rafmacalaba/datause-encoder-data huggingface dataset:
1980s previously used for the “$1-a-day” global poverty counts. Importantly, the new compilation of national lines is more representative of low-income countries, given that the sample size is larger and it corrects the sample biases in the old data set. The pure effect of the PPP revisions is to bring the poverty count down but this is outweighed by the higher level of the national poverty lines in the poorest countries, as used to determine the international line.

Our new calculations using the 2005 ICP and new international poverty line of $1.25 a day imply that 25% of the population of the developing world, 1.4 billion people, were poor in 2005, which is 400 million more for that year 2005 than implied by our old international poverty line based on national lines for the 1980s and the 1993 ICP. In China alone, which had not previously participated officially in the ICP, the new PPP implies that an extra 10% of the population is living below our international poverty line. But the impact is not confined to China; there are upward revisions to our past estimates for all regions, consistent with the higher cost of living in developing countries implied by the results of the 2005 ICP. The higher global count is in no small measure the result of correcting the sample bias in the original compilation of national poverty lines used to set the old “$1-a-day” line.

Although there are a number of data and methodological issues that caution comparisons across different sets of PPPs, it is notable that our poverty count for 2005 is quite robust to using alternative PPPs anchored to the consumption patterns of those living near the poverty line. Of course, different methods of determining the international poverty line give different poverty counts. If we use a line of $1.00 a day at 2005 PPP (almost exactly India’s official poverty line) then we get a poverty rate of 16%—slightly under 900 million people—while if we use the median poverty line for all developing countries in our poverty-line sample, namely $2.00 a day, then the poverty rate rises to 50%, slightly more than two billion people.

There is greater somewhat greater sensitivity to mixing national accounts consumption with survey means. We have proposed a simple Bayesian mixed method, in which the survey mean is replaced by the geometric mean of the survey mean and its predicted value based on prior national accounts data. This is only justified under certain assumptions, notably that consumption is identically log-normally distributed between the (national-accounts-based) prior and the surveys. These assumptions can be questioned, but they do at least provide a clear basis for an alternative hybrid estimator. This gives a lower poverty count for 2005, namely 19% living below $1.25 a day rather than 25%. A large share of this gap—two thirds of the drop in 33
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# **4 Methodology**

To nowcast a series at a particular point in time, we produce an estimate of the series before that variable has been observed but when other contemporaneous variables in our information set have been observed. For instance, we might use data available to us now to get an estimate for economic growth or inflation before official statistics are released. As a concrete example, suppose that in mid-April 2014, we have either a few weeks of Google Trends data or perhaps some preliminary monthly estimate of a search term, but we do not yet know the current inflation. Lags in publication of inflation could mean that we only have estimates for inflation through March or even February 2013. If a policymaker is interested in knowing inflation today, we would nowcast at a monthly _m_ horizon of _hm ≥_ 1.

Our strategy for this exercise is as follows. For each series in each country we will compare nowcasts using Google Trends data and one-step ahead forecasts from a best effort ARIMA model to some benchmark models to assess if the information available from Google Trends data improves our forecasting ability. We now introduce our benchmark models. In the following subsection, we discuss what we mean by a “best effort” ARIMA model.

## **4.1 Benchmark Models**

Five simple models are estimated to provide a baseline for the candidate models described below. The estimated baseline models are the simple mean of the series, the median of the series, the value of the series in the previous period, an AR(1) model, and an _A, A, N_ exponential smoothing model. This exponential smoothing model written in its recursive form is given by where _lt_ and _bt_ are the level and growth rate, respectively, and the parameters along with the initial states are estimated as described in section 3.1. This model is otherwise known as Holt’s linear method with additive errors and is equivalent to an ARIMA (0 _,_ 1 _,_ 1) model [Hyndman et al., 2008]. Our one-step ahead point forecasts are given by 15
variables are part-time employment (-0.009), being retired (-0.312), being a student (-0.165) and being unemployed (-0.316). Lastly, income and religiosity (‘how important is God in your life?’) have a positive and significant effect on perceived freedom of choice and control (0.135 points and 0.059 points respectively).

The specification for which the results are reported in Column (3) additionally includes a set of dummy variables to control for the country income group (low income, lower middle income, and upper middle income, with high income as the omitted reference category), obtained from the 2016 World Development Indicators.<sup>13</sup> The coefficients show how perceived control is highest among individuals living in high-income countries and decreases along with the country income-level. Specifically, being from a low-income country decreases the score by 0.96 points, while being from an upper-middle-income country only reduces the score by 0.36 points.

