rafmacalaba/datause-encoder-data huggingface dataset:
# WPS3910-IE

<mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 1</mark>

**Getting Girls into School: Evidence from a Scholarship Program in Cambodia**<sup>♣</sup>

Deon Filmer Norbert Schady

## World Bank Policy Research Working Paper 3910, May 2006

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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._

> ♣ We thank Luis Benveniste, Pedro Carneiro, Eric Edmonds, T. Paul Schultz, Miguel Urquiola, and participants at a seminar at the World Bank for many useful comments.  All remaining errors are our own.  This work benefited from generous support by Government of the Netherlands through the BNPP program.
# **_oil subsidies_ on economic growth: all countries, MENA, and MENA oil group**

|**Dependent**<br>**Variable:**||**Rate of Growth**||
|---|---|---|---|
||**Full Sample**|**MENA subsample**|**MENA oil Group**<br>**subsample**|
|**Long run results:**<br>**log GDP**|-0.168<sup>***</sup><br>(-11.00)|-0.301<sup>***</sup><br>(-9.34)|-0.316<sup>***</sup><br>(-6.87)|
|**log populaton**|0.132<sup>***</sup>|0.0493|0.0849|
||(2.59)|(0.45)|(0.61)|
|**log subsidy**|-0.000469|-0.00311|-0.000923|
||(-0.26)|(-0.23)|(-0.05)|
|**Short run results:**||||
|<br>**Error correcton**<br>|-1.014<sup>***</sup>|-1.022<sup>***</sup>|-1.020<sup>***</sup>|
|**coef.**||||
||(-85.40)|(-55.14)|(-42.38)|
|**d (log GDP)**|1.180<sup>***</sup>|1.213<sup>***</sup>|1.220<sup>***</sup>|
||(65.36)|(41.52)|(33.05)|
|**d (log**<br>|0.942<sup>***</sup>|0.359|0.332|
|**oulaton)**||||
|**pp**|(4.96)|(1.12)|(0.56)|
|**d (log subsidy)**|0.000281|0.0118|0.0165|
||(0.17)|(0.65)|(0.58)|
|**lagged d(log**<br>**subsidy)**|0.000209|-0.00573|-0.0179|
||(0.13)|(-0.35)|(-0.71)|
|**constant**|2.261<sup>**</sup>|7.413<sup>***</sup>|7.232<sup>***</sup>|
||(2.41)|(3.85)|(2.84)|

_t_ statistics in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. Based on panel autoregressive distributive lag model with error correction (ARDL-EC). Subsidies do **_not_** have an adverse effect on long-run and short-run growth. The long-run results are consistent with the single country projections from CPAT which until 2036. Error correction coefficient is negative and significant indicating cointegration among variables as expected. ARDL-EC accounts for this cointegration without the need to test for it. In all regressions robust errors are reported. Using clustered error option did not significantly impact the general direction of the results, although in some instances the significance dropped slightly as would be expected under clustered error.

41
13 figures is three. Med = median in percentage of GDP, LM = lower middle, UM = upper middle, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MENA = Middle East and North Africa, SSA = Sub-Saharan Africa.

Changes in vulnerability arising from changes in oil intensity and the exchange rate are shown by income in figure 7. High-income countries had the largest proportion of countries with declining oil intensity of GDP and currency appreciation against the U.S. dollar. Upper-middleincome countries had the highest proportion of countries with offsetting coefficients exceeding 20 percent.

Figure 7: Impact of oil intensity of the economy and exchange rate on the change in vulnerability by income, 1999–2008 <!-- Start of picture text --> 90<br>82 82<br>Low income<br>80 73<br>Lower-middle income<br>70<br>61 Upper-middle income<br>58<br>60<br>High income<br>50<br>39 38<br>40 35 35<br>30<br>23<br>18<br>20 13<br>10<br>0<br>Declining oil intensity Offset exceeding 20% Appreciating exchange rate<br>% of countries in each category<br><!-- End of picture text --> _Sources:_ Table A1.4 and author’s calculations.

It is sometimes argued that the energy intensity of an economy growing from a low base may need to increase as the economy ramps up its use of modern commercial energy. The percentage of countries with declining energy intensity was indeed smallest for low-income countries and increased with rising per capita GDP (see table A1.6 in appendix 1), but more than half of low-income countries reduced energy intensity. During the same period, more than onethird of low-income countries reduced the oil share of energy consumption. All in all, oil intensity declined in more than half of low-income countries, and one-fifth even managed to offset 20 percent or more of the effect of soaring world oil prices via consumption with a reduction in oil intensity.

