rafmacalaba/data-use-mentions-extended huggingface dataset:
covers urban adults aged 15-64.<sup>7</sup> To inform areas that surveys should cover on collecting data on different types of skills, and in turn how to evaluate outcomes from different types of employment-related programs, guidelines were developed during the 20<sup>th</sup> ICLS on the measurement of qualifications and skills mismatch — where skills span (a) jobspecific/technical skills, (b) basic skills such as literacy, numeracy and ICT skills, and (c) skills that can be transferred from one job to another, including cognitive as well as non-cognitive skills such as personal initiative and managerial acumen) (ILO, 2018e). In low-income, wage labor contexts, relevant job-specific and basic skills would typically be focused on physical/labor-intensive capacities, but would range more widely for those running their own business/farm operations where numeracy-related skills (including financial acumen, for example) are relevant. Better data on non-cognitive factors associated with employment are also highly relevant in low-income, rural contexts, where gender norms on employment opportunities tend to be highly entrenched — approaches used in psychology have also been introduced in the WEAI survey module to understand the extent to which respondents’ economic actions are driven by self-motivation as opposed to others’ preferences (Malapit et al, 2017). Similarly, the importance of personal initiative (as opposed to acting out of necessity) for women entrepreneurs has been raised by Campos et al (2017) and Buvinic and Furst-Nichols (2014).

> 6 Few studies are focused in rural areas, however (see Calderon et al, 2013 for one exception from rural Mexico).

> 7 See http://microdata.worldbank.org/index.php/catalog/step/about for more details.

18
# **4. Exploratory Analysis of the Mechanisms Underlying the Trade Impacts**

Our results to this point indicate that preferential trade agreements containing elevated standards of IPRs protection generate reasonably consistent changes in the trade of IP-intensive goods on the part of member countries with third markets, netting out trade with _demandeur_ partners. While the effects overall are modest, there are identifiable impacts on sectoral trade, especially where the major partner is the United States, the EU, or EFTA, and in IPAs in which IPRs standards are legally enforceable as measured by the World Bank database. These agreements tend particularly to expand exports of biopharmaceuticals and medical equipment, both in the aggregate and on a bilateral basis.

Such findings raise the question of what mechanisms may drive the trade expansion. One important channel is the responses of global firms to what they may perceive as improved investment climates associated with IPAs. It may be, for example, that increased third-country exports of high-IP products reflect increases in domestic production capacity, which may be the result of increased inward technology flows. In the absence of data regarding firm-level responses of both affiliates and domestic firms, this is a difficult question to answer. Here we explore this issue on a preliminary basis by bringing in additional data from the U.S. Bureau of Economic Analysis that may capture the role of FDI, via affiliate activities in IPA-member countries. We also employ data on U.S. related-party trade to explore the effects of IPAs on trade in intermediate inputs.

Specifically, we study first the impact of IPAs on the sales of local affiliates of U.S. majority-owned affiliates in broad manufacturing sectors that most closely track our high-IP products. The closest aggregate sectors in the BEA data are chemicals, computers and electronic products, and electrical equipment, appliances, and components. Our approach is to use affiliate sales as the dependent variable in the basic regressions above, with these sectors constituting a set of high-IP sensitive goods.<sup>21</sup> Given our prior results,
summary of the projected changes for

> 9 As another potential mechanism, we explore subnational migration flow between the period 2005 and 2010. Using a simple OLS regression, we find some suggestive evidence that hotter temperature could lead to more migration (Appendix A, Figure A8). However, since migration could help households obtain better economic opportunities and escape poverty, our estimation results could be considered as the net impacts of hotter temperature (after factoring in the beneficial effects of migration on poverty reduction).

> 10 The CMIP6 models capture future trends in climate change under alternative scenarios of human activities. It has been bias-corrected and downscaled to ERA-5 to address deficiencies identified in earlier models like Global Meteorological Forcing Dataset (GMFD) and NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). This data set aligns with the emissions scenarios of the 2021-2023 IPCC report, rendering it more current and relevant than previous models which were based on the scenarios of the 2013 IPCC report (IPCC, 2021).

