rafmacalaba/datause-encoder-data huggingface dataset:
7 among the public goods (DeWulf, 1981). Given these difficulties, many recent authors consider the allocation of general benefits so problematic that they exclude such goods from their studies. Specific Benefits and Transfer Payments There are three main methods to allocate specific benefits and transfers. The first approach is to make ad hoc assumptiu- , about the incidence of particular programs. For instance, agricultural benefits are often distributed in proportion to farm income. The second approach is to infer the incidence from legislative intent, making a strong assumption that benefits flow to intended beneficiaries. The third, and by far the most informative, approach uses household survey data to deternine household members' utilization of particular programs.

Of course, allocable public expenditure may also have external (or "nonrecipient") benefits, as when education affects general productivity and social order, as well as the productivity of the individual receiving the education. One way of accounting for extemal benefits is the Tax Foundation (1967) approach of treating portions of some specific and transfer benefits as if they were general. However, in most cases the amount of indirect benefits and the allocation of these benefits are arbitrary choices that may provide misleading measures of benefit progressivity.

Also, it is important to consider benefit shifting. As noted, public transfers crowd out private interfamily transfers, so that some of the benefits from new public transfeis may accrue to private donors who may diminish their inter-family transfers. Agricultural program benefits can also be shifted from farmers to consumers in the fonn of lower food prices or to landlords in the form of higher demand and rents for land. In fact, Allen (1982) has shown that even lump-sum transfers can have large enough relative price and income effects to offset the benefits from cash transfer programs. Most incidence studisq do not analyze benefit shift or extemal benefits. In cases where shifting and external effects may be important, researchers should consider a range of possible shifting and external benefit assumptions in assessing the distributional impact of certain programs.
# **Better Than Most: Teacher Beliefs about Effort and Ability in Uganda**

Shwetlena Sabarwal Kanishka Kacker World Bank Indian Statistical Institute James Habyarimana Georgetown University Keywords: Education; Teacher Absenteeism; Teacher Performance JEL Codes: I20; O15; H52 Acknowledgements: We thank the staff of the Uganda Ministry of Education and Sports and Innocent Mulindwa (World Bank) for their support. Survey design and field‐work support was provided by Mariam Adil, Jonathan Gartenberg, Dharini Natarajan, and Lindsay Dawson. Filder Ayremo and George Okwir led the field‐work. Lucine Munkyung Park and Malek Abu‐Jawdeh provided superb research assistance. Financial support from the Strategic Impact Evaluation Fund is gratefully acknowledged. We thank Tara Beteille, David Evans, Tazeen Fasih, Ezequiel Molina, Andrew Ragatz, and participants at the RISE 2017 conference for helpful comments.
about 0. _15-0.17._ When the coefficients of wages of the current period and one and two months lags are added-up the resulting coefficient rises to 0.28 (Table 2, eqn. 1).

The coefficient of current period money growth appears with the wrong sign. The sum of the coefficients with one and two month lag is around 0.40-0.46 for the unrestricted specifications.

Let us turn now to the estimation with the restrictioi, that the coefficients sum one. The parameter of the exchange rate (plus foreign inflation) variable lies in the range of 0.40 - 0.62 depending upon the specification and the number of lags included. This value of the coefficient of the exchange rate shows that a 10 percent devaluation would produce an increase in the CPI between 4 and 6.2 percent, ceteris paribus, say holding wages and money fixed after the devaluation. The coefficient of wages is between 0.20, (specification with no lags) and 0.23 wheii two lags are added up. The coefficient of the sum of two months lagged mo-ney growth (MI) is between 0.40 (eqn. 4, table 2) and 0.44 for the specification represented by (eqn. 7, table 2).

