rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> World Bank projects to isolate exogenous changes in public expenditures<br>from those endogenously responding to the state of the business cycle..<br>Romer and Romer (2010) also adopt a narrative approach and document<br>the time of legislative changes to the U.S. tax code in an attempt to identify<br>tax policy shocks and classify whether tax changes were anticipated and<br>whether they were explicitly aimed at addressing business cycle conditions.<br>The second approach is the Structural Vector Autoregression (SVAR)<br>methodology introduced by Blanchard and Perotti (2002). They identify fis-<br>cal shocks using information about fiscal institutions. On the expenditure<br>side, their identifying assumption is that discretionary government expendi-<br>ture requires at least one quarter to respond to business cycle conditions.<br>For taxes, they use the output elasticities of tax revenues, estimated from<br>information about tax codes, to differentiate between the endogenous reac-<br>tions of tax revenues to output fluctuations and unanticipated shocks to tax<br>revenues.<br>There has been much debate as to the relative merits of these two ap-<br>proaches. Perotti (2004) and Ramey (2009) have extensive comparisons of<br>the SVAR and narrative approaches to identifying government expenditure<br>shocks. Ilzetzki, Mendoza and Végh (2010) discuss extending the two ap-<br>proaches to countries outside the OECD. Favero and Giavazzi (2010) and<br>Perotti (2011) compare and attempt to reconcile the very different results<br>arising from Romer and Romer’s (2010) and the SVAR approaches to iden-<br>tifying tax policy shocks. Our study does not innovate on identification of<br>fiscal shocks; we rather take off the shelf the Blanchard and Perotti (2002)<br>methodology. But it is worth making two points on identification.<br>First, whatever the merits of the narrative and natural experiment ap-<br>proaches in general, when studying a broad cross-section of countries, these<br>methods are difficult to implement. While Barro (1981) and others have<br>13<br><!-- End of picture text -->
<!-- Start of picture text --> 8<br>S<br>so02 .<br>cv .<br>3<br>oO ° . ry . o<br>4<br>4 % .<br>a +<br>= } °<br>Lo<br>ae<br>. . o ¢<br>.<br>°<br>0 20 40 60 80 100<br>Women with an account (%)<br><!-- End of picture text --> <!-- Start of picture text --> o<br>=] °<br>°<br>a<br>9 + . °<br>2 °<br>2 9 r<br>= .<br>8 ° , 3<br>a . °<br>@ °<br>°<br>3 ry r %e<br>2 -,<br>Eo<br>2s<br>°<br>° a<br>° 2<br>0 20 40 60 80 100<br>Women with an account (%)<br><!-- End of picture text -->
PRE Working Pa;r Series

||AuAhor|1<br>ga|Contact<br>for<br>per|
|---|---|---|---|
|WPS402 The GATT as International Discipline<br>Over Trade Restrictions: A Public<br>Choice Approach|J. Michael Finger|March 1990|N. Artis<br>38010|
|WPS403 Innovative Agricultural Extension<br>for Women: A Case Study uf<br>Cameroon|S. Tjip Walker|||
|WPS404 Labor Markets in an Era of<br>Adjustment: The Chilean Case|Luis A. Riveros|||
|WPS405 Investments in Solid Waste<br>Management: Opportunities for<br>Environmental Improvement|Carl Bartone<br>Janis Bernstein<br>Frederick Wright|April 1990|S. Cumine<br>33735|
|WPS406 Township, Village, and Private<br>Industry in China's Economic<br>Reform|William Byrd<br>Alan Gelb|||
|WPS407 Public Enterprise Reform:<br>AChallenge for the World Bank|Ahmed Galal|April 1990|G. Orraca-Tetteh<br>37646|
|WPS408 Methodological Issues in Evaluating<br>Debt-Reducing Deals|Stijn Claessens<br>Ishac Diwan|||
|WPS409 Financial Policy and Corporate<br>Investment in Imperfect Capital<br>Markets: The Case of Korea|Mansoor Dailami|April 1990|M. Raggambi<br>37657|
|WPS410 The Cost of Caphal and Investment<br>in Developing Countries|Alan Auerbach|April 1990|A. Bhalla<br>37699|
|WPS411 Institutional Dimensions of Poverty<br>Reduction|Lawrence F. Salmen|||
|WPS412 Exchange Rate Policy in Developing<br>Countries|W. Max Corden|||
|WPS413 Supporting Safe Motherhood:<br>A Review of Financial Trends|L. M. Howard|||
|WPS414 Supporting Safe Motherhood: A<br>Review of Financial Trends,<br>Executive Summary|L. M. Howard|||
|WPS415 How Good (or Bad) are Country<br>Projections?|Norman Hicks<br>Michel Vaugeois|April 1990|M. Berg<br>31058|
|WPS416 World Bank Efforts at Poverty<br>Measurement in the Third World: Th<br>Living Standards Measurement Stud|Paul Glewwe<br>e<br>y|||
the public on the basis of an anticipated relationship between price and service quality. I first explore how such a relationship between pay and service quality may lead to either a “lowlevel” or a “high-level” equilibrium, or possibly the co-existence of both. I then go on to demonstrate that when the public is uncertain about the true type of the utility administration (whether it is “corrupt” or “non-corrupt”), the utility market described by my model tends to exhibit multiple equilibria, in the sense that a “low-level” (“high-level”) equilibrium obtains when the (Bayesian) prior probability of “non-corrupt” is low (high).

