rafmacalaba/datause-encoder-data huggingface dataset:
is always above 0.84, suggesting that our instrumental variables are doing a reasonable job in explaining observed trade flows. This is not surprising given that we use the gravity framework.

Table 6 provides the instrumental variable estimates of equation (1). In the first three columns we report the estimates using one of the three types of instruments for each export flow (online and total). In columns (4) to (6) we use two instruments for each trade flow, which allows us to test for the validity of our instruments. IV (2) + (3) corresponds to the first two instruments for each trade flow (i.e. obtained form equations (2) and (3) for online exports, and from equations (5) and (6) for total exports. The same logic applies to IV (2)+(4) and IV (3)+(4). In column (7) we use all instruments.

All the estimated coefficients on the share of online exports are negative as expected and statistically significant at the 1 percent level. The estimates in column (4) suggest that a 1 percent increase in online exports would lead to a 0.01 percent decline in the wage gap, which is very close to the OLS estimate. The estimated coefficient on total exports is also always negative and statistically significant with a point estimate around -0.05. The coefficient on the relative supply of skilled workers remains negative across the four specifications, but is always statistically insignificant. The share of Internet users is negative and statistically significant at the 10 percent level. At the bottom of the table we report various statistics attesting the overall validity of our instruments. The p-value of the F-test for the first stage is strongly below the admitted threshold of 5% in all specifications, and the Hansen J test shows that the overidentification restriction are valid (except perhaps in column (6) when we only use demand and tariff shocks as instruments). The Cragg-Donald F-test also attests of the strength of our instruments.

# **5 The role of small**

As discussed earlier a potential explanation for the negative impact of online exports on the wage gap is that the reduction in the fixed cost of exporting benefits small firms which tend to be less skill-intensive. While small firms are under-represented in the population of regular exporters, anecdotal evidence suggest that they account for a significant share of the firms engaged in crossborder e-commerce. The company “Ecommerce Europe” which describes itself as “the voice of the e-commerce sector in Europe” represents over 25,000 online shops across Europe. Its 2016 e-commerce report surveyed 585 European firms engaged in e-commerce and shows that 334 of the 10
# **6** **<u>Conclusion</u>**

The paper has provided evidence on the evolution of the global income distribution during a crucial period of accelerated globalization spanning the period from the end of Communist regimes in Eastern Europe and the Soviet Union to the beginning of the global financial crisis. In many respects, this might have been the most globalized (and most optimistic, in the sense of trust in the potentials of globalization) period ever in history. It is very important to study what were its effects on the level and distribution of income among the world population. Our results confirm earlier findings that the level of global inequality remains high, with a Gini of around 70%, and while inequality appears to have declined in the most recent years, these changes are probably not robust to plausible standard errors (if one could formulate and calculate them).

A robustness check whereby missing incomes, defined as the (positive) gap between per capita household consumption from National Accounts and mean per capita income from household surveys, are allocated to the top 10% of individual recipients, and top incomes are supposed to follow a Pareto distribution, returns estimates of the global Gini coefficient which are about 5 points higher. Most of the increase is due to the allocation of the entire gap to the top decile, not to the Pareto-elongation of the top of the income distribution. A value of 75% may be considered an upper bound on the “true” global Gini.

The approach whereby the entire gap is allocated to the top national deciles seems to us a more realistic one than the alternative used so far in the literature of spreading the gap evenly. In effect, we argue that the two problems that have recently been discussed separately - namely the large, and in some cases, growing gap between National Accounts consumption and means from household surveys, and the realization, due to the results obtained from fiscal data, that surveys tend to underestimate top incomes - are really one and the same problem.

Our approach returns another important result. Global inequality, measured by the Gini index, might not have gone down at all if the entire (or most) of missing income comes from the underestimation of incomes at the top of national income distributions. This important issue 48
# PPR Working Paper Series

