rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 0.5<br>0.45<br>Urban areas<br>0.4<br>Rural areas<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100<br>Unemployment ratio<br>Probit probability<br><!-- End of picture text --> We also investigate the determinants of poverty along geographical lines. When the entire sample is restricted along urban and rural geographical lines, the spatial location of households is a significant determinant of poverty in rural areas, and less so in urban areas where it has a lower marginal effect. The results of the probit analysis are summarized in Table A.9 in the Annex (determinants of poverty in urban areas) and Table A.10 in the Annex (determinants of poverty in rural areas). However, even though the parameter estimates for spatial effects are significantly different from zero at the 95% level, the scope of the marginal effects associated with these variables is limited. In particular, when the spatial location of households is controlled for, to account for a possible interaction between poverty and geographic regions, the marginal effect increases significantly to over 11% in rural areas. This relatively large increase and effect on the event probability suggests the existence of a strong interaction and geographical effect on poverty, and possibly expected high payoffs from internal migrations. Nonetheless, the costs of inter-regional migration may be prohibitive for the poor, which have limited assets endowment , and especially in a context of high unemployment, and shortage of supply in the housing market.<sup>45</sup> In addition to the interaction term between poverty and geographical location of households in rural areas, the “ _burden of age dependency,_ ” “SEG,” and the interaction term constructed by conditionally ranking “ _SEG_ ” to be consistent with the poverty map also produces a relatively large marginal effect on the response probability space. When the “SEG” is taken alone, the marginal effects on the response is about 25%; and when it is combined with the poverty mapping of SEG, the marginal effect increases to over 48%. This result is consistent and suggests that the likelihood of being poor varies considerably across SEG, and the payoffs from moving across socioeconomic groups are likely to be even higher for the poor.<sup>46</sup>

> 45 Despite ongoing reforms, the housing market is not yet sufficiently flexible to permit easy migration to areas experiencing higher growth in labor demands (Braithwaite, Grootaert and Milanovic [2000]).

> 46 For instance, the probability of being poor is very high when the main income source is from “ _Social Benefits,_ ” and poor individuals moving into a different socioeconomic group, that is growth oriented with prospects for employment creation will greatly increase their potential for welfare improvement.

28
# _Risks_

_Reputation risk arising from service disruptions._ As with the original Small Investors Program, the Treasury is fully dependent on the performance of the service provider, in this case the Development Bank of the Philippines and its Data Center. The Treasury has experience handling catastrophic disruption and is using lessons from the original program. For its part, the Data Center has sought to reassure the Treasury by negotiating the escrow of the program’s source code from Smetrix.

_Liquidity risk._ The Treasury plans to offer both Treasury bills and notes on the electronic platform. (At first only bills will be offered.) To avoid skewed maturity profiles, a carefully crafted online issuance strategy has to be developed.

_Dependence on the sales and promotion efforts of dealers._ The Treasury is dependent on the sales and promotion efforts of the dealers involved in the program. Given the program’s major benefits for the Treasury, it should consider creating a promotions budget that drives interested investors into dealer offices.

# **Retail investors**

# _Incentives_

_Better access to savings products._ The Expanded Small Investors Program is especially useful in providing access to savings instruments in areas of the Philippines outside the three main cities (Manila, Cebu, Davao), because bank branches in these areas do not carry the full range of deposit and investment products.

_More choices and better yields._ If Treasury notes are made available as well, it would be possible for investors to construct their own portfolios using a variety of savings products. These could be used for different needs, such as education plans, pensions, and provident savings, and could provide alternatives to under performing products.

With the program’s low delivery costs and direct access to retail investors, the Treasury has an opportunity to realign Treasury yields relative to private debt instrument yields. Currently, inefficiencies in the distribution of securities sometimes result in private debt instruments being placed in the hands of savers and investors at yields far below those for Treasury securities with similar maturities. These misalignments are sustained for extended periods simply because investors lack choices, especially outside major urban areas.

