rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2. Summary of studies of metal price forecast performance**

# **A. Metals being evaluated**

<!-- Start of picture text --> Number of studies<br>15 14<br>11<br>10<br>8 8 8<br>5<br>5<br>0<br>Aluminum Copper Lead Nickel Tin Zinc<br><!-- End of picture text -->

# **C. Benchmarks used for evaluation**

<!-- Start of picture text --> Number of studies<br>10<br>8 8<br>8<br>6<br>4<br>2<br>2<br>0<br>Judgment No-change Univariate<br>models<br>E. Data frequency<br>Number of studies<br>50 Aluminum Copper Lead<br>Nickel Tin Zinc<br>40<br>30<br>20<br>10<br>0<br>Daily/Weekly Monthly Quarterly<br>Source : World Bank.<br><!-- End of picture text --> <!-- Start of picture text --> B. Forecasting methodologies evaluated<br>Number of studies<br>10 9<br>8 7<br>6<br>4 3<br>2 1<br>0<br>Machine Multivariate models Univariate models<br>learning models Futures prices<br><!-- End of picture text -->

# **D. Time period for evaluation**

<!-- Start of picture text --> Number of studies<br>50 Aluminum Copper Lead<br>40 Nickel Tin Zinc<br>30<br>20<br>10<br>0<br>F. Forecast horizon<br>Number of studies<br>50 Aluminum Copper Lead<br>40 Nickel Tin Zinc<br>30<br>20<br>10<br>0<br><3 months <=1 year > 1 year<br>Before Sept '08 Before Jul '14 Before Jan '20 After the pandemic<br><!-- End of picture text -->

# **F. Forecast horizon**

_Note_ : Figures show the number of studies that included each commodity or applied different forecasting methods. Since several studies examine more than one metal price, the total can be larger than the number of studies. B. Number of studies that examine the forecast performance of futures prices, machine learning techniques, multivariate models (including structural VARs and Bayesian VARs), and univariate time series models against a benchmark. The one study that examine both machine learning techniques and univariate models are shown in the category for machine learning techniques. The one study that examines both futures prices and univariate models is shown in the category for futures prices.

C. Number of studies that benchmark forecast performance against latest spot prices (“no change”), futures prices, multivariate models (including structural VARs and Bayesian VARs), and univariate time series models. Studies that benchmark against both no-change forecasts and futures prices (1) and against both no-change forecasts and univariate models (1) are shown in the category for no-change benchmarks. The one study that conducts a qualitative analysis is included in the category for judgment-based forecasts.

D. Number of studies that evaluate forecast performance for each metal price.

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reported measures provided by respondents. However, tax data may lack demographic information on individuals or households unless it is merged with other administrative or survey data. Also, it does not contain information on capital income or other key worker characteristics, such as education and occupation (only the NACE code of the company is recorded). The income of employees is reported by the employer, who has fewer incentives to misreport and is often more tightly monitored than the employees. This increases the data quality. In our data, labor income is measured at the individual level. Therefore, we cannot investigate tax evasion at the household level. Although the quality of tax data is usually high, it still may be plagued by missing variables, zero values challenging to interpret, and technical errors. Administrative tax data may also be challenging to extract from the administrative databases because they are often not integrated and not optimized to extract data (often, tax administration is interested only in checking the single taxpayer's income). Künn (2015) documents the increasing usage of linked administrative and survey data in economic research and discusses the challenges of data linking. Given the very large volume of information for approximately 7 million taxpayers, different identifiers were provided for the same individual, making the process of aggregating monthly tax data for each individual into an annual income measure difficult/quite challenging.

**In Romania, several income categories are exempt from personal income tax and, therefore, are not included in the definition of income used here.** Exemptions include social benefits other than pensions and temporary work incapacity benefits, such as allowances, indemnities, social insurance benefits, and scholarships. Invalidity pensions for the 1st degree of invalidity and benefits for war invalids are also exempt. Additionally, income from damages, insurance payouts, and punitive damages is tax-free, but there are other exempt categories.<sup>3</sup>

> 3 These encompass income from inheritance or donation, real estate transfers under specific conditions, and income from diplomatic missions and international organizations. Exemptions also extend to consultancy income under nonreimbursable agreements, subsidized interest on credits, and sports prizes from European, World, and Olympic championships.

