rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 5.1: Testing the models with 2005 and 2011 as training and “holdout” data**

||Baseline|Model 1|Model 2|Model 3|Model 4|
|---|---|---|---|---|---|
|Original R<sup>~~2~~</sup>|0.245|0.246|0.247|0.247|0.251|
|<br>Original Adj.R<sup>2</sup>|0.244|0.245|0.245|0.246|0.249|
|Original AIC|20047.21|20022.04|20072.26|20065.60|20001.73|
|Original BIC|20271.92|20270.00|20335.72|20360.06|20319.43|
|Num.obs.|17134|17134|17134|17134|17134|
|boot.cv|0.170|0.171|0.170|0.170|**0.169**|
|RMSEtest05|**0.0232**|0.0281|0.0338|0.0272|0.0291|
|RMSEtest11|0.0438|0.0431|**0.0427**|0.0429|0.0490|

Notes: cv=cross‐validation, R2=r‐squared; AIC=Aikike information Criterion; BIC= ; boot=bootstrap; RMSE=root mean squared error. HRE=Highlands, Reliable Region; HDR=Highland, drought‐prone Region; LOE=Lowlands Enset growing region; LOR=Lowlands reliable region. 05,11 refer to the datasets collected in 2005, 2011 respectively (and defined above).

We then repeat the exercise, but this time considering the whole dataset for 2005/2011 as
, such as the US, but to a much lower extent. This suggests that German foreign investment played an important role. This pattern is confirmed also when considering an alternative measure of foreign ownership based on the country of origin of the Global Ultimate Owner (GUO) as shown in Figure A.5. Data on firm performance in the Orbis database, however, are limited to information on employees and revenues.<sup>6</sup> Due to missing data on employment, the final sample is reduced to about 61,000 firms.

The rest of the empirical analysis draws on a survey of firms with more than 10 employees collected by the Central Statistical Office of Poland. These data are best suited to examine overall effects on firm performance, given the limited coverage for performance variables, especially revenues, in the Orbis database. Survey data are available for the period 1995-2013 and contain information on a set of firm

> 5On the other hand, when comparing the distribution of firms across sectors in Orbis and in the survey of firms provided by the Central Statistical Office of Poland (Figure A.3), the two distributions are broadly aligned suggesting that coverage is likely to be similar across sectors.

> 6In our empirical analysis of firm performance, we exclude the year 1999 due to anomalies in the data.

9
are implemented to lift other binding constraint to growth. Therefore, we give this binding constraint a score of 4 in terms of its relevance to gender inclusion (see column (5) of Table 1).