# _Risks_

The main implicit cost for investors buying government securities through the Expanded Small Investors Program is the liquidity constraint due to the program’s buy and hold nature. If investors purchase these securities through dealers, they will be able to redeem them before maturity (with a certain capital gain or loss). But under the program, securities cannot be sold before maturity except in extraordinary situations.

37
_Decentralization:_ _The Way Forward for Rural Developrnent?_ percent of government spending between 1960-85. The revenues from export taxes accrued to central governments, however, resulting in rural communities at best receiving only limited direct benefit from the product of their own labor, and often receiving no benefit at all.

Realizing the negative incentive effects that export taxes have on agricultural producers, developing country governments have tended to reduce levels of export tax over the past few years. These types of agricultural taxes are, therefore, unlikely to provide a significant contribution to overall revenues in the future.

**Agricultural Land** Taxes. Binswanger et al (1993) summarize three main advantages that a land tax has over a tax on agricultural output or exports: (i) it has minimal disincentive effects if the tax is based on the potential monetary yield of a certain plot under normal conditions; (ii) it facilitates taxation of the domestic subsistence sector, while being much less regressive than a poll tax; and (iii) provided the tax base is not changed too frequently, a land tax will not discourage investment in land improvements.

Although land-based taxes are considered to be the highest-yielding tax instruments available to local governments, nowhere in the world have land and property taxes, in both rural and urban areas, taken together contributed more than ten percent of total tax revenue (Bird and Wallich 1993). Administering land taxes effectively and equitably requires detailed information on land size, value, ownership, and its productive capacity. Even in the few developing countries that maintain a sufficiently detailed cadastral survey, land taxes are relatively unimportant. Often associated with previous extractive colonial or military regimes, rural land taxes typically incur high political costs that offset any revenue gains. Developing-country governments are therefore unlikely to turn to land taxes as a major source of revenue for rural areas.

**User Charges and Fees.** The use of user charges and fees as potential sources of additional local resources has received considerable recent attention (Bahl and Linn 1992), not so much as mechanisms to boost overall local revenues, but to

_7_
# **Table 7 Projected and Actual Real GDP Growth Rates for Low- and Middle-Income Countries, 1965–2010**

<u>(annual percent change)</u>

||**_History_**||**_Projecti_**<br>**_199_**|**_ons for_**<br>**_0s_**|**_Actu_**|**_al_**|
|---|---|---|---|---|---|---|
|_Region or Income Group_|_Trend 1965–87_|_Then recent_<br>_experience_<br>_(1980–88)_|_Baseline_|_Low_<br>_case_|_1990–2000_|_2000–10_|
|All low- and middle-income countries|5.3|4.2|5.0|4.2|4.9|5.8|
|Sub-Saharan Africa|3.2|0.5|3.8|3.7|2.6|5.1|
|Asia|6.6|7.4|6.3|5.5|7.2|7.9|
|Europe and Middle East and North Africa|4.6|2.8|3.9|3.3|2.9|3.4|
|Latin America and the Caribbean|4.6|1.6|3.9|3.0|3.3|3.3|
|Memo items:|||||||
|All low- and middle- countries income excluding<br>China and India|4.9|2.6|4.3|3.6|3.7|3.7|
|High-income OECD countries|3.3|3.7|3.0|2.4|2.5|1.4|

_Source_ : This is table 14 of _A Long-Term Outlook (_ Fardoust and Dhareshwar 1990), to which actual figures from World Development Indicators for the 1990s and the 2000s were added.

_Note_ : The country coverage for the actual data is slightly different from the projection. It excludes Myanmar and Taiwan, China, data for which were not available. Instead of Yugoslavia, the GDP data for Croatia, Macedonia, Serbia, and Slovenia are used; data were not available for Bosnia-Herzegovina, Kosovo, or Montenegro. The 2010 values are estimates.

