rafmacalaba/datause-encoder-data huggingface dataset:
not high enough to justify fears of monopolization, the share has doubled from 7 to 14 percent between 2003 and 2010. China‘s FDI is unusual in another respect: in recent years, nearly 80 percent of FDI flows have originated from state-owned or controlled firms, raising concerns in some recipient countries (Rosen and Hanemann, 2009).

Should this response of China to its own perceived dependence be a source of concern for outsiders? Much will depend on how concentrated are the markets for natural resources. As long as markets are competitive and sources are diversified, China‘s actions will have limited effect on other countries. For example, in the key energy sector, energy types are becoming more diversified and within particular ones—for example oil and natural gas—there are more sources of supply.

**Table 5: China's Major Imports of Natural Resource Products (Excluding Agricultural Raw Materials and Fuels) and their Shares, 2009**

|HS-4|Product Description<br>|Imports ($ mill.)|Imports as<br>% of<br>Natural<br>Resource<br>|Imports as<br>% of World<br>Imports||Major Suppli|ers (and the|ir share|s)||
|---|---|---|---|---|---|---|---|---|---|---|
|2601|Iron ores and concentrates,incl roast|50,140|69.3|64.9|Australia|Brazil|India|40.1|25.7|15.2|
|2603|Copper ores and concentrates.|8,479|11.7|27.9|Chile|Peru|Australia|24.2|16.1|12.7|
|2608|Zinc ores and concentrates.|1,883|2.6|36.8|Peru|Australia|EU27|28.7|27.2|9.9|
|2602|Manganese ores and concentrates|1,773|2.5|52.3|Australia|South Africa|Gabon|36.6|22.4|12.2|
|2607|Lead ores and concentrates.|1,737|2.4|44.1|Peru|USA|EU27|30.4|16.8|12.1|
|2610|Chromium ores and concentrates.|1,311|1.8|75.3|Australia|Brazil|India|40.1|25.7|15.2|
|2604|Nickel ores and concentrates.|1,057|1.5|54.2|Indonesia|Philippines|Australia|30.4|26.2|22.4|
|2515|Marble,travertine,ecaussine|861|1.2|55.0|Turkey|EU27|Egypt|39.2|29.1|15.0|
|2613|Molybdenum ores and concentrates.|766|1.1|24.4|Chile|USA|EU27|45.8|20.4|7.1|
|2503|Sulphur of all kinds,other than sublime|705|1.0|37.8|Canada|Saudi Arabia|USA|18.0|17.9|10.7|
||Natural resources,excl agricgoods|72,332|100.0|41.1|||||||
|0-97|Allgoods|1,005,555||8.3|||||||

Source: UN COMTRADE database.

Moreover, just as WTO bindings constrain the scope for preferential access to markets, a WTO provision – GATT 1994 Article XVII on State Trading Enterprises – is meant to prevent state traders and investors from departing from the MFN principle. Thus, in principle, even a stateowned firm based abroad could not divert its output in a discriminatory fashion to its home country. However, even though this provision extends to all such enterprises ―wherever located,‖ it is not clear that WTO rules can oblige a country to ensure its firms honor its obligations even when they are located outside its territory. How this provision is interpreted and applied will have a crucial bearing on how much scope countries like China have to secure privileged access to resources by investing abroad.