Turkey’s attempt to address the binding constraint associated with low performance in technology absorption and innovation will produce limited results if the supply of skilled labor, including skilled female labor, is low. This is because skilled labor has been shown to complement new technologies (Acemoglu 1998) and the distortion associated with the undersupply of skilled female labor will give rise to a negative indirect welfare effect, d[μGs (τ) −μGp(τ)]/dτY < 0. According to Acemoglu (1998), a higher supply of skilled female labor initially means a decline in the skill premium, but the expansion of the market size for skill-complementary technologies encourages faster upgrading of the productivity of skilled workers and eventually strengthens the process of skill-biased technical change. In other words, Acemoglu’s theory – confirmed using data for the United States – suggests that new technologies are complementary to skills by design, not by nature. His approach suggests that countries that educate women but fail to provide conditions for their inclusion in the economy would grow more slowly because of two reasons: (1) a direct negative effect on potential growth due to lower supply of skills; and (2) an effect on productivity because of slower skill-biased technical change which manifests itself in lower technology absorption and innovation. Closing the gender employment gap of 35 percentage points among professional and technical workers, with women employed at 39% and men at 61%, would be hugely beneficial for growth, although this is not the largest participation gap. As a way of addressing Turkey’s low performance in technology absorption and innovation, public policy must deal specifically with the obstacles to economic participation of skilled female labor. We therefore rate the binding constraint associated with low performance in technology absorption and innovation as 4 from a gender inclusion perspective (see column
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Wang,
_Ingo Borchert and Mattia Di Ubaldo_ Figure 2: Average Value of Services Trade Outside and Within Services PTAs <!-- Start of picture text --> GBRFRA DEU<br>LUX IRL AUT BEL NLD ESP ITA<br>GRCDNK POLSWE USA<br>HRV HUNROM CZ EPRTFIN<br>CYP SVNBGR SVK CHE GBR DEU JPN USA<br>BMU MLT ISL ESTESTCYPLVALVAPAN LTULTUSVNBGRL UX HRV SVKMAR UKRHUNNZLNZLROMPHLPHLEGYCZECHLCHLSGP S HKGHKGISRIRLGPRTMYSMYS P FINCOLARE GRC NGATHATHADNKZAFVENARGAUTIRNNORNORPOLBELSWESAUCHETURIDNIDNNLDKORKORMEXAUSAUSESPRUSINDINDCANCANBRAITA FRA CHNCHNJPN<br>FJI MLTMKD ISL PAN CRIUZBURYAZETUN VNMVNMUKRKAZ<br>SWZMDA PNG BRN JOR MAR<br>MHL SYC LBR MNGBHSMKDALBGEOBRNBIH TKMJOR KENTUN LKACUBLBYECUAGO QATKWTIRQDZA MEX<br>COL<br>PER<br>CRI<br>TZA<br>20 25 30<br>Log (GDP)<br>Outside PTAs Within PTAs<br>15<br>10<br>5<br>Average Share<br>0<br>-5<br><!-- End of picture text --> Source: Authors’ elaboration using the World Bank DTA 2.0 Database and ITPD-E data. Note: dashed lines denote the log value of average trade within (red) and outside of (black) services PTAs.

have greatly increased the number of partners with which they have signed a services PTA, there was no change at all over the 2000-16 period for Brazil and South Africa (Figure 3), whose connectivity remains at very low levels.<sup>1</sup> When countries sign or join plurilateral agreements, their connectivity displays a discrete jump, as is apparent from Figure 3 when China signed a PTA with ASEAN in 2007, as did Australia—jointly with New Zealand—shortly afterwards in 2010. Overall, Since 2000, economies in Europe, Asia-Pacific and in North America have been most active in concluding services PTAs (Appendix Figure 9). Singapore concluded 19 services PTAs in that period, followed by Chile which signed 18 agreements.

Latin America and the Caribbean is a region with
Table 4 presents the estimation results of elasticity regression (8). Columns (2)-(5) show that the elasticity is around 1.55. The coefficient of 1 _/N_ is statistically significant at the 1 percent level in column (2) and at the 10 percent level in column (5), supporting our identification assumption with the number of effective observations capturing cross-country variability in the variance of nighttime light growth. The coefficients of area and cloud cover are also statistically significant at the 1 percent level when included alone, indicating that they also affect the noise in nighttime light growth. Across the different specifications, the estimated elasticity stays nearly the same, varying from 1.51 to 1.60 when the controls are included separately and rising to 1.64 when they are included together. The elasticity is hence higher than suggested by the simple regression presented in table 3. It is also higher than the central unity estimate in Henderson, Storeygard, and Weil (2012) and the 1.3 estimated in Hu and Yao (2021), both of which use annual DMSP nighttime light data from 1992.<sup>18</sup> However, our estimate is somewhat below the implied elasticity between VIIRS nighttime lights and GDP per capita in China, which Chor and Li (2021) estimate to be 2.1.

Table 4: Elasticities between Nighttime Lights and GDP

||Var|iance of nig|ht light g|rowth: va|r(_z_)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|cov(_y, z_)|1.46***|1.55***|1.51***|1.60***|1.64***|
||(0
inclusion status of OIC countries, we calculated and compared the financial access related indicators of OIC countries with those of other groups of countries, i.e., GCC, OPEC, OECD, OIC excluding GCC, MENA region, low Income countries, and developing countries.<sup>26</sup> We use the data from OECD countries as benchmark, because OECD countries have achieved a highly developed financial environment after overcoming many similar issues facing OIC countries currently.

