rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Calibration of Remaining Parameters

|Parameter|Calibration||
|---|---|---|
|_β_|0.9747||
|_u_(_c_)|ln(_c_)||
|_δn_|0.0594||
|_α_|0.3476||
|_λl_|0.028||
|_pc,t_|_∈_[0_._28_,_1_._62]|(BEA data)|
|_δc,t_|_∈_[0_._142_,_0_._208]|(BEA data)|

_Notes:_ The ICT price and depreciation rates are measured directly based on the consider is time variation in the depreciation rate of ICT capital, which, as we document, is quite substantial (see Figure 8).<sup>24</sup> To assess the implications of the declining ICT price, we can therefore compare our baseline simulation with a counterfactual scenario in which the ICT price remains constant at its 1968 level.<sup>25</sup> Our simulations are based on the calibration of the core model parameters (Table 3) and the values reported in Table 4. The value of _β_ , the discount factor, is calibrated based on our estimates of the returns to capital.<sup>26</sup> Given our focus on long run trends, we assume log utility, or a unitary inter-temporal elasticity of substitution, which is consistent with the wide range of empirical estimates (see, for example, the discussion and references listed by Guvenen, 2006). Moreover, log utility is a useful benchmark for expositional purposes, as income and substitution effects cancel out.

For the depreciation rate of non-ICT capital we take the average value of our BEA-based estimates. Likewise, we calibrate the non-ICT capital intensity, _α_ , using the average of our estimated non-ICT capital income share, ignoring any short term fluctuations around this average. As initial conditions for capital, we use the 1968 capital stocks in the US, to make our simulated transitions

> 24We do this to improve the fit in our calibration but this does not materially affect our main results concerning the impact of the declining ICT price.

> 25While we start our simulation in 1968 purely due to data availability reasons, it is a curious coincidence that 1968 is also the founding year of Intel Corporation, which produced the first commerically available single-chip central processing unit (CPU) in 1971.

> 26The steady state Euler equation implies that _β_ (1 + _r_ ) = 1, where _r_ is the return to capital. We assume that the returns to capital before 1980 are roughly at their steady state level and calibrate _β_ based on this relation.

29
the incidence of IrT is high among manufactures, there is a sense in which this measure is a broad complement to those above. No obvious unconditional relationship appears in figure le.

Each of these variables is of interest in itself. However each also may represent a channel through which the other variables of interest affect growth. For instance, resource abundance may also imply a high level of export concentration or low level of intra-industry trade. We attempt to disentangle these effects as well.

# 3. Estimation Techniques

We begin with a basic specification that can nest much of the existing work on the empirics of economic growth: Where **_yit_** dot is the log difference of per capita GDP of country i in period t, yj,i-1 log income per capita at the beginning of the period, Xit the matrix of conditioning variables and T the particular trade variable of interest. j.4 is an individual country fixed effect, p, is a sample wide time effect and **,,t** a country and time specific effect.

Most of the previous work discussed above, and in fact much of the growth literature until recently has been based on estimations of an equation similar to (1) using cross sectional regressions data which lack any time dimension, although the drawbacks are well known.8 As Levine and Renelt (1992) first pointed out in the growth context, cross-country growth regressions are sensitive to the variables included in the specification. Further, substantial bias may be induced by the correlation of unobserved country-specific factors and the variables of interest; E(p.j, tit), may be large. Casselli, Esquivel and Lefort (1996), for instance, pointed out that the difference with respect to

> 8 More recently, distinguished economists have raised serious concerns about the general practice of testing a plethora of hypotheses about economic growth by relying exclusively on cross-country growth regressions. See for example, Solow (2001).