18
6|3.3|1.7|
|Basic|10.4|9.5|9.1|9.2|10.3|9.8|
|Less than<br>Basic|1.4|1.2|1.0|1.0|1.1|0.8|

Notes:<sup>a</sup> Including general and technical secondary, as well as postsecondary vocational. _Source_ : LFS data and Calculation

# **_<u>Box 3  Data and Methodology</u>_**

The conventional approach to the analysis of wage structure is to estimate an earnings function. This function describes the relationship between the (logarithm of) wages and observable workers’ and firms’ characteristics. To ensure reliability of the estimated relationship, one needs a representative sample of several thousand employees.

The Latvian LFS provides a large, representative sample of the Latvian population. Until recently, however, wages in the LFS were reported only in broad intervals. This meant, for example, that it was not possible to distinguish between workers who earn 250 and those who earn 400 LVL per month. The highest earnings category included in the questionnaire was “more than 200”. Moreover, respondents were asked to report gross rather than net wages, which may also create some confusion for those receiving “envelope wages”, who may not know their gross salaries. The Household Budget Survey (HBS), the leading alternative source of labor market information, collects data on wages but has much smaller sample. It does not contain as detailed information about respondents' jobs as the LFS. As well, NORBALT (1999) dataset, which recently became available, contains detailed information on individuals and their wages, but includes less than 2000 observations for which wages are reported or can be derived.

Overall, the LFS appears to be the most appropriate data source to use in analyzing wage structure in Latvia. Since 2002, LFS
in column (5), and one additional test — for age — rejects when I use the randomization inference p-values in column (6). This is in line with what one would expect to happen by chance. However, the normalized differences in column (7) are small to moderate. None of them is greater than 0.16 in absolute value, much smaller than the cutoff used by Imbens (2015). Second, I show that all 15 variables do not jointly predict exposure to a female supervisor by estimating a linear regression of _exposed_ on all 15 variables. The p-value of the overall F test of the regression is 0.41.<sup>42</sup> Lastly, I investigate whether the lines of the operators who were exposed to the female su-

> 41As in Section 3.5.1, to take attrition into account, I only include women in these checks whose data is also used in the analysis, i.e. who were interviewed in the household survey and had another adult decision-maker in the household. In the ideal scenario, I would be able to use data of line operator movements to exclude sorting towards lines exposed to new female supervisors based on characteristics that determine the operators’ household outcomes of interest. Unfortunately, the factories in this sample do not keep a reliable account of operator movements.

> 42See Table C.7 in the Online Appendix. As in Section 3.5.1, to include the variables only defined for married women, I follow two steps. First, I set these variables to zero if they are missing and, second, I include dummy variables that indicate whether the variables are missing in the regression. As Table C.7 in the appendix shows, I also do not find that covariates predict selection if I include all covariates but limit the sample to married respondents, or if I only include covariates which are defined for all respondents in the regression. The p-values for the F-statistic of the regression in these
are Aweil, Bentiu, Bor, Kajok, Juba, Malakal, Rumbek, and Wau. 6 For Sorghum, for example, data for Malakal and Wau are available since 2006 and 2007 respectively. For Sesame, most data are available since 2010. For diesel and gasoline, data are available only since 2012.

3
|Length (yrs)|Average|Max|Length (yrs)|Average|Max|Length (yrs)|Average|Max|Length (yrs)|Average|
||Breach|of breach|breach|Breach|of breach|breach|Breach|of breach|breach|Breach|of breach|breach|
|Original|||||||||||||
|Fiscal Financing|4|3|2|25|5|15|||||||
|CAB financing|9|7|6|50|6|28||||6|4|3|

Source: Authors’ calculations based on country DSAs.

# **<u>Annex Table 4B: Effects of oil price shock on projected fiscal DSA indicators, 2015-2034</u>**

|**FISCAL DSA**<br>|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**PV of debt-to GDP ratio**||**CHAD**|||**NIGERIA**|||**CAMEROON**||**R**|**EP. OF CON**|**GO**|
|**Difference with original baseline (pp)**|Max|Length (yrs)|Average|Max|Length (yrs)|Average|Max|Length (yrs)|Average|Max|Length (yrs|) Average|
||Deviation|of deviation|deviation|Deviation|of deviation|deviation|Deviation|of deviation|deviation|Deviatio|n of deviation|deviation|
|Fiscal Financing|10|17
where _ε_ is a vector of iid standard normal random variables and we take _σ_ = 2 _._ 5. In Model 1 we use _n_ = 50 _,_ 100 and for Model 2 we use _n_ = 50 _,_ 100 _,_ 1000 _,_ 10 _,_ 000 in order to illustrate consistency.