We also separately examined financial inclusion in GCC and non-GCC among the OIC countries to see the dispersion of the financial inclusion status among all OIC countries. GCC are economically more developed and thus more comparable to its counterparties, such as OPEC and MENA region countries, whereas a comparison between non-GCC and low-income countries should provide more meaningful implication regarding the financial inclusion gap of the poorer

> 25 (Kuran 2011)

> 26 The low income countries and developing countries are defined by the country classification by the World Bank published in January 2011.

25
- [6] Moraes, Marcia, 2004. Analysis of the labor market of the Brazilian sugar alcohol sector. Mimeo. University of S ̃ao Paolo.

- [7] Mitchell, Donald, 2005. Sugar policies: an opportunity for change. in A. Aksoy and J. Beghin, eds., Global Agricultural Trade and Developing Countries. The World Bank.

- [8] Mundlak, Yair and Donald Larson, 2002. On the transmission of world agricultural prices. World Bank Economic Review 6(3), 399-422.

- [9] Pavcnik, Nina, Andreas Blom, Pinelopi Goldberg and Norbert Schady, 2004. Trade liberalization and industry wage structure: evidence from Brazil. World Bank Economic Review 18(3), 319-320.

- [10] Porto, Guido, 2003. Using survey data to assess the distributional effects of trade policy. Policy Research Working Paper #3137. The World Bank

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- [14] Nicita, Alessandro, 2004b. Efficiency and equity of a marginal tax reform: income, quality and price elasticities. Policy Research Working Paper #3266.

27
distributions of consumption and income from 1981 to 2017 that closely align with the World Bank’s official estimates.<sup>10</sup> For comparisons of growth as estimated by national accounts and surveys, there is the additional concern that heterogeneity in the survey data over time can potentially create noisy growth estimates. To partially address this concern, consumption and income surveys are considered separate series for each country, so that we do not calculate growth rates from an income survey in one year and a consumption survey in another. But there remain many ways in which growth estimates of consumption surveys (or income surveys) can be problematic due to changes in survey methods and practices. Jolliffe (2001), Beegle et al. (2012), Jolliffe et al., (2014) all document how changing instrument design over time within a country can change measured consumption, which will appear as growth (or contraction) over time independent of whether national well-being has in fact changed. As survey methods and questionnaires change over time within a country, PovcalNet typically does not post-adjust data from previous years for that country. Metadata on survey comparability suggests revisions in survey methods are widespread and create a break in comparability for two-thirds of the 164 countries that have data over time.<sup>11</sup> As further evidence of this problem, the World Bank is only able to produce comparable data for mean household survey growth required for the shared prosperity measure (requiring comparable data over a five-year period) for 88 of the 168 economies in its database.<sup>12</sup> For the assessment of growth rates, where comparability in the measures matters significantly, we create an ancillary analysis file based on the World Bank’s _Global Database of Shared Prosperity_ . This database is designed with the objective of assessing growth as measured 10 Following an approach similar to that taken by Lakner et al. (2019), we use the ungroup command included in the DASP Stata Package (Abdelkrim and Duclos,
Following Giles and Murtazashvili (2013), who investigate poverty outcomes in linear and nonlinear settings, our dynamic panel data model with lagged variables takes the following form: where _yit_ is food insecurity for household _i_ at time _t_ . _yit−_ 1 is the lagged value of food insecurity and _divit−_ 1 is the lagged value of the diversity index. We lag these values to account for the time it takes for livelihood diversification to actually affect welfare. Diversification does not have an instant impact. Rather, households may use diversification, _ex ante_ , to cope with shocks and thus improve food security in the future. Including lagged food insecurity _yit−_ 1 in our specifications ensures that the variation we observe in our dependent variable is due to livelihood diversification rather than household level characteristics or differences. In this specification, _β_ 2 is our variable of interest, measuring how lagged income diversification impacts a household’s food insecurity, dependent on that household’s food insecurity status in the prior round of data.<sup>8</sup> In estimation, we account for regional and time differences in COVID-19 policies and mitigation strategies. We include time (i.e., round) indicators ( _δt_ ) to capture variation in COVID-19 cases and COVID-19-related policies occurring nation-wide. This also captures other large-scale temporal events such as elections in Malawi and civil unrest in Ethiopia. Regional indicators ( _rj_ ) are interacted with a time trend _tt_ to control for regional differences in COVID-19 mitigation strategies over the evolution of the pandemic as well as other regional shocks such as drought or conflict. Lastly, _ui_ is a household fixed effect to control for time-invariant, unobservable household heterogeneity, and _εit_ is an idiosyncratic error term. Robust standard errors are clustered by household to correct for within-household correlation over time.