9
## **Figure 12: Total amount of LCs by Turkish banking sector (million USD)**

**Figure 13: Total amount of export credits by Turkish banking sector (million USD)**

<!-- Start of picture text --> 25000<br>25000<br>20000<br>20000<br>15000<br>15000<br>10000<br>10000<br>5000<br>5000<br>0 0<br>Apr‐04 Nov‐04 Jun‐05 Jan‐06 Aug‐06 Mar‐07 Oct‐07 May‐08 Dec‐08 Apr‐04 Sep‐04 Feb‐05 Jul‐05 Dec‐05 May‐06 Oct‐06 Mar‐07 Aug‐07 Jan‐08 Jun‐08 Nov‐08<br><!-- End of picture text --> _Source: BRSA_ The demand effect seems to explain to a larger extent the drop in banks’ volumes of trade loans. In Ghana where exports of cocoa kept up, banks reported an increase in the volume of letters of credit and short-term financing for the period September-December 2008 versus comparable period in 2007 despite an increase in the cost of financing for both instruments. Similarly, in Kenya where both imports and exports increased, trade credit increased in 2008 despite an increase in lending rates making trade credits to the private sector more expensive.

**_B. Banks reported not lacking liquidity but implementing more stringent lending requirements_**

# **_Very few surveyed banks have reported lack of liquidity in local currency as_**

**_constraining their supply of trade finance._** Banks in Sierra Leone have traditionally been liquid but not providing export finance because of a scarcity of creditworthy customers, among other things.  The banking systems in most of the other countries, in particular in Chile, Egypt, Kenya, South Africa and Tunisia, also appear as well-capitalized in local currency and have not experienced liquidity constraints since the onset of the crisis<sup>12</sup> .  In contrast, some banks have reported more difficulty accessing liquidity in foreign currencies which have become more scarce and expensive as a consequence of the liquidity crunch in western banks in a first stage, and the drop in export revenues. Banks had more difficulties financing large import transactions such as big purchases of equipments or large investments by public companies as reported by Tunisian banks.

> 12 The relatively secured position of the Chilean Banks seems to be the consequence of a delayed impact of the crisis and the macro position of Chile. The banking sector in Chile has become strongly capitalized, highly regulated, and fairly concentrated after the dramatic crisis of 1982.  Furthermore, in Chile, while the banking sector and the private corporations are net debtors from the rest of the world, the Government and the Pension Funds are net creditors.

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|**Table 7: Impacts on Formality**||**Communic**|**ation vs Contro**|**l Blocks**|**Ins**|**pector vs**|Inspector <br>**Control Blo**|Indirectly<br>**cks**|
|---|---|---|---|---|---|---|---|---|
||Sample<br>Size|Control<br>Mean|Free Cost<br>Difference|Communication<br>Difference|Sample<br>Size|Control<br>Mean|Assigned <br>Difference|Inspected<br> Difference|
|_Administrative data measures of formalizing after interve_|_ntions began_||||||||
|Definite match for SIMPLES|1346|0.007|0.00618|-0.00300|5186|0.006|0.00390|-0.00144|
||||(0.00662)|(0.00446)|||(0.00443)|(0.00229)|
|Definite or probable match for SIMPLES|1346|0.015|0.00177|-0.00706|5186|0.014|0.00421|0.00102|
||||(0.00773)|(0.00761)|||(0.00586)|(0.00384)|
|Definite match for MEI|1346|0.060|-0.0349***|-0.0155|5186|0.026|0.00313|-0.00557|
||||(0.0131)|(0.0139)|||(0.00826)|(0.00469)|
|Definite or probable match for MEI|1346|0.067|-0.0370***|-0.0143|5186|0.033|0.0138|-0.00339|
||||(0.0142)|(0.0161)|||(0.0106)|(0.00561)|
|Definite match for ALF|1346|0.030|0.00311|0.000227|5186|0.032|0.0218**|-0.00540|
||||(0.0113)|(0.0117)|||(0.0110)|(0.00535)|
|Definite or probable match for ALF|1346|0.041|-0.000335|-0.00597|5186|0.041|0.0327***|0.00206|
||||(0.0125)|(0.0125)|||(0.0124)|(0.00653)|
|**Definitely got any type of formal status**|1346|0.083|-0.0281*|-0.0120|5186|0.056|0.0245*|-0.0100|
||||(0.0159)|(0.0174)|||(0.130)|(0.0068)|
|**Definitely or probably got any type of formal status**|1346|0.104|-0.0350*|-0.0182|5186|0.075|0.0392***|-0.0034|
|Notes: Standard errors in parentheses, clustered at the bl<br>respectively, after controlling for randomization strata. S|ock level. *, *<br>ampling weigh|*, and *** i<br>ts are used|(0.0183)<br>ndicate statisti<br>for the Inspec|(0.0209)<br>cally different from<br>tor vs Control blocks|control mean<br>comparison|at the 10<br>s.|(0.0149)<br>, 5 and 1 pe|(0.0083)<br>rcent levels|