Column (4) includes all independent variables included in the specification (2), and adds year and country fixed effects<sup>14</sup> . Finally, Column (5) additionally calculates cluster-adjusted robust standard errors to account for within-country correlation. Aside from being married, the impact and direction of gender and the other demographic and socio-economic variables remains the same in both specifications.

> 13 The World Values survey dataset (accessed August 2016) is available at http://www.worldvaluessurvey.org/WVSDocumentationWVL.jsp; the  World Development Indicators (also accessed in August 2016) are available at http://data.worldbank.org/data-catalog/world-development-indicators.

> 14 We do not add year and country fixed effects to specification (3), since the income-level status of the country does not change across the years in our data, and is collinear to the fixed country effects.

22
Table 11. Technology diffusion and multiple supply chain linkages outside firm boundaries

||Dep v|ariable: Norm|alized share o|f technology-|related job ad|verts|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Pioneer|4.630***|4.479***|4.501***|4.476***|4.487***|4.427***|
||(0.398)|(0.393)|(0.394)|(0.395)|(0.394)|(0.393)|
|Pioneer_×_years since emergence|-0.202***|-0.193***|-0.195***|-0.194***|-0.194***|-0.191***|
||(0.036)|(0.035)|(0.035)|(0.036)|(0.035)|(0.035)|
|**Linkages of pioneer frms outside frm boundary**|||||||
|Buyer and supplier||2.311***||||1.257*|
|||(0.475)||||(0.763)|
|Supplier and innovation partner|||1.947***|||1.029**|
||||(0.371)|||(0.442)|
|Innovation partner and buyer||||2.350***||1.402*|
|||||(0.457)||(0.728)|
|Innovation partner and buyer and supplier|||||2.717***|-0.713|
||||||(0.581)|(1.272)|
|**Linkages of pioneer frms outside frm boundary over time**|||||||
|Buyer and supplier_×_years since emerg.||-0.055||||-0.073|
|||(0.048)||||(0.065)|
|Supplier and innovation partner_×_years since emerg.|||-0.032|||-0.006|
||||(0.037)|||(0.041)|
|Innovation partner and buyer_×_years since emerg.||||-0.040||-0.015|
|||||(0.046)||(0.072)|
|Innovation partner and buyer and supplier_×_years since emerg.|||||-0.043|0.051|
||||||(0.060)|(0.122)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.041|0.041|0.041|0.041|0.041|0.041|
|Observations|10,072,709|10,072,709|10,072,709|10,072,709|10,072,709|10,072,709|

Notes: Standard errors clustered at the firm level in parentheses.The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **4.3 Robustness checks**

## **4.3.1 Studying international linkages only**

A central objective of the analysis above was to document the role of pre-existing firmto-firm linkages to pioneer firms in shaping the cross-country spread of new technologies. Given that almost 40% of matched firms in our dataset are based in the US, and that all identified pioneer cities are also within the US, one potential concern about the baseline results is whether they are driven by firm-to-firm linkages within the US (and not by international linkages). In this section, we first explore whether there is a differential effect for firms within and outside the US.

We take the baseline specifications defined above and add further interaction terms with a US dummy for each supplier, buyer, innovation partner and affiliate indicator. Consider the pioneer and buyer’s regression again, for example, we now adopt the following specification: 28
product aggregation and country bilateral variations):<sup>35</sup> To compare the results obtained using these different elasticities, we first aggregate the EU MFN tariff schedule, from the HS 6-digit level to both the corresponding ISIC and SITC levels. We then use these elasticities to calculate TRI, deadweight loss, OTRI and trade impact accordingly, with adjustments based on (21). Columns (4) and (5) of Table 6 present the results. Overall, while these elasticities are fundamentally different, the results are remarkably consistent with trade losses of 5 to 8 percent, and welfare losses of less than 1 percent.

Column (6) of Table 6 presents the result when we purposely reshuffle the heterogeneous trade elasticities to minimize the correlation between trade elasticities and tariffs. This is to show that even with heterogeneous elasticities, if there is no negative correlation between tariffs and elasticities, the welfare and trade impacts calculations will be larger in magnitude: the UK’s potential trade loss from Brexit is around 33 percent, and the EU’s welfare loss is about 1.2 percent. Both of these impacts are larger than the results presented in Table 4 when the empirical negative correlations between tariffs and elasticities are allowed.