The issues I describe by my model are highly relevant for describing actual public utility markets in several world regions. One is Latin America, where a great wave of regulatory changes has swept over the region largely in the electricity and telecommunications sectors but increasingly in water and sanitation. This has largely taken the form of privatization, so far more in telecommunications and power than in water and sanitation.<sup>7</sup> In the latter sectors public operation is however still often the most realistic alternative.

Another region where endemic inefficiency problems in both the electric power and water sectors take similar forms is South Asia. A main case in point is the market for electricity to households and farmers in India. This market is plagued by a chronic “low-level equilibrium” where willingness to pay for electricity supply is low (often zero at the margin for farmers, and typically determined at the state level through a political process), service quality is poor with frequent power outages and voltage distortions (leading to damaged equipment), and there are large system losses.<sup>8</sup> Surveys have indicated substantial willingness to pay for more reliable service, which could also raise more revenue so as to solve some of the problems). There is however a lack of political will to increase electricity prices in part because consumers do not trust the power utilities to actually deliver higher-quality services. The

> 7 See Estache, Foster and Wodon (2000) for an early discussion and overview of privatization of utilities in Latin America over this period, and World Bank (2009) for a more recent discussion, covering all basic utility services.

> 8 Such issues are discussed e g in World Bank (2010).

6
<!-- Start of picture text --> Manufacturing value-added in GDP<br>6 8 10 12<br>GDP per capita PPP (log)<br>50<br>40<br>30<br>20<br>10<br>Manufacturing value added in GDP<br>0<br><!-- End of picture text --> Source: WDI; author’s calculations. Note: each dot in both panels represent a 1‐year observation of one country, which means that each country has 15 dots showing the trend over time.

**Turkey’s service sector growth falls behind many comparator countries.** The top panel of Figure 2.4 shows the shares of services, manufacturing and agriculture in GDP for Turkey and comparator countries. Turkey falls roughly in the middle of the comparator group. However, the bottom panel of Figure 2.4 shows that the 2000‐2015 growth of Turkey’s services economy was much lower than many of its comparators.

**Figure 2.4: Value‐added shares and growth of shares for Turkey and peers (2000‐2015)**

<!-- Start of picture text --> Value-added shares in GDP, 2015<br>80<br>72.7<br>68.5<br>60 64.0 63.4 62.7<br>60.5 59.7<br>52.9<br>50.2<br>40 43.3<br>29.7<br>27.0<br>20 24.6<br>19.9 19.0 17.516.6 21.0<br>13.4 13.8 13.5<br>11.8<br>7.8 8.8<br>0 5.0 2.3 4.1 2.5 4.6 2.5<br>BRA ZAF HUN POL RUS TUR CZE IND CHN IDN<br>Agriculture Manufacturing Services<br>Share of value-added in GDP<br><!-- End of picture text --> 8
# **Impact of U.S. Market Access on Local Labor Markets in Vietnam**

Trung X. Hoang Vietnam Academy of Social Sciences Ha Nguyen<sup>1</sup> Development Research Group The World Bank JEL: F16 Key words: U.S.-Vietnam Bilateral Trade Agreement, employment, propagation, exports

> 1 We are grateful to Brian McCaig for kindly sharing tariff data. We thank Brian McCaig, Norman Loayza, Minh Nguyen, Anh Pham, Luis Serven, four referees and seminar participants at the World Bank for insightful comments and feedback. All errors are our own. Contact addresses: Trung X. Hoang (hoangxuantrung3012@gmail.com) and Ha Nguyen (hanguyen@worldbank.org). This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 502.01-2016.10.
4 <!-- Start of picture text --> 100% 7 : ; :<br>Pe aya ee<br>20% 2 rg a<br>0% 20% 40% 60% 80% 100%<br>To OECD<br><!-- End of picture text --> <!-- Start of picture text -->
-25% 0% 25% 50% 75% 100%<br>Gross emigration rates<br><!-- End of picture text -->
# **3.1. Creating a general environment that will have appeal to foreign investors**

- The economic and political environment should be relatively stable. Economic and political volatility creates pressures on newly reformed systems, thus undermining the confidence of potential foreign participants in the systems.