In general these results show that the exchange rate (and foreign inflation) has a quantitatively important impact on CPI inflation. Besides, this variable is the only one that is statistically significant across different specifications. Moreover, the impact of changes in the rate of growth of Ml on CPI inflation is weak in the short term and operates with seveal lags.'5 The rate of growth of wages also has an impact on CPI inflation though its quantitative importance is not very large. These findings suggests that a policy of exchange 15 Unpublished estimations conducted at the National Bank of Hungary suggest that changes in domestic credit have also a large impact on consumer prices.

18
|Technical Assistance Project||
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|Technical Assistance Project||
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|Rubber Project(04)<br>||
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|Structural Adjustment Project(02)||
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|ForestryProject(02)||
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|Industrial Finance and Development Project||
|Industrial Finance and Development Project||
|Industrial Finance and Development Project||
|Industrial Finance and Development Project||
|Industrial Finance and Development Project||
|Industrial Finance and Development Project||
|Industrial Finance and Development Project||
|Industrial Finance and Development Project||
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|PALMINDUSTRIE Technical Assistance Project|Ousmane Sissoko|
|Structural Adjustment Project(03)||
|Structural Adjustment Project(03)||
|Health and Demographic Project|Ethna M. Johnson|
|Health and Demographic Project|Ethna M. Johnson|
|Health and Demographic Project|Ethna M. Johnson|
|Health and Demographic Project|Ethna M. Johnson|
|Health and Demographic Project|Ethna M. Johnson|
|Health and Demographic Project|Ethna M. Johnson|
|Health and Demographic Project|Ethna M. Johnson|
21

# Section 6. Measures of regional decentralization and subordination.

To evaluate the level of fiscal decentralization within the Russian regions, we examine the distribution of total expenditure responsibilities and revenue assignments between regional and local governments. We construct two simple measures of the level of fiscal decentralization, using the data respectively from the revenue and expenditure side of consolidated regional budgets:

- The ratio between local governments total revenuesl and the consolidated regional budget revenues12

- The ratio between local governments total expenditures'3 net of transfers to regions and the consolidated regional budget expenditures14

The measure of fiscal decentralization on the expenditure side of consolidated regional budgets is defined in such a manner to provide information about the share of local governments in final budget outlays<sup>15</sup> (purchases of goods and services from the rest of the economy).

Furthermore, we explore the degree of subordination of local to regional budgets using the following measure -- the ratio between regional transfers to local governments and local governments total expenditures16.

Thus, the two measures of fiscal decentralization within regions refer to the downward shift of control over budget revenues and expenditures from regional to local governments. The measure of fiscal subordination is used to describe the dependence of local governments on transfers from regions.

The statistical analysis presented below is conducted with data on 85 of the 89 regions of the Russian Federation. The regions removed from the sample are Chechnya, Ingushetia, Moscow City and St. Petersburg City. The first two are dropped because of the poor quality or lack of data on most of the variables. As mentioned above, the special status of the last two as federal cities puts the issue of fiscal decentralization out of context.

Table 12 presents the values of the two measures of fiscal decentralization together with selected summary statistics for the period 1994 - 1996.

- Calculated as the sum of the total revenues of all tiers of local government minus the revenues from transfers from one local government to another.

- 12 Calculated as the sum of the total revenues of local and regional govermments minus the revenues from transfers from regions to local governments and from local governmen **t** s o regions.

- 13 Calculated as the sum of the total expenditures of all tiers of local government minus the expenditures on transfers from one local government to another.

- 14 Calculated as the sum of the total expenditures of local and regional governments minus the expenditures on transfers from regions to local governments and from local governmen **t** s o regions.

- **15** Our database does not provide data on regional transfers to the federal government and consequently this type of interbudgetary transfers is not netted out of the consolidated regional budget expenditures.

