rafmacalaba/datause-encoder-data huggingface dataset:
# FIGURE 8 Pass-through associated with global shocks

# A. Global demand shocks

<!-- Start of picture text --> Ratio<br>1.2<br>Interquartile range Median<br>0.8<br>0.4<br>0.0<br>-0.4<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# C. Global supply shocks

<!-- Start of picture text --> Ratio<br>0.4<br>Interquartile range Median<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# E. Oil price shocks

<!-- Start of picture text --> Ratio<br>0.5 Interquartile range<br>Median<br>0.0<br>-0.5<br>-1.0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# B. EMDEs: Global demand shocks

<!-- Start of picture text --> Ratio<br>3.0 Interquartile range Median<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>D. EMDEs: Global supply shocks<br>Ratio Interquartile range Median<br>0.8<br>0.4<br>0.0<br>-0.4<br>-0.8<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>F. EMDEs: Oil price shocks<br>Ratio<br>1.0 Interquartile range Median<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> Note: Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. EMDEs = emerging market and developing economies; IT = inflation targeting. B.D.F. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications. Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

31
2 0 g Private

# rnvestmat

Capital accumulation occurs only i' the urgan 6orTaal privato C.cr. To examine the decision to invest, defne Izrst- t'c after-tax rate ^l ,cW-on private physical capital, IK, as the ratio of net profits to th2e sto'COk f c apptal: The desired capital stock by firms ir the private cor.ai -: ban sxctor is determnined so as to equate the after'tax rate of returzn on. ca- taI plu.s the rate of capital gains due to changes iE the price of capi'c., and mnus depreciation (at the rate 63) to the opportunity cost of ingEcstTr-<.t, which (assuming the absence of "effective" restrictions to capital mobil _,y)_ is here taken to be the world interest rate, adjusted for the rate of cen:--.ation of the nominal exchange rate, e: where 0 _<_ 63 < I. One could also add to the marginal cost offor&gn caoitai on the right-hand side of this equation a risk premikim, to re..c_t 'type of imperfections that developing count:res face on wo-ld cakts _maar-kets._ Such a premium could be specified, for instance, as a convex f _Lr_ of the difference between the value of tbe private capital stoc!<, P'KK3, -rlative to firms' foreign borrowing, _ER * FL3 ,_ WhSci in the prese nt setting e-resents a measure of firms' net worth.

Using equation (76), and setting E _=_ 0, tais arbitrage condt:;'-.- y-Ilds<sup>1 6</sup> Actual investment in each period is determined by a paotial g .j-kStM ent process, and is given as a functioD of the ratio between t7ne &esirzA capital stock and last period's capital stock: 1<sup>6</sup> When checking for homogeneity **ok-** degree zero in prices (including the e.:chrnge rate, which is the numeraire here), the rate of deoreciation must indeed be accc-.:-ted for in (78).

28
**Table 4: Georgia’s elasticity of labor market outcomes with respect to exporting, OLS**

|**Destination market**|**Coefficient**|**Employment**|**Female employment**|**Wages**|**Female wages**|
|---|---|---|---|---|---|
|World|Export share|0.00074|-0.00083|-0.00136**|0.00037|
|||[0.00068]|[0.00072]|[0.00058]|[0.00068]|
|EU|Export share|0.00078|-0.00077|-0.00132**|0.00040|
|||[0.00068]|[0.00072]|[0.00058]|[0.00068]|
||EU market share|-0.00028+|-0.00044*|-0.00021|-0.00014|
|||[0.00021]|[0.00026]|[0.00021]|[0.00027]|
|Russia|Export share|0.00067|-0.00090|-0.00142**|0.00031|
|||[0.00068]|[0.00072]|[0.00057]|[0.00068]|
||Russia market share|0.00066+|0.00094*|0.00077+|0.00095|
|||[0.00047]|[0.00052]|[0.00051]|[0.00091]|
|ECA|Export share|0.00064|-0.00091|-0.00136**|0.00040|
|||[0.00068]|[0.00072]|[0.00058]|[0.00069]|
||ECA market share|0.00034**|0.00029*|0.00001|-0.00008|
|||[0.00015]|[0.00016]|[0.00014]|[0.00019]|
|HIC|Export share|0.00075|-0.00079|-0.00129**|0.00041|
|||[0.00068]|[0.00072]|[0.00057]|[0.00068]|
||HI market share|-0.00009|-0.00021|-0.00032+|-0.00016|
|||[0.00020]|[0.00022]|[0.00020]|[0.00025]|
|UMIC|Export share|0.00059|-0.00092|-0.00144**|0.00032|
|||[0.00068]|[0.00073]|[0.00058]|[0.00069]|
||UMI market share|0.00034**|0.00022+|0.00021*|0.00013|
|||[0.00015]|[0.00016]|[0.00013]|[0.00020]|
|LMIC|Export share|0.00071|-0.00088|-0.00132**|0.00039|
|||[0.00068]|[0.00072]|[0.00058]|[0.00069]|