||i:C|Author|Dat|Contact<br>paper|
|---|---|---|---|---|
|WPS307|On the Accuracy of Economic<br>Observations: Do Sub-Saharan<br>Trade Statistics Mean Anything|Alexander J. Yeats|November 1989|J. Epps<br>33710|
|WPS308|Harmonizing Tax Policies in Central<br>America|Yalcin M. B'ran|November 1989|T. Watana<br>31882|
|WPS309|Honduras-Public Sector Finances,<br>Issues, and Policy Suggestions<br>to Resolve Them|Yalcin M. Baran|November 1989|T. Watana<br>31882|
|WPS310|A Macroeconomic Consistency<br>Framework for Zimbabwe|Ali Khadr<br>Klaus Schmidt-Hebbel|||
|WPS311|Macroeconomic Performance<br>Before and After Disinflation in<br>Israel|Leonardo Leiderman<br>Nissan Liviatan|November 1989||
|WPS312|Improving Public Enterprise<br>Perrormance: Lessons from<br>South Korea|Mary M. Shirley|October 1989|R. Malcolm<br>61708|
|WPS313|The Evolution of Paradigms of<br>Environmental Management in<br>Development|Michael E. Colby|||
|WPS314|Primary Commodity Prices and<br>Macroeconomic Variables: A Long-<br>run Relationship|Theodosios Palaskas<br>Panos Varangis|November 1989|D. Gustafson<br>33714|
|WPS315|Notes on Patents, Distortions,<br>and Development|Julio Nogu6s|||
|WPS316|The Macroeconomics<br>of Populism<br>in Latin America|Rudiger Dornbusch<br>Sebastian Edwards|||
|WPS317|Price and Quality Competitiveness<br>of Socialist Countries' Exports|Zdenek Drabek<br>Andrzej Olechowski|||
|WPS318|Debt Buybacks Can Indicate<br>Sovereign Countries' Willingness<br>to Raise Investment and Repay<br>Debt When Partial Debt Relief is<br>Offered: Theory and Tests|Sankarshan<br>Acharya<br>Ishac Diwan|||
|WPS319|Trends in South-South Trade and<br>the Potential in Non-Discriminatory<br>Liberalization of Barriers|Refik Erzan|||
In order to safeguard competition, bank supervision should be conducted in a way that ensures a level-playing field for all institutions. There are a number of reasons to be concerned with how bank supervision is conducted in Russia.<sup>**8**</sup> First, small and transparent banks could be disadvantaged compared to large and complex banks due to the absence of proper consolidated supervision. In other words, due to their complexity and because of the lack of adequate consolidated supervision larger banks could “get away with things” (like taking on excessive risks) that smaller banks cannot. Second, the regional and functional fragmentation of the Russian banking system could be causing dispersion of supervisory practices across different types of banks and regions due to the potential for supervisory capture on one side or abuse on the other side. Third, the allocation of supervisory resources to regions decreased over time and, hence, supervision in the regions could be lacking and favoring improper competition in the regions compared with the Moscow district (see Table 4). Fourth, the overall operating environment for banking supervision could be distorted by relatively higher political influence compared to other large developing countries.

The cross-country comparison of some institutional arrangements for supervising problem banks and dealing with failed banks also suggests some differences that may obstruct competition in Russia’s banking sector (see Table 5). Russia is the only BRIC country unequipped with legally enforced cease and desist orders. Further, together with Brazil, Russia’s banking supervisors are not required to make timely disclosure of enforcement actions. While all BRICs’ banking supervisors have the power to suspend distribution of dividends, in Russia the banking supervisors cannot suspend the distribution of bonuses and management fees. This can have negative implications for market transparency, discipline, and effective dealings with

> **8** For a detailed discussion see World Bank (2010).

11
## **Figure 3.11: Real private consumption and investment for government investment simulations**

**(% level deviation from base)**

<!-- Start of picture text --> Private Consumption Private Investment<br>3 5<br>2<br>0<br>1<br>0 ‐5<br>‐1 ‐10<br>‐2<br>‐15<br>‐3<br>‐4 ‐20<br>ginv‐tdir ginv‐dbor ginv‐fbor ginv‐tdir ginv‐dbor ginv‐fbor<br>2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Source: Authors’ calculations based on simulation results.

**Figure 3.12: Real private consumption and investment for external shock simulations (% level deviation from base)**

<!-- Start of picture text --> Private Consumption Private Investment<br>4.0 0.8<br>3.5 0.7<br>3.0 0.6<br>2.5 0.5<br>2.0 0.4<br>1.5 0.3<br>1.0 0.2<br>0.5 0.1<br>0.0 0.0<br>pwe remit pwe remit<br>2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Source: Authors’ calculations based on simulation results.

# **4. Conclusions**

This paper presents GEM‐Core, a model for country‐level CGE analysis, covering its structure and data needs, and demonstrating its use in a macro application. The simulations in this paper are examples of the types of shocks that can be simulated with a two‐sector database. Other examples include macro aspects of taxation, foreign aid, demography, labor force participation, savings, investment, and domestic and foreign debt sustainability.