21
|P162760|2017|2019|6/10/2019|4 Active|Zambia|
|---|---|---|---|---|---|
|P162760|2017|2020|12/12/2019|5 Active|Zambia|
|P162760|2017|2020|5/26/2020|6 Active|Zambia|
|P162762|2020|2020|1/25/2020|1 Active|Turkey|
|P162762|2020|2020|6/29/2020|2 Active|Turkey|
|P162789|2018|2018|5/4/2018|1 Active|Cote d'Ivoire|
|P162789|2018|2019|12/4/2018|2 Active|Cote d'Ivoire|
|P162789|2018|2019|6/10/2019|3 Active|Cote d'Ivoire|
|P162789|2018|2020|11/7/2019|4 Active|Cote d'Ivoire|
|P162789|2018|2020|6/27/2020|5 Active|Cote d'Ivoire|
|P162789|2018|2021|7/16/2020|6 Active|Cote d'Ivoire|
|P162792|2018|2018|6/28/2018|1 Active|Vietnam|
|P162792|2018|2019|6/18/2019|2 Active|Vietnam|
|P162792|2018|2021|7/19/2020|3 Active|Vietnam|
|P162833|2019|2020|10/21/2019|1 Active|Peru|
|P162833|2019|2020|4/18/2020|2 Active|Peru|
|P162835|2019|2020|9/20/2019|1 Active|Egypt,Arab Republic o|
|P162835|2019|2020|4/28/2020|2 Active|Egypt,Arab Republic o|
|P162849|2018|2019|9/28/2018|1 Active|India|
|P162849|2018|2019|2/17/2019|2 Active|India|
|P162849|2018|2020|9/10/2019|3 Active|India|
|P162849|2018|2020|6/29/2020|4 Active|India|
|P162850|2018|2019|8/18/2018|1 Active|West Bank and Gaza|
|P162850|2018|2019|4/3/2019|2 Active|West Bank and Gaza|
|P162850|2018|2020|10/5/2019|3 Active|West Bank and Gaza|
|P162850|2018|2020|2/24/2020|4 Active|West Bank and Gaza|
|P162850|2018|2021|7/23/2020|5 Active|West Bank and Gaza|
|P162890|2018|2018|6/22/2018|1 Active|Gambia,The|
|P162890|2018|2019|1/10/2019|2 Active|Gambia,The|
|P162890|2018|2019|6/4/2019|3 Active|Gambia,The|
|P162890|2018|2020|12/25/2019|4 Active|Gambia,The|
|P162890|2018|2020|4/9/2020|5 Active|Gambia,The|
|P162890|2018|2021|7/23/2020|6 Active|Gambia,The|
|P162893|2018|2018|1/23/2018|1 Active|Liberia|
|P162893|2018|2019|8/3/2018|2 Active|Liberia|
|P162893|2018|2019|11/30/2018|3 Active|Liberia|
|P162893|2018|2019|5/9/2019|4 Active|Liberia|
|P162893|2018|2019|6/17/2019|5 Active|Liberia|
|P162893|2018|2020|11/4/2019|6 Active|Liberia|
|P162893|2018|2020|6/8/2020|7 Active|Liberia|
|P162901|2019|2019|2/8/2019|1 Active|Djibouti|
|P162901|2019|2020|9/13/2019|2 Active|Djibouti|
|P162901|2019|2020|3/15/2020|3 Active|Djibouti|
|P162902|2019|2019|10/12/2018|1 Active|Solomon Islands|
|P162902|2019|2019|4/2/2019|2 Active|Solomon Islands|
|P162902|2019|2020|10/4/2019|3 Active|Solomon Islands|
|P162902|2019|2020|4/24/2020|4 Active|Solomon Islands|
# **Table 2: Effects of a Workerʹs Unexpected Illness on the Labor Supply of Other Household Members**

|Dependent Variable|1(Work)|Days worked|Hours worked|
|---|---|---|---|
|_Panel A: Overall effects_||||
|Other worker ill|0.013|0.130|1.618|
||[0.027]|[0.164]|[1.482]|
|Observations|3,951|3,948|3,948|
|R‐squared|0.501|||
|_Panel B: Effects by gender of responder_||||
|Other worker ill|0.088**|0.394*|4.715**|
||[0.036]|[0.237]|[2.332]|
|Other worker ill X Female|‐0.131**|‐0.496|‐5.718|
||[0.052]|[0.327]|[3.494]|
|Mean dependent variable|0.807|4.279|37.660|
|Observations|3,951|3,948|3,948|
|R‐squared|0.502|||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. All specifications include individual fixed effects. Column 1 is estimated by OLS and columns 2 and 3 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐ interview dummies interacted with gender, week‐of interview dummies interacted with city, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
22