13
1

# **Introduction**

According to UPU data, on average across countries, postal employees account for approximately 0.2 percent of the labor force. The global operating income of post offices is $240 billion, accounting on average for 0.4 percent of GDP. Development of postal services and broader development have traditionally gone hand in hand. A “postal Jipp curve” can be constructed linking letters posted per capita with GDP per capita (data from UPU postal database and World Bank World Development Indicators).<sup>2</sup> About 73 percent of the variation in the number of letters per capita can be predicted by GDP per capita (Figure 1). This suggests that there is a significant relationship between economic development and expansion of postal networks.

# **Figure 1.**

<!-- Start of picture text --> 1000<br>100<br>10<br>1<br>0.1<br>0.01<br>100 1000 10000 100000<br>GDP per Capita (USD)<br>Letters/Capita/Year<br><!-- End of picture text --> A belief in the economic and social importance of the sector has been one factor behind pushing for universal access to postal services. The European Union (EU) defines universal service to post in the following terms:

> 2 Letters/capita is clearly not a directly equivalent measure of telephones per capita that the original Jipp curve concentrated on —it is more of a measure of use (equivalent, perhaps, to minutes of call time/capita) than of infrastructure rollout.  Another measure might be post offices/capita. However, as post offices are public, whereas the great majority of telephones are private, this is not a good comparator, either.  For example, Hong Kong could probably get away with very few really large post offices to serve the entire city, obviously it couldn't do the same with telephones.  The most direct equivalent may be home letterboxes per capita, but that does not appear to be available in the UPU database.
_Figure 3: Lack of implementation capacity is the largest barrier to improving student learning according to officials in EAP and globally._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Indonesia Philippines Vietnam Mongolia Lao PDR Non-EAP<br>Implementation capacity Money<br>Lack of interest from govt Political resistance from others<br>Political resistance from teachers' unions Other<br>No barrier No answer<br><!-- End of picture text --> Source: CDG–World Bank survey, 2022.

- 4 The Effects of Poverty and Malnutrition on Student Learning

- 4.1 EAP policy makers underestimate the contribution of poverty to low levels of learning.

In Indonesia, Vietnam and Mongolia, surveyed policy makers attributed low levels of student learning to school-based factors, such as poor instruction, lack of books and learning materials, inadequate school facilities and lack of internet connectivity (Figure 4). These factors can be influenced by education policy and financing. The impacts of poverty and malnutrition are acknowledged in some EAP countries, especially in Lao PDR and the Philippines. But in other countries, officials tend to undercount the influence of socioeconomic factors in favor of school-based factors. This may indicate optimism about the power of education and the potential for disadvantaged students to benefit from learning. There is also a clear and statistically significant correlation between per capita GDP and the share of officials attributing low learning to poverty and malnutrition across all countries in the dataset. The higher incomes in EAP are associated with officials being less likely to explain low learning by economic hardship.