Notes: Standard errors in parentheses, clustered at the block level. *, **, and *** indicate statistically different from control mean at the 10, 5 and 1 percent levels respectively, after controlling for randomization strata. Sampling weights are used for the Inspector vs Control blocks comparisons.

39
panel that allows us to identify cohorts according to their year of birth, and their schooling and labor market trajectories. This permits a richer dynamic analysis as compared to other approaches in the same spirit including for instance Behrman et.al. (2006) and Cárdenas et.al. (2011), which focus on snapshots of cross sections of individuals observed at the same age during different time periods. By construction, these studies mix together different generations, which implies not observing the evolution of specific cohorts over time.

Synthetic panels have been used recently to study school to work transitions, and to identifying scarring effects from school dropout and unemployment at young ages. Our analysis is the first to our knowledge to focus on the Osow youth phenomena, and to simultaneously study the causes and consequences of the problem. Additionally, as compared to other studies which focus on individual country cases, our approach differs by pooling together synthetic panels from 18 Latin American countries covering more than two decades, which allow for a wider and more comprehensive view.

Focusing on Osow youth entails several important differences. One is that the literature on youth transitions generally considers only the school to work or school to unemployment status, where remaining in the education system and entering the labor market tend to be viewed as substitutes without considering the “Osow” status as an option. This rules out a significant population subgroup. Another difference is that the future consequences of being Osow are obviously very different to those of either working or remaining in school, as mutually exclusive decisions.

The paper is divided into five sections. Section 1 presents the data and discusses the advantages of a cohort approach. By illustrating the main stylized facts derived from our data the section also puts forward some points of reference to guide the empirical investigation. Section 2 describes Osow youth patterns and disentangles the age, cohort, and time effects underlying its dynamics. The results are used to characterize countries into three different groups depending on the patterns observed. Section 3 presents our empirical results for identifying the community and macro factors associated with the persistent levels of Osow youth for men and women in LA. Section 4 attempts the identification of future scarring effects observed later in life, based on the information on belonging to the Osow group at young ages. Section 5 concludes.

# **1. Data, Stylized Facts and Reference Points**

This section presents the data used for our empirical analysis. We also show an illustration of its use for descriptive analysis and for deriving some general stylized facts for comparison.

_Household Survey Database and the cohort approach_ 3
manufacturing, information and communication technology, hospitality, entertainment, and construction). We targeted firms that had 2 to 15 employees (the same range as the experiment in Anderson and McKenzie, 2020), and that had been in business for at least one year. Firms were sampled using a combination of list-based interviewing and door-to-door screening. Kantar began with its own database of firms, and set of appointments to survey firms which met the sector and firm size criteria. Once the survey team were traveling to a given commercial area or industrial cluster, they then went door-to-door to find and interview other firms that met the sampling criteria. This resulted in an overall sample of 1,054 firms, of which 397 came from the original lists and 657 from door-to-door sampling. Baseline data collection took place between September 2 and October 14, 2019.