52
**Figure 7 Pre-COVID, the Middle East and SubSaharan Africa had the hardest challenge, as both the gap and gap-inequality of learning are the highest**

**Figure 8 Post-COVID, the learning gap might increase approximately the same in several regions, however, the gap-inequality could increase the most in the Middle East and SubSaharan Africa**

<!-- Start of picture text --> Bigger and more Bigger and more<br>14.5 heterogenous 1.6 heterogenous<br>problem problem<br>12.5 MNA 1.4 MNA<br>1.2 SSF<br>10.5<br>1.0<br>8.5 WLD<br>SSF<br>0.8<br>6.5<br>0.6 LAC<br>4.5 WLD EAP<br>0.4<br>2.5<br>EAP LAC 0.2<br>ECA<br>0.5 ECA<br>0.0<br>Smaller and more -0.5 4.5 9.5 14.5 19.5 24.5 29.5 Smaller and more homogenous 0.0 1.0 2.0 3.0 4.0<br>homogenous -1.5problem Learning gap (p.p.) problem Increase on the learning gap (p.p.)<br>Inequality sensitive learning gap (p.p.) Increase in the inequality sensitive learning gap (p.p.)<br><!-- End of picture text --> <!-- Start of picture text --> problem<br><!-- End of picture text --> Note: variances on types of Arab script poses specific challenges to the teaching and the assessment of learning in the Middle East region. Source: Author’s calculation. All underlying numbers can be found in table A.2 in the annex.

# Final remarks

This paper has expanded the concept of learning poverty, drawing from the poverty measurement literature to present axiomatic properties of poverty measures and building on the FGT family of poverty measures to provide an extension of distribution-sensitive measures of learning poverty. The paper has presented conceptual, numerical, empirical, and practical reasons why this extension can be useful for measuring the consequences of COVID-19 school closures and comparing those consequences over time and across countries.

Three simulation scenarios explored potential learning losses, expressed in terms of the World Bank/UIS learning poverty indicator. In the most pessimistic scenario, learning poverty in low- and middle-income countries was expected to increase 20 percent, or 10 percentage points, going from 53 to 63 percent. Most of that increase might occur in lower-middle-income and upper-middle-income countries, especially in East Asia and the Pacific, Latin America and the Caribbean, and South Asia. Africa, and the group of lowincome countries, both with the highest levels of learning poverty before the COVID-19 pandemic, could have the smallest absolute and relative increases in learning poverty due to COVID-19-related school closures. The small size of those increases simply reflects the magnitude of the learning crisis in those countries before the pandemic.

Building on the FGT class of poverty measures to complement learning poverty measures with measures of a learning poverty gap and learning poverty severity, the discussion showed that Africa is the only region where the learning poverty depth might increase more than the learning poverty breadth.

30
|Observations|5,038|5,038|5,038|5,038|5,038|5,038|5,038|5,038|5,038|5,038|
|---|---|---|---|---|---|---|---|---|---|---|
|R-squared||0.073||0.049||0.119||0.131||0.033|
|Number of households||2,675||2,675||2,675||2,675||2,675|

Source: Generated by authors using LSMS data Note: PSLC=Primary School Leaving Certificate. JCE=Junior Certificate Examination. MSCE=Malawi School Certificate of Education Examination.

***, **, *, and + indicate that the corresponding regression APE is statistically significant at the 1%, 5%, 10%, and 15% levels, respectively. The table presents APEs on all explanatory variables. The APEs from the CRE Probit models are estimated using GEE approach. The unconditional APEs from CRE Tobit are estimated using pooled Tobit regression methods.  The linear FE results are presented for comparison purpose. P-values between brackets are based on cluster robust standard errors.

60
**_Energy demand models for policy_** **_<u>formulation</u>_**

The end-use oriented studies of transport demand attempt to capture the fuel demand by considering individual components contributing to demand and accordingly, they tend to cover the relevant demand drivers for the developing countries. The disaggregated approach also allows a detailed representation of the vehicle stock, vehicle vintages, and changes in the fuel mix, modal mix and technologies, as well as rural-urban dichotomy. This method is also capable of capturing introduction of new technologies or fuels, and traditional modes of transport. This method has been applied to the developing countries in the past. Although most of the end-use models do not consider price-induced effects, the problem may not be acute due to inelastic demand of transport fuels. The use of hybrid end-use models can also address this specific problem.