Figure 8 shows the above results by region, excluding high-income countries. Oil intensity declined in at least half of the countries in every region, and in three-quarters or more of the countries in Europe and Central Asia, the Middle East and North Africa, and South Asia. Europe and Central Asia had the highest percentage of countries in which oil intensity declined, the offsetting coefficients exceeded 20 percent (although even after recent improvement, energy efficiency remains relatively low in that region), and the local currency appreciated against the U.S. dollar.
**Figure 11:  Incentives to Queue for Jobs in the Public Sector**

<!-- Start of picture text --> 15 200%<br>10 20% probability 10% probability 150% 20% probability<br>5 10% probability<br>0.5% probability 100%<br>no borrowing<br>0 5% probability<br>25 30 35 40 45 50 50%<br>-5<br>0%<br>-10 1% probability 25 30 35 40 45 50<br>-15 5% probability -50%<br>1% probality<br>-20 -100%<br>Source: Authors’ calculations<br>Net gain from waiting<br>Effect of pensions on the gain (loss) from waiting.<br><!-- End of picture text --> How much of the incentive to delay taking the job in the private sector is explained by the pension system? To answer this question we calculate equation (15) assuming that pensions in the civil service respond to the same rules as pensions in the private sector. We then compare the resulting ngw(.) functions. The percentage differences between the net gains of waiting at different ages are graphed in the right panel of Figure 11. The results show that differences in the pension system have strong effects around the age where waiting is no longer optimal. Depending on the probability of finding a job in the public sector, differences in pensions can increase by 10 to 40% of the net gain of waiting and therefore provide strong incentives to let the job in the private sector pass. Similarly, pensions can reduce by 10 to 30% the loss from waiting and also contribute to queuing.

The final question that we address is what are the incentives to switch from a public sector to a private sector job? We do this analysis for an individual with higher education considering the possibility of switching at various ages. The relevant measure in this case is the change in the financial wealth, which is the present value of future wages and pensions. The results are presented in Figure 12. Not surprisingly our calculations show that individuals switching would face important losses; the financial wealth would be reduced by 46% to 48%.

Most of the losses in financial wealth are of course attributable to lower salaries in the private sector. A sizable part of the losses, however, is also explained by the move to a less generous pension system. Hence, the change in pension system reduces the financial wealth by between 5% and 15% depending on the age of the switching. The longer the individual waits, and therefore the higher the expected pensions are, the higher the loss (see dotted line in Figure 12).

21
3 governmental institutions and by public sector investment in physical and human infrastructure in development.<sup>8</sup> How regional and local governments are financed thus directly affects the well-being of billions of people around the world. This paper looks at what theory has to tell us about which taxes should be assigned to such governments. This paper focuses solely on general taxes and not on revenues that are presumably offset by direct benefits received by those who pay them. Section 2 describes the rather limited role prescribed for such taxes by the standard theoretical literature on fiscal federalism. Section 3 reconsiders this question in light of an expanded theoretical framework that has come to be called "second-generation fiscal federalism."<sup>9</sup> Following a brief discussion in Section 4 of several other issues relevant to tax decentralization, Section 5 provides an overview of what an appropriate subnational tax system might look like. Section 6 then considers in more detail the major taxes that appear suitable for subnational governments in emerging countries in theory. Section 7 concludes.

# **2. Tax Assignment: The Standard Model**

"Who should tax, where, and what?" is how Richard Musgrave (1983) once characterized the question of tax assignment in a multilevel government.<sup>10</sup> The answer, at least in theory, has long seemed to be clear to most economists.

## **2.1. Rules for Tax Assignment**

As Oates (1996) summarizes the position, standard fiscal federalism theory suggests essentially three rules with respect to the taxes that should be assigned to subnational governments: (1) Lower levels of government should, as much as possible, rely on benefit taxation of such mobile economic units as households and mobile factors of production.

> 8 Interestingly, Cantarero and Perez Gonzalez (2009) suggest a connection between the degree of revenue decentralization and regional economic growth in Spain, although no significant connection between expenditure decentralization and growth was found. The importance of infrastructure to economic growth continues to be vigorously discussed in the literature: for two recent contributions, see Straub, Vellutini, and Warlters (2008) and Seethepalli, Bramati, and Veredas (2008).  The World Bank’s governance data base (http://info.worldbank.org/governance/wgi/index.asp) has been used in countless studies to show the importance of government institutions in affecting policy outcomes, including fiscal outcomes: for one example, see Bird, Martinez-Vazquez and Torgler (2008).