# 4.2.2 Inteat **tea And _ther nimtnrtiona**

There is the possibility that an observed relationship between interest rates and growth merely reflects the effect of other policies which are correlated with financial market policies. It could be expected that countries with tight controls oni interest rates and high inflation might also have tight, import-biased trade restrictions and other distortionary policies. Twenty three countries are common to the present sample and that of Agarwala, 1983, which specified distortion indices for a range of policies. For these countries, a modified distortion index, ODI, was formed from Agarwala's data, by subtracting from his aggregate distortion index, DI, the component due to interest rates, RI. The results of including ODI together with RR in the regression for the second sub-period are: DYY - c + .192 RR RsqAdj - .423 (6) (4.23) DYY - c + .139 RR -.019 ODI RsqAdj - .493 (7) (2.78) (-2.00) The inclusion of ODI does not raise the overall explanatory power much, but it does reduce the coefficient of RR. There may therefore be something in the suggestion that part of the coefficient of RR in simple equations really reflects other policies, but these do not dominate the regression and a strong relationship with interest rates remains. The weakness of the relationship between interest rates and growth found in Agarwala's study appears to have been due to the loss of information caused by uneven country grouping into ranges.

## 4.2.3 Financialization and Efficiency

The next stei ls to specify and estimate a "causal chain", defined as a set of structural equations with more plausible one-way causality. One such chain consists of two hypotheses: 1) the degree to which savings flows are intermediated through the financial system is related to the real rate of return available on financial assets;
**Figure 16. Export Sophistication: Argentina versus Comparators (1975-2000)**

<!-- Start of picture text --> 8500 110<br>105<br>8000<br>100<br>7500 95<br>90<br>7000<br>85<br>6500<br>80<br>6000 75<br>70<br>5500<br>65<br>5000 60<br>1975 1980 1985 1990 1995 2000<br>GDPpc EXPY EXPY/GDPpc<br><!-- End of picture text --> _Source_ : Sanchez and Butler (2008).

**A low level of export sophistication can explain the low export growth of Argentina’s manufacturing sector compared to primary products and agro-industry** . However it is also well known that FDI has a significant role in the development of the manufacturing sector, and as a consequence it is interesting to analyze how Argentina has integrated into the global production chain. In this scenario, it is important to distinguish between two different value chains: _buyer-driven_ and _producer-driven_ . Buyer-driven networks refer to the situation where global buyers create a supply base upon which production and distribution systems are built without direct ownership.<sup>12</sup> Producerdriven networks emerge from production fragmentation in vertically integrated sectors. Both types of networks are analyzed below, with buyer-driven networks considering textiles and clothing (T&C), footwear and furniture; and producer-driven networks focusing on automotive and information and communication technology (ICT).

**T&C represents the most important network for Argentina, with exports representing on average 68 percent of total buyer-driven exports over the last decade** (Figure 17). However T&C also registered the steepest increase in imports over the same period, well above the value of exports. A comparison between Argentina and its main comparators illustrates that Brazil is the net best performer, showing high exports in the three networks, but a clear comparative advantage in the footwear chain. Neither Argentina nor Chile are net exporters in any of the three networks. Clearly Brazil has been able to exploit the benefits of buyer-driver networks, as its exports have maintained strong growth over the years, with an impressive trade surplus.  Overall, it seems that Argentina has not been able to integrate its production into the global chain of buyer-driven networks. In fact, import values significantly exceed export values in the three sectors. The conclusion seems to be that buyerdriven networks have not fostered Argentinean exports and have not shown important spillovers on the structure of its export-oriented industrial production.

> 12 This is common in industries in which large retailers, branded marketers and branded manufacturers play the central role in setting up decentralized production networks in a variety of exporting countries. This pattern has become common in labor-intensive, consumer goods sectors; this is the reason why the three main sectors considered in this analysis are textiles and clothing, footwear and furniture.