6
NQ’s efforts to improve the rigor of research on such an important policy question, the data they painstakingly assembled to allow them to explore patterns in food aid allocation and conflict that remain poorly understood, and the care they take in subjecting their findings to a range of robustness and falsification checks all speak to a careful and deliberate effort to improve understanding of sensitive policies.<sup>1</sup> Nonetheless, we show that the estimation strategy they employ is vulnerable to this hitherto underappreciated source of confounding in panel data IV estimation that calls into question the exclusion restriction on which their causal identification depends. Using placebo tests, randomization inference tests, and Monte Carlo simulations, we find that their core claim – that food aid causes civil – conflict in recipient countries does not stand up to close scrutiny. Randomization inference tests in which we randomly assign the variation that underlies identification show that the spurious and endogenous trends in the data almost always lead in these data to a positive effect of aid on conflict, no matter the withinyear allocation of aid. The Monte Carlo simulations show that this upward bias can even be consistent with a data generating process in which food aid shipments _prevent_ conflict, the opposite of the effect found in the NQ strategy that is reported as causal.

This specific, high profile example more generally highlights a potential source of inferential error in other attempts to use a similar panel data IV strategy based on a continuous DID estimator. The essence of the problem in a time series framework is that if the longer-run trend dominates the year-on-year variation in the plausibly exogenous time series and the contemporaneous trends in the outcome variable are not parallel across the exposure domain – especially if the non-parallel trends are nonlinear and thus not controlled for adequately with fixed effects or

> 1 Chu et al. (2016) undertake another set of robustness checks using a semiparametric endogenous estimation procedure and cannot reject NQ’s parametric specifications and declare their findings robust.

3
Where _H_ (.) is an increasing function, set to be iso-elastic with elasticity .<sup>15</sup> The level of employment determines the number of firms in the sector since (from labor demand), 2 ). ww εε ii 2ii 2ii ii ii **_Implementation of the two-sector model:_** Parameters are given in table 5 below. We assume that there are rr = LL ww /(γγ pp EE no productivity spillovers in sector 2, and that commercial building in this sector uses the residential construction technology.

|**Table 5: B**<br>Preferences|**enchmark pa**<br>α = 0.75;|**rameters:**<br>ε = 9;|<br>Preferences|**Two-sector param**<br>α = 0.25;<br>μ = 0.5;|**eters**<br>ε = 9;<br>εw= 40.0;|
|---|---|---|---|---|---|
|Firms|β = 0.6;|γ = 0.2;|Firms|β1=0.6;<br>β2=0.6;|γ1= 0.2;<br>γ2= 0.2;|
|Commuting:|utility left af<br>c = 0.7:|ter 1 radius|Shipping sector 2<br>goods/ services:|Iceberg left after<br>T = 0.7:|1 radius|

Adding this sector to the benchmark model – with agglomeration, χ = 0.10 – gives the city configuration illustrated in figure 8a. The main feature is the addition of ‘employment-N’, i.e. employment in this nontradable consumer services sector. As shown in panel (c) the tradable (T-) sector – as modeled in the benchmark case – remains closest to the center, while the N-sector locates next to consumers, except where central land rents crowd it out. The T-sector is now small, but somewhat more crowded into the center and it is this that gives marginally higher central productivity and land rents compared to figure. 1.2.

While the N-sector is assumed not to have agglomeration economies, it does have costs of shipping goods to consumers (or equivalently, of consumers visiting the firms), and this gives a centripetal force to the location of this industry. This becomes the dominant force if T-sector agglomeration economies are small enough, in which case the locations of the N and T sectors are reversed as illustrated in Figure 8.2. The N-sector and a large mass of consumers now locate near the center, while the T-sector attains greatest employment density near the edge of the city.

The different patterns in figures 8.1 and 8.2 accord with perceptions of some urban areas with centers occupied by tradable sectors, while others have consumer services such as retail in the center, while tradable sectors – factories rather than tradable services -- are at the edge. Outcomes depend on building requirements, as well as the extent of agglomeration economies. Such outcomes may be associated with clusters of tradable activity at the city edge, this destroying the circular city structure, and possibly creating a multiplicity of equilibria, the subject to which we now turn.