> 9 Oates (2008) distinguishes two strands of the second-generation literature.  However, this distinction is ignored here since both strands emphasize the two common elements missing from the standard fiscal federalism model that are stressed in the present paper -- the treatment of politicians and officials as having their own objectives and the need to pay close attention to the political and fiscal environments within which these agents operate.

> 10 A more detailed discussion of Musgrave’s arguments may be found in Bird (2009).
# **4. DATA**

The data underlying this study were obtained through a panel survey of Javanese farm households conducted by the Indonesian Center for Agrosocio-economic Research (CASER) in April/May 1991 and again in June 1999. The baseline sample included rice-growing villages that had already been covered by the program, as well as villages that were not yet covered by the program, but were in areas where the program was planned to be implemented. All villages were visited in the repeat survey in 1999, but our analysis focuses only on those villages that had not yet been exposed to an FFS at the time of the baseline survey in 1991. In most of these villages, the village sample contains randomly selected farmers who participated in the training in various years throughout the period covered, as well as farmers from the same village who did not participate. Of the 268 sample households from the 22 sample villages where a field school had been implemented between 1991 and 1999, only 112 had actually participated in the training while the remaining 156 households had not attended a program, but had been potentially exposed to some of its (new knowledge) benefits through informal communications and joint activities with graduates of the program who lived in their village.

The 1991 survey collected information on households’ farm operations and characteristics for the 1990/91 wet rice season and on their household attributes, activities and assets. It also documented the farmers’ knowledge of specific aspects of pest management that were to be included in the training program, through a set of questions on specific curriculum components. The 1999 survey repeated the same questions and collected additional data regarding the household and the village participation in FFS training, and more information about the community. The farmers’ responses to the identical knowledge questions in both 1991 and 1999 were scored, and the number of correct answers relative to the total number of pest management questions serves as an indicator of pest management knowledge.<sup>2</sup> More detailed information on the construction of the knowledge score is provided in Appendix A.

10
# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|The Modeling Framework|4|
|3.|Experiments and Results|10|
||Trade Liberalization Experiment<br>Prodt'ctivity Growth Experiment|11<br>17|
|4.|Conclusions|25|
|Refe|rences|27|
|Tabl|es||
|1|Ten Percent Reduction of Manufacturing Protection in<br>Developing Countries<br>|13|
|2|<br>Ten Percent Productivity Increase in Production of||
||<br>Manufactures for Export|20|
|3|<br>Shares of Manufactures Exports in Total Output and<br>Manufactures Imports in Total Demand|24|
O.A.M.D.G.

|**Asset**|**Estimated Price in USD**|Table 4: Prices used for<br>the valuation of assets|
|---|---|---|
|bicycle|52||
|boat/canoe|140|_Source: Survey of people_<br>_of SISBEN 1-3 in rural_<br>_Boyacá_|
|chicken|4|_Note: the Familias_<br>_database contains already_|
|cow|245|<br>_house values as well as_<br>_information about whether_|
|diesel generator|1,048|_a TV is b/w or colour._|
|donkey|28||
|duck|17||
|fan|21||
|fridge|87||
|goat|35||
|horse|44||
|kerosene lamp|17||
|manual sewing machine|44||
|mixer|14||
|motorcycle|2,271||
|average piece of land for a house<sup>21</sup>|1,398||
|pig|26||
|rabbit|7||
|sheep|24||
|stereo equipment|70||
|television b/w|17||
|television colour|157||

> 21 The familias dataset contains the actual house value, so no external average prices have to be applied here.

18
||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Servic**<br>**es**||||||||||||
|**Transp**<br>**ortatio**<br>**n**|0.00|0.00|0.01|0.01|0.03|0.05|0.07|0.08|0.09|0.09|0.09|
|**Bankin**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**g-**<br>**Financ**<br>**ing**||||||||||||
|**Real**<br>**Estate**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Public**<br>**Admin**<br>**istrati**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**on**||||||||||||
|**Educat**<br>**ion**|0.00|0.00|0.00|0.01|0.01|0.02|0.02|0.03|0.03|0.03|0.03|
|**Health**|0.00|0.00|0.00|0.01|0.01|0.02|0.03|0.04|0.04|0.04|0.04|
|**Other**<br>**Servic**<br>**es**|0.00|0.01|0.01|0.02|0.04|0.06|0.09|0.11|0.11|0.11|0.12|