- 16 Calculated as the sum of the total expenditures of all tiers of local government minus the expenditures on transfers from one local government to another.
# Remittances and Vulnerability

# in Developing Countries<sup>_∗_</sup>

Giulia Bettin<sup>_†_</sup> Andrea F. Presbitero<sup>_‡_</sup> Nikola Spatafora<sup>_§_</sup> March 20, 2014

**JEL Codes:** F33, F34, F35, O11

**Keywords:** Remittances, Shocks, Business Cycle, Vulnerability, Gravity Model

> _∗_ We thank Giacomo Oddo, Roberto Tedeschi and Simonetta Zappa at the Bank of Italy for clarifications on the remittances data set. We also thank Caglar Ozden, Dilip Ratha, and participants at the 4th International Conference “Economics of Global Interactions: New Perspectives on Trade, Factor Mobility and Development” and at seminars at the International Monetary Fund and KNOMAD (The World Bank) for useful comments. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent the views of the International Monetary Fund, the World Bank, or the policies of these institutions.

> _†_ Giulia Bettin, Università Politecnica delle Marche (Italy) and MoFiR. E-mail: g.bettin@univpm.it.

> _‡_ Andrea F. Presbitero, International Monetary Fund and MoFiR. E-mail: apresbitero@imf.org.

> _§_ Nikola Spatafora, The World Bank. E-mail: nspatafora@worldbank.org.

1
Figure 1: Local linear non-parametric regression of average total fertilizer use per hectare by total number of hectares cultivated by household in main season <!-- Start of picture text --> Ethiopia Malawi<br><!-- End of picture text --> <!-- Start of picture text --> Local polynomial smooth Local polynomial smooth<br>80 400<br>60<br>300<br>40<br>200<br>20<br>100<br>0<br>-20 0<br>0 1 2 3 4 5 0 1 2 3<br>Total hectares of land under cultivation Total hectares of land under cultivation<br>95% CI lpoly smooth 95% CI lpoly smooth<br>kernel = epanechnikov, degree = 1, bandwidth = .39, pwidth = .59 kernel = epanechnikov, degree = 1, bandwidth = .13, pwidth = .2<br>Niger Nigeria<br>Local polynomial smooth Local polynomial smooth<br>15 200<br>150<br>10<br>100<br>50<br>5<br>0<br>0<br>-50<br>0 5 10 15 20 0 1 2 3 4<br>Total hectares of land under cultivation Total hectares of land under cultivation<br>95% CI lpoly smooth 95% CI lpoly smooth<br>kernel = epanechnikov, degree = 1, bandwidth = 2.02, pwidth = 3.04 kernel = epanechnikov, degree = 1, bandwidth = .34, pwidth = .51<br>Tanzania Uganda<br>Local polynomial smooth Local polynomial smooth<br>30 20<br>20 10<br>10 0<br>0 -10<br>-10 -20<br>0 5 10 15 20 0 2 4 6<br>Total hectares of land under cultivation Total hectares of land under cultivation<br>95% CI lpoly smooth 95% CI lpoly smooth<br>kernel = epanechnikov, degree = 1, bandwidth = 1.32, pwidth = 1.98 kernel = epanechnikov, degree = 1, bandwidth = .43, pwidth = .64<br>Average inorganic fertilizer use (kg/ha) Average inorganic fertilizer use (kg/ha)<br>Average inorganic fertilizer use (kg/ha) Average inorganic fertilizer use (kg/ha)<br>Average inorganic fertilizer use (kg/ha) Average inorganic fertilizer use (kg/ha)<br><!-- End of picture text --> Note: Farm size is defined as the total number of hectares under cultivation in the main season and does not include rented out or fallow land. Multiply imputed plot size variables, as described in Section 3, are used in the aggregation to the household level. Farm size observations above the 99<sup>th</sup> percentile in each country are excluded from these figures. Inorganic fertilizer application rates are representative of total inorganic fertilizer, not nutrients.