The remaining model structure used in the simulations uses regressors with pairwise correlations of 0.5, generated as _r_ ( _i_ ) = _r_ (<sup>_⋆_</sup> _i_ )<sup>+</sup><sup>_e_where each</sup><sup>_r_</sup> (<sup>_⋆_</sup> _i_ )<sup>and</sup><sup>_e_are vectors of</sup><sup>_n_independent</sup> standard normal elements. After demeaning to obtain _Z_ we generate _n_ observations as Model 3: _y_ = _ιn_ +<sup>�7</sup> _h_ =1<sup>_z_(</sup><sup>_h_) +</sup><sup>_σε,_</sup> where now we use _σ_ = 2. Values of _k_ used are 20, 40 and 80, while we adopt _n_ = 50 and 100.<sup>11</sup> We analyse 100 simulated data sets for each model and value of _k_ and _n_ mentioned above. The prior on model space is constructed with a random _θ_ as in (5) with a prior mean model size _m_ = _k/_ 2. The different priors for _g_ discussed above have been used, as well as two fixed values for _g_ ( _g_ = _n_ and _g_ = _k_<sup>2</sup> ). For the hyper- _g/n_ prior we take _a_ = 3.

Rather than provide exhaustive results from these simulations, we merely highlight the most important findings here. The different
foreign bank ownership on firm access to finance and innovation (Mao and Wang, 2022; Lee et al., 2015; Bakhouche, 2022; Agénor and Canuto, 2017; Amore et al., 2013; Brown et al., 2009; Chava et al., 2013; Hsu et al., 2014; Kerr and Nanda, 2015) using a merged cross-country data set of firm-level data from the World Bank Enterprise Survey and bank-level data from FitchConnect covering the period from 2016 to 2022 and about 22,000 firms in 49 countries. In our regressions, we control for firm-level characteristics, bank-level financial soundness, and legal origins of bank ownership. We then zoom specifically on foreign state ownership and its effect on innovation outcomes. We try to address the concerns about possible endogeneity (reverse causality) in the link from access to credit to firm innovation by isolating a positive credit shock based on a scoring model estimated on data for countries with (more) efficient national credit markets. We also conduct several robustness checks, including specifications with alternative dependent variables such as firm research and development (R&D) spending. In addition, we test whether baseline estimation results for firms that introduced a new or improved product or service in the past three years can also hold for a subset of firms that introduced a new/improved product or service that is also new to their main market. We thus contrast the potential difference between less risky innovation ("innovation diffusion or new to the firm") and more risky innovation ("radical innovation or new to the market") to confirm the limit of bank credit, or debt in general, versus equity in financing radical innovation (Cornelli and Yosha, 2003; Hall and Lerner, 2010; Lerner and Nanda, 2020).

We find that firms with credit from state banks tend to innovate more than firms with credit from other types of banks. We do not find a positive effect on firm innovation outcomes when the firm borrows from a foreign-owned bank—
mark> **<mark>demand surge</mark>**<sup>**15**</sup> <mark>. For the purposes of the exercise presented herein, the equivalent would be impact</mark> amplification.

<mark>Claims data from large catastrophes in developed insurance markets shows that the cost of a given level of damage increases when that damage is situated within a catastrophe of such severity as to have a significant impact at the national or regional scale. In the context of building damage, modelling firm RMS (Souch, C,</mark> 2010) describes the drivers of this loss amplification from the perspective of insurers as being:

- <mark>Economic demand surge leading to inflated costs. This arises from an imbalance in supply and demand for key materials and labour required for recovery and reconstruction due to the extent of the disaster footprint.</mark>

- <mark>Deterioration vulnerability leading to damage escalation. This arises from delays in addressing the impacts from the disaster.</mark>

- <mark>Claims inflation leading to increased payouts from insurers. This arises from a relaxation in insurers’ claims assessment procedures due to the scale of the task.</mark>

- <mark>Coverage expansion leading to increased payouts from insurers. This can arise from political pressure for insurers to honour claims that would not have qualified for payouts under the terms of the insurance</mark> contracts in place at the time of the event.