# **Other approaches**

In line with the general trend in energy model developments, the use of hybrid models in the transport sector in gaining ground. Jaccard et al (2004) provide an example of a hybrid model where the rich technological representation of the transport system is complemented by using a top-down behavioral choice model. Similarly, Robert et al (2007) apply forecasting and back-casting approaches to analyze energy demand in the Stockholm transport system in 2030 after oil peak is reached.

# **4.2.3 Residential demand**

Residential sector generally accounts for a substantial share of final energy demand in many countries. As energy sector policies or energy market conditions tend to have serious welfare consequences for the households, this sector has traditionally been well analyzed. Given that both aggregated and disaggregated demand forecasting has been carried out, we shall present the application of econometric method first, followed by that of the end-use approach.

69
# **Feeding Five Billion Asians: A Socioeconomic Perspective**

# **Jose Cuesta**<sup>**1**</sup>

**JEL:** O13, O53, J11

**Key Words:** Food security, poverty, hunger, middle class, Asia **Sector Board:** Poverty

> 1 World Bank, Poverty Reduction and Equity Unit, 1818 H Street, NW, Washington DC 20433, USA. The author would like to thank C. Malmberg-Calvo, J. Saavedra and participants to the Economist Conference on “Feeding the World: Asia’s prospects of Plenty”, Hong Kong, for valuable comments. Usual disclaimers apply.
(4') Δln( _GDPi,t_ ) = _κi_ + _λt_ + η<sup>3</sup> Δabs( _rrai,t_ ) + (η<sup>3</sup> + η<sup>4</sup> )abs( _rrai,t-1_ ) + _σi,t_ Hence, η<sup>3</sup> +η<sup>4</sup> captures the effect that a permanent change in the level of the RRA has on the GDP per capita growth rate.<sup>14</sup> In Table 11 we report the relevant estimates for testing whether there is a significant growth effect associated with changes in the level of the absolute RRA. As discussed, we can conclude there is a significant growth effect if the sum of the coefficients on the contemporaneous and lagged RRA is significantly different from zero. In column (1) we report estimates of the sum for the _t_ and _t_ -1 effect, i.e. η<sup>3</sup> +η<sup>4</sup> as suggested by the simplest possible model in equation (4). To ensure that we are really picking up the effect of permanent changes in the level of the absolute RRA, we report results from regressions that add additional lags of the level of the absolute RRA on the right-hand side of the estimating equation. Hence, in column (2) we report the results for the sum of the _t_ to _t_ -2 effect, column (3) the sum of the _t_ to _t_ -3 effect, and column (4) the sum of the _t_ to _t_ -4 effect. In columns (5) to (8) we repeat the exercise using the growth rate of consumption per capita instead of the growth rate of GDP per capita as the dependent variable. As can be seen, the main result is that the sum of coefficients on the current and lagged level of the RRA is negative and significant at the conventional confidence levels. Quantitatively, the estimates in columns (4) and (9) suggest that a one standard deviation increase in the absolute relative rate of assistance (0.23) reduced GDP per capita and consumption growth by over one and half percentage points per annum. Hence, our analysis suggests that distortions to the relative price between agricultural and non-agricultural products had a significant negative effect on Sub-Saharan African countries' GDP and consumption per capita growth rates.

To strengthen the above result of a significant long-run growth effect of distortions to the relative price between agricultural and non-agricultural products, we report in Table 12 estimates that are based on 5-year, non-overlapping panel data. Before discussing those results, we would like

> 14 See, for example, Bond et al. (2010) for an application of this strategy to testing whether the positive effect of an increase in the level of investment is limited to the level of GDP per capita, or whether it also has an effect on the GDP per capita growth rate.