26
Table 16: Price index (survey round 2)

||(1)<br>National|(2)<br>North|(3)<br>Center|(4)<br>South|
|---|---|---|---|---|
|PWP|-3.335<br>(16.613)|-2.404<br>(17.735)|-36.293<br>(23.572)|30.750<br>(27.488)|
|R-squared|0.27|0.63|0.10|0.26|
|Observations|2793|339|959|1495|
|Mean of dep. var.|485.25|556.2|512.531|452.08|
|in control group|||||

OLS estimates. Standard errors clustered at the EA level. Estimates include district and week-of-interview fixed effects. _∗p <_ 0 _._ 10 _<u>, ∗∗ p <</u>_ 0 _._ 05 _<u>, ∗∗∗p <</u>_ 0 _._ 001 47
<!-- Start of picture text --> 2500<br>« General<br>___.s<br><— The Analysis of Household Surveys:<br>AMicroeconometric Approach to Development Policy * Policy Research Reports<br>« World Development Reports<br>2000<br>=<br>ai)<br>5<br>za)<br>” 1500<br>wv<br>LJ]<br>o<br>[=]<br>Oo<br>£<br>£<br>2 1000<br>5 Breaking the Conflict Trap: Civil<br>xz &— Warand Development Policy<br>U :<br>Land Policies for Growth and<br>i Poverty Reduction:<br>500 +z<br>WODR 2005:A Better Investment -<br>, Climate<br>Privatization, Regulation, and<br>/ for Everyone Reforming Infrastructure:<br>We Competition<br>. _ ——— WDR 2002: Building Institutions<br>ieWOR 1978 Markets for<br>0<br>0 500 1000 1500 2000<br>Cumulative # books, ranked in descending order of citations<br><!-- End of picture text -->
# **Annex 3: Micro-model Regression Results**

**Mincer E** **<mark>q</mark> uation Results for the Different Labor Market Se** **<mark>g</mark> ments**

||Agricultural<br>Sectors|Non-<br>Agricultural<br>Sectors|Informal<br>Activities in Non-<br>Agricultural<br>Sectors|Formal Activities<br>in Non-<br>Agricultural<br>Sectors|
|---|---|---|---|---|
||lnY_lab||||
|Urban|0.344|0.191|0.099|0.167|
||(3.04)**|(5.55)**|-1.86|(4.13)**|
|Household Head|0.317|0.306|0.237|0.261|
||(2.97)**|(9.10)**|(4.03)**|(7.17)**|
|Gender|0.193|0.237|0.187|0.123|
||-0.95|(7.64)**|(3.61)**|(3.54)**|
|Years of Schooling|0.011|0.058|0.114|0.008|
||-0.3|(3.95)**|(4.65)**|-0.4|
|Years of Schooling Sq|0.006|0.005|-0.001|0.005|
||(2.61)**|(7.62)**|-0.53|(6.66)**|
|Experience|0.088|0.080|0.085|0.066|
||(8.98)**|(25.74)**|(17.33)**|(17.57)**|
|Experience Squared|-0.001|-0.001|-0.001|-0.001|
||(7.85)**|(18.91)**|(12.95)**|(12.02)**|
|Constant|5.113|5.078|4.624|6.179|
||(19.32)**|(57.91)**|(36.65)**|(50.75)**|
|Observations|997|6907|2793|4114|
|Adjusted R-squared|0.18|0.33|0.19|0.28|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 30
including the “ _cajas de compensación_ ” associated with certain employee organizations) . The ‘unbanked’, on the other hand, have no deposits or savings in any such products mentioned above.

6. **Less than 40% of Bogotá’s adult population use formal sector financial services.** This percentage is higher than in Mexico, but lower than the proportion prevailing in Brazil (Figure 3.1).