The dynamic panel data specification captures causal impacts if the livelihood diversification index is not correlated with the error term. We address issues of endogeneity which might arise from (1) simultaneity or reverse causality between the independent and dependent
line with the evidence available in the literature. For instance, Autor et al. (2019) study the large-scale labor demand shocks stemming from rising international manufacturing competition during 1990–2014 and its consequences. Among their conclusions, they find that this trade shock significantly reduced employment and earnings of young adult males. Recent research has also found that trade reforms can reduce exports of advanced economies when they reduce the fixed costs of foreign direct investment (FDI) significantly more than trade barriers (Baek and Hayakawa, 2022).

Another worth mentioning case is Spain. The Spanish economy went from autarky at

> 10 Spain requested accession to the EU on 26 July 1977.

> 11Note that 1986 seems to be the year globalization dynamics took off in China. But these dynamics were impulse by previous reforms in the Chinese economy. These reforms started in 1978 in the agricultural sectors, followed in 1984 with reforms in urban areas and industrial sectors. See Prasad (2004) for an overview of the key aspects of developments in China’s macroeconomy and economic structure.

> 12As discussed in the data section, the trade data on services tend to be of lower quality than goods data, but these results are intuitive and a solid based for an analysis for countries like India.

14
- Bagwell, Kyle, and Robert W. Staiger (2016), “The Design of Trade Agreements,” Chapter 8 in Bagwell and Staiger (eds) Handbook of Commercial Policy, Elsevier, December.

- Bagwell, Kyle and Robert W. Staiger (2018a), “Multilateral Trade Bargaining and Dominant Strategies,” International Economic Review, 59(4): 1785-1824.

- Bagwell, Kyle, and Robert W. Staiger (2018b), National Sovereignty in an Interdependent World, in Lili Yan Ing and Miaojie Yu (eds.), World Trade Evolution: Growth, Productivity and Employment, Routledge, November.

- Bagwell, Kyle, Robert W. Staiger and Ali Yurukoglu (2018), “Quantitative Analysis of Multi-Party Tariff Negotiations,”mimeo.

- Bagwell, Kyle, Robert W. Staiger and Ali Yurukoglu (2019), “Multilateral Trade Bargaining: A First Look at the GATT Bargaining Records,”mimeo.

- Bond, Eric W. and Jee-Hyeong Park (2002), “Gradualism in trade agreements with asymmetric countries,” Review of Economic Studies 69: 379-406.

- Curzon, Gerard (1966), Multilateral Commercial Diplomacy: The General Agreement on Tariffs and Trade and its Impact on National Commercial Policies and Techniques, Praeger: New York.

- Devereux, Michael (1997), “Growth, Specialization, and Trade Liberalization,” International Economic Review 38: 565-585.

- Freund, Caroline L. and John McLaren (1999), “On the Dynamics of Trade Diversion: Evidence from Four Trade Blocs,” Board of Governors of the Federal Reserve System International Finance Discussion Papers Number 637, June.

- Furusawa, Taiji, and Edwin Lai (1997), “Adjustment Costs and Gradual Trade Liberalization,” Journal of International Economics 49: 333-361.

