rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Causes**

# **II.A. What Caused Hyper-globalization?**

The factors that have contributed to the hyper-globalization of the 1990s and 2000s have been extensively analyzed in the literature. The World Bank’s 2020 World Development Report (WDR) on Global Value chains provides a succinct summary of the main drivers, which involve a combination of technology and policy. Major technological advances after World War II led to dramatic declines in transportation and communication costs that enabled the fragmentation of the production process, so that different stages of production could be outsourced to different countries to take advantage of international cost differences. This process led to the emergence and growth of Global Value Chains and to unprecedented growth in gross trade as intermediate goods and services crossed borders many times before final products reached their intended destinations. But technology alone would not have achieved this growth, had it not been for a set of policies that created the right environment for trade and GVCs to flourish: sharp reductions in tariff and nontariff barriers, often within the context of unilateral trade liberalizations by developing countries<sup>11</sup> ; bilateral and regional trade agreements that liberalized trade for the member countries; and last but not least, the expansion of the World Trade Organization (WTO) to include several developing countries including China. Multilateralism played an important role during this period by reducing uncertainty. When goods and services are traded across the entire globe, regional agreements do not suffice; only multilateralism can offer the degree of stability and predictability required for global trade to flourish<sup>12</sup> . At the same time, the ideological shift towards neoliberalism, fueled by the fall of communism in Eastern Europe and the Soviet Union, provided a supporting intellectual backdrop for the changes that were taking place, encouraging the entry of several countries in Eastern Europe, Central Asia and East Asia into the global trading system.

Against this background, it is natural to ask what has changed since that time, causing a possible reversal of earlier trends. Not surprisingly, the main drivers are the same factors that led to the hyper-globalization of the past: technology and policy.

> 11 See Irwin (2022) for an overview of the trade reform wave of 1985-1995, the years leading up to the era of hyperglobalization.

> 12 For a more detailed discussion see Goldberg and Pavcnik (2016), Goldberg (2019) and Goldberg (2023).

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# **Figure 3**

<!-- Start of picture text --> Energy consumption (KgOE) and share of energy on expenditure (rural)<br>100% 13.0%<br>12.0%<br>80%<br>11.0% Natural Gas<br>60%<br>10.0% LPG<br>40%<br>9.0% Electricity<br>20% Gasoline<br>8.0%<br>0% 7.0% Energy %<br>0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Deciles<br>% energy expenditure Energy / total expenditure<br><!-- End of picture text --> Source: own calculations using ENIGH 2010

**Figure 4**

<!-- Start of picture text --> Energy consumption (urban)<br>160<br>140<br>120<br>100<br>Natural Gas<br>80<br>Electricity<br>60<br>LPG<br>40<br>Gasoline<br>20<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>KgOE<br><!-- End of picture text --> Source: own calculations using ENIGH 2010 According to Figure 4, urban households have a steep increase in their use of gasoline when income increases, while for the lower deciles there is an increase of up to 20 KgOE in the consumption of electricity and LPG. While the lower income households can consume half of the energy units of gasoline that they consume of LPG or electricity, the upper decile consumes more than double the energy units of gasoline 14
Gfais & Zheng Strategic Intcrdependence inthe East-West Gas Trade

# _Notes:_

1. An additional amount of about 30 bcm could be supplied with limited investments in the order of $325 million. Expansion of supplies by additional 60 bcm could be made with further investments at still a reasonable rate of retum. Beyond these amounts transport costs rise significandy. Moreover, energy efficiency improvement in Russia through structural adjustment and appropriate energy pricing policies can also be expected to contribute to energy conservation in the domiestic market and thus generate additional exportable gas.

2. In addition each transit country has its own privatization and coxporatization objectives that bear on the fate of the companies managing the transit services and the commercial policies these would follow.

- 3- All of these policies seem to be pursued by Gazprom. For instance, in the fall of 1990 Gazprom joined Wintershall/BASF in a new partnership.

4. Considering individual countries as idependent players can be done at some cost but would not add insights.

5. From (1) we have <!-- Start of picture text --> aP e 0 - c0e = a=-o,<br>afa eD aT eD OtR 6 D ijDn -C<br><!-- End of picture text --> 6. In fact one would take the utility funcdon of a epresentative consumer and the size of the populaton. Here we abstract from that to simplify the presentation without loss of generality.