**Figure 3. 1  Use of Financial Services In Bogotá, Mexico City, urban Brazil and OECD countries**

<!-- Start of picture text --> 100 50,000<br>91<br>90 45,000<br>80 37,610 40,000<br>70 35,000<br>60 30,000<br>50 43 25,000<br>39<br>40 20,000<br>30 24 15,000<br>20 10,000<br>6,230<br>10 1,810 2,710 5,000<br>- -<br>Mexico D.F Bogotá Brazil USA<br>Banked population GNI per capita<br>GNI per capita in 2003 (US$)<br>Percentage of banked population<br><!-- End of picture text --> _Source: Mexico, Bogotá and Brazil data, own studies,US Federal Reserve Bank, 2002_

**Figure 3. 2  Use of Banking Services as a Percentage of Population**

<!-- Start of picture text --> Funds/ Stocks/ Bonds 0.7<br>CDT 2.5<br>Mortgage 2.9<br>Savings Account w ith special aw ards 3.0<br>Personal Credit 3.8<br>Use of telephone for banking services 3.8<br>Current Account 7.2<br>Credit Card 8.0<br>Debit Card 17.9<br>31.7<br>Savings Account<br>0 5 10 15 20 25 30 35 40<br>% of population that use each product<br>Type of product<br><!-- End of picture text --> _Source: Colombian Association of Media Investigation ACIM, General Media Survey EGM for the year 2000_ 46
33 TABLE 4

# THE BENEFIT INCEDENCE OF GOVERNMENT HEALTH PROGRAMS, BY COUNTRY AND REGION.

|Author. Year of Data. Country|<br>Inpatient<br>|IOutatientand Other<br>I<br>VPublc Health<br>I|<br>TotaL|
|---|---|---|---|
|l|To~~~~~~~ta<br>_Count_|l<br>_a-_||
|F03leX.196.6CbiJeb|NA|NA|+(+I|
|Sabota- 1970._Pnnnea'_|NA|MA|NA|
|Meernno. 1974.Malaufa,b|**_±(+)_**|+(+O||
|Selow-lcy1974Colobmnb|+<br>_4+_|**_?_**(+)|?(+'|
|Petrei||||
|Argentina|NA|NA|NA (+)|
|Costa Rica|NA|NA|NA (+)|
|Chile|NA|NA|NA(+)|
|Dominican Republic|NA|NA|NA (+)|
|Uruguay|NA|NA|NA (+)|
|Meldau, 1970, Colombia'|**-**<br>(NA)|+ (+)|+ (+)|
|Meesook, 1978,Indonesia|_d(NA)|**_o**(NA)|_(NA)|
|van de Walle, 1987, Indonesia|**-**<br>(+)|**-**<br>(+)|-<br>(+)<br>||
|Riboud,1986, Costa Rica|+(+)|+(+)|+ (+)|
|Hammer,Nabi, Cerone,1989,Malaysia|4.(+)|4(4)|+(+)|
||Urban|||
|Selowsky, 1974, Colombiab|+ (+)|**_7_**(+)<br>|+(.)|
|Meesook,1978,Indonesia<br>_|(NA)<br>|**o**(NA)<br>|**-**<br>(NA)|
|van de Walle, 1987, Indonesia|-<br>(+)<br>|t___ __(+)|**-**<br>**(+)**|
||Rural|_||
|Selowsky, 1974, Colombiab|?**(+)**|0**(4)**|? (+)|
|Meesook, 1978, Indonesia|**_d**(NA)|**_-o_**(NA)|-<br>(NA)|
|van de WaUe, 1987, Indonesia|-<br>(0)|-<br>(+)|-<br>(+)|

- '+ = pro-poor, = pro-rich; 0= neutral; ? = no clear picture; NA= not<sup>available. Results are based on</sup> benefits per household. Results based on benefits/income are presented in parentheses. bResults based on survey data

'Results based on ad hoc allocatois across households.

dAlI hospital care.

'Public health clinics only.

SOURCES. Foxley, 1979, p. 108; Sahota, 1977, p. 216; Meemnan,<sup>1979, pp. 158-160; Selowsky, 1979, pp.</sup> 89-96; Petrei 1987, p. 47; Meldau, 1980, p. 138; Meesook, 1985, p. 69; Riboud, 1990,<sup>Table</sup> lLt4; van de Walle, 1992, Table 18.
- Collier, P., van der Ploeg, F., Spence, M., and Venables, A. (2010). Managing resource revenues in developing economies. _IMF Staff Papers, 51_ , pages 84–118.