16
_opportunity, would you like to move permanently to another country, or would you prefer to continue living in this country?"_ In Table 6 we document the relationship between corruption, confidence, and these three outcomes. In the first column, we report the simple bivariate relationship between the confidence variable and the three outcome variables of interest, and in the second column we introduce the full set of control variables from Table 3. We find strong evidence that a lack of confidence in public institutions raises sympathy for violent protest, raises the desire to migrate, and reduces political participation. We next investigate the extent to which this reflects the effect of corruption perceptions and corruption experiences. In columns three and four we estimate regressions of the three variables on the two corruption variables alone (but still controlling for the full set of control variables from Table 3). Here we find evidence those individuals who have experienced corruption or who perceive corruption to be high in their country show support for violent protest and express increased desire to permanently leave their country. In addition, we find that having had a corruption experience lowers the likelihood of individuals voicing their opinion to public officials.

Finally, we introduce both corruption measures together with confidence in institutions as explanatory variables. Doing so sheds light on whether the effects of corruption on these outcomes operate only through confidence in institutions (in which case the corruption variables would not enter significantly), or whether there are direct effects of corruption (in which case they would enter significantly even after controlling for confidence in public institutions). In the case of corruption experiences, there seems to be fairly clear evidence of both direct and indirect effects, as both the corruption and confidence variables enter significantly. In the case of corruption experiences however the effects seem to run more through confidence in institutions. These findings provide some support to the findings of Putnam (2000) and Uslaner (2002) that institutional trust contributes to citizen‟s involvement in the political process.

# **7.  Conclusions**

In this paper we have used data from the Gallup World Poll, a unique and very large global household survey, to document a quantitatively large and statistically 24
small private ones (Figures 9 and 11). It should be noticed that this process has not been monotonic over time.

The continuation of the transformation of the economic structure has also played a role in compressing the wage distribution. During this decade, the shift from manufacturing and agriculture towards services continued (Gimpelson 2015) enabling young workers to move to higher paying jobs. This is suggested by the changes over time in the coefficients of the occupational variables. Particularly, the coefficients for low-skill occupations (unskilled workers, operators and assemblers, clerks) increased at low levels of the wage distribution. Also supporting this hypothesis, Gimpelson’s analysis of the labor force survey, which is richer in terms of occupation and industry information, suggests that there has been convergence of wages both between and within industries.

27
# **Measuring the Unmeasured: Combining Technology and Behavioral Insights to Improve Measurement of Business Outcomes**

Stephen J. Anderson Christy Lazicky Bilal Zia<sup>*</sup>

**JEL Codes:** O12, O16, C81, C93, M41 **Keywords:** Microenterprises, SMEs, Measurement, Underreporting, Evaluation, Survey Methods

