rafmacalaba/datause-encoder-data huggingface dataset:
# 7.2 Market access gains to developing countries

A key empirical issue is the extent to which changing the dispersion of overall protection, for a given average cut in tariffs, affects the average reduction in protection facing developing countries. In contrast with the efficiency issue addressed in 7.1, there is little that theory can say to guide whether tops-down cuts will be more successful in reducing the barriers facing developing countries. However, we do know that developing countries generally face higher MFN tariff barriers against their exports than do industrial countries (World Bank 2003, Chapter 2). Many have concluded that this difference reflects to a significant degree the limited presence before the Uruguay Round of developing countries in the “main game” of multilateral negotiations—the exchange of market access concessions. Whatever the cause of this discrepancy, it implies that approaches that attack tariff peaks more aggressively are more likely to be beneficial to developing countries, the greater the extent that developing countries face disproportionately high tariffs.

To examine this issue, we increase the _b_ parameter in equation (4) to reduce the concavity of the tariff cutting formula. At one extreme, we consider the classic Swiss formula set in such a way that it brings about a 50 percent reduction in tariffs with the greatest reductions, by far, in the highest tariffs. Then, we allow for increasing degrees of flexibility up to the point where all tariffs are reduced by a flat 50 percent. This is done by increasing the flexibility parameter in the extended Swiss formula from 1.0 to 2.0.

The results of this analysis are presented in Figure 4 for EU tariffs faced by exports from low-income countries, and in Figure 5 for US tariffs faced by the same countries. In each case, the values of the flexibility formula are shown on the horizontal axis and the proportional reduction in tariffs on the vertical axis. We find that a classic Swiss formula used to target a 50 percent cut in the simple average tariff on imports into these two large economies does indeed lead to larger reductions in the weighted average tariffs facing low-income countries. In the case of the USA, the reduction in tariffs is 56 percent, while in the European Union, the reduction is 55 percent. Then, as the flexibility of the cuts is increased, the reductions in average tariffs faced by the low income countries fall progressively, to 50 percent when tariffs are cut proportionately.

16
|**Panel A: All**||Au|to|||Elect|ronics||
|---|---|---|---|---|---|---|---|---|
||log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|log of<br>imports|
|(Dummy share 2004-2010 > 15)|0.018|||-0.115|0.105**|||0.115**|
||(0.108)|||(0.129)|(0.043)|||(0.052)|
|(Dummy share 2004-2010 > 10)||0.108<br>||0.129<br>||0.050<br>||-0.007<br>|
|||(0.103)||(0.145)||(0.038)||(0.048)|
|(Dummy share 2004-2010 > 5)|||0.125|0.092|||0.006|-0.017|
||||(0.105)|(0.124)|||(0.034)|(0.036)|
|Observations|5,562|5,562|5,562|5,562|52,597|52,597|52,597|52,597|
|R-squared|0.947|0.947|0.947|0.947|0.962|0.962|0.962|0.962|
|Treated|74|109|177|177|683|1024|1708|1708|
|Total country-products|405|405|405|405|3971|3971|3971|3971|
|**Panel B: Auto**||Intermed|iate auto|||Final|auto||
||log of|log of|log of|log of|log of|log of|log of|log of|
||imports|imports|imports|imports|imports|imports|imports|imports|
|(Dummy share 2004-2010 > 15)|-0.030|||-0.058|0.366|||0.099|
||(0.076)|||(0.093)|(0.284)|||(0.508)|
|(Dummy share 2004-2010 > 10)||0.017||0.008||0.369||0.321|
|||(0.067)||(0.108)||(0.244)||(0.487)|
|(Dummy share 2004-2010 > 5)|||0.076|0.085|||0.191|-0.032|
||||(0.096)|(0.119)|||(0.213)|(0.278)|
|Observations|3,818|3,818|3,818|3,818|1,744|1,744|1,744|1,744|
|R-squared|0.973|0.973|0.973|0.973|0.935|0.936|0.935|0.936|
|Treated|49|75|121|121|25|34|56|56|
|Total country-products|285|285|285|285|120|120|120|120|
|**Panel B: Electronics**||Intermediat|e electronics|||Final ele|ctronics||
||log of|log of|log of|log of|log of|log of|log of|log of|
||imports|imports|imports|imports|imports|imports|imports|imports|
|(Dummy share 2004-2010 > 15)|0.214***|||0.132|0.054|||0.104*|
||(0.079)|||(0.095)|(0.051)|||(0.061)|
|(Dummy share 2004-2010 > 10)||0.197***||0.082||-0.013||-0.045|
|||(0.075)||(0.095)||(0.043)||(0.055)|
|(Dummy share 2004-2010 > 5)|||0.152**|0.086|||-0.046|-0.050|
||||(0.071)|(0.075)|||(0.038)|(0.041)|
|Observations|14,323|14,323|14,323|14,323|38,274|38,274|38,274|38,274|
|R-squared|0.962|0.962|0.962|0.963|0.961|0.961|0.961|0.961|
|Treated|208|315|537|537|475|709|1171|1171|
|Tld|1034|1034|1034|1034|2937|2937|2937|2937|
|ota country-proucts|||||||||

