rafmacalaba/datause-encoder-data huggingface dataset:
42

# TABLE C4: COMPARISON OF ANNUAL AND QUATERLY PRIVATE CONSUMPTION SERIES (billons $ 1980)

|YEAR<br>ANNUAL|QUARTERL|Y|
|---|---|---|
|(1)|(2)|(1)y(2)|
|1980<br>2909|2910.85|0.999|
|1981<br>3123|3097.525|1.00i8|
|1982<br>3046|3020.5|1.008|
|1983<br>2883|2858.625|1.008|
|1984<br>2977|2952.85|1.008|
|1985<br>3083|3094.325|0.996|
|1986<br>3011|3029.9|0.994|
|1987<br>2984|3042.025|0.981|
|1988<br>3047|3122.95|0.976|
|1989<br>3220|3311.4|0.973|
|1990<br>3387|3496.125|0.969|
# of on in Impacts Temporary Migration Development Origin Countries<sup>∗</sup>

Laurent Bossavie<sup>1</sup> and C ̧a ̆glar Ozden<sup> ̈2</sup>

**JEL codes:** J61, F22, O15

**Key words:** Temporary migration, economic development, origin countries.

> ∗We would like to thank our colleagues and co-authors Syud Ahmed Amer, Erhan Artuc, Joseph-Simon Gorlach, Maggie Liu, Mattia Makovec, Aaditya Mattoo, Daniel Garrote-Sanchez, Siddharth Sharma, Mauro Testaverde and He Wang for countless discussions and for sharpening our thinking on temporary migration, without implicating them for the shortcomings in this paper. Funding for this research was provided by the Rapid Social Response Multi-Donor Trust Fund, the DEC Research Support Budget (RSB), Knowledge for Change Program (KCP) and the Multi-Donor Trust Fund for International Trade within the World Bank.
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region is indeed well below what could be expected given the countries’ level of development. Public spending per child aged 5 to 19 expressed as a share of per capita GDP lags behind the global pattern from one percentage point in Vietnam to 18 percentage points in Lao PDR (Figure 16).

_Figure 16: Public funding per child and level of development_ <!-- Start of picture text --> 50%<br>45%<br>40%<br>35% R2 = 0.3679<br>30%<br>25% Philippines<br>Vietnam<br>20% Lao PDR<br>Mongolia<br>15% Vietnam Indonesia<br>Indonesia<br>Philippines<br>10%<br>Mongolia<br>5% Lao PDR<br>0%<br>500 2000 8000 32000 128000<br>EAP actual EAP preferred by officials<br>of per capita GDP<br>Public funding for education by child as a proportion<br><!-- End of picture text --> Source: Authors’ calculations from UNESCO SDG 4 and World Development Indicators. The gray disks indicate actual levels for the other countries for which public funding per child could be computed using UNESCO data. The dotted line is the global pattern corresponding to the linear regression of funding per child over the levels of development (as measured by per capita GDP in PPP).

Per child spending is about a third of what one would expect in Lao PDR given the country’s per capita GDP. It is also 40 percent lower than expected in Indonesia and Mongolia and about 30 percent lower than expected in the Philippines and Vietnam. This lower-than-expected level of public funding for education helps explain why learning poverty is so elevated in Lao PDR and the Philippines.

Learning poverty tends to decline rapidly among low- and middle-income countries as public funding for education reaches about 30 percent of per capita GDP, before stalling above that threshold (Figure 23 in the Annex). Although officials in the EAP region may not be fully aware of the extent of the learning crisis, they still believe that public funding for education needs to be increased.<sup>17</sup> 17 This is true even in countries such as Indonesia, which already spends 20 percent of its national budget on education. While this is a large amount as a proportion of budget expenditure, it is a small amount in GDP terms, since Indonesia does not collect enough taxes in proportion to the value of its economy (World Bank, 2020).

20
technologies needed to promote green growth will already be available. Second, even when technologies are available, adaptive R&D can improve the fit of new technologies to local market conditions. For example, production processes can be adapted to take advantage of cost savings in local markets. Third, technology transfer will be most likely to promote green growth when it promotes knowledge spillovers. Fourth, financial constraints and ease of use play important roles determining diffusion of green technologies within developing countries. Fifth, policy incentives are important for creating markets for green technologies, as market forces typically do not reward pollution prevention completely.

While not exclusive, much of the focus in the paper is on energy use and climate change. This is consistent with general trends in the literature, given recent interest in the climate problem. Developing countries now play a key role in post-Kyoto negotiations. At the same time, secondary benefits from energy policies (e.g. lower costs from improved energy efficiency, reduced local air pollution) are important for low-income countries. While not emphasized here, other technologies will also be important for low-income countries, particularly pertaining to resource use (such as water) and agricultural productivity. Future work should also explore the differences that emerge among various technological fields.