> * Anderson: Stanford Graduate School of Business (sjanderson@stanford.edu); Zia: World Bank (bzia@worldbank.org); Lazicky: IDinsight (christy.lazicky@idinsight.org). We thank the Innovation for Poverty Action (IPA) country offices in Ghana, Rwanda and Uganda for hosting our study and for excellent data collection activities. Special thanks to Lukas Autenried for valuable research assistance, as well as Augustine Owusu, Alyson Caito, and Stephen Kagera for great fieldwork. We are indebted to David McKenzie for detailed comments and suggestions for improving the paper. We also thank Paul Gertler and seminar participants at the IPA SME Initiative conference, London Business School, Notre Dame, Wageningen, and the World Bank for valuable comments and suggestions. All errors remain our own. This research was supported by grants from: Private Enterprise Development in Low-Income Countries (PEDL), the John A. and Cynthia Fry Gunn Faculty Scholar award, Stanford Graduate School of Business, and the World Bank.
|To|tal AmountSales <br>(1)|Total CountSales <br>(2)|NewCustomers <br>(3)|Logof<br>NewCustomersSales <br>(4)|ContactAmountSales <br>(5)|ContactCountSales<br>(6)|
|---|---|---|---|---|---|---|
|PanelA:Differen|ce-in-differencesr|egressionswith the|fullunmatchedsa|mple|||
|TreatxPost|0.400***<br>|0.401***<br>|0.066***<br>|O.177***<br>|-0.009***<br>|-0.091***<br>|
||(0.003)|(0.003)|(0.004)|(0.006)|(0.003)|(0.003)|
|Constant|9.574***<br>|6.412***<br>|4,147***<br>|7.561***<br>|9.253***<br>|5.852***<br>|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.001)|(0.001)|
|Fixed effects|||Merchan|t, year-month|||
|Observations|2,260,776|2,260,776|2,260,776|2,260,776|2,260,776|2,260,776|
|R-squared|0.934|0.943|0.853|0.755|0.929|0.926|
|Panel8: Weighte|d difference-in-dif|ferencesregression|s with thefullunm|atchedsample|||
|Treatx Post|0.362***<br>|0.355***<br>|0.221***<br>|0.330***<br>|-0.038***<br>|-0.128***<br>|
||(0.004)|(0.004)|(0.006)|(0.008)|(0.004)|(0.004)|
|Constant|9.702***|6.703***|4,375***|7.683***|9.317***|6.031***|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.001)|(0.001)|
|Fixed effects|||Merchan|t, year-month|||
|Observations|2,260,776|2,260,776|2,260,776|2,260,776|2,260,776|2,260,776|
|R-squared|0.943|0.937|0.843|0.782|0.941|0.933|
|**Table 8: Earnings Regr**<br>Dependent variable:log|**essions - OLS**<br> of monthly ear<br>(1)|nings<br>(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Days per month (log)|0.525***|0.519***|0.522***|0.542***|-0.208|-0.209|-0.230|
||(0.10)|(0.10)|(0.104)|(0.11)|(0.28)|(0.28)|(0.28)|
|CasualDummie|-0.377***|-0.385***|-0.384***|-0.425***|-0.412|-0.381|-0.413|
||(0.09)|(0.09)|(0.073)|(0.10)|(0.26)|(0.26)|(0.26)|
|Age|<br>0.005|<br>0.002|<br>0.004|<br>0.0009|<br>-0.017|<br>-0.021|<br>-0.020|
||(0.02)|(0.02)|(0.026)|(0.02)|(0.03)|(0.03)|(0.03)|
|AgeSquared|0.007|0.010|0.006|0.011|0.035|0.039|0.039|
||(0.03)|(0.03)|(0.038)|(0.03)|(0.04)|(0.04)|(0.04)|
|Sex|<br>0.383***|<br>0.398***|<br>0.399***|<br>0.403***|<br>0.359***|<br>0.354***|<br>0.373***|
||(0.08)|(0.09)|(0.084)|(0.09)|(0.11)|(0.11)|(0.11)|
|Schooling|<br>-2.60e-06|<br>-0.020|<br>-0.021|<br>-0.008|<br>0.016|<br>0.014|<br>0.015|
||(0.03)|(0.04)|(0.055)|(0.04)|(0.05)|(0.05)|(0.05)|
|Schooling2|<br>0.002|<br>0.004*|<br>0.004|<br>0.004|<br>0.003|<br>0.003|<br>0.003|
||(0.00)|(0.00)|(0.003)|(0.00)|(0.00)|(0.00)|(0.00)|
|Program*Schooling||0.022|0.057|0.003|-0.079|-0.079|-0.078|
|||(0.07)|(0.114)|(0.07)|(0.09)|(0.09)|(0.09)|
|Program*Schooling2||-0.003|-0.005|-0.002|0.0009|0.001|0.0008|
|||(0.00)|(0.005)|(0.00)|(0.01)|(0.01)|(0.01)|
|Prior-Casualwork|0.214**|<br>0.213**|<br>0.213**|<br>0.243***|<br>0.305***|<br>0.298***|<br>0.301***|
||(0.09)|(0.09)|(0.099)|(0.09)|(0.12)|(0.12)|(0.12)|
|Prior-employee firm|0.219***|0.216***|0.218***|0.221***|0.147|0.147|0.147|
||(0.07)|(0.07)|(0.076)|(0.07)|(0.09)|(0.10)|(0.09)|
|Appr. in the past|<br>0.085|<br>0.085|<br>0.086|<br>0.084|<br>0.135|<br>0.156*|<br>0.153|
||(0.07)|(0.07)|(0.065)|(0.07)|(0.09)|(0.09)|(0.09)|
|v|||0.005|||||
||||(0.025)|||||
|v2|||<br>0.000|||||
||||(0.004)|||||
|v*program|||0.003|||||
||||(0.040)|||||
|v2*program|||0.003|||||
||||(0006)|||||
|Contractor|0.711***|0.685***|.<br>0.685***|0.668***|0.716***|0.79***|0.653***|
||(0.13)|(0.13)<br>|(0.136)<br>|(0.14)|(0.21)<br>|(0.20)<br>|(0.22)<br>|
|Program*Contractor|-0.204|-0.076|-0.250|-0.002|0.554|0.536|0.540|
||(0.15)|(0.34)|(0.637)|(0.35)|(0.48)|(0.48)|(0.48)|
|Program-Constructor|<br>0.255***|<br>0.348|<br>0.188|<br>0.417|<br>0.849*|<br>0.915**|<br>0.861*|
||(0.07)|(0.31)|(0.608)|(0.32)|(0.45)|(0.45)|(0.45)|
|Workers||||<br>0.024|<br>0.085||<br>0.103*|
|||||(0.04)|(0.05)||(0.05)|
|Capital-Intensity||||||-0.008|-0.008|
|||||||(002)|(002)|
|Input-Intensity||||||.<br>0040|.<br>005*|
|||||||.<br>(0.03)|.<br>(0.03)|
|Constant|2.955***|3.028***|2.997***|2.857***|5.402***|<br>5.334***|<br>5.07***|
||(0.52)|(0.53)|(0.59)|(0.54)|(1.01)|(1.03)|(1.04)|
|N|<br>550|<br>550|<br>550|<br>536|<br>345|<br>345|<br>345|
|R2|0.353|0.357|0.358|0.367|0.26|0.259|0.268|
|Adjusted R2|0.325|0.327|0.322|0.334|0.199|0.196|0.203|