In contrast to the average perceptions of EAP officials, the weight of socio-economic conditions and individual characteristics remain large in many surveyed EAP countries (Afkar et al., 2023). Standardized assessments frequently show large variation in the impact of socio-economic background on student performance, including EAP countries, though not all (OECD, 2019). Some high performing education systems are able to attenuate the challenges of poverty and provide students an opportunity for academic success, while other systems do this less well and have a strong association between poverty and low 7
**Figure 3a: IFLS School Data – Sample Size 30**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average in-school dummy (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>2.5<br>2<br>1.5<br>3 years later1<br>.5<br>0<br><!-- End of picture text -->

# **Figure 3b: IFLS School Data – Sample Size 100**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average in-school dummy (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>4<br>3<br>2<br>3 years later<br>1<br>0<br><!-- End of picture text -->

# **Figure 3c: IFLS School Data – Sample Size 300**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average in-school dummy (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>8<br>6<br>4<br>3 years later<br>2<br>0<br><!-- End of picture text -->
<!-- Start of picture text --> Government Handling Living Standards of the Poor Government Handling Job Creation<br>Percentage of respondents with "fairly well" & "very well" by mine location Percentage of respondents with "fairly well" & "very well" by mine location<br>yay We ALL % Italy Vw. SN<br>f Spain a. ie a ~? Spain aaa ayes ;<br>> Turkey jw . ‘ Turkey WY<br>foes > Iraq & | y ji. ae<br>ee e e y ; fc } ~ )<br>Piro Algeria= Libya 7 4 Dd Hee Algeriaee \ V > wy+<br>Egypt e { ® (<br>t Libya E<br>b Saudi Arabia i r gypt<br>( a. % , = { = : ah P has Saudi Arabia ~, {<br>= L, { e° e ; e j<br>lee<br>~e@ , fa f e ce _<br>& e Sudang ge Yemen es e ® er f<br>ae, use. ee tee : O45) j ye mcucenas FS - Yemen<br>ts.e { DemocraticRepublic owee |a6" a: { Democraticz reat Wea%<br>RD=_— of Congo Orye : eeid ofRepublic Congo %.<br>ae 2 zat Ln ie ?;<br>S“ao2me," me“8 , Ff* Pyb/ %8ee| Heise: - %<br>a? a. 3 © o€. ,<br>eel e & e we ee | e ee<br>ahs a’ 2°." ‘& i<br>Or’ eee Py we<br>South Aw ; ep,<br>‘we soa Vit:<br>gouth4<br>‘ee Se<br>© 2019 Mapbox © OpenStreetMap © 2019 Mapbox © OpenStreetMap<br>Scale<br>%1 00:<br><!-- End of picture text -->
as a percentage of the total amount of fees collected, treatment schools spent more on non-salary items like classroom materials and construction than the comparison schools, but the di¤erence is not statistically signi...cant. (Table 2) The total increase in payments was approximately one tenth of the cost of the food that was delivered to the schools.

On average, non-baseline children paid lower fees than baseline children (Table 15, columns 2 to 6). One explanation for this is that they may have belonged to families that were more marginal to school participation, possibly due to a lower socio-economic status. Non-baseline children were not enrolled prior to the program and did not have any siblings enrolled in the school. If the teacher operated price discrimination on the basis of socio-economic status, she would have charged a lower price to those children.

Case 2 predicts that the price of education in comparison schools where pupils could transfer to treatment schools should have been lower than the price in comparison schools were this was not possible. We assume that pupils could transfer from a comparison school to a treatment school if the two schools were less than 4 km apart. We ...nd evidence that the price of a day in school in comparison schools that were located within 4 km of treatment school was indeed lower than the price of a day in school in comparison schools that were located more than 4 km from the closest treatment school (Table 15, column 5). In treatment schools, the price tended to be lower when there was at least one other treatment school nearby, though the e¤ect is not statistically signi...cant at conventional levels (Table 15, column 6). This is in line with the argument in case 2: having another treatment school nearby decreased the number of potential transfers, because children from other schools had several treatment schools to choose from if they wanted to transfer to a treatment school.