Table 2 provides some descriptive statistics on these firms. The entrepreneurs have an average age of 38, have been running their firms for a median of 8 years, and 64 percent have at least two years of university education, and 26 percent have post-graduate education. Twenty-five percent are female. The mean (median) firm has 5.9 (5) employees and USD $2291 ($833) in monthly sales. Baseline use of the market for business service providers is low, with only 5 percent having used a human resources (HR) firm to find a worker in the past year, and only 3 percent of firms currently using an outside accounting or marketing firm. We used the indirect method to assess the reasons for the lack of use of this market, asking firms why they thought SME owners in Nigeria (including themselves) did not use this market. Firms answered on a 5-point Likert scale, and we consider the percent of firms who think a factor is very likely or extremely likely to be a key factor for not using this market. Many of the answers given as key factors by firms are consistent with information frictions: 27 percent say not knowing any providers, 37 percent say finding it difficult to judge the quality of providers, 40 percent say not being confident they can find someone they can trust, and 37 percent say it is too much time and hassle to find such a provider. Seventy-two percent of firms think at least one of these information-related reasons is a key factor, and only about one-third of firms (35% for HR providers, 31% for marketing and accounting) are confident they could quickly find a provider if they needed one.

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Table 5. Estimates of the effect of the crisis on potential individual-level mechanisms in migrant households

||Diarrhea last<br>month = 1|Respiratory<br>infection last<br>month = 1|Has vaccine<br>card = 1|Has any polio<br>shots = 1<br>|Has any DPT<br>or Pentavalente<br>shots = 1|<br> <br> <br>Any individual<br>health expenses<br>last month = 1|
|---|---|---|---|---|---|---|
|_All households_|||||||
|Migrant household = 1|-0.001|-0.062|-0.023|0.028|0.023|-0.002|
||(0.059)|(0.057)|(0.046)|(0.036)|(0.032)|(0.047)|
|Year 2012 = 1|0.015|-0.066*|0.131***|0.084***|0.073***|-0.109**|
||(0.043)|(0.037)|(0.026)|(0.023)|(0.021)|(0.048)|
|DD|0.087|0.157*|-0.008|-0.006|-0.020|0.078|
||(0.092)|(0.087)|(0.061)|(0.039)|(0.038)|(0.080)|
|Constant|0.435***|0.603***|0.814***|0.850***|0.869***|0.559***|
||(0.028)|(0.030)|(0.025)|(0.024)|(0.020)|(0.032)|
||985|985|947|939|956|985|

Notes: Sample of all children under three years old at time of survey in 2008 or 2012 with valid information (cross-sectional cohorts). Standard errors estimated allowing for clustering at the sample cluster level in parentheses . *** indicates significantly different at p<0.01, ** at p<0.05, and * at p<0.10.
# **5 Robustness and Sensitivity**

In this section, we provide robustness checks and sensitivity analysis. Our main concern is that the instrumental variables estimator has relied on the use of one instrument only. In order to perform specification tests, we redo the whole analysis using two instruments.

We add export market density in 1995 to our main instrument, that is, the out-of-district cost of transportation to Kampala. This lag captures the fact that markets are sometimes focal points that tend to perpetuate. Although it is fairly common to use lagged variables as instruments, there are some concerns to address. First, it is important to ask if the same correlation between market availability and poverty in _t_ may be present between market availability in _t − T_ and poverty in _t_ (where _T_ is the number of year between household surveys, 1999 and 1995 in this case). For instance, if there is autocorrelation in the residuals in (10), then the endogeneity argument that invalidates the results from OLS may also invalidate the results from the use of lagged instruments. Similarly, lagged instruments will not work if there are persistent omitted variables. Here, we claim that the autocorrelation in the errors becomes a second order problem. We are actually merging data from the 1999 UNHS with an instrument taken from the 1995 UNHS. Since there are four years between these surveys, there is good reason to believe that the correlation will be practically absent. Another problem is that, since our instrument varies across districts, the instrument is required to be uncorrelated with all lags of the residuals, a requirement that might fail in the presence of district fixed effects. Since we are accounting for district effects by including a comprehensive set of district controls **z** _c_ , we argue that this requirement is met.