Note:

- Subcity dummies included but not reported

- Hausman test of endogeneity for column 3: Chi2(24)=0.72

- V is the residual from the education model

- Standard errors in column 3 bootstrapped.

- *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-18-
- 16 -

Tablo 3s Quota Growth Rates in the EC and US<sup>Markets: Average Annual</sup> Percentage Change in Quota Volumes, 1986-1989

||EC<br>Market'|Us<br>Market|
|---|---|---|
|A. All<br>Quotas|||
|EAST EUROPEAN FIVE|5.2<br>|2 <sup>. </sup>5 <sup>b</sup>|
|Bulgaria|5.1|_1.9¢|
|Czechoslovakia|1.8|6.6e|
|Hungary|5.3<br>|-7.5<br>|
|Poland|4.7|3.7|
|Romania|5.6|1.9|
|East Asian Four|5.1|3.4<br>|
|All MFA Suppliers|5.2|4.0|
|B. Binding Quotasd|||
|EAST EUROPEAN FIVE|4.2|2.2|
|Bulgaria|5.4<br>|**|
|<br>Czechoslovakia|3.7|**<br>|
|Hungary|4.0|13.7|
|<br>Poland|5.1|9.6|
|Romania|4.5|1.7|
|East Asian Four|4.9<br>|3.4<br>|
|All MFA Suppliers|4.8|4.2|

Source: World Bank computer files on the MFA.

# Notes!:

"**" **=** not applicable (not under quota) Quota growth computations consider only continuous quotas,<sup>i.e., quotas</sup> which were in place in all four years between<sup>1986 and 1989.</sup>

- a EC(12) in all four years.

- b 1986-1989 average excludes Bulgaria and Czechoslovakia<sup>as 1989 data</sup> for these suppliers are not available.

- c 1986-1988 average.

- d Includes those products which had a quota<sup>utilization rate of 90</sup> percent or greater in at least two years between<sup>1986 and 1989.</sup>