7. The first order condition for the transit revenue ma on can be written as <!-- Start of picture text --> MxR'= & =CT- S)(- a)_ MC T<br><!-- End of picture text --> 8. The first order condition for Russia's gas exports can be wrten as _AMRR_ = _GG_ = +RC(aG _aT PR C) aGYa -a;_ -= **_MC"I_** 9. Cooperation here is understood in the game theory terminology, i.e. sharing of information, of resources and pay-offs. Players would cooperate if they ex ante agree 17
## **4.6.1 Spatial disparities**

Transport and border investments reduces the transport and trade costs between locations leading to the relocation of economic activities within and across countries, and the movement of people within countries. The differential impacts of the investments will not only bring different welfare benefits across countries but also affect economic disparities across and within country.

Transport and trade investments can lead to either convergence or divergence at the regional and national levels. We use coefficients of standard deviation to quantify the impacts of the investments on spatial disparities in terms of real wages. The higher the coefficient, the higher the disparities between all the locations. At the regional level of West Africa, the coefficients of standard deviation will drop by 0.08% when investing only in the Dakar-Lagos corridor, by 2.2% when only investing in the road corridor, and by 3.5% when removing transit delays and reducing final border delays by half. For all scenarios, reducing trade costs will reduce spatial inequality in real wages across locations for the whole of West Africa.

Within country, spatial inequality between regions will increase in some countries and decrease in others. Tables A6 and A7 present the changes in the coefficients of standard deviation for each scenario at the country level. Road investments only will reduce spatial inequality in Guinea-Bissau and Sierra-Leone. Additional reductions in trade costs will lead to further reductions in within-country spatial inequality for most countries, but the changes remain small. Similar tables are presented for the four other corridors in the Appendix.

# **5 Conclusion**

This paper estimates the welfare gains from upgrading several major regional corridors in West Africa. It uses the quantitative economic geography framework from Redding (2016) with trade within and across countries, and mobility of people within countries to assess the economic impacts of reduction in trade costs from road and border infrastructure investments. First, we quantify the direct gains for locations along the corridors as well as the indirect spillovers for more distant locations. We find that the upgrade of Dakar-Lagos regional road corridor brings sizable economic benefits relative to investment costs, with a benefit-cost ratio estimated around 3. The economic benefits of road corridor upgrades are doubled and more widely spread when combined with measures to reduce current massive border delays. The benefits are negligible for Nigeria, but large for small, fragile states (Guinea-Bissau, Liberia, and Sierra Leone). The gains are highest for corridors connecting large economies, and smaller and more fragile countries gain proportionally more from accessing larger markets. Finally regional investments, including border time reduction policies, will reduce spatial inequality in the whole region but might increase inequality in some countries.

More work remains to be done to better assess the effects of transport investments. First, the paper relies on strong assumptions on speed and border delays because of the lack of reliable and 29
**Table 6: Effects of PH lockdown policies on its export and import trade**