‐27‐
TABLE 8 EGYPT 1975-80 THE DIRECT IMPACT OF NEXT PREGNANCY ON **WEANING** (before age 60 months) % per cent pregnant (mother became pregnant prior to weaning) RR relative risk of weaning (pregnant / not pregnant) AR attributable risk of weaning

-------------FERTILITY CONTROL--------------ALL (N-7375 children) NO (N=4714 children) YES (N-2661 children) EST. (95 % CI) EST. (95 % CI) EST. (95 % CI)

PREGNANT at any age % 17.33 (16.46 - 18.19) 17.59 (16.50 - 18.67) 16.87 (15.45 - 18.30) RR 2.92 ( 2.72 - 3.14) 3.24 ( 2.96 - 3.55) 2.21 ( 1.94 - 2.52) AR 24.96 (22.68 - 27.18) 28.27 (25.24 - 31.18) 16.97 (13.30 - 20.49) PREGNANT before 12 months % 9.36 ( 8.69 - 10.02) 9.86 ( 9.01 - 10.71) 8.45 ( 7.40 - 9.51) RR 2.92 ( 2.71 - 3.15) 2.90 ( 2.63 - 3.20) 2.19 ( 1.88 - 2.54) AR 15.23 (13.49 - 16.93) 15.77 (13.44 - 18.04) 9.12 ( 6.59 - 11.59) PREGNANT at 12 months or later % 14.87 (13.77 - 15.98) 15.29 (13.85 - 16.74) 14.24 (12.51 - 15.97) RR 3.20 ( 2.87 - 3.58) 3.91 ( 3.43 - 4.47) 2.27 ( 1.82 - 2.82) AR 24.68 (21.31 - 27.90) 30.82 (26.37 - 35.01) 15.29 ( 9.82 - 20.43) based on Weibull proportional hazards model controlling for: parity, age of mother, preceding birth interval length, sex, region, urban/rural residence, fertility control, education of mother, education of father based on data from the Egyptian Fertility Survey 1980 restrictions: children born within 60 months of survey to married women last two children only no multiple births no marital disruption prior to child death Muslim only note: some mothers do not become pregnant by survey

*-not statistically distinguishable from 0.0 for per cent (%) and attributable risk (AR) and from 1.0 for relative risk (RR)

40
**Annex C: Participating Schools, Students and Teachers**

|**School**|**Num. of**<br>**students**<br>**(Control and**<br>**Treatment)**|**CBT**<br>**facilitators**|**Math**<br>**tutors**|
|---|---|---|---|
|DGB|181|4|4|
|DGETI A|120|4|4|
|DGETI B|137|6|3|
|DGETI C|363|5|8|
|DGETI D|227|4|5|
|DGETI E|181|4|4|
|DGETI F|183|5|4|
|COLBACH A|605|14|13|
|COLBACH B|227|15|12|
|COLBACH C|459|15|9|
|COLBACH D|491|13|8|
|COLBACH E|413|5|5|
|COLBACH F|363|6|10|
|CONALEP A|137|5|5|
|CONALEP B|181|5|4|
|CONALEP C|192|3|4|
|CONALEP D|226|3|3|
|CONALEP E|225|3|5|
|CONALEP F|181|6|5|
|CONALEP G|182|3|4|
|**Total**|**5274**|**128**|**119**|

27
15 wealth, area of study, and the subject’s belief that the charity was paid in accordance with the instructions.<sup>9</sup>

**Table 3:  Treatment effects on effort in pro-social organizations, pre-selection (all subjects)**

|Dependent Variable:|Effort for Charity<br>Ability-<br>adjusted Effort<br>I|Relative to Abilit<br>Ability-<br>adjusted Effort<br>II|y<br>Ability-<br>adjusted Effort<br>III|
|---|---|---|---|
|Pay-for-Ability (D)|0.021|0.020|0.021|
||(0.03)|(0.03)|(0.03)|
|Pay-for-Performance (D)|0.086**|0.087**|0.093**|
||(0.04)|(0.04)|(0.04)|
|Amount Sent in Dictator Game||-0.015|-0.013|
|||(0.02)|(0.02)|
|Gender (D)||0.016|0.015|
|(1 = Female)||(0.03)|(0.03)|
|Age (in years)||-0.002|0.000|
|||(0.01)|(0.01)|
|Religious Attendance|||0.004|
|(5 = More than Once a Week)|||(0.01)|
|Family Income (Relative to Others)|||0.027*|
|(5 = Much Above Average)|||(0.02)|
|Accounting Major (D)|||0.017<br>(0.03)|
|Tax Major (D)|||0.023<br>(0.03)|
|Belief that Charity was Paid|||-0.023|
|(5 = Complete Confidence)|||(0.02)|
|Constant|0.999***|1.048***|0.991***|
||(0.02)|(0.21)|(0.24)|
|R-squared|0.013|0.015|0.029|
|P-Value|0.061|0.268|0.257|
|Observations|430|430|430|