For instance, advances in agriculture, such as drought resistant crops and more efficient irrigation, are of particular importance to developing countries. In health care, neglected tropical diseases are a prominent issue.<sup>25</sup> These diseases, primarily of an infectious or parasitic nature, occur almost exclusively in developing countries, so that for-profit pharmaceutical companies have had little incentive to invest in new medicines to combat these diseases (Ridley _et al_ . 2006). Thus, the same problems of creating demand for innovations needed in developing countries that green technologies face occurs in other fields as well, such as agricultural economics and health

> 25 See, for example, http://www.who.int/neglected_diseases/en/, accessed March 17, 2011.

47
establish the robust explanatory power of engineering density and patenting, controlling for a variety of economic and geographic variables; measures of secondary and tertiary education that may be correlated, as well as attempting to control directly for endogeneity. Finally, we show the persistence of the impact of engineering density in a multi-country panel working at the “state” level.

We then provide historical evidence that broadly confirms the ranking of countries emerging from our engineering data and which suggests that precisely an inability to manage the new technologies emerging from the Second Industrial Revolution slowed growth in the Southern US, and led to a loss of competitiveness in a sector long an area of comparative advantage in Latin America– mining. The fact that the US was able to leverage this sector into a well-educated workforce, and world leadership in scientific research while Latin America lost the industry confirms the importance of innovative capacity, and the persistent effect of early human capital investments. It also lends support to the idea of multiple equilibria arising from differences in innovative capacity.

35
and states associated with the reported production quantities. Even if the questionnaire instrument allows for the distinction of crop condition and state, the conversion factor database would need to cover each crop-unit-condition-state combination – a more demanding challenge for survey implementers to address compared to having a simpler conversion factor database at the crop-unitlevel.

As the literature on the IR largely overlooks the abovementioned issues with relying on farmerreported production values, we now turn to the description of the Ugandan context and the data that allow us to explore whether, and the extent to which, errors in farmer-reported production information exist and how, if at all, they affect the often-observed IR.

# **3. Country Context**

Uganda is a landlocked East African nation of approximately 39 million people, with an annual population growth rate of 3.25 percent and 83.9 percent of the population living in rural areas (2015). National and rural rates of the population living in poverty, with respect to the national poverty line, are estimated at 19.5 and 22.4 percent, respectively (2012), and gross domestic product per capita in current US dollars stands at 705 (2015). The economy is heavily dependent on agriculture, such that agricultural land constitutes 71.9 percent of total land area (2014), agriculture value added corresponds to 25.8 percent of the GDP (2015), and agricultural employment makes up 71.7 percent of total employment (2013).<sup>7</sup> Agriculture, specifically increasing agricultural production and productivity, is identified as an essential vehicle for wealth creation in the country’s key policy documents (GoU, 2013; 2015; MAAIF, 2013).

Maize is a major staple, commercial, and export crop in Uganda. It is the leading cereal crop grown in almost all parts of the country. In Eastern Uganda, the country’s leading maize producing region (UBOS, 2010), the crop accounts for the highest share (25 percent) of crop income (World Bank, 2016). At the same time, Eastern Uganda, following Northern Uganda, is also the region with the highest concentration of the country’s poor, and the latest estimate of the regional absolute poverty rate stands at 24.5 percent (World Bank, 2016). Commercialization of maize is relatively low in Eastern Uganda, with less than 41 percent of maize producing households selling _any_ quantity of maize.<sup>8</sup> According to FAOSTAT data depicted in Figure 1 and Figure 2, the trends in maize area harvested and maize yield are positive for the period of 1995-2014. The national area harvested increased steadily over time, from 571,000 hectares to 1.1 million hectares in 2014. The national yield

> 7 The official World Bank statistics on Uganda are obtained from <u>https://goo.gl/IqGLQh. The reference years are noted</u> in parentheses.

> 8 The statistic is based on the Uganda National Panel Survey (UNPS) 2015/16 data.

6
# EXBCUTIVE **SUMMARY**

1. The scale and magnitude of water vending activities in metropolitan areas of developing countries has not been widely realized, nor has the value of information on such water vending systems been adequately appreciated by water resource engineers or policy analysts. This paper presents a case study of water vending in Onitsha, Nigeria--a city of 700,000 people and one of the most important market towns in West Africa. The paper illustrates that a rapid reconnaissance survey of water vending activities and the willingness of households to pay for improved water services can yield valuable inforn tion for water supply planning and unexpected policy insights. Such studies are very inexpensive relative to the capital costs of urban water projects and should become a standard part of water supply project design and planning in developing countries.