|**Lockdown event dummies**|**Export value**|**Export quantity**|**Import value**|**Import quantity**|
|---|---|---|---|---|
|Lead14|-17.175|68.871|-387.447***|-362.702**|
||(62.466)|(72.963)|(138.960)<br>|(141.047)<br>|
|Lead13|-16.577<br>|55.537<br>|-407.726***<br>|-388.132***<br>|
||(50.014)<br>|(57.619)<br>|(124.583)<br>|(128.420)<br>|
|Lead12|-10.657<br>|41.271<br>|-325.559***<br>|-288.970**<br>|
||(50.179)|(56.845)|(115.160)|(118.536)|
|Lead11|3.653<br>|75.148<br>|-310.940***<br>|-295.110**<br>|
||(42.900)|(53.652)|(112.382)<br>|(117.824)<br>|
|Lead10|-2.288<br>|52.657<br>|-303.603***<br>|-259.155***<br>|
||(40.875)|(47.299)|(95.906)<br>|(100.382)<br>|
|Lead9|-19.585<br>|46.125<br>|-260.634***<br>|-252.310***<br>|
||(39.438)|(46.206)|(88.876)|(96.657)|
|Lead8|35.136<br>|65.661<br>|-221.388***<br>|-174.882**<br>|
||(35.200)|(44.911)|(83.145)|(85.772)|
|Lead7|-28.043|6.475|-272.991***|-242.466***|
||(35.546)|(46.027)|(76.487)|(79.867)|
|Lead6|0.130|47.862|-183.414***|-150.583**|
||(32.454)|(41.584)|(68.329)|(68.519)|
|Lead5|1.055<br>|25.113<br>|-186.607***<br>|-191.841***<br>|
||(31.349)|(38.952)|(68.282)|(68.691)|
|Lead4|-30.587<br>|25.887<br>|-149.440***<br>|-109.007**<br>|
||(27.575)|(36.311)|(48.381)|(48.336)|
|Lead3|-12.207<br>|2.765<br>|-26.218<br>|-50.175<br>|
||(25.091)|(29.793)|(41.885)|(42.205)|
|Lead2|-2.229<br>|9.785<br>|-56.942*<br>|-20.457<br>|
||(31.652)|(50.305)|(32.347)|(34.474)|
|Lag0|-9.357|17.588|-79.845|-105.788|
||(56.128)|(62.262)|(77.656)|(81.106)|
|Lag1|-21.245|11.347|-27.555|-61.241|
||(50.554)|(62.033)|(77.704)|(74.505)|
|Lag2|-22.359<br>|-2.877<br>|-32.622<br>|-60.719<br>|
||(48.822)|(52.224)|(66.657)|(67.755)|
|Lag3|-17.406|5.252|-19.429|-41.418|
||(44.439)|(54.473)|(59.706)|(59.326)|
|Lag4|-39.523<br>|-46.982<br>|-5.294<br>|-68.011<br>|
||(41.074)|(48.523)|(54.306)|(55.190)|
|Lag5|-2.393|-6.701|-13.549|-38.502|
||(41.051)|(47.574)|(45.666)|(46.742)|
|Lag6|-17.121<br>|-3.925<br>|-32.666<br>|-50.086<br>|
||(32.130)|(42.357)|(33.183)|(36.617)|
|Lag7|-5.367|6.524|-29.424|-46.100|
||(28.022)|(38.684)|(37.709)|(36.186)|
|Lag8|-41.028<br>|-27.619<br>|-|-|
||(32638)|(41821)|-|-|
|Lag9|.<br>-|.<br>-|-9.744|-20.598|
||-|-|(31.496)|(27.281)|
|Constant|918.692***|549.824***|1266.384***|892.179***|
||(78.346)|(87.163)|(153.775)|(154.173)|
|Observations|243526|243526|848983|848983|
|Number of pair id|44792|44792|120298|120298|
|R-squared|0.011|0.020|0.017|0.027|

Note: Estimation was conducted on the monthly trade data using a fixed effects estimator that accounts for cross-country-product heterogeneities. Robust standard errors are reported in parentheses. Asterisk indicate the level of significance, *** _p <_ 0 _._ 01, ** * _p <_ 0 _._ 05, _p <_ 0 _._ 1.

34
<!-- Start of picture text --> Funnel plot on baseline coefficients [N=1,391; skweness = 0.460]<br>1000 |<br>ao '<br>?\ 800 !<br>[o} 1<br>2 1<br>!<br>2 600 i<br>=@ 11<br>28 400 Pex<br>3 Es<br>5o =|"<br>‘3a2 200ol ten ee eee attFob+ Saye.tteAtte 1 LER tata ae + OtherAgr.Agelydr/Min. anockshock o(+)~shockke)(+<br>-0.20 -0.10 0 0.10 0.20 0.30<br>PCC (+: more risks of conflicts; -: less risks of conflicts)<br><!-- End of picture text -->

<!-- Start of picture text --> Funnel plot on negative agricultural shocks [N=481; skweness = 1.175]<br>1000 t<br>1<br>Ww—?\ 00 11!<br>2°Q 1 1<br>1<br>£& 600 1<br>g 1<br>3 1<br>2 400 j<br>225\I<br>2 200 ra<br>a i r<br>0<br>-0.20 -0.10 0) 0.10 0.20 0.30<br>PCC (+: more risks of conflicts; -: less risks of conflicts)<br><!-- End of picture text --> <!-- Start of picture text --> Funnel plot on positive agricultural shocks [N=448; skweness = 0.429]<br>500 I<br>ity I<br>2 400<br>fo} 4<br>° #1<br>= anal<br>2 300 1<br>=@ batt+|<br>28 200 &:|<br>rc) |<br>53 iineteeaf |e ae<br>img2 1006 + + te te eee.tetRiceeagreeett4a ee Fe a|1 ae”Raett 4+atotheete hte“ .+<br>-0.20 -0.10 0 0.10 0.20 0.30<br>PCC (+: more risks of conflicts; -: less risks of conflicts)<br><!-- End of picture text -->

<!-- Start of picture text --> Funnel plot on positive hydrocarbon/mineral shocks [N=325; skweness = -0.386]<br>1000 I<br>1<br>Ww—21 800 11!<br>2°Q !1<br>= 1<br>2 600 1<br>g !<br>B 1<br>23 400 i\<br>25 Hli<br>2 200 :<br>a2 i l<br>1<br>0<br>-0.20 -0.10 0) 0.10 0.20 0.30<br>PCC (+: more risks of conflicts; -: less risks of conflicts)<br><!-- End of picture text -->
marry men who are on average 3 years older than them. On average, women participate in 0.3 decisions out of a maximum of 5. Women agree to an average of 0.2 statements out of 5 on when it would be justified for a man to beat his wife. These characteristics are similar across the two samples.