Note: * p<0.1, ** p<0.05, *** p<0.01. Dependent variable is ability-adjusted effort in the Pro-social task (task 2). OLS specification, standard errors in parentheses. The dictator game variable is divided by 1000 for presentational convenience.

Table 3 presents the results of the analysis. The first two rows, displaying the coefficients of the pay for ability and pay for performance dummy variables, are the results of greatest 9 Wealth is measured by responses to the question “Relative to other students at your institution, would you say your family income is:” where 1 = “Much below average” and 5 = “Much above average”
# ���� ����� ��� ���� �����       (3)

where i refers to specific capital flows, c to the country and t to quarter-year. We regress ����, capital flows of type i (normalized by subtracting from each observation its country specific mean and dividing it by the country specific standard deviation) on a dummy for the global stop, country-fixed effects, ��, and country-specific time trends, ���.

**Table 9: Capital Inflows around Global Stops**

||FDI|Portfolio Equity|Portfolio Debt|Other flows|
|---|---|---|---|---|
|������ �����|0.262**|-0.536***|-0.712***|-0.761***|
||[2.35]|[-4.59]|[-7.65]|[-5.59]|
|Country fixed-effects|Yes|Yes|Yes|Yes|
|Country specific trends|Yes|Yes|Yes|Yes|
|Observations|3,237|2,962|3,084|3,213|
|# of countries|34|33|34|34|
|R-squared|0.11|0.05|0.07|0.07|

_Notes:_ The dependent variables are capital flows of the respective type as a percentage of trend GDP. They are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level.

**Table 10: Capital Outflows around Global Stops**

||FDI|Portfolio Equity|Portfolio Debt|Other flows|
|---|---|---|---|---|
|������ �����|0.006|-0.028|-0.342**|-0.473***|
||[0.06]|[-0.24]|[-2.61]|[-4.21]|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Country specific trends|Yes|Yes|Yes|Yes|
|Observations|3,201|2,709|2,871|3,209|
|# of countries|34|32|32|34|
|R squared|0.176|0.071|0.038|0.042|

_Notes:_ The dependent variables are capital flows of the respective type as a percentage of trend GDP. They are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level.

Results are in Tables 9 and 10. While portfolio equity, portfolio debt and other inflows all decline in global stops, FDI inflows do not, suggesting that they are heavily influenced by other factors. Strikingly, FDI inflows behave “countercyclically,” rising significantly during global stops. On the other hand, resident outflows decline around global stops only in the case of portfolio debt and other flows (the change in outward FDI and portfolio equity outflows is essentially zero). To the extent that there is stabilizing behavior during episodes of global stops, it comes through rising 17
|**Administration and procedures 2003**|||||-0.8587|
|---|---|---|---|---|---|
||||||(1.351)|
|**Change in per capita agricultural**<br>|-0.0001**|0.0001|-0.0001**|-0.0001*|-0.0001**|
|**spending 06-07 (“incremental “ effect)**|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|<br>**Change**<br>**squared**<br>**in**<br>**per**<br>**capita**||-0.0000||||
|**agricultural**<br>**spending**<br>**06-07**<br>**(“incremental” effect)**||(0.000)||||
|<br>**Chuquisaca**|0.3492***|0.3479***|0.3507***|0.3411***|0.3322***|
||(0.117)|(0.118)|(0.114)|(0.119)|(0.122)|
|**Cochabamba**|0.1234|0.1135|0.1235|0.1231|0.1304|
||(0.095)|(0.094)|(0.095)|(0.095)|(0.096)|
|**Oruro**|0.1238|0.1136|0.1245|0.1383|0.1262|
||(0.104)|(0.103)|(0.104)|(0.104)|(0.104)|
|**Potosí**|0.3438***|0.3485***|0.3444***|0.3485***|0.3385***|
||(0.097)|(0.099)|(0.097)|(0.096)|(0.097)|
|**Tarija**|-0.2784***|-0.2794***|-0.2779***|-0.2739***|-0.2799***|
||(0.090)|(0.078)|(0.090)|(0.094)|(0.094)|
|**Santa Cruz**|-0.2895***|-0.2823***|-0.2892***|-0.2874***|-0.2803***|
||(0.062)|(0.061)|(0.063)|(0.063)|(0.064)|
|**Beni**|-0.3124***|-0.3003***|-0.3125***|-0.3053***|-0.3038***|
||(0.067)|(0.063)|(0.067)|(0.070)|(0.071)|
|**Pando**|-0.3320***|-0.3197***|-0.3313***|-0.3237***|-0.3298***|
||(0.064)|(0.062)|(0.065)|(0.072)|(0.070)|
|**Observations**|322|322|322|322|322|
|**R2**|0.236|0.239|0.236|0.239|0.245|
|***** p<0.01, ** p<0.05, * p<0.1**||||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