<mark>These impacts are tied to the overall scale of the event, rather than the local intensity used to calculate damage in the vulnerability methodology laid out in this paper, and as such, require data from a variety of catastrophes of different magnitudes in order to derive factors for amplifying losses/impacts. Although we are not looking through the lens of insurance, a number of aspects of post‐event loss amplification as described</mark> above are relevant to our analysis
model (1), estimated using the 2001 and 2007 HESs, to impute household expenditure into the LFSs for all available years (2001 to 2010), and then use the imputed expenditure data to estimate poverty for the entire 2001-2010 period.

We will be relying on a number of assumptions:

# **Assumption 1** : _The model is time-invariant, i.e._ βt = β _._

This assumption cannot be avoided, since we are looking to apply the model for imputing data into years other than the year for which the model can be estimated. This has the potential to introduce a bias if the two different time periods are in fact associated with different models. This bias can only be ruled out if _Assumption 1_ holds true. Note that this assumption can be tested both directly and indirectly. For a direct test one could use any test statistic that evaluates the difference between the estimate for β2001 and the estimate for β2007 . An indirect test can be obtained by estimating the model for one of the two HES years and then apply this model to obtain an imputed poverty rate for the other year. Since both years also allow us to compute the actual poverty rates based on observed data, we are able to verify how well the observed data compares to the imputed data. See section six for the test results.

**Remark 1** : The assumption of a time-invariant model also means we have to be careful with the choice of units in which both the dependent and the independent variables will be measured. One may want to avoid including independent variables that are expressed in time _t_ monetary value; we will be working with quantities and dummy variables for our model. This prevents potential model inconsistencies. Let us refer to the model parameters as betas. If one variable measures the monetary value of owned bicycles, while another variable simply checks whether the household has a car or not by equaling one or zero, then this could become problematic. The beta associated with the car ownership
where _Yt,i_ is a particular flow type of country _i_ at time _t_ , _Dt,t′_ is the set _Y t_ and _t_<sup>_′_</sup> and _N ′_ is the of countries with data for flow for time periods _t,t_ number of such countries. As done in the main text, the flow data are standardized by subtracting the mean and dividing by the standard deviation of the respective time series (before computing the covariance matrix (C.1)).

Comparing the first two rows of Table C.1 we see that using the simple principal component estimator yields a very small increase of the average adjusted _R_<sup>2</sup> ( _R_<sup>2</sup> ), from 0.054 (corresponding to the 0.05 average shown in their appendix figure A7), to 0.075 when analyzing disaggregated flow data at quarterly frequency. Considering instead total inflows and outflows (obtained by summing all flow types, in the fourth line of the table) leads to another increase in _R_<sup>2</sup> . In, turn, the same happens when we consider the disaggregated data at yearly, rather than quarterly, frequency (lines 6-7 of Table C.1). With any of these two changes we obtain _R_<sup>2</sup> around 0.125. We note that when considering flows at the yearly level, the factor estimation method seems to make less of a difference. Normalizin <u>g</u> by trend instead of nominal GDP also leads to a modest increase in _R_<sup>2</sup> . If flow aggregation and yearly frequency are combined, the _R_<sup>2</sup> rises above 0.2, almost four times the initial 0.054 value.

Unlike ours, the framework of Cerutti et al. (2017c) includes only two factors and no factors.
duction of bound tariffs by the WTO meant that the Ghanaian government could not raise applied tariffs above the established ceiling. Hence, even though the government did adjust tariffs slightly upwards – as it can be seen in fig. 1 – tariff rates generally stayed below the 1991 levels on average for the remainder of the period under consideration.