# **7.3 Sectoral Impacts**

In this robustness check we show how heterogeneous elasticities result in very diverse sectoral effects. Table 7 presents the sectoral breakdown of the trade impact post-Brexit. The first two columns show the composition of the UK’s exports to the EU by SITC sectors. Nearly 60 percent of the UK’s exports to the EU are in the Machinery and Transport Equipment

> 35Please refer to Section A1 of the Appendix for the proof.

22
|**Table 11. Comparativ**|**e Statistics, over time**|GRO|WTH|CAP|ITAL|T|FP|
|---|---|---|---|---|---|---|---|
|||**10 year**|**20 year**|**10 year**|**20 year**|**10 year**|<br>**20 year**|
|Manufacturing Trade|SSA|0.33%|0.34%|0.30%|0.31%|0.18%|0.19%|
||Resource Rich|0.13%|0.59%|0.12%|0.54%|0.07%|0.32%|
||Non-Resource Rich|0.26%|0.26%|0.24%|0.23%|0.15%|0.14%|
|Product<br>concentration|SSA|0.14%|0.24%|0.12%|0.21%|0.06%|0.10%|
||Resource Rich|-0.06%|0.58%|-0.05%|0.50%|-0.02%|0.25%|
||Non-Resource Rich|0.22%|0.24%|0.19%|0.21%|0.10%|0.11%|
|Intra-regional Trade|SSA|0.00%|0.08%|0.00%|0.04%|0.00%|0.11%|
||Resource Rich|-0.08%|0.06%|-0.04%|0.03%|-0.11%|0.08%|
||Non-Resource Rich|0.06%|0.02%|0.03%|0.01%|0.08%|0.03%|

During the past 20 years, the growth benefits of higher manufacturing trade, lower product concentration, have been transmitted through an acceleration of capital accumulation and TFP growth. Yet in terms of intra-regional trade, the progress has just started to accelerate. What dimension of trade integration explains over the past 20 years the highest growth per worker benefits? The SSA region benefited the most from increasing manufacturing trade products: 0.34 percentage point per year increase in growth per worker, and the impact is primarily driven by faster capital accumulation (0.31 percentage point per year).

Greater export product diversification over the past 20 years in SSA resource rich countries and non-resource rich countries also renders higher growth benefits. There are two takeaways from these results: (a) the growth benefits from product diversification are larger among non-resource rich than among resource rich countries in SSA, and (b) the impact on the sources of growth in SSA is greater through faster capital accumulation than through an acceleration of TFP growth.

Finally, we observe an analogous result when explaining the growth effects of increasing intraregional trade in SSA groups classified according to their extent of natural resource abundance. An increase in this type of trade over the past decade has led to greater growth benefits among non-resource rich countries and a stronger transmission through TFP growth. However, for the resource rich countries, the growth benefits were smaller in the past 10 years, yet higher than the non-resource rich countries in the past two decades.