Note: Robust standard errors clustered at the cluster importer-product level are in parentheses. All specifications include importeryear, importer-product, and product-year fixed effects. The samples include the top 15 producers of automobile or top 15 exporters of final electronics other than Japan.

*** p<0.01, ** p<0.05, * p<0.1

15
Policy Research Working Paper 10591

# **Abstract**

This paper reports the views and perceptions of randomly selected education policy makers in the East Asia Pacific region, based on surveys of 651 senior public officials in 14 middle-income countries. The findings show that officials tend to prioritize increasing secondary school completion over improving learning quality, and they severely underestimate learning poverty and do so by a larger margin than officials in other countries. Officials were most likely to cite system capacity as the primary constraint to improving learning. The findings show that officials’ support for gender equality and disability inclusion is high. Interviewed officials tend to oppose violence against students and prefer to invest in in-service teacher training or early-grade reading compared to other options, such as EdTech or inclusion for students with disabilities. This mix of alignment and misalignment between policy makers’ goals and the stated goals of development partners can inform future engagement in policy dialogue, analysis, and information campaigns.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at nyarrow@ worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The dependent variable is the standardized anthropometric height-for-age z-score (HAZ). HAZ is defined as _(h-hr)/ur where_ h is the observed height of a child of a specified sex/age group, h, is the median height in the reference population of children of that sex/age group, and or, is the standard deviation of height measurement for the reference population of that sex/age group. The standard reference population recommended by the World Health Organization is that of the United States National Center for Health Statistics. As several studies have indicated that less than 10 percent of the worldwide variance in height can be ascribed to genetic or racial differences (Martorell and Habicht 1986), this reference population is appropriate. Children with a HAZ score less than -2 are usually classified as stunted.

The set of predictors consists of characteristics of the child (e.g., age, gender, birth order), household demographic variables such as household size and age-sex composition, characteristics of the parents (e.g., educational attainment, and mother's age and height), access to public services (e.g., piped drinking water), and an urban dummy.

We seek not only to model the overall determinants of nutrition, but also to see if the VL program has an impact on nutrition. However, as households chose to participate, we have an issue of self selection if we include households' VL participation as an explanatory variable. If we use the VL allocation to the community in lieu of participation, we solve the issue of endogenous household choice, but only at the expense of introducing potential bias from endogenous program placement (Rosenzweig and Wolpin 1986). The bias, if any, can not even be signed with confidence since an estimated impact may be an overestimate (if programs are placed where the anticipated return is higher than average) or negative (if programs go to favored but more developed communities). We address this issue by taking advantage of the fact that we have two observations on the VL expenditures that correspond to different rounds of DHS surveys. Thus, we can use fixed effects estimations that control for the initial conditions in the communities. The general form of the models that we estimate is as follows: _hi=d ,, + +_ **_t+_** _t_ **_i,d,_** + **_i,d, +_** _YVLd,, +_ E **_t_** _5 d+6i,d,t d_ **_d_** where i is the index for individual children, d is the indicator for the district in which the child resides, and t is the year (1996 or 2000). _VL_ is the district level per capita VL expenditure, and _D_ is the set of district dummies. We merge the district-level VL expenditure data with the DHS data for 1996 and 2000 to create a dataset with 19,053 observations on child heights upon which we estimate the model. The per capita VL district expenditure variable is the district average amount spend on in the two years prior to and including the survey (i.e., 1994-1996 for the 1996 DHS, and 1998-2000 for the 2000 DHS).

