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though it has reduced staff numbers slightly. Salaries were increased, though not dramatically. The controversial performance bonus tied to collections has been phased out, and it remains to be seen whether the salary scale will suffice to attract and keep talented labor. SARS has also been flexible in using special initiatives to deal with specific personnel problems, and provides excellent training opportunities for its staff.

The Venezuelan, Mexican, and Ugandan cases show less progress in this important area. SENIAT introduced a voluntary retrenchment program, which reduced staff numbers initially, and hired top private sector accountants and lawyers to manage the reform. The SAT’s experience with personnel reform was lackluster, and Mexico continued to have the lowest ratio of taxpayers to tax officials of the countries considered. The URA did not initially downsize the organization, and, in fact, increased the staff complement over time, though it did sporadically increase salaries. Increasing salaries without undertaking some kind of retrenchment program might actually have adverse effects.

# _Financial Resource Management_

The cases show that these gains in performance have not come at great financial cost. As Table 1 shows, all ARAs, with the exception of Uganda, had reasonably low collection costs (taking 2% as the benchmark). Moreover, the more autonomous ARAs tend to have lower average collection costs (recurrent costs expressed as a share of collections) than the less autonomous ones. Kenya, Peru, and South Africa are all at the low end of the set. At the same time ARA collection costs (as an average of these cases) are comparable to or less than those of non-autonomous developing country tax agencies.

**<u>TABLE 1:  AVERAGE COLLECTION COST AS A PERCENTAGE OF REVENUES COLLECTED</u>**

|COUNTRY|AVERAGECOLLECTIONCOST|
|---|---|
|Peru|1.9% (1996-1998)|
|Mexico|1.7% (1995, 1997-1998)|
|Venezuela|2.0% (1995-1998)|
|Kenya|1.2% (1995-2000)|
|South Africa|1.1% (1998-2001)|
|Uganda|3.6% (1991-2001)|
|_Average_|_1.9%_|

_Source: Author’s calculations from ARA and MOF data, and Taliercio (2000)._ 22
Rosenzweig, M.R. and Udry, C.R. (2019) _Assessing the Benefits of Long-Run Weather Forecasting for the Rural Poor: Farmer Investments and Worker Migration in a Dynamic Equilibrium Model_ . National Bureau of Economic Research.

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_Figure 22: Gaps between expected and actual education premiums by country, gender and levels of education_ <!-- Start of picture text --> 120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>-20% Indonesia Lao PDR Mongolia Philippines Vietnam<br>-40%<br>-60%<br>-80%<br>Secondary vs. primary Male Secondary vs. primary Female<br>University vs. secondary Male University vs. secondary Female<br><!-- End of picture text --> Source: CGD–World Bank 2022 survey for envisioned premiums and Patrinos and Montenegro (2014) for a compilation of the actual premiums.

# 7 How is this research relevant for EAP education reform and globally?

Survey responses of officials from twelve middle-income EAP countries about their perceptions of education highlight that these officials, in many cases, have surprising views on key areas of policy interest. Officials in EAP prioritize increasing school completion over improving learning, underestimate learning poverty by a higher margin than other regions and believe implementation capacity is the greatest barrier to improving student learning. If officials think children know more than they do, and prioritize increased years of schooling over increased learning, it seems less likely that they will take the necessary steps to ensure that all children are learning.

Surveyed senior public officials in EAP tend to have positive views on utilizing education technology as a tool for equitable learning, supporting access for students with disabilities, supporting gender equality, and opposing violence against children. Despite this support for inclusion, they tend to select teacher training or other investments over additional teachers for children with disabilities when given the choice.

Surveyed EAP officials tend to overestimate public financing for education and question the effectiveness but not the need for additional education spending. Officials tend to believe distance learning was effective during COVID-19-related school closures and underestimate the risk of increased learning inequality from EdTech. They tend to think that the returns to completing an advanced degree are much higher than they likely are, and officials from the ministry of finance overestimate this by a larger margin than other officials. This may lead to an overinvestment in tertiary education, to the detriment of improvements in primary and technical education sectors.