# 7 Concluding remarks

There are several lessons to be learned from the Kenyan preschool meals program. The ...rst point is that the context in which school meals are implemented is very important. A program that increases school participation in an environment with low teaching quality is likely to fail to translate into better educational achievement. We ...nd evidence of a very strong complementarity between teacher characteristics, i.c. the amount of experience, and school meals in improving test scores. School meals can hardly be complementary to teaching if there is little teaching going on, or if the teaching is of very poor quality.

This study also provides an insight on the generalizability of results from randomized evaluations. Randomized provision of inputs can provide schools, or other economic agents, with a 43
# **Jobs and Land Use within Cities: A Survey of Theory, Evidence, and Policy**

_Arti Grover Goswami and Somik V. Lall_ _JEL Classification:_ R12, R13, R14, R3, R52 _Key words:_ Agglomeration, spatial, monocentric, polycentric, land use regulations.

**Acknowledgements** : This paper is a part of a World Bank Regional study on the Spatial Development of African Cities. We are grateful to the World Bank Multi‐donor Trust Fund on Sustainable Urbanization and UK DFID for financial support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors, they do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent and should not be attributed to them.  The authors can be contacted at <u>agrover1@worldbank.org</u> and <u>slall1@worldbank.org.</u>
different slopes of the income-satisfaction relation across countries and across groups of people (Clark et al., 2005; Frijters et al., 2004a; Lelkes, 2006). A few cross-country longitudinal studies also used GDP per capita instead of income controlling for country fixed-effects and found a positive relation in contrast with the Easterlin paradox (Di Tella et al., 2003; Helliwell, 2003; Alesina et al., 2004).

Despite the fact that the overwhelming majority of studies find a positive relation between income and happiness, there are a few studies that find a negative relation. This case has been labeled the case of the _frustrated achievers_ (Graham and Pettinato, 2002; Becchetti and Rossetti, 2009; Brockmann et al., 2009) to underscore the fact that even an increase in income can result in decreased satisfaction. For example, using the German Socio-Economic Panel Survey (SOEP), Becchetti and Rossetti (2009) found that half of the achievers - defined in terms of people who improved on their income during the last period – are frustrated, meaning that they declared a reduction in life satisfaction. This is one of the few studies that actually splits happy and unhappy achievers into two groups avoiding in this way the averaging of negative and positive incomesatisfaction relations.

Several studies also find that income exhibits decreasing marginal returns on satisfaction for individuals within countries and across countries and that this is true especially over time (Diener and Luca, 1999; Layard, 2005). This would be partly explained by the role of positional goods and the reference group, two factors that change along with income (Duesemberry, 1949, Frank, 2005). In essence, expectations, needs and the reference group are continuously adjusted to higher incomes so that the effect of an increase in marginal income is short-lived. This literature provides a possible explanation for the Easterlin paradox. In low income countries, an increase in average incomes increases average happiness but in high income countries this elasticity is no longer at work and factors other than income such as religion, social networks and freedom become more important for explaining changes in happiness.

The sign and significance of the relation between happiness and **relative income** in empirical studies is less consistent than the observed relation between happiness and absolute income. The majority of studies find a negative and significant relation as postulated by the relative income hypothesis (when controlling for absolute income). Studies that use averages of incomes across pre-defined reference groups as relative income are perhaps the most common and results are quite consistent in finding a negative relation between relative income and life satisfaction (Ferrer-i-Carbonell, 2005; Blanchflower and Oswald, 2004; Graham and Felton, 2006; Helliwell 20
- 38 -

invest. This may be why Abel and Blanchard conclude that "our data are not sympathetic to the basic restrictions imposed by the q theory, even extended to allow for simple delivery lags." Does Irreversibility Matter?

Unfortunately, incorporating irreversibility into models of aggregate investment spending is not a simple matter. As we have seen, the equations describing optimal investment decisions are extremely nonlinear, even for very simple models. Partly as a result of this, there has been very limited empirical work to date that tests the importance of irreversibility for the modelling of investment spending.