23
# **<u>3.2. How has the supply of teachers changed over time?</u>**

Any discussion of teacher earnings makes more sense if we understand the relative supply of workers in the field (which is, in turn, endogenous to teacher earnings). We track the proportion of primary school teachers relative to the overall labor force along with the ratio of primary school teachers to students across the countries in our sample (Figure 1).<sup>27</sup> There are no countries in which the fraction of teachers in the labor force has fallen substantively. The proportion has risen sharply in several countries (e.g., Burkina Faso, Democratic Republic of Congo, Sierra Leone, Tanzania, and Zambia), risen modestly in some (e.g., Côte d’Ivoire and Malawi), and stayed roughly constant in others (e.g., The Gambia). Furthermore, the teacher-student ratio has also risen in many countries (i.e., smaller class sizes), so the rise in teachers cannot entirely be explained by rising enrollment. The average rise across the continent suggests a combination of increasing demand for teachers and that teaching is seen as a desirable profession.

# **<u>3.3. What do teachers and other workers earn in Africa?</u>**

We assess teachers’ earnings relative to other wage workers in two ways. In this section, we compare monthly earnings, hourly earnings, hours worked weekly and the proportion having a second job (Table 4). In the next section, we report the results of multivariate analyses that simultaneously control for age, gender and urban/rural residency. For this comparison of earnings, we restrict the sample to wage workers with secondary education or more.

The median earnings of teachers identified in our study surveys are about $680 per month (measured at the 2011 purchasing power parity (PPP) exchange rate) (Column 1, Table 4). There is, however, very large cross-country variation in these monthly earnings. Teachers make $100 per month in the Democratic Republic of Congo, $220 in Liberia, $400 in Niger, $660 in Ghana, $800 in Tanzania, $1,600 in Zambia, and $2,300 in Namibia.

One often-used way to put these numbers in context is to express teacher earnings in relation to GDP per capita. Column 3 of Table 4 reports that, on average, teachers in the sample earn 300 percent of GDP per capita, again with large variation across countries. Teachers in Burkina Faso and Zambia earn around 500 percent of GDP per capita and, at the other extreme,

> 27 There are far more missing data among secondary school teachers and so those results are not informative.

15
26 Table 9 Major sources of settler arrivals, 1986487(p)

|_To s_<br>_Rank_|_uttlaer_<br>_arrvals_<br>_Cowvryof lastresuidae_|_Nwnber_|_%_|_Escud_<br>_Rank_|_ing RefugieSHP_<br>_Couwyoflat residrce_|_Nwnber_|_%_|
|---|---|---|---|---|---|---|---|
|1|United Kingdom|19 887|17.6|1|United Kingdom|19 882|19.5|
|2|New Zealnd|15 967|14.1|2|New Zealand|15 966|15.6|
|3|he Philippins|6 431|5.7|3|The Plippliws|6 173|6.0|
|4|**SouthAfrica**|5265|4.6|4|SouthAfrica|5247|5.1|
|5|Hong Kong|5 139|4.5|5|Hong Kong|4669|4.6|
|6|Malaysia|5 025|4.4|6|Malaysia|3 477|3.4|
|7|Yugoslavia|2 961|2.6|7|Yugoslavia|2 589|2.5|
|8|Thailand|2 868|2.5|8|Viemam|2425|2.4|
|9|Lebanon|2676|2.4|9|UrltedStatsofAmerica|2316|2.3|
|10|Vienm|2 473|2.2|10|Lebanon|2262|2.2|
|11|United Stan of Ameica|2 322|2.0|11|Sri Lanka|2 247|2.2|
|12|SriLanka|2314|2.0|12|Ireland|2115|2.1|
|13|Singapore|2222|2.0|13|Singapore|2 047|2.0|
|14|Ieland|2 115|1.9|14|China|2027|2.0|
|15|India|2073|1.8|15|India|1 969|1.9|
|16|Indonesia|2 067|1.8|16|Fiji|1565|1.5|
|17|China|2028|1.8|17|Germany|1 549|1.5|
|18|Chile|1960|1.7|18|Korea|1 500|1.5|
|19|Germany|1 637|1.4|19|Canada|1 425|1.4|
|20|Fiji|1 565|1.4|20|Mauritius|1 395|1.4|
||Other|24 316|21.5||Other|19 364|18.9|
|Total||113311|100|Total||102209|100|

Source: Department **oTf** fmmigration and Ethnic Affairs, 1987:63.

(p) Prelinminary