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# 1. **Global trends in interest rate levels anfd spreads (annual data)**

# 1.1 Global trends since 1960

Broad trends in global interest rates since 1960 are summarized by the world medians shown in Table 1 .2 There appears to have been a general upward trend in the level of world median real interest rates, but the most striking feature is a pronounced secular swing in real rates over the past forty years, with a sharp dip into negative rates in the 1970s followed by a recovery to higher than previous levels in the 1980s and 1990s, and the beginnings of a reduction again more recently. The swing is evident in both money market and deposit rates.

From a theoretical point of view, variations over time in the general level of unregulated wholesale _ex post_ real interest rates can be explained by deviations of actual from expected inflation, and because of cyclical or trend changes in the productivity of capital and the propensity to save and perceptions of risk. Changes in the degree to which these interest rates are administratively controlled will also be a factor.

The causes of the secular swing in world interest rates since the 1960s, a we][l known feature of industrial country data, have been debated in the literature at length.<sup>3</sup> Was there a downturn in the marginal efficiency of capital (possibly associated with the surge in petroleum and other primary product prices); or was there a transitory an increase in the propensity to save? These are probably the leading explanations. In a fully integrated world capital market, these real factors would be fully transmitted across all markets, and would not retain any national features. Nominal, currency-specific factors such as shifts in the relation between actual and expected inflation are of greater interest in the present context, where we are looking at differences in the behavior of interest rates from country to country. Thus, a fairly plausible and

> **2** In this section, unless otherwise stated, "real" rate dala shown are computed as _ex post_ real interest rates simply adjusted for consumer price inflation, see the data appendix. In Table 1, for each year the median is formed from all of the countries for which _IFS_ data exists for that year.

> 3 An important early analysis of the episode is Blanchard and Summers (1984). Bank of England (1996) presents a useful overview of empirical work explaining long-trends in real interest rates in the industrial countries.

2
smooth consumption to better insure themselves against macroeconomic volatility. On the other hand, with weak institutions, those well connected to financial institutions might have disproportionate access to the financial flows to the detriment of the poor, which can exacerbate inequality.<sup>11</sup>

# **3.2.5.Empirical Estimates of Multiple Drivers of Growth and Inequality**

Various empirical studies have estimated the impact of several factors mentioned above that concurrently affect growth and inequality. For instance, Jaumotte, Lall, and Papageorgiou (2013) focus on two important drivers of economic growth in recent decades—technological change and globalization— and evaluate their joint impact on inequality. Relying on a panel data set of 51 countries covering 1981 to 2003, they find that technological change has a greater impact on income inequality than globalization does. The overall impact of globalization on inequality is limited, reflecting two offsetting effects. Trade globalization reduces inequality by raising the income of the bottom four quintiles, while financial globalization—manifested through an expansion in FDI flows—increases inequality. Technological innovation is the key channel increasing inequality: it increases the demand for skilled workers and the returns to capital, and disproportionally boosts the income in the top quintile of the income distribution. The authors also find that an increase in access to education could offset the negative effects of technological change and financial globalization, thus reducing inequality.

More recently, Furceri and Ostry (2019) have corroborated the different roles of technological change and globalization in driving inequality. Using model-averaging techniques in a sample of 108 countries covering the more recent period of 1980 to 2013, they find econometric results consistent with Jaumotte, Lall, and Papageorgiou (2013): namely, that financial globalization and technological improvements contribute to a rise in inequality while trade globalization is associated with lower inequality, especially in developing countries.<sup>12</sup>

# **4. How Does Poverty and Inequality Affect Growth?**

# **4.1. Empirical Estimates of the Impact of Poverty and Inequality on Growth**

# **4.1.1. From Poverty to Growth**

> 11 Globalization and technological change influence growth and inequality through different components of GDP. Trade globalization and technological change impact the income distribution through labor income and the skill premium, whereas financial flows affect capital income.