Annex 5 conducts robustness checks by including additional political and managerial controls of the municipality and further investigating the association of agricultural spending with specific improvements of vulnerability (as well as specific cases of vulnerability deterioration). Key messages do not change.

# **6. Conclusions**

This paper explores the reduction of food insecurity in Bolivia, adopting a supply side approach that analyzes the role of agricultural spending on vulnerability. Previous findings on the role of 21
# **Table A2: OECD Development Centre African Economic Outlook (AEO)**

**Data Provider** OECD Development Centre **Description** Multilateral organization headquartered in Paris, France **Website** www.oecd.org/dev **Data Source** African Economic Outlook **Type** Expert Assessment **Respondents** OECD Development Center Staff **Frequency** Annual since 1996 **Coverage** African countries **Public Access** Publicly available through African Economic Outlook report tables. **Description** Indicators are based on the frequency of newspaper reports on incidents related to two indicators:  "hardening of the regime" and "political troubles". Newpaper reports are taken from the weekly newspaper Marchés Tropicaux et Méditerranéens, and are either coded as 0-1 for the occurrence of relevant stories or 0-3 on the severity of reported incidents.  Total scores are reported for each country and year, and the distribution is highly skewed by a few countries with very high frequency of reported events.  We therefore convert to a threepoint scale corresponding to observations in the first, second, and third terciles of the distribution in each year.

|**Voice and Accountability**|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|
|Hardening of the Regime|X|X|X|X|X|X|X|X|X|
|**Political Stability and Absence of Violence**||||||||||
|Political troubles|X|X|X|X|X|X|X|X|X|
|**Government Effectiveness**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|..|..|..|..|..|..|..|||
|NA|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Control of Corruption**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Country Coverage**|**36**|**33**|**33**|**32**|**26**|**26**|**26**|**26**|**25**|
|**Year of Publication**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**1996**|

40
Structural estimates of the average “kin tax” are significantly different from zero for women, estimated at roughly 4.3 percent for those whose relatives did not attend the experiment, but at 8.0 percent for those with relatives present. Our model of stochastic choices in the experiment fits the data well, explaining both investment and exit decisions. We see no evidence that this behavior can be explained by household bargaining with a spouse, aversion to public announcements, or aversion to risk-taking in general.

We hypothesize that the behavior observed in this experiment is a sign that village sharing norms distort investment incentives towards less visible, but potentially less profitable, investments, and may consequently slow economic growth. The negative correlations we observe between the extent of income hiding at the village level and the level of prosperity in the village, measured several different ways, are in agreement with this interpretation. However, such results should be interpreted with caution, since the direction of causality is unclear. Moreover, the efficiency impacts of social pressure to share income will clearly depend on the range of income-hiding technologies available, and correlation between observability and profitability. There is still much to be learned about the range of observable and unobservable investment opportunities available to poor households, the set of technologies for hiding or protecting income from pressure to share, and the mechanics of that pressure. Whether the levels of social pressure we observe are large or small will depend on the ways in which social pressure is exerted outside of the lab: for example, whether individuals are pressed to share all cash income (for example, business revenues) or only their profits after compensating themselves for their indirect costs or labor time.

Studies of mutual insurance typically assume that transfer arrangements are on the efficient frontier, though the analogous assumption has been called into question in intrahousehold bargaining contexts. Our work suggests that relationships with close kin outside the household may be similar to within-household interactions, and that social sanctions which encourage cooperation and sharing may also have important disincentive effects.

38