29
Table B1: Summary Statistics of pooled household-level data

||N|Mean|Median|SD|Min|Max|
|---|---|---|---|---|---|---|
|**Consumption (per day USD in 2017 PPP terms):**|||||||
|Real per capita consumption expenditures|167,101|3.96|2.90|8.03|0.06|2304.38|
|Nominal per capita consumption expenditures|167,260|3.38|2.14|8.82|0.04|2521.88|
|**Spatial defator**|||||||
|Spatial defator<br>|167,128|1.00|0.98|0.13|0.76|1.75|
|**Demographic:**<br>|||||||
|Household size|167,357|6.55|6.00|3.80|1.00|62.00|
|Age of household head|167,253|40.31|40.00|18.46|0.00|119.00|
|Household head is male|167,303|0.78|1.00|0.41|0.00|1.00|
|**Marital status (of household head):**|||||||
|Married|167,293|0.75|1.00|0.43|0.00|1.00|
|Never married|167,293|0.04|0.00|0.18|0.00|1.00|
|Living together|167,293|0.07|0.00|0.25|0.00|1.00|
|Divorced/Separated|167,293|0.06|0.00|0.23|0.00|1.00|
|Widowed|167,293|0.09|0.00|0.29|0.00|1.00|
|**Education (of household head):**|||||||
|No education|151,321|0.37|0.00|0.48|0.00|1.00|
|Primary incomplete|151,321|0.25|0.00|0.44|0.00|1.00|
|Primary complete|151,321|0.13|0.00|0.34|0.00|1.00|
|Secondary incomplete|151,321|0.16|0.00|0.37|0.00|1.00|
|Secondary complete|151,321|0.02|0.00|0.15|0.00|1.00|
|Post secondary but not university|151,321|0.03|0.00|0.16|0.00|1.00|
|University incomplete and complete|151,321|0.04|0.00|0.19|0.00|1.00|
|Can read and write|164,312|0.56|1.00|0.50|0.00|1.00|
|**Employment (of household head):**|||||||
|Not in labor force|147,133|0.12|0.00|0.32|0.00|1.00|
|Unemployed|147,133|0.03|0.00|0.16|0.00|1.00|
|Employed in Agriculture|147,133|0.52|1.00|0.50|0.00|1.00|
|Employed in Industry|147,133|0.08|0.00|0.27|0.00|1.00|
|Employed in Services|147,133|0.26|0.00|0.44|0.00|1.00|
|**Access to basic services**|||||||
|Improved water|167,286|0.69|1.00|0.46|0.00|1.00|
|Improved sanitation facility|167,286|0.24|0.00|0.43|0.00|1.00|
|Access to electricity|156,228|0.48|0.00|0.50|0.00|1.00|
|**DOU urban classifcation:**|||||||
|Urban center|167,357|0.35|0.00|0.48|0.00|1.00|
|Urban cluster|167,357|0.19|0.00|0.40|0.00|1.00|
|Rural|167,357|0.45|0.00|0.50|0.00|1.00|
|**DB urban classifcation:**|||||||
|Core|167,357|0.31|0.00|0.46|0.00|1.00|
|Suburb|167,357|0.09|0.00|0.29|0.00|1.00|
|Town|167,357|0.21|0.00|0.41|0.00|1.00|
|Other rural|167,357|0.60|1.00|0.49|0.00|1.00|

Source: International Urban Poverty Database.

Note: The statistics above are based on the household-level data pooled for 16 SSA countries. Real per capita consumption expenditures are deflated using a spatial deflator calculated using the GHSPOP 1km dataset and the DOU method.

60
Overall illicit income earned by drug lords in Colombia from cocaine tra¢ cking can be estimated using the estimated projected production and price of cocaine. The relevant cocaine price however is not clear, as local prices of cocaine are quite low while international prices from the United States during the period of study are too high. Caulkins and Reuter (2010) report cocaine prices from raw production in Colombia up to the retail prices in the United States for the years 1997, 2000 and 2007. They note that the export cocaine price in the ports of Colombia is around one-third of the mid-level wholesale price reported in the United States while the import cocaine price in the ports of the United States corresponds roughly to two-thirds of the mid-level wholesale price reported inside the United States. Assuming that somewhere between the ports of Colombia and the United States, Colombian drug lords deliver their production to others that continue the tra¢ cking in the United States, the cocaine price relevant for estimating the illicit income of Colombian drug lords would be between these two bounds. Accordingly, we use a price of half the mid-level wholesale cocaine price per kilogram reported for the United States by UNODC, as in the following measure Note that we use the consumer price index with base year 2005 to get illicit aggregate income from cocaine tra¢ cking in pesos in real terms. Hence, the aggregate real undetected illicit income from cocaine production per year RUICOCt is given by the following proxy variable Similarly, we do not observe total potential income from common crimes but only income from reported crimes. We use National Police Department statistics for the period under study on the income stolen through common crimes against property which includes 56 o¤enses, such as robbery, theft, burglary, motor vehicle theft, larceny etc., and excludes drug tra¢ cking from aggregate values. This allows us to obtain the following measure of reported income stolen where �tqt measures the probability of detection and con...scation of income from common crimes for some �t > 0. This re‡ects the idea that empirically (though not in the theoretical model, where qt is determined to be the same for both illicit sectors) the probability of detection and con...scation of income from cocaine can be di¤erent than for common crimes.