> 12 More specifically, Furceri and Ostry (2019) estimate the drivers of inequality using weighted-average least square (WALS) techniques, whereby the reported coefficients are a weighted average of the estimated coefficients across all possible models. This technique addresses model uncertainty and endogeneity issues related to omitted variables typically present in empirical studies focused on income inequality.

22
outcomes measured in ENAHO. _εipt_ is an error term clustered at the province level as we measure the number of Venezuelan immigrants at this level and suspect there is strong serial correlation in many of our outcomes.

_β_ identifies the effect of the number of Venezuelan immigrants in year _t_ in region _p_ . Importantly, we also control in all models for time (either year or month*year) fixed effect ( _αt_ ) and province fixed effects ( _αp_ ). Hence, we control for any time-invariant differences in outcomes across provinces and aggregate changes in outcomes, both of which may be related to the location choice decisions of Venezuelans. In our preferred specification, we also control for province-specific time-trends ( _time ∗ αp_ ) which account for any local trends in the outcome variable. In this model, the impact of the presence of Venezuelans is identified by examining how outcomes for Peruvians change when more Venezuelans arrive in an area conditional on the trend in that outcome.<sup>8</sup> It is possible that local shocks impact both the destination choice of Venezuelans and outcomes for Peruvians, hence we also use an instrumental variable strategy where we exploit the intuition that immigrants are more likely to move to localities where immigrants from the same nationality are located. We therefore instrument our measure of the number of Venezuelans in a province _p_ with the presence of Venezuelans in that province as recorded in the 2007 census interacted with year dummy variables. This is a semi-parametric version of the traditional immigrant network instrument as recommended by Goldsmith-Pinkham et al. (2020) as it allows the strength of the network effect to potentially vary in each year. An overidentification test can be used to examine whether the instrument has a consistent relationship over time.

# **5 Results**

## **5.1 Labor Market Conditions and Discrimination**

Table 2 shows our main results on the effects of local labor market conditions on self reported discrimination. We first present the OLS results, and then turn to provide the estimates from our IV specification. Importantly, given that the types of jobs in which men and women work differ, in Table 2 we show the main results for the full sample of immigrants who responded the survey, and split the sample by gender.

Columns (1)-(3) show the OLS relationship between the (log) local informal employment 8 Our results are robust to controlling for district fixed effects and time-trends as well, but we believe this is over-fitting the model as many individuals commute across district boundaries for work.

14
# **<u>Table B7. Outstanding nominal Swedish government bonds, March 31, 2006</u>**

||Date of i|ssuance|Date of re|demption|Rate of interest|Amount,<br>billions of SEK|
|---|---|---|---|---|---|---|
|1.|20/4|1999|20/4|2006|3.50|35.3|
|2.|25/10|1996|25/10|2006|6.50|1.1|
|3.|15/8|1995|15/8|2007|8.00|79.9|
|4.|5/5|1997|5/5|2008|6.50|58.2|
|5.|28/1|1998|28/1|2009|5.00|71.6|
|6.|20/4|1993|20/4|2009|9.00|1.0|
|7.|15/3|2004|1/12|2009|4.00|50.0|
|8.|8/11|2000|15/3|2011|5.25|62.0|
|9.|13/3|2002|8/10|2012|5.50|44.7|
|10.|5/5|1997|5/5|2014|6.75|63.8|
|11.|6/9|2004|12/8|2015|4.50|36.5|
|12.|19/9|2005|12/7|2016|3.00|32.0|
|13.|28/1|2004|1/12|2020|5.00|40.2|
|All bonds|-||-||5.50<sup>1</sup>|576.3|
|Money weighted duration||5 y|ears||-|-|
|Total government debt|-||-||-|1267.7|

1Money weighted average. Sources: The Swedish National Debt Office and the author’s calculations.

70
accession commitments. The figures for changes are for percentage points reduction, e.g., from 15% ad valorem to 9% ad valorem would be -6.0, a reduction of 6 percentage points.