- Goldstein, Judith, and Joanne Gowa (2002), “U.S. national power and the post-war trading regime,” World Trade Review, Volume 1, Issue 2, pp. 153-170.
relatively short time periods of 5-year averages. In order to account for the cyclical reversion to the trend, they include in the standard growth model (3) the output gap at the start of the period: where _s=5_ and ln _yit_ − _s_<sup>−ln</sup> _yitT_ − _s_ is the output gap. They also add a time specific effect<sup>μ</sup> _t_<sup>, and</sup> a country-specific effect<sup>η</sup> _i_<sup>. Controlling for the initial output gap allows them to improve the</sup> regression fit and avoid overestimating the speed of transitional convergence. The output gap in the regression is given by the difference between (the log of) potential (trend) and actual GDP per capita around the start of the period. The trend output is obtained using the BaxterKing filter for each country in the sample (Baxter and King, 1999).

The inclusion of the initial output gap in the growth regression model is contentious because with the steady state shifting over time there is no stable long-run trend that can be identified using the filter. We therefore forecast real per capita GDP growth using a pure cross section version of the regression model (4). The model includes all variables representing convergence factors and structural policies mentioned earlier, and only one variable in the category of stabilization policy (inflation). These are the most pertinent to growth over a long time span and the most commonly found in the empirical growth literature.

In this paper we use Loayza _et al._ ’s panel data and panel estimates to forecast real per capita GDP growth for the set of all developing countries for the period 2005-14. We do so without having to forecast each of the steady state growth determinants in _X_ .<sup>14</sup> Only for the set of 35 developing countries in Loayza _et al._ ’s panel data (the panel contains 55 developing and
<!-- Start of picture text --> Figure 4: Observed and simulated labor market status proportions and time usage per spouse and having children<br>Husbands Sample Wives Sample<br>No Children Children No Children Children<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>NP 56 UE 19 PT 10 FT 15 NP 43 UE 24 PT 12 FT 21 NP 82 UE 10 PT 3 FT 5 NP 83 UE 10 PT 2 FT 4<br>Husbands Simulated Wives Simulated<br>No Children Children No Children Children<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>100%<br>30%<br>50%<br>20%<br>0%<br>10%<br>NP 53 UE 16 PT 10 FT 21<br>0%<br>NP 53 UE 16 PT 10 FT 21 NP 41 UE 20 PT 12 FT 27 NP 86 UE 5 PT 3 FT 6 NP 85 UE 5 PT 3 FT 7<br>Leisure Work Home production<br>Source:<br><!-- End of picture text --> _Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

_Note:_ Abbreviations: FT= employed full-time; PT= employed part-time; U= unemployed; NP= nonparticipant. The numbers next to the labor market labels are the proportion of husbands and wives in each labor market state for each country. Per panel, the proportion of husbands and wives should sum up to 100. Because of approximation, for some panels, the sum is equal to 101
cial networks still fits within a two-hour survey. Although the speed with which respondents can go through the photo books alleviates concerns about respondent fatigue, the network module came near the end of the survey. Randomly ordering photos and using multiple photo books with pictures placed in a different random order in each location allows us to precisely quantify and correct for respondent fatigue. We estimate the likelihood that a given woman is identified based on the page where her picture appears and the position of her picture on the page in the relevant photo book.<sup>17</sup> We find that women appearing later in the photo book may be identified up to 73% fewer times than those appearing on the first page, and that women appearing lower down on each page may be identified up to 18% fewer times than women appearing at the top (see Table A11.) Given that these differences are distributed randomly, they do not bias our estimates; however, we use estimates of respondent fatigue to re-weight the network data for ease of interpretation.

We complement sales and profit data from our three in-person surveys with high-frequency data collected through SMS surveys. Starting the week after baseline surveys were completed, all women in our sample received a weekly text message on a randomly selected day asking them to report total sales revenue from the previous day. Those who did not have a business were told to reply with zero. We incentivized responses by offering participants UGS 1,000 (USD 0.30) in airtime. Each month, an enumerator supplemented the SMS surveys by calling each woman who had not responded to any SMS survey in the past month. We find that average daily revenues reported in SMS surveys in the month that baseline, midline, or endline surveys took place in-person have a correlation of 0.31 with in-person reports, with 37% of reports being lower in the SMS survey than the in-person survey, 50% being higher in the SMS reports than the in-person reports, and 13% matching precisely. Given