> 15 If = ∞ then there is, in general, specialization of cells.  Any non-specialized cell would pay the same wage in both sectors. ww εε

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**Figure 2.14 Morocco: Distribution of the Labor Force by work status and gender, Average 1999‐2013**

**(In percent)**

<!-- Start of picture text --> National<br>60%<br>Female Male 53.4%<br>50%<br>43.7%<br>40%<br>31.9% 31.3%<br>30%<br>19.3%<br>20%<br>13.3%<br>10%<br>3.0%<br>0.6%<br>0%<br>Wage and salaried Own‐account Employers Contributing family<br>workers workers workers<br><!-- End of picture text --> _Source_ : Ragbi et al. (2017).

**Figure 2.15 Morocco: Distribution of the Labor Force by sector and gender, Average 1999‐2013 (In percent)**

<!-- Start of picture text --> 70%<br>60.0%<br>60%<br>Female Male<br>50%<br>40.8%<br>40% 36.6%<br>30%<br>24.4%<br>20% 15.3%<br>11.6% 10.8%<br>10%<br>0.2% 0.1%0.1%<br>0%<br>Agriculture Industry Construction Services Misdirected<br>activities<br><!-- End of picture text --> _Source_ : Ragbi et al. (2017).

19
<!-- Start of picture text --> $1000<br>3 600 424<br>Z 400 192<br>E Pere2s ei9 6 sss<br>gE Segegeggessesggszege“Fee esseses 282s<br>Sgag7 g 8° 98S eee 2585<br><!-- End of picture text --> <!-- Start of picture text --> S000 qi Se<br>ae ht<br>= bavt cA<br>Es Age bh<br>ia Kted: }<br>As Vo !<br>pom<br>Ba es 1<br><!-- End of picture text --> Xy = {L,R,1,F}
demand: all firms charge a constant mark-up over variable cost in every market. As a consequence, the fob price charged by firms is not determined by the characteristics of the destination market. Nevertheless, more productive firms have lower marginal costs, charge lower prices, sell higher quantities and earn higher revenues. As a result, fob prices will differ across firms exporting the same product.

Melitz and Ottaviano (2008) retain the price competition framework of Melitz (2003), but modify the assumption of CES demand by including a linear demand and variable mark-ups. Therefore the fob price depends on the degree of competition present in a destination market: a firm might set different prices across destinations. However, the prediction on the sign of the correlation between fob export prices and destination characteristics remains ambiguous. In Melitz and Ottaviano (2008) market size (GDP) and trade affect the degree of competition, which in turn affects the selection of producers and exporters in that market. Larger, more integrated markets exhibit higher productivity and lower mark-ups.

Berman, Martin and Mayer (2012) build on Melitz and Ottaviano (2008) and develop a model to analyze the heterogeneous reaction of exporters to real exchange rate changes. This paper is close to the class of models where incomplete pass through is generated by introducing additive trade costs (like distribution costs). High additive trade costs lower the relative importance of the producer price as a share of consumer price, so that firms can charge higher mark-ups in destinations with higher additive trade costs (more distant markets, markets with higher distribution costs, etc.). This is also shown in Martin (2012), who explains that a necessary condition for firms to set higher mark-ups or to quality upgrade their product toward more distant countries is the presence of per unit rather than iceberg transport costs.

The quality competition (or quality sorting) models depart from the price competition models by incorporating quality differentiation across firms: more productive firms – apart from being more efficient - will make use of better-quality, more expensive inputs, and might face higher marginal costs and prices. Baldwin and Harrigan (2011) propose a model in which high quality firms are the most competitive and quality increases with firms' heterogeneous cost. They use product-level U.S. trade data to show that export unit values are positively related to distance.

The main difference between price (Melitz 2003) and quality competition (Baldwin and Harrigan 2011) models is that in price competition models price is the only element upon which firms compete and market entry thresholds are defined in terms of a maximum price<sup>6</sup> . In quality competition models, firms’ competitiveness is based on the quality-adjusted price and market-entry thresholds are based on qualityadjusted price. As per Baldwin and Ito (2011), “ _the lower quality-adjusted prices (unobserved) are_

> 6 Ito (2011).