One paper that does provide such a test is by Bizer and Sichel (1988). They develop a model of capital accumulation and utilization with asymmetric costs of adjustment, i.e., the costs of adjusting the capital stock up or down can differ. If irreversibility is important, one would expect to find that downward adjustment costs exceed upward ones. Bizer and Sichel derive an Euler equation, which they estimate using Hansen and Singleton's generalized instrumental variable procedure. They do not have firm data, so instead they use 2-digit SIC industry data. They measure asymmetry of adjustment costs with respect to two reference points: a zero level of investment, and a "normal' (average) level of investment.

Their preliminary results indicate some evidence of irreversibility, in particular in primary metals, fabricated metal products, and possibly the paper industry. But they also find thet upward adjustment costs exceed downward ones in the food and petroleam industries. This may simply mean that aggregation is maskirg irreversibility. Other problems include the use of a single discount factor (the S&P dividend/price ratio, which is very volatile) and cost of capital for all industries. Nonetheless, their
- 13 -

Table 2 (Continued)

|The Comparat|ive Importance<br>ofAir<br>1985<br>1987 Exports to|and Vessel Sh<br>, 1986 and 198<br>U.S. (S mill)|ipments In<br>7<br>3/ Share|Total U<br>of Expor|.S. Imports;<br>ts by Air|
|---|---|---|---|---|---|
|Exporter|by air|<br>by vessel|1985|1986|<br>1987|
|Kenya|21.4|<br>56.5|26.6|18.3|<br>27.4|
|Mozambique|0.3|25.1|0.7|1.1|0.1|
|Rep. of South Africa|827.3|<br>494.9|52.7|52.4|62.6|
|All Other Countries|98.0|540.9|10.5|11.6|<br>15.3|
|Near East Asia|2,081.2|<br>6,454.4|32.7|31.2|24.4|
|Iraq|0.3|286.9|0.2|0.2|0.1|
|Iran|85.3|1,452.1|7.1|11.5|5.5|
|Israel|1,914.1|<br>664.5|69.4|73.0|74.2|
|Kuwait|15.4|<br>431.5|1.6|2.4|3.4|
|Saudl Arabia|23.7|2,660.7|8.0|2.7|0.9|
|United Arab Emirates|15.3|<br>601.0|2.7|3.1|2.5|
|All Other Countries|27.1|357.7|10.5|10.9|7.0|
|South Asia|1,655.1|<br>2,066.7|40.3|44.5|44.5|
|India|1,309.4|<br>1,195.7|44.5|51.2|52.3|
|Pakistan|143.7|<br>257.4|32.2|31.4|35.8|
|Nepal|24.5|5.9|96.9|90.0|79.5|
|Bangladesh|61.1|305,2|17.9|21.5|16.7|
|Sri Lanka|115.4|297.8|21.3|23.3|27.9|
|All Other Countries|1.0|4.7|31.3|29.4|17.5|
|Other Asia|15,388.7|<br>52,851.3|20.1|22.2|22.6|
|Thailand|799.0|1,381.2|20.1|20.1|36.6|
|Malaysia|1,511.8|<br>1,376.0|57.4|57.2|52.4|
|Singapore|3,509.1|2,633.8|46.6|57.3|57.1|
|Indonesia|98.6|3,256.4|1.6|2.3|2.9|
|Philippines|878.1|1,362.6|38.6|37.3|39.2|
|Republic of Korea|2,625.6|14,239.4|15.9|16.2|15.6|
|Hong Kong|3,359.7|6,343.5|32.9|35.0|34.6|
|Taiwan, China|2,531.8|21,790.7|9.2|10.5|10.4|
|All Other Countries|75.0|467.7|10.7|12.7|13.8|
|ALL EXPORTEiRS<br>2/|67,007,0|244,025.0|19.7|21.9|21.5|

1/ In U.S. terminology the French West Indies are Guadeloupe and dependencies and Martinique. The Leeward and Windward Islands consist of Antigua, St. Kitts-Nevis, Anguilla and Montserrat, St. Vincent, St. Lucia, Dominica and Grenada.

# 2/ Includes developed, developing and socialst countries.

3/ Shipments made partly by air and partly by vessel are oxcluded from these tabulations. For this reason, the sum of the "air"' and t"vessel" shipments may be less than the total export figures reported elsewhere in this report.