- Each country's system of laws, enforcement, and litigation should be comprehensive, rational, fair, generally consistent with international standards and practice, and well documented. Foreign investors should feel comfortable with their ability to function under the system.

- . Each country's program of taxation should be reasonable, fair, balanced in its treatment of different participants in the economic and financial system, and balanced in its treatment of different types of financial institutions, instruments, and investments.<sup>38</sup>

- Each country's system of regulation should be limited to that which is needed and productive; balanced in its treatment of different types of financial institutions, instruments, and investments; and fair to various participants.

- Each country's economic, financial, legislative and regulatory systems should strive for transparency.

# **3.2. Consistent and stable macroeconomic environment**

- A country with fixed exchange rate and free capital mobility that follows an overly expansionary monetary policy will be vulnerable to a speculative attack.<sup>39</sup>

- Eichengreen, Rose, and Wyplosz, 1996a studied the behavior of macroeconomic variables in the period leading up to attacks in 22 countries from 1967 to 1992. For non-ERM countries they found evidence that inconsistent fundamentals (e.g., larger than normal budget deficits, inflation, and credit expansion) preceded the attack.

- The public debt must be kept within reasonable bounds, and its maturity and currency structure must be prudently managed. Beyond the sovereign, excessive leverage,

> 38 In Spain, although double taxation agreements had provided partial exemption, slow and cumbersome procedures for refunding taxes withheld at the source acted as a strong disincentive for foreign investors before full exemption of withholding tax for non-residents in 1990. The exemption has contributed significantly to the maturity of both the primary and secondary markets. However, factors of first order importance in explaining foreign investment flow to the Spanish debt market were a substantial spread between Spain and Germany and a very strong political commitment to create a pan-European political entity. Many foreign investors considered the acquisition of high-yielding debt from Spain as a sure bet. 39 Inconsistent policies would eventually lead to a crisis even in the absence of international capital mobility. The crisis would be delayed, because agents would have to reduce their holdings of domestic currency through current account transactions. (Auernheimer, 1987)

12
selected non-head produces much lower levels of household usage. Product-related cues appear to be important to gain a full understanding of usage of insurance, formal credit, and informal savings, but do not appear necessary for more basic services such as bank accounts, formal savings, and informal credit.

# **5. Regressions**

In this section, we test whether the differences across treatments described in the previous section hold up when we control for other factors that could affect usage in regressions. Some of those regressions are also designed to identify the characteristics of the individuals and households that reported lower levels of usage for institution-based versus product-based questions. Another set of regressions examines the household characteristics of the randomly selected informants who reported lower household usage rates than those obtained from the head of household or the full enumeration of individual usage. In this way, we hope to identify the types of respondents that have difficulty with certain question formats.

# **a. Household Usage: Full Enumeration vs. Informants**

To describe household usage of financial services, we use the following specification, which we estimate via probit:

# **_Finance_ i =α**

**+β1 Agei +β2  Rurali +β3 Sizei**

**+β4  Dependent Sharei**

**+β5  Age of Head of HHi**

**+β6  Highest Grade, Headi**

**+β7  HH Head Numeratei**

**+β8  Share of Agricultural Workersi**

**+β9  Share Employedi +β10 Share Self-employedi**

**+β11 Informant is HH Headi**

23
**Table 4.1. Results for maize livelihood zone**

|||**Soil moistu**|**re**||**NDVI**|||**SPEI**||**Total**|**seasonal rain**|**fall**||**WRSI**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|**Landed h**|**ouseholds**|||||||||||||||
|Hazard|**0.055*****|**0.059*****|**0.119*****|**0.024****|**0.034*****|**0.071*****|0.019|0.019|**0.062*****|-0.019|-0.017|0.012|0.001|0.012|-0.015|
||**[3.852]**|**[4.097]**|**[4.792]**|**[2.122]**|**[2.755]**|**[4.120]**|[1.616]|[1.547]|**[2.843]**|[-1.570]|[-1.407]|[0.701]|[0.685]|[1.169]|[-0.433]|
|Hazard<sup>2</sup>||**0.022***|0.011||**0.020*****|-0.001||0.003|-0.004||**-0.022*****|**-0.015****||-0.000|0.000|
|||**[1.831]**|[0.839]||**[2.807]**|[-0.082]||[0.243]|[-0.250]||**[-3.021]**|**[-1.964]**||[-1.162]|[0.653]|
|Hazard<sup>3</sup>|||**-0.028*****|||**-0.021*****|||**-0.021*****|||-0.011**|||-0.000|
||||**[-2.954]**|||**[-3.814]**|||**[-2.600]**|||[-2.205]|||[-0.863]|
|**Landless**|**households**|||||||||||||||
|Hazard|**0.043****|**0.038***|0.032|0.013|**0.030***|**0.077****|**-0.028****|**-0.041****|0.071|**-0.045*****|**-0.042*****|**-0.075****|**-0.003*****|0.000|-0.016|
||**[2.239]**|**[1.844]**|[0.875]|[0.801]|**[1.696]**|**[2.441]**|**[-2.006]**|**[-2.249]**|[1.550]|**[-3.161]**|**[-3.128]**|**[-2.526]**|**[-3.064]**|[0.056]|[-0.422]|
|Hazard<sup>2</sup>||0.002|0.004||**0.029****|0.005||-0.022|**-0.039****||**-0.033****|**-0.039****||-0.000|0.000|
|||[0.101]|[0.181]||**[2.189]**|[0.347]||[-1.305]|**[-2.213]**||**[-2.114]**|**[-2.571]**||[-0.444]|[0.374]|
|Hazard<sup>3</sup>|||0.007|||**-0.023****|||**-0.041*****|||0.012|||-0.000|
||||[0.399]|||**[-2.352]**|||**[-2.917]**|||[1.215]|||[-0.406]|
|Obs.|50,266|50,266|50,266|50,242|50,242|50,242|50,266|50,266|50,266|50,266|50,266|50,266|48,506|48,506|48,506|
|R-sq|0.362|0.363|0.364|0.358|0.358|0.359|0.361|0.361|0.361|0.361|0.362|0.362|0.370|0.371|0.371|

_Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index. Results are from a weighted regression such that each household is assigned its survey-specific population weight. Countries and years included are Ethiopia (2011, 2016), Lesotho (2017), Malawi (2010, 2016, 2019), Mozambique (2014), Zambia (2015), and Zimbabwe (2017). Dependent variable is the log of per capita (or adult equivalent) consumption. Other variables included and not shown are education of household head, gender of household head, whether the household location is remote, agroecological zone indicators, whether the household was surveyed during the lean season, survey year, country, country x survey year, and administrative fixed effects for district (Ethiopia. Lesotho, Malawi) and province (Mozambique, Zambia, Zimbabwe). Robust t-statistics are shown in parenthesis. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent. When variable is significant the coefficient and standard error are in bold font.

18
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

# **APPENDIX A PROJECTION METHODOLOGY AND KEY ASSUMPTIONS**

## **_Projection methodology_**

Financial projections were carried out for the Public Service Pension Fund of Uganda under a no-reform scenario (baseline scenario) to assess pension liabilities of the current system assuming it does not change. Also, several reform scenarios were modeled and evaluated using the baseline scenario as a benchmark.

The World Bank Pension Reform Options Simulation Toolkit (PROST) model, version 12, was used for the projections. PROST is a computer-based pension model designed to simulate the behavior of pension systems and assess their financial sustainability under different economic and demographic assumptions over a long time frame. The model can be adapted to a wide range of country circumstances and it allows us to model various types of pension reform options. A general description of the model can be found in APPENDIX D.

The simulation horizon spans from fiscal year 2003/2004 (the base year) to fiscal year 2075/2076 (the ending year) – approximately a lifetime of one generation which is viewed to be an adequate length for pension system modeling. As with any simulation model, the outcome from PROST depends largely on the nature and quality of data and assumptions used in the making of projections. Three main groups of assumptions are important in pension modeling: (1) macroeconomic and labor market; (2) demographic; and (3) pension system related variables. The key assumptions used in the projections for the PSPF and summary results are presented in the next sections.