2. The vast majority of Onitsha obtains its water from an elaborate and well-organized water vending system which has been created and is operated by the private sector. Approximately 275 tanker trucks purchase water from about 20 private boreholes and then sell it to households and businesses equipped with water storage facilities. Many of the households which purchase water from tanker trucks resell the water by the bucket to individuals who cannot afford large storage tanks or who cannot be reached by tanker trucks. There are literally thou!1ands of small retail water vendors in Onitsha. These small retailers not only sell directly to individuals, but also to other water vendors who carry two 4-gallon tins on their shoulders with a pole and deliver water directly to a customer.

3. During the dry season households obtain approximately 2.96 million gallons per day (mgd) from the water vending system, for which they pay about US$28,000. In 1987 the public water utility was supplying about 1.5 mgd during the dry season, only 50 percent of the amount supplied by the water vendors. For this 1.5 mgd the water utility only managed to collect about US$1,100 in revenues. During the dry season the private sector water vending system was thus collecting about 24 times as much revenue as the water utility. In the rainy season the sales of water vendors were still 10 times the revenue collected by the water utility. On an annual basis households in Onitsha are already paying water vendors over twice the operation and maintenance costs of a piped distribution system.
Africa, Brazil, China and India allude to the possibility, but the long-run dynamics of green growth are still not fully understood. There is of course a downside to gales of competitive destruction and a risk that more rapidly shifting comparative advantage will give rise to more abrupt structural change, putting strains on the operation of labor markets.

Besides innovation, there are other areas of policy, such as network infrastructure provision, where there is also a presumption that private markets will provide less investment than is socially desirable. Here too, the consequences of new policies for labor markets will be very largely dictated by the way in which those markets function.

# **5. Heterogeneity of developing countries**

The previous section of this paper noted the importance of recognizing the different circumstances of developing countries compared with developed ones. But there is much variation across developing countries, too, not least with respect to the likely ease of transition to a world of low-carbon growth. For example, the scope for developing a comparative advantage in the production of equipment for low-carbon electricity production depends on the manufacturing base of the country concerned and whether there are scale and learning economies in the particular technology. Thus the Greenpeace/EREC (2011) study envisages that, in its Advanced Energy [R]evolution scenario, 20% of the growth in renewable capacity in the rest of Africa by 2020 will be supplied by South African manufacturing, and by 2030 30%. Some countries have a comparative advantage in particular renewable energy sources because of natural endowments. Brazil, for example, has the right climatic conditions and soils to give it a substantial cost advantage in biofuels, although other characteristics of the Brazilian economy also help (Kojima and Johnson, 2005).

The developing countries that currently produce a high level of greenhouse gas emissions per unit of GDP face a difficult challenge of structural adjustment. They are the ones in which more labor is likely to have to be reallocated from currently GHG-intensive activities, either by switching technologies within an industry or by moving labor between industry sectors. Given the importance of carbon dioxide emissions from energy production, energyintensive economies will comprise a large part of this group. The following scatter diagrams illustrate the wide variation across developing countries according to measures of carbon 24
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Table 2: Changes in multidimensional inequality from additive or proportional changes in the first outcome.<sup>*</sup>

||**M**<br>|**LD**<br>|**Th**<br>|**eil**<br>|**G**<br>|**ini**<br>|
|---|---|---|---|---|---|---|
||Period 1|Period 2|Period 1|Period 2|Period 1|Period 2|
|_Scenario 1_<br><br><br>|_The first o_|_utcome variable_|_additively incre_|_ases from 100 in_|_Period 1 to 120 i_|_n Period 2_|
|M(x�)<br><br>|0.270|0.216|0.281|0.234|0.400|0.364|
|M(x)<br><br><br>|0.158|0.129|0.170|0.142|0.311|0.283|
|A(x)<br><br>|0.302|0.222|0.302|0.242|0.400|0.364|
|R(x)<br><br>|0.112|0.087|0.111|0.092|0.089|0.081|
|S(x)<br><br>|0.032|0.006|0.021|0.008|0.000|0.000|
|_Scenario 2_<br><br><br>|_The first outco_|_me variable prop_|_ortionally increa_|_ses from 100 in P_|_eriod 1 to 120 i_|_n Period 2_|
|M(x�)<br><br>|0.270|0.284|0.281|0.296|0.400|0.409|
|M(x)<br><br>|0.158|0.171|0.170|0.185|0.311|0.323|
|A(x)<br><br>|0.302|0.316|0.302|0.316|0.400|0.409|
|R(x)<br>|0.112|0.113|0.111|0.111|0.089|0.086|
|S(x)<br>*) The simulate<br>mean 30, Gini|0.032<br>d distributions ha<br>0.3; Correlation: p|0.032<br>ve the following<br>eriod 1: 0; period|0.021<br>parameters: First<br>2: ≈ 0.3.|0.020<br>outcome: mean|0.000<br>100, Gini 0.5; Se|0.000<br>cond outcome:|