Most importantly, the introduction of bound tariffs meant that whichever adjustments the government made, tariff rates could not exceed the upper limit. WTO accession, therefore, reduced uncertainty about Ghana’s trade policy and strengthened the commitment of Ghana toward trade liberalization. Handley (2014) presents a model and empirical evidence showing that the binding tariffs commitment introduced by the WTO reduced trade policy uncertainty. This can be observed in fig. 1, where there were fewer variations in tariff rates after 1996 compared with the pre-WTO period and tariff rates appear to converge to similar levels across sectors. Additionally, tariffs rate in the post-WTO era generally stayed below 25 percent demonstrating the effect of the bound tariffs absent from the pre-WTO era of trade liberalizations. Therefore, the two phases of Ghana’s trade liberalization policy – tariff reductions and restructuring enforced under the SAP, followed by the WTO entry that reduced trade policy uncertainty – will be taking into account in evaluating the impact of trade policy on market power in Section 5.

# **2.2 Data**

As part of the SAP, the World Bank launched the Regional Project on Enterprise Development (RPED) with the aim of collecting manufacturing firm-level survey data in many African countries, including Ghana. The dataset was jointly collected by the Centre for the Study of African Economies (CSAE) at the University of Oxford, the University of Ghana, and the Ghana Statistical Service between 1992 and 2003. The first survey involved an original sample of 200 firms randomly drawn from the 1987 Ghana manufacturing census. The sampled firms were broadly representative of the entire national economy across size distribution and the four major industries: food processing, textiles and garments, wood and
the e¤ects of migration upon other residents of the migrants’ countries of origin and destination, Journal of Political Economy, 85, 5: 1001-20.

Vidal, J.P. (1998): The e¤ect of emigration on human capital formation, Journal of Population Economics, 11, 4: 589-600.

Woodru¤, C. and R. Zenteno (2001): Remittances and micro-enterprises in Mexico, Mimeo., University of California at San Diego.

World Trade Organization (2004): Trade Statistics, Historical Series 1980-2003 (http://www.wto.org/english/res_e/statis_e/statis_e.htm).

38
<sup>_∗∗_</sup>|
|||(0_._04)||(0_._04)||(0_._02)||(0_._09)||(0_._16)||(0_._04)|
|MA, 1996|0_._11<sup>_∗∗∗_</sup>|0_._10<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|_−_0_._01<sup>_∗_</sup>|_−_0_._01<sup>_∗_</sup>|0_._06<sup>_∗∗∗_</sup>|0_._03|0_._02|_−_0_._01|_−_0_._002|_−_0_._01|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._003)|(0_._003)|(0_._01)|(0_._02)|(0_._02)|(0_._01)|(0_._004|)<br>(0_._005)|
|Log mean light, 1996|0_._02|_−_0_._13<sup>_∗∗_</sup>|0_._46<sup>_∗∗∗_</sup>|0_._43<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._06<sup>_∗∗_</sup>|_−_0_._11|_−_0_._34<sup>_
deviations.<sup>12</sup> This is also highlighted by a low _R_<sup>2</sup> of 0.021. These results indicate that survey predictions are a useful but very weak indicator of actual future spending.

There are several possible explanations for this finding. One possibility is that individuals may make consumption decisions spontaneously and are therefore not very good at predicting their spending over longer time horizons. Alternatively, individuals may have a clear idea of their future spending but may fail to follow through on their plans, for example due to a lack of self-control or financial constraints (although the latter is unlikely given that all participants of our experiment have access to credit card borrowing by definition). Another explanation could be measurement error and different types of response bias. Consumers may have a clear idea of their future spending but fail to communicate this accurately in their survey responses. This interpretation is somewhat unlikely to apply in our population, given that the participants of our experiment are highly educated, and financially experienced (87% of respondents in our sample have a college degree) and that we elicited survey expectations following standard questionnaires that were adapted and tested for this specific population.

Taken together, the evidence suggests that using survey data to measure treatment effects may be misleading, and provides a strong rationale for using administrative data to measure actual rather than planned consumption.

# **6.2 Prior Beliefs**

Figure 6 shows the distribution of inflation and exchange rate expectations at baseline. In Figure 6.a, we plot the distribution of prior beliefs about the future inflation rate. On average, prior expectations about inflation (3.39 pp) are close to the expert forecast (2.3 pp) and close to a recent rate (1.4 pp).<sup>13</sup> There is, however, significant dispersion in predictions across individuals, with individuals in the bottom decile of the distribution predicting an inflation rate of 0 pp and individuals