5
We then build on this logic turning it into a strategy to infer entry barriers. Essentially, the strategy involves answering the following question: what is the magnitude of the entry barrier that is required so that, given the estimated elasticity between idiosyncratic distortions (TFPR) and idiosyncratic productivity (TFPQ), the equilibrium average size attains the level in the data? Consider the example of Chile, one of the 21 countries in our collection of firm-level databases<sup>7</sup> . In Chile’s manufacturing sector, firms employed an average of 94 workers in 2013, slightly below the average size of 118 in the US manufacturing sector, our benchmark of efficiency.<sup>8</sup> From the firm-level data, we estimate a productivity-elasticity of distortions of 0.17.<sup>9</sup> According to our quantitative model, the idiosyncratic distortions should have induced a strong decline in average firm size to 36 workers. To reconcile the equilibrium with the data, our model identifies a countervailing entry tax rate of 230 percent, about two times the entry cost of a firm in the undistorted economy. Extending this logic to all the countries in our data, we come up with a model-based distribution of entry barriers.

Equipped with model-based estimates of entry barriers, our final contribution concerns the quantitative assessment of the aggregate effects of entry barriers and idiosyncratic distortions. As stated earlier, the TFP gains from lifting all distortions are substantial, ranging between 20 and 50 percent in the most distorted economies, with both distortions weighing in prominently in accounting for these gains. We complement these findings with two additional exercises. The first one quantifies the role of the interaction between 7Our data combine census-based information of manufacturing sectors in Latin America, South Asia, and sub-Saharan Africa with a selected sample of countries from the Amadeus database, the selection being driven by the extent to which the size distribution portrayed in these data is comparable with the one reported in Eurostat’s Structural Business Statistics database. More details are provided in Section 4.

> 8Since the Chilean manufacturing data cover the universe of manufacturing plants with 10 or more workers, we apply the same truncation to the US data to arrive at the average size of 118 workers in the US. We explain data coverage details for all countries in the data section.

> 9The actual estimate of the elasticity is 0.32. However, we take into account that there is correlated misallocation in the US, our benchmark of efficiency. By subtracting the US’ estimate of the distortion elasticity of 0.15 from the estimate for Chile, we reach the value of 0.17. We proceed in this fashion for all of the countries.

5
E-2 We are developing IPPS in two phases. We have estimated the first prototype from a massive U.S. data base, developed by PRDEI in collaboration with the Center for Economic Studies of the U.S. Census Bureau and the U.S. Environmental Protection Agency. This data base was created by merging Manufacturing Census file data with US EPA data on air, water and solid waste emissions. It contains complete environmental, economic and geographic information for approximately 200,000 factories in all regions of the United States. The first prototype of IPPS spans approximately 1,500 product categories, all operating technologies, and hundreds of pollutants. It can separately project air, water, and solid waste emissions, and incorporates a range of risk factors for human toxic and ecotoxic effects. It can also project emissions of some greenhouse gases and several compounds which are hazardous to the ozone layer. Since it has been developed from a database of unprecedented size and depth, it is undoubtedly the most comprehensive system of its kind in the world.

We recognize, however, that this is only the beginning. Although much more detailed empirical research is needed on the sources of variation in industrial pollution, it is already clear that great differences are attributable to cross-country and cross-regional variations in relative prices, economic and sectoral policies, and strictness of regulation. The second phase of IPPS development will, therefore, have to be even more ambitious than the first. We are now undertaking an econometric research project which will use plant-level data from many countries to quantify the major sources of international and
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- Energy audits of buildings for Sustainable energy Action Plans of Covenant of Mayors cities, USAID’s “Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) /Clean Energy Program”, internal reports of Sustainable Development Centre Remissia, 2014.