Table 8.2: Summary of the impact of the December 2008 draft agricultural and <u>NAMA</u><sup>a</sup> <u>modalities; Flexibilities take-back as percentage of the formula cut</u>

||Applied Rates<br>Agriculture|NAMA|Bound Rates<br>Agriculture|NAMA|
|---|---|---|---|---|
|High income countries|47|0|36|20|
|Developing non-LDC|95|50|59|11|

Source: Calculated from Martin-Mattoo, 2008 Table 1. Note: a Non-Agricultural Market Access

# **9. CONCLUSIONS AND COMMENTS**

The strength of traditional GATT/WTO flexibility provisions is that they have allowed adjustment within a system that provides for general pressure toward liberalization – flexibility to take restrictive action within a system that provides general discipline not to do so. The effectiveness of GATT/WTO safeguard or flexibility provisions to sustain long-run liberalization programs stems from their requiring objective, transparent and participatory national decisions on the application of new/exceptional import restrictions. Their usefulness does not stem from the ‘formulas’ they have provided, e.g., injury or injurious dumping.

The proposed SSM (like the existing SSG) includes no procedural requirements; defines by arithmetic formulas when a Member may impose a new import restriction. The formulas expand Members’ scope for imposing restrictions but they provide a poor guide as to when it makes sense to do so. Simulations indicate that the formulas frequently prescribe action when it is not needed, frequently fail to prescribe action when it is appropriate. To the extent that the proposed SSM is more than a defense of Members’ latitude to unilaterally increase their tariff rates, it is bad guidance on when to do so – or not to do so.

Even without the proposed SSM, the tentatively agreed flexibilities to name special and sensitive products provide great scope to avoid the tariff reductions the agricultural formula would otherwise imply. Such modalities suggest lack of agreement rather than agreement on what liberalization Members will undertake. Failure to agree on the additional flexibilities the SSM would provide is part of this broader lack of agreement.

The current pause in the Doha Round negotiations provides an opportunity to think farther about what the negotiations are about. In the spirit of general commitment to policies that will support development, I offer two comments.

The first is to remind all of us, analysts and negotiators, that the objective of a safeguard provision is to support good choices over bad rather than to support restriction over liberalization, or vice versa. In the mercantilist environment of a trade negotiation it is easy to slip into a mercantilist perspective. While I have 38
syndrome among both lower middle and upper middle-income countries, it is particularly extreme in the case of Morocco.” (Foa, Liverani and Chau↵our 2017). It is noteworthy that these patterns of interpersonal trust appear to be a characteristic shared with other MENA countries.

As shown in Table 7, there is a strong correlation between social and institutional trust. Among individuals who strongly believe that Moroccans tend to trust each other, 66.5 percent also report trusting political institutions, compared to 41.0 percent of those who believe that Moroccans do not trust each other at all – a gap of over 25 percentagepoint. One explanation for that correlation is that interpersonal trust beyond in-group ties creates an environment conducive to the establishment of the rule of law and thus the building of trust in political institutions.

Table 7: Social and institutional trust (ordered logit model)

||(1)|(2)|
|---|---|---|
||Institutional|Institutional|
||trust|trust|
|Interpersonal trust|0.383***|0.375***|
||(0.0483)|(0.0495)|
|Region||X|
|Urban/rural||X|
|Gender||X|
|Income||X|
|Marital status||X|
|No. of HH members||X|
|Income||X|
|Education||X|
|Employment status||X|
|Constant|-0.793***|-0.233|
||(0.156)|(0.426)|
|Observations|5,916|5,913|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Data source: World Bank survey on trust (2021) Socio-demographics also matter for trust. Figure 6 plots the coefficients of an ordered logit regression of trust in government on relevant geographic and socio-demographic variables. The coefficients show the direction and strength of the correlation between respondents said they do not trust other Moroccans. The least trusted part of society is strangers or people whom they have met the first time: only 19.4 % of respondents said they trust people they have met for the first time. These numbers were consistent with those observed in 2021 and in 2022.

22