In assessing the effect of VL expenditures on child nutritional outcomes (HAZ), the parameter of interest is y. Even using district fixed-effects, ordinary least squares (OLS) 10
**<mark>Table 7 – Impact of FD Law on Firm Outcomes Given State Public Debt and Municipality Bank Exposure</mark>** <mark>This table reports OLS estimates of the impact of the FD Law on real outcomes of private firms in states of varying ex ante public</mark> indebtedness and across municipalities of varying ex ante bank exposure to local public debt. The variable _Liabilitiesf,y_ is the volume (in logs) of liabilities of firm _f_ in year _y_ . _Assetsf,y_ and _Fixed Assetsf,y_ are the volume (in logs) of total and fixed assets of firm _f_ in year _y_ . _Salesf,y_ is the volume (in logs) of sales of firm _f_ in year _y_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _DebtBankMunim,Mar16_ measures the average exposure to local public debt of banks operating in municipality _m_ in the month prior to the reform. It is calculated as the municipality’s weighted average of _BankExposureGovs,b,Mar16_ which is a measure of the exposure of a bank to local public entities of a state in March 2016. The standard deviation of this variable for our sample of firms is equal to 0.02. _Posty_ is an indicator variable that equals one from 2016 onwards.  Observations at the firm-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels.  Detailed variable definitions are provided in Table A1 in the <u>Appendix.</u>

||Liabil<br>|itiesf,y<br>|Ass<br>|etsf,y<br>|Fixed A<br>|ssetsf,y<br>|Sal<br>|esf,y<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Posty*DebtStates,16Q1|0.024**||0.019*||0.037||0.023*||
||(0.009)||(0.012)||(0.029)||(0.014)||
|Posty*DebtBankMunim,Mar16||0.743||0.339||-0.176||0.022|
|||(0.762)||(0.463)||(0.989)||(0.914)|
|Posty*DebtStates,16Q1||1.452***||1.280***||0.329||2.224*|
|*DebtBankMunim,Mar16||(0.358)||(0.285)||(0.842)||(0.938)|
|Observations|4,452|4,292|4,460|4,318|4,387|4,139|4,443|4,206|
|R-squared|0.967|0.979|0.983|0.991|0.976|0.988|0.941|0.963|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|-|Yes|-|Yes|-|Yes|-|
|State-Year FE|No|Yes|No|Yes|No|Yes|No|Yes|

54
of fighting, which in turn can affect societal, demographic and household-level norms around women’s decision-making.

Our preliminary results find an increase in intrahousehold women’s decision-making since the conflict started in areas under the control of the International Recognized Government in Yemen. Using a difference-in-difference approach that controls for individual and household characteristics, we find that this result is driven by households living in districts with medium intensity conflict as compared to low intensity conflict. This result holds up to a series of robustness checks, and could be explained by changes in household composition, whereby men are more likely to leave the household in conflict affected districts, leaving women in charge of household decisions.

# II. Context

Yemen is the poorest country in the Middle East and has been facing systematic deprivations in key dimensions of human wellbeing over decades. According to the World Bank’s Human Capital Index (2018), a person born today in Yemen can expect to achieve only 37% of their full productive potential by the time they reach the age of 18, underlining the large scale of unfulfilled potential due to poverty, and low investments in good quality nutrition, health and education services. The vulnerabilities in terms of economic wellbeing and development have been driven and exacerbated by political instability. After the unification of South and North Yemen in 1990, the country was on a path to relatively stable economic and political consolidation for a few decades.

In the 2010s resentment over corruption, unjust representation and rising food prices built up in the Middle East, culminating in the Arab Spring. Yemen was no exception, as years of corruption and the dwindling oil reserves were not enough to maintain the peace in such a fragmented society. Protests began in Sana’a in 2011 and reached a turning point in 2014 when the government removed a series of subsidies. Houthi rebels had been engaged in military confrontation in the North since 2004, but seized the opportunity to make unprecedented territorial gains, forcing the government from Sana’a to Aden. This was followed by Saudi Arabialed airstrikes over Houthi-controlled territory beginning in 2015, as well as on-the-ground confrontations between the Houthis, Internationally Recognized Government (IRG) forces, as well as other armed groups (such as the AQAP and the STC). Eight years of brutal war have seen different frontlines, shifts in territorial control of areas, and the involvement of several political and armed actors. The De-Facto Authority (DFA), formed by the Houthis, command control of the Northern areas. While the IRG maintain control of parts of southern Yemen, the UAE-backed Southern Transitional Council (STC) and other groups have also been vying for power. In the last year the prospects for peace have been more encouraging than ever, and the fighting has been less intense.

Nonetheless, in addition to the conflict, Yemen has been beset by a series of shocks including the COVID-19 pandemic, a massive depreciation of the Yemeni rial and resulting inflation, natural 3
weak, in all applications of the SYS‐GMM estimator to cross‐country growth empirics. Beyond this, my analysis in the last section of the paper also emphasizes the importance of systematically verifying that conclusions are robust over a reasonably large set of alternative possible instrument sets. Paying more careful attention to issues of internal instrument strength will help to improve the credibility of empirical evidence based on the SYS‐GMM estimator, not only in the inequality and growth literature, but in a much broader range of applications as well.