- <mark>European Bank for Reconstruction and Development (EBRD) (2014). Regulatory Support for Renewable Heating and Cooling Policy, EBRD, Armenia.</mark>

- <mark>Global Environmental Facility (GEF) (2014a). The potential of improving EE in panel multiapartment buildings in the Republic of Armenia, GEF, Armenia.</mark>

<mark>Global Environmental Facility (GEF) (2014b). Green Urban Lighting, GEF, Armenia.</mark> <mark>Government of Armenia (GOA) (2015). On approving the Intended Nationally Determined Contributions of the Republic of Armenia under the UN Framework Convention on Climate Change.</mark> <mark>-</mark> <u><mark>http://www4.unfccc.int/submissions/INDC/Published%20Documents/Armenia/1/INDC Armenia.pdf</mark></u> <mark>Government of Georgia (GOG) (2015). Georgia’s Intended Nationally Determined Contribution Submission to the UNFCCC.</mark> <u><mark>http://www4.unfccc.int/submissions/INDC/Published%20Documents/Georgia/1/INDC_of_G eorgia.pdf</mark></u> Jackson, T. (1991). Least-cost greenhouse planning supply curves for global warming abatement. Energy policy, Vol. 19, No. 1, pp. 35-46.

Jackson, T. (1995). Joint Implementation and Cost Effectiveness under the Framework Convention on Climate Change, Energy Policy, Vo. 23, pp 117- .

Johnson, T.M., C. Alatorre, Z. Romo and F. Liu (2009). Low-Carbon Development for Mexico, World Bank, Washington, DC.

Knowledge, Attitude and behavior Baseline Survey, USAID’s “Enhancing Capacity for Low Emission Development Strategies (EC-LEDS) /Clean Energy Program”, Winrock International Georgia, 2014.

23
respondents.<sup>12</sup> The results therefore suggest that mental health screening questionnaires can be meaningfully added to multi-purpose household surveys such as the Living Standards Measurement Surveys, and in addition to their intrinsic interest, provide additional predictive power for economic behavior beyond the physical health measures and socioeconomic variables traditionally collected.

A concern with self-reporting of physical health status is that self-reports result in measurement error which is correlated with socioeconomic characteristics, including income (see Strauss and Thomas (1998) and Lokshin and Ravallion (2007)). Strauss and Thomas note that reports of specific functionings, such as activities of daily living, are believed to be more accurate and less subject to these sources of measurement error than questions about general health status. In this regard, the questions used to construct the mental health indices, such as those about the frequency of having difficulty falling asleep, or being distracted from everyday activities, may be less prone to reporting bias than general health status.

Nevertheless, one may still be concerned about potential biases in the reporting of mental health symptoms. Sociologists and Psychologists have identified three different effects that may influence accurate reporting of internal states: (a) the overall tendency to say yes or no, (2) the need for social approval and (3) the perception of the desirability of a trait--boys don't cry, so men often do not report whether they tend to "suddenly cry without reason". Though the literature is not extensive, an early study (Gove and Geerkin 1977) suggests that these biases are uncorrelated with demographic variables such as sex, race, education, income, age, marital status and occupation--the categories examined in this paper. These results were corroborated by Vernon et al. (1982) and Hunt et al. (2003). In all 3 studies, reporting bias tends to result in underestimates of _prevalence_ of psychiatric disorders such as depression, but does not alter observed correlations between mental health and demographic characteristics. One caveat is that these studies are all in high-income countries, and there so there is a need for further research on this in developing country contexts.

<u>5.2. Mental health and welfare measurement: Going beyond measures of “happiness”</u> An additional motivation for including mental health measurement in household surveys is that it can allow consideration of a broader notion of welfare than is offered by the traditional focus on income and expenditure. An antecedent for this is found in the burgeoning literature on happiness and its correlates (see Kahneman and Krueger (2006) for a recent review). World Value Surveys, for instance, collect information on global life satisfaction or happiness with the single question “All things considered, how

> 12 Indeed, the depression and anxiety components of the SCL-90R are found to contain most of the relevant information for the 9 dimensions covered under the questionnaire (Das and Das 2006).

- 14 -