Table A3 presents the summary statistics for the men in each sample. The size of the male sample is 75 to 80 percent of the female sample, since women of childbearing age are over-sampled in the DHS. About 53 percent of the men in the sample are working. The average age of the cohort is 21, a year older than the women’s average age. About a quarter of men have more than secondary education, slightly lower than the women’s share.

# **3 Results**

## **3.1 Main results**

The results of the main marriage penalty estimation are in Table 1. The model collapses the yearly event study indicators into pre- and post-marriage periods, with the full set of birth cohort and country-year fixed effects. The post-marriage treatment indicator is then interacted with a binary variable indicating whether the cohorts exclude women with children (the “no child sample”) or allow for childbirth (the “ignore child sample”).

Panel A shows the results for women across the region (column 1) and then the four constituent countries (columns 2-5). On average, marriage reduces labor force participation by 11.7 percentage points across South Asia among childless women in the five years after marriage. Among our sample countries, the penalty is largest in India, at 12.1 p.p., and lowest in Maldives, at 2.3 p.p., where it is not significant.

Across the region, the marginal effect of allowing for childbearing in the regression specification is small. Whereas the marriage penalty among all women is 15.7 p.p., the marriage penalty for childless women is 11.7 p.p., suggesting that childbearing explains just 25 percent of the aggregate family formation penalty. However, the gaps between the – ignore child and no child samples vary widely. They are smallest in India and Bangladesh – the large countries that dominate the aggregate sample where the total penalty and the 11
and the direct experience data contained in the _Enterprise Surveys_ , we have the ability to explore the existence and nature of the gap between the two.

# **2.2 Are perceptions correlated with the incidence of bribes?**

In likely response to the criticisms and limitations of perceptions data on corruption, there is a nascent empirical literature developing on the relationship between experience with corruption and perceptions of 7 corruption. Olken (2006) takes a detailed, micro approach and examines corruption perceptions and incidences of skimming off the top from road construction in Indonesia. He finds that perceptions on the extent of corruption and objective levels are a good reflection of each other when the extent of corruption can be easily and objectively confirmed. However, when corruption is carried out in ways that are easily hidden and unverifiable, perceptions and the extent of corruption begin to diverge. Two country studies, one in Ukraine (Čábelková and Hanousek 2004) and one in Uruguay (Moyal et al. 2004), show that media sources are influential in forming perceptions of corruption and, most importantly, these studies show how negative perceptions of corruption may reinforce people’s willingness to offer bribes. However, these two studies do not have data that tally direct experiences with bribing or other forms of corruption.

While these micro studies show that it is difficult to disentangle the relationship between perception and experience, Tirole (1996) provides a theoretical model as to the dynamic nature of perceptions and experience and under what conditions perceptions and experience of corruption diverge. With respect to the dynamics, collective reputations are difficult to change. With respect to their divergence, Tirole points out that the corrupt acts of others stick to all officials, even when there may be only a handful of corrupt officials. Furthermore, this collective reputation provides few incentives for even honest civil servants to maintain their integrity and remain incorruptible. In sum, both the nature of the dynamics and of the collective reputation of corruption requires many repeated acts of honesty from public officials to wipe out the perception that corruption is prevalent.

Informed from what we know of the empirical and theoretical literature, we fill the gap of research on the nature and (possibly) its dynamics on the relationship between perception and experience with corruption. We do so by matching perceptions data on corruption to the transaction data to determine if perceptions differ when a firm is or is not a victim of graft and for the first time, to the best of our knowledge, determine the relationship between the two.