Results are reported in Table 8. The instruments work well, both according to the predictive power in the first stage regression and to the Hansen specification test of overidentifying restrictions. The magnitudes of the impacts are similar to those in previous sections.

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workers from sectors exposed to trade with the US or by input-output linkages, this specification aims to capture direct impacts. Panels A and B use wage employment to population (12 years or older) ratios from the Population Census counts. One limitation of this measure is that the publicly available counts do not allow to restrict the sample further to people older than 15 years of age, or to the private sector. Panel C provides results for private wage employment in the private sector for people aged 15 years or older using the IPUMS samples. We also provide results for separate age groups below. In addition to the control variables included in the previous section, we also include the initial share of exports and imports in the region’s value added as right-hand side variables. This would control for any differential employment trends by regions with different degrees of trade openness. In addition, we control for the initial share of manufacturing employment in all specifications, as this variable is highly correlated with employment growth in the tradable sector, possibly capturing secular trends in manufacturing jobs. Finally, we control simultaneously for employment- and export-weighted exposure to local automation. This is because export-weighted exposure to local automation is correlated with exposure to US automation. Thereby, its omission would likely bias the estimated impact of US automation on employment, as it would also capture any impacts that Mexico’s automation in sectors with a comparative advantage may have on the labor market. At the same time, we need to include employment-weighted exposure to local automation to control for the direct impact of local automation on employment that operates outside the trade channel. The reduced-form estimates show that US automation did not affect employment rates in Mexico’s tradable sector, since the coefficients are not statistically different from zero in all specifications.

Table 12 shows the estimates of trade with the US on Mexico’s employment in the tradable sector, driven by exposure to US automation. In other words, changes in trade outcomes at the area level are instrumented with exposure to US automation. Thereby, the coefficients would reflect the impacts of changes in trade with the US that are driven by US automation. The first-stage equation is the main equation 5, whose estimates are reported in Table 3.

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Table 5: Cross-establishment Drivers of MAX-MOST Gap

||_GA_<br>(1)|_Pj_<br>(2)|
|---|---|---|
|_Hj_|-0.07<sup>_∗∗∗_</sup>|-0.06<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|_NOLoan_|0.04<sup>_∗∗∗_</sup>|0.05<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|_Bias_|0.01<sup>_∗∗∗_</sup>|0.01<sup>_∗∗∗_</sup>|
||(0.00)|(0.00)|
|Management (Z-Score)|-0.00<sup>_∗∗∗_</sup>|-0.00<sup>_∗∗∗_</sup>|
||(0.00)|(0.00)|
|Age: 6 - 10 Years|-0.01|-0.00|
||(0.01)|(0.01)|
|Age : 11 - 15 Years|-0.04<sup>_∗∗∗_</sup>|-0.04<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|Age : 16+ years|-0.02<sup>_∗∗∗_</sup>|-0.02<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|_Multiple_||-0.08<sup>_∗∗∗_</sup>|
|||(0.01)|
|N|16605|16605|
|R-squared|0.10|0.11|
|2-Dig. Sector FE|Yes|Yes|
|Country FE|Yes|Yes|

Notes : MAX-MOST Gap, _GAPj_ , is defined at establishment level as number of BFs with _MAXfj > MOSTfj_ , over number of BFs with _NUMf,j >_ 1. The base categories are Size: Small, and Age _≤_ 5 Years. Establishments weighted by sampling weights. _NOLoan_ is a binary variable taking value 1 if the establishment has any loan rejected in the last year. _Bias_ is defined as [(1 + 4 _/_ 9 _∗ SelfPerceptionScore_ ) _− Sj_ ], where _SelfPerceptionScore_ asks the establishments their perceived technology ranking (scale from 1 to 10) as compared to the whole world. _Multiple_ is the fraction of BFs in the establishment with _NUMf,j >_ 1. *, ** and *** denote 10%, 5% and 1% significance respectively.

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