## **_Macroeconomic and covered wage growth assumptions_**

Macroeconomic variables highly influence future projections. In the short- to medium term, IMF macroeconomic projections for Uganda were used for assumptions regarding GDP growth, inflation rate, and discount rate. In the long run, it is assumed that the Ugandan economy reaches a steady state with real GDP growing at about 3.5% and inflation stabilizing at 3.5%. Average wage in the private sector is assumed to increase in line with GDP per capita growth. The macroeconomic assumptions used in the simulations are summarized in Table 15. These assumptions can be considered to be a moderate economic growth scenario. It is always advisable to experiment with various scenarios for the time paths of the main economic variables (i.e., a more and/or a less optimistic economic growth scenarios) which was not done in the presented set of simulations but may be considered in the future.

- 79 -
fundamental interest is in ensuring that the new PPP data on price levels do not affect the global count of poor. It is important to recognize though, that the number of poor is only kept constant at the global level – the poverty headcounts would change in most every country.

While it might be attractive to communicate that the global poverty rate is unchanged, fixing the poverty rate at 14.5 percent in 2011 is essentially a statement asserting that we place more faith in the global level of poverty as estimated by 2005 PPP data that has been extrapolated out to 2011 as opposed to the poverty level implied by the new 2011 PPP data. Yet, at the same time, such an approach would imply that for comparison between countries, the 2011 PPPs are most appropriate. This approach would also have the awkward property that any update or correction to the data that affected the estimated poverty level for a single country would force a revision to the global poverty line, and this revision would then lead to a change in poverty lines in all countries of the world.

This paper proposes answers to the questions of whether new PPP data should affect global poverty estimates, and if so, how. Our view differs from the view that poverty estimates should not change with the new PPP data. Extrapolations of old PPP data should provide reasonable approximations to new PPP data, but as with most all data collection efforts, there are always so-called seam issues when new data are introduced to a time series of statistics. There are likely flaws in the 2011 PPP data, but this will always be the case for efforts to collect price data across the world where product variation is significant. Our analysis essentially assumes that the signal in the 2011 PPP data provides a better portrayal of relative price levels across countries as compared to using the 2005 PPP data (combined with local inflation rates). And this assumption leads us to the view that current global poverty estimates should be based on the most recent price data.

29
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 3.6. Power generation mix**

<!-- Start of picture text --> a. Trade expansion b. Trade stagnation<br>4%<br>5%<br>28%<br>48%<br>49% 3%<br>64%<br>2%<br>Hydro Coal Gas Other<br>Hydro Coal Gas Other<br><!-- End of picture text --> Source: Derived from Rosnes and Vennemo 2009.

**Table 3.5. Differences in electricity production and CO2 emissions in trade expansion and stagnation**

||**WAPP**|**SAPP**|**EAPP**|**CAPP**|**Total**|**WAPP**|**SAPP**|**EAPP**|**CAPP**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|
||**_Productio_**|**_n differenc_**|**_e (TWh)_**||||**_Emissio_**|**_ns savings_**|**_(M ton)_**||
|Coal||–41.5|0.7||–40.8||–37.8|0.6||–37.2|
|Diesel|–0.8|–0.3|0.3||–0.8|–0.6|–0.2|0.2||–0.6|
|Gas|–9.2|–5.3|–42.4||–56.8|–4.7|–2.7|–21.5||–28.9|
|HFO|0.2||0.4|–4.9|–4.3|0.1||0.3|–3.6|–3.2|
|Hydro|11.5|47.5|43.4|5.1|107|||||0|
|**Total**|**1.6**|**0.5**|**2.4**|**0.3**|**4.7**|**–5.2**|**–40.7**|**–20.4**|**–3.6**|**–69.9**|

Source: Derived from Rosnes and Vennemo 2009.

Note: The East African Power Pool is expanded to include key Nile Basin trading partners Egypt, Ethiopia, and Sudan. CAPP = Central Africa Power Pool; SAPP = Southern African Power Pool; WAPP = West African Power Pool.

Ultimately, power trade expansion in EAPP would save less than $0.01 per kilowatt-hour, less than for all other power pools. In addition to the financial savings, however, the deepening of regional power trade would benefit the region by reducing the long-run marginal cost of power. Given that power is a key production input to the economy, any reduction in power costs will improve productivity and competitiveness.

The magnitude of power cost savings varies across individual countries in the EAPP (table 3.6). Small countries that have traditionally relied on gas-fired generation have the most to gain from switching to imported hydropower. In particular, Burundi stands to gain $0.04 per kilowatt-hour, the highest savings in the region. Kenya and Uganda would gain $0.01 per kilowatt-hour. Even in countries where per unit savings are more modest—$0.01 per kilowatt-hour or less—the aggregate value of the savings can be quite significant. Ethiopia, which would become a major power exporter under trade expansion, would 44