For perceptions, we use data from cases where firms are asked to rank the top three obstacles, out of a list of sixteen, that affect the operations of the establishment. Answers from all entrepreneurs asked 7 For a cross country examination of the relationship between perception of and experience with corruption, see Weber (2007).
annualized rate over the entire period ( ). We have two scenarios with a positive shared prosperity: ( ) implies that the mean of the bottom 40% grows 1pp (2pp) faster than mm= 0% the mean. Similarly, we have two scenarios with a negative shared prosperity premium, where the mm= 1% mm= 2% bottom 40% grows slower than the mean ( and ). mm= −1% mm= −2% Figure 4 compares these values of against the observed differences in household survey growth rates for the bottom 40% and the mean. Over 271 comparable growth spells observed in PovcalNet, for all mm years (Panel A), the median value of observed is practically zero. For the sample of 72 official shared prosperity spells from approximately 2006-2011 (Panel B), released in October 2014, the median value mm of is 0.7pp. More than a quarter of the most recent spells show . However, only time will tell whether this change in the shared prosperity premiums is a one-off event or the beginning of a trend mm mm> 2% and, if so, how widespread a phenomenon it is. While we clearly impose an optimistic shared prosperity scenario, our maximum premium of is not unprecedented in past spells. mm= 2% **Figure 4: Observed shared prosperity premiums ( ) compared with shared prosperity premiums ( ) used in simulations** (A) Historical spells in PovcalNet of 5 years or more mm (B) Official shared prosperity spells for most recent mm period of approximately 5 years <!-- Start of picture text -->
-2% +2% -2% +2%<br>-.1 -.05 0 .05 .1 -.1 -.05 0 .05 .1<br>observed m (growth in the bottom 40 - growth in the mean) observed m (growth in the bottom 40 - growth in the mean)<br>20 40<br>15 30<br>10 20<br>Density Density<br>5 10<br>0 0<br><!-- End of picture text -->

Source: PovcalNet. Based on 271 non-overlapping spells of five years Source: Global Database of Shared Prosperity, circa 2006-2011<sup>14</sup> or longer between 1978 and 2012. Six outliers are excluded.

Our simulations start with the ‘line-up’ for 2011, which are the World Bank’s most recent estimates of mean income or consumption and its distribution within the developing countries for which there exist

> 14 Global Database of Shared Prosperity, circa 2006-2011, available at: <u>http://www.worldbank.org/en/topic/poverty/brief/global-database-ofshared-prosperity</u>

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concentration would benefit those households at the bottom of the income distribution more (in relative terms), since households in the lower income deciles allocate a larger share of their consumption to corn products. For instance, improvement among households in the four lowest deciles of moving from a concentrated market to perfect competition would be equivalent to 2.89, 2.25, 1.90, and 1.57 percent of their consumption, respectively. In contrast, the average relative impact among households in the three highest decile is only about 0.4 percent (though the absolute impacts increase as one moves toward higher income deciles), since corn is a staple in the Mexican diet especially relevant among low-income groups.

**Table 7. Expenditures on corn products and potential distributive impact of higher competition**

||**All**|Poo|rest||Hous|ehold in|come d|eciles||Rich|est|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||1|2|3|4|5|6|7|8|9|10|
|Average pc income<sup>a</sup>|3,499|558|1,012|1,326|1,634|1,987|2,395|2,933|3,772|5,338|14,041|
|Absolute impact<sup>b</sup>|23.1|16.1|22.7|25.2|25.6|24.6|25.4|26.0|24.6|23.1|17.6|
|Relative impact<sup>c</sup>|0.66|2.89|2.25|1.90|1.57|1.24|1.06|0.89|0.65|0.43|0.13|

_Source:_ World Bank estimates using ENIGH 2014. a. According to current income (MX pesos of August of 2014). b. The average impact on well-being (moving from concentrated to competitive market) c. The average absolute impact over the average income (in %).

Regarding the impact of reducing concentration on poverty and inequality, Table 8 shows that moving from a concentrated to a more competitive market (and the price changes derived from this change) reduces the poverty headcount by 0.66 percentage points and the poverty gap by 0.53 percentage points. As shown in Figure 8, short-run estimates—corresponding to the first step of the stepwise simulation—suggest that the poverty headcount falls by 0.45 percentage points as the market share of incumbent firms decreases by 50 percent. The extreme case of perfect competition estimates an upper bound reduction of 0.88 percentage points. Inequality will also decline since households at the lower end of the income distribution have larger expenditure shares in corn products than those at the higher end of the distribution. Finally, the improvements in inequality are modest relative to the gains in poverty reduction.

**Table 8. Estimated effects on poverty and inequality of changes in competition in corn products**

||**Pov**|**erty indic**|**ators**|**Ine**|**quality indicators**||
|---|---|---|---|---|---|---|
||Headcount|Poverty<br>gap|Squared<br>poverty gap|Gini|Atkinson<br>(epsilon=0.5)|Entropy<br>(theta=0.0)|
|Baseline|53.35|21.09|11.01|0.4970|0.2096|0.4328|
|Moving into competition|52.69|20.55|10.61|0.4938|0.2071|0.4263|
|Difference|-0.66|-0.53|-0.39|-0.0032|-0.0025|-0.0065|

_Source:_ World Bank estimates using ENIGH 2014.

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