The rest of this paper proceeds as follows. In the next section, I summarize the findings of the four empirical studies reviewed in this paper. I also briefly review the SYS‐GMM estimator and the use of weak instrument diagnostics and weak instrument robust approaches to inference in this setting. Section 3 contains my main empirical results, which show that weak instrument‐consistent confidence sets for the estimated effect of inequality on growth in the benchmark specifications of these papers are very large and include a wide range of positive and negative values. In Section 4, I investigate how the performance of the internal instruments in the SYS‐GMM estimator and the size of weak instrument‐ consistent confidence sets for the estimated effects of inequality on growth depend on some of the many possible choices for constructing the instrument set in the SYS‐GMM estimator. Section 5 offers concluding remarks.

# **2.  Background**

# **2.1 Four Recent Inequality and Growth Studies**

In this section I briefly summarize the main findings of the four studies considered here.<sup>6</sup> All four studies use the SYS‐GMM estimator to identify the effects of inequality on growth, using cross‐country panel data sets in which each observation is a five‐year period for a given country. However, the studies differ in the specific hypotheses being tested, in their choice of inequality measures, and in their choice of control variables.

> 6 Three other prominent earlier papers in this literature are similar to the ones considered here, in the sense that they also use lags of variables as internal instruments.  Barro (2005) uses lags of inequality as instruments in a 3SLS regression but does not implement either the differenced or the levels equation that make up the SYS‐GMM estimator.  Forbes (2000) uses the differenced GMM estimator, and as such is likely to suffer from concerns about instrument strength similar to those documented here in more recent papers.  Parenthetically, this paper is notable for being the only one among those discussed here to find a positive effect of inequality on growth. Interestingly, in all of the papers I replicate below, the differenced equation yields a positive (although not often significant) correlation between inequality and growth, consistent with difference‐GMM estimates of Forbes (2000).  Finally, Voitchovsky (2005) implements the full SYS‐GMM estimator, and Bazzi and Clemens (2013) demonstrate that the internal instruments are weak in this paper, so I do not repeat this analysis here.

5
# **Appendix 4: Description of Route Groups**

ICAO uses a set of 17 route groups between regions that have been applied to this data set, with some modifications as noted in the footnotes below. The groups carry numbers and are defined follows in Appendix 1 of ICAO Circular 255-AT/105:

1. _Between North America and Central America/Caribbean._ Includes routes between on the one hand Canada and/or the United States (including Alaska and Hawaii) and on the other hand Central America and the Caribbean. Routes between the United States and Puerto Rico /Virgin Islands are considered domestic and are excluded. Central America/ Caribbean is defined as the geographical area covered by route group 2 but excluding Mexico.

2. _Between and within Central America and the Caribbean._ Includes routes between or among the Bahamas, Belize, Bermuda, Costa Rica, El Salvador, Guatemala, Honduras, the islands of the Caribbean Sea (including Puerto Rico and the Virgin Islands), Mexico, Nicaragua and Panama.

3. _Between Canada, Mexico and the United States._ Includes routes between or among the above States. The United States includes Alaska and Hawaii but excludes Puerto Rico and the Virgin Islands<sup>41</sup> .

4. _Between North America/Central America /Caribbean and South America._ Includes routes between the geographical areas defined on one hand by route group 1 and /or Mexico and on the other hand by route group 5 (“local South America”) 5. _Local South America._ Includes routes between or among the following States: Argentina, Bolivia, Brazil, Chile, Colombia (including the San Andres Island), Ecuador, Falkland Islands (Malvinas), French Guiana, Guyana, Paraguay, Peru, Suriname, Uruguay and Venezuela.

6. _Local Europe._ Includes routes between or among the States of geographical Europe, Algeria, Azores, Canary Islands, Greenland, Iceland, Madeira, Malta, Morocco, Tunisia and Turkey.

7. _Local Middle East._ Includes routes between or among the following States: Bahrain, Cyprus, Egypt, Islamic Republic of Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, United Arab Emirates and Yemen.

8. _Local Africa._ Includes routes between or among the States of continental Africa and offshore islands, but excluding Algeria, Azores, Canary Islands, Egypt, Madeira, Malta, Morocco, Sudan, and Tunisia.

9. _Between Europe and the Middle East._ Includes routes between the two geographical areas defined by route group 6 (“local Europe”) and route group 7 (“local Middle East”) respectively.

> 41 The August 2010 version of the ICAO Carbon Emissions Calculator, in Appendix A on page 12, Bermuda is included in this group. Bermuda has thus also been included in the calculations presented here.

37