_Table B. 8. Annual spending associated with investments and households’ consumption under the SUMP Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricul**<br>**ture**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Forest**<br>**ry**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Minin**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**g**||||||||||||
|**Food**<br>**Manuf**<br>**acturi**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**ng**||||||||||||
|**Textile**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Wood**<br>**and**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Paper**||||||||||||
|**Chemi**<br>**cal and**<br>**Plastic**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Produ**||||||||||||
|**cts**||||||||||||
|**Metal**<br>**Produ**<br>**cts**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Machi**<br>**nery**<br>**and**<br>**Equip**<br>**ment**|0.00|0.12|0.12|0.06|0.00|0.00|0.00|0.00|0.00|0.00|0.00|

42
# What Explains Global Inflation

Jongrim Ha, M. Ayhan Kose, Franziska Ohnsorge and Hakan Yilmazkuday<sup>∗</sup> Keywords : Oil prices; demand shocks; supply shocks; interest rate shocks. JEL Classification : E31, E32, Q43

> ∗ Ha: World Bank, Prospects Group; jongrimha@worldbank.org. Kose: World Bank, Prospects Group; Brookings Institution, CEPR, and CAMA; akose@worldbank.org. Ohnsorge: World Bank, South Asia Region, CEPR, and CAMA; fohnsorge@worldbank.org. Yilmazkuday: Florida International University; hyilmazk@fiu.edu. We would like to thank Pierre-Olivier Gourinchas, Andrei Levchenko, Mirco Mozzanica, Franz Ulrich Ruch, Hillary Stein, participants at the 2023 IMF Annual Research Conference, and seminars in the World Bank for their helpful comments. We gratefully acknowledge support from World Bank PHRD and KCP Funds. The findings, interpretations, and conclusions expressed in this paper are those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
In order to mitigate some of these impacts of the pandemic, an exceptional social and economic emergency plan was announced, enacted and gradually implemented by the Government of Tunisia on March 21, 2020. These compensatory measures targeted the poorest strata of the population and the most vulnerable groups affected by the pandemic to be able to compensate them with incurred losses due to the pandemic. Table 3 below provides a comprehensive description of the measures including the amount of support, period and target number of needy families, families with limited income, families caring for a person without family support and low retirement pensions.

**Table 3:  Compensatory measures announced by Government of Tunisia on March 21, 2020**

|**Target Population**|**Amount of support**|**Period**|**Target number in**<br>**theory**|
|---|---|---|---|
|Needy families (PNAFN / AMG1)|50TND (15 dollars)|April 2020|260 000<br>|
||60TND (17 dollars)|May 2020|Households|
|Families with limited income (AMG2)|200 TND<br>200 TND<br>(70 dollars)|April +May 2020|370 000<br>Households|
|Families caring for a person without<br>family support|200 TND<br>(70 dollars)|April 2020|779<br>Households|
|Low retirement pensions (less than<br>180 TND-60 dollars)|100 TND Avril<br>(30 dollars)|April 2020<br>pension<br>increased to 180<br>dinars from<br>August|140 000<br>Households|
|Famille « Istimarat » (excluding<br>Families with limited income)<br>|200 TND<br>(70 dollars)|May 2020|301149<br>Households|

Source: Data accessed from Ministry of Social Affairs Simulating the impacts of the pandemic on welfare in the presence of all the above mitigation measures announced,<sup>14</sup> we observe that compensatory transfer measures initiated by the government will have a positive impact on poverty. Specifically, the increase in poverty will be 6.5 percentage points if mitigation measures are in place vis-à-vis in their absence when the increase in poverty will be 7.3 percentage points<sup>15</sup> (see Table 4).

**Table 4: Impact of COVID19 with mitigation measures**

|Before||After mitigation|Difference|Difference|
|---|---|---|---|---|
|COVID|After COVID|measure|(without|(with|

> 14 We provide a description of the government measures announced on March 21, 2020. The simulations are based on the announced measures as there is no access to information on actual spending under these measures announced.

> 15The data used to estimate these welfare impacts in the presence of mitigation effects do not allow us to focus on a region, hence we do not provide estimates of these impacts across regions. Moreover, the activities targeted by government measures are more concentrated in coastal regions. The South Region, where some of the riots are concentrated, is characterized by the oil extraction industry and some chemical industries, which do not employ a large workforce.

17
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Rural Income Generating Activities (RIGA) data. <u>http://www.fao.org/economic/riga/en/</u>

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18