Importantly, this empirical variable only includes reported crimes while we also need to consider undetected crimes. To adjust these values we must take into account the reporting rates for these types of crimes. According to Soares (2004a, 2004b) there is a huge di¤erence between o¢ cial statistics on 33
_41_ TABLE 16 AVERAGE RATE OF GROWTH OF TFP BY INDUSTRY 01=0 FOR 1976-80. 01=1 FOR 1981-85 DO-PR FOR PRIVATE FIRMS AND DO-PU FOR PUBLIC FIRMS

|OBS<br>52|IND<br>385|DO<br>PR|Dt<br>1|NTFP<br>0.08762|
|---|---|---|---|---|
|53|311|PU|0|-0.02506|
|54|313|PU|0|-0.12258|
|55|314|PU|0|0.01654|
|56|321|PU|0|-0.03294|
|57|322|PU|0|-0.22660|
|58|324|PU|0|-0 10126|
|59|331|PU|0|-0.09174|
|60|332|PU|0|0.04366|
|61|341|PU|0|-0. 10436|
|62|342|PU|0|-0.09494|
|63|351|PU|0|-0.01226|
|64|352|PU|0|-0.04958|
|65|353|PU|0|-0. 18164|
|66|354|PU|0|-0.17038|
|67|356|PU|0|-0. 13385|
|68|361|PU|0|-0 12552|
|69|369|PU|0|-0.05302|
|70|371|PU|0|-0.04802|
|71|372|PU|0|0.04200|
|72|381|PU|0|-0.03336|
|73|382|PU|0|-0.06714|
|74|383|PU|0|-0. 12758|
|75|384|PU|0|-0.02806|
|76|311|PU|1|0.06914|
|77|313|PU|1|0.20844|
|78|314|PU|1|0.15168|
|79|321|PU|1|0.01110|
|80|322|PU|1|0.20933|
|8I|324|PU|1|-0.07018|
|82|331|PU|I|0.00114|
|83|332|PU|I|-0.07670|
|84|341|PU|t|0.07072|
|a5|342|PU|I|-0.00296|
|86|351|PU|I|-0.02220|
|87|352|PU|1|0.02096|
|88|353|PU|t|0.08560|
|**8S**|354|**PU**|**t**|0.14142|
|90|**356**|PU|1|-0.03610|
|91|361|PU|1|0.12464|
|92|369|PU|1|0.01042|
|93|371|PU|I|0.07164|
|94|372|PU|1|0.06744|
|95|381|PU|1|0.06758|
|96|**382**|PU|I|0.05514|
|97|383|PU|I|0.11302|
|<br>98|<br>384|<br>PU|<br>1|0.02094|
|**Appendix Table 6: Robustness of Primary **|**Outcome Resul**<br>Alternative<br>Survival|**ts**<br>Sales<br>Inverse H|Profits<br>yperbolic|Log<br>Sales|Log<br>Profits|Number<br>Errors|Inventory<br>Reporting|
|---|---|---|---|---|---|---|---|
|Assigned to Training*1 Year|0.006|0.124|0.098|0.094**|0.068|‐0.014|0.060|
||(0.010)|(0.116)|(0.101)|(0.037)|(0.047)|(0.026)|(0.063)|
|Assigned to Training* 3 Years|0.031**|0.569***|0.428***|0.172***|0.138***|0.006|0.078|
||(0.014)|(0.136)|(0.119)|(0.048)|(0.051)|(0.030)|(0.063)|
|Spillover Group * 1 Year|0.001|‐0.015|‐0.008|‐0.027|0.010|0.004|‐0.053|
||(0.011)|(0.120)|(0.103)|(0.040)|(0.045)|(0.029)|(0.066)|
|Spillover Group * 3 Years|0.013|0.237|0.182|0.009|0.043|‐0.014|0.028|
||(0.014)|(0.151)|(0.130)|(0.047)|(0.047)|(0.029)|(0.074)|
|Mean of Pure Control Group|0.835|7.270|6.240|8.265|6.889|0.542|0.849|
|Sample Size|13666|12909|12881|10420|10790|11541|4391|
|P‐value: Training 1 year=3 years|0.046|0.001|0.003|0.036|0.112|0.607|0.836|
|P‐value: Spillover 1 year=3 years|0.374|0.069|0.110|0.385|0.463|0.638|0.375|
|P‐value: Trainingeffect zero bothyears<br>Notes: Robust standard errors in parenth<br>*, **, and *** indicate significance at the<br>All regressions control for randomization<br>and for survey round fixed effects. See tes|0.074<br>eses, clustered<br>10, 5, and 1 per<br>strata fixed effe<br>t for variable d|0.000<br>at the mar<br>cent levels<br>cts, the ba<br>efinitions|0.001<br>ket level.<br>respectivel<br>seline valu|0.002<br>y.<br>e of the ou|0.027<br>tcome wh|0.847<br>ere availa|0.344<br>ble,|

51