We now apply the methods to analyze changes in multidimensional inequality in Azerbaijan from 2016 to 2018. We use data from the second (2016) and the fourth (2023) rounds of the Life in Transition Survey (LITS). The LITS is a survey run by the European Bank of Reconstruction and Development and the World Bank covering the so-called “transition countries” of Europe and Central Asia and several comparator countries of Western Europe, the Middle East, and North Africa (EBRD 2023). The survey included a nationally representative sample of around 1,000 households in Azerbaijan in the second and fourth rounds. Azerbaijan experienced rapid growth in per capita GDP between 2016 and 2023, which makes the example of this country helpful in illustrating the properties of the methods. We construct the multidimensional inequality index for three dimensions captured by the monthly per capita income (in 2017 PPP terms), years of education, and respondent’s health GGiinnii MM assessment. The measure is calibrated for 2016 using normative weights of 1⁄2 for the income dimension and 1⁄4 for the education and health dimensions. Table 3 presents the specific and multidimensional inequality levels for Azerbaijan in 2016 and 2023. The mean monthly per capita income increased by almost 59 percent from about 852 PPP dollars in 2016 to 1350 PPP dollars in 2023. The average years of education and health self-assessment remained relatively stable. Income growth was accompanied by an increase in income inequality, from a Gini of 0.253 in 2016 to 0.339 in 2023. The inequality in years of education grew while the inequality in health assessment slightly declined.

26
Table 7: Split OLS regression of the effects of IT use on firm-level productivity: High competition with China

|Dependent variable log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Computers-per-worker|1.168***|1.079***|1.353***|1.282***|1.220***|1.219***|
||(0.285)|(0.319)|(0.286)|(0.201)|(0.164)|(0.170)|
|_R_<sup>2</sup>|0.810|0.814|0.822|0.830|0.834|0.834|
|Share of labor with Internet|1.107***|1.110***|1.260***|1.102**|1.014***|0.999***|
||(0.255)|(0.249)|(0.476)|(0.449)|(0.372)|(0.381)|
|_R_<sup>2</sup>|0.804|0.811|0.817|0.824|0.828|0.828|
|Share of labor with computer|1.234***|1.155***|1.319***|1.156***|1.038***|1.023***|
||(0.211)|(0.211)|(0.423)|(0.407)|(0.332)|(0.333)|
|_R_<sup>2</sup>|0.809|0.814|0.821|0.826|0.830|0.830|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|730|730|730|730|730|730|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI 22
priority-setting exercises. As cost elasticities increase, affecting regional unit costs of administration (csi , cni), the effects propagate across objectives (through ΘS and ΘN) and regions (through the effects of ΘS and ΘN on ∆). In principle, for a "perverse" cost case, the result could be reversal of the provincial rank-ordering suggested by a budget shares analysis that ignores administrative costs.

# **4.  An Application to Lao PDR: Cobb-Douglas Results**

In Section 2, we have computed alternative provincial budget shares using relative measures of poverty incidence, the poverty count, and the poverty gap. Our discussion in Section 3.6 shows that this is equivalent to the use of poverty indicators as η-values in the optimal allocation model under Cobb-Douglas assumptions (γ = 0, δ = 0, or both). In this restricted case, provincial budget shares depend only on the η-values, and are not affected by variations in welfare function weights and cost elasticities. However, the levels and distributions of activities within provinces are strongly affected by such variations. Using provincial poverty incidence as the η-variable, we illustrate these effects with two examples: the impact of changes in regional welfare function weights (the β's in equation (1)) on the number of poor people served, program quality (proxied by expenditure net of administrative costs), and environmental improvement; and the impact of changes in the administrative cost elasticity on the number of poor people served.

In the first example, we assume that administrative cost elasticities are zero and focus on provincial outcomes when parameters change for the four variables in the regional welfare equation: expenditure (Ei), poor people served (Si), the proportion of poor people served (Si / Pi), and environmental improvement (Ni). In four cases, we assign a weight of .70 to each objective and equal weights to the others. In the fifth case, we assign equal weights to all 15