->

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