- Dagher, J., Gottschalk, J., and Portillo, R. (2012). The short-run impact of oil windfalls in low-income countries: A dsge approach. _Journal of African Economies, 21_ , pages 343–72.

- Davis, J., Owssowski, J., Daniel, J., and Darnett, S. (2001). Stabilizing and saving funds for non-renewable resources: Experience and fiscal policy implications. _(Washington, D.C.: International Monetary Fund). IMF Occasional Paper, no. 205_ .

- Devarajan, S., Dissou, Y., Go, D., and Robinson, S. (2015). Budget rules and resource booms and busts: A dynamic stochastic general equilibrium analysis. _Policy Research Working Paper Series 6984, The World Bank_ .

- Economides, G., Park, H., and Philippopoulos, A. (2011). How should the government allocate its tax revenues between productivity-enhancing and utility-enhancing public goods? _Macroeconomic Dynamics, 15_ , pages 336–64.

- Gelb, A. and Grasmann, S. (2010). How should oil exporters spend their rents? _Center for Global Development, Working Paper No. 221_ .

- Giovanni, M., Shu-Chun, S. Y., and Zanna, L.-F. (2014). Debt sustainability, public investment, and natural resources in developing countries: the dignar model. _IMF Working Paper WP/14/50_ .

- Go, D., Robinson, S., Thierfelder, K., and Utz, R. (2013). Dutch disease and spending strategies in a resource-rich low-income country – the case of niger. _Policy Research Working Paper Series 6691, The World Bank_ .

- Goldberg, J. (2011). Kwacha gonna do? experimental evidence about labor supply in rural malawi. _Manuscript, Economics Department, University of Maryland_ .

- Jihad, D., Gottschalk, J., and Portillo, R. (2009). What are the effects of fiscal policy shocks? _Journal of Applied Econometrics, vol. 24_ , pages 960–992.

52
# B. Productivity and Rural Welfare

Comparing poverty classifications with yields of sorghum and millet reveals a negative relationship for sorghum and a positive relationship for millet (Figure 34). The NHBPS 2014/15 classifies households into four income classifications. Households are first classified into the poor and non-poor. Within the poor, households may be extremely poor; within the extremely poor, households may additionally be food insecure. Therefore, the four income classifications—the food insecure, the extremely poor, the poor, and the non-poor—represent households from increasing to decreasing levels of poverty. Each classification represents individuals who meet that classification but not a lower poverty classification, that is, the poor represent households that are neither extremely poor nor food insecure. The non-poor have the highest yields of sorghum; the food insecure have the lowest yields. The non-poor have the lowest yields of millet; the food insecure have the highest yields. Poverty rates by yield quantiles show a similar relationship for sorghum and millet (Figure 35).

Figure 34: Sorghum and Millet Yield by Poverty Classification, 2014/15 <!-- Start of picture text --> (a) Sorghum (b) Millet<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Food Extremely Poor Non-Poor<br>Insecure Poor<br>Yield (100 kg/acre)<br><!-- End of picture text --> <!-- Start of picture text -->
1.7<br>1.65<br>1.6<br>1.55<br>1.5<br>1.45<br>1.4<br>1.35<br>Food Extremely Poor Non-Poor<br>Insecure Poor<br>Yield (100 kg/acre)<br><!-- End of picture text -->

Source: Authors’ calculations using NHBPS 2014/15.

Figure 35: Poverty Rates by Yield Quantile for Sorghum and Millet <!-- Start of picture text --> (a) Sorghum (b) Millet<br>50% 55%<br>40% y = -0.0248x + 0.4156<br>50%<br>30%<br>45%<br>20%<br>40%<br>10%<br>0% 35%<br>Lowest Low Middle High Highest Lowest Low Middle High Highest<br>Yield quantile Yield quantile<br>Poverty Rate Poverty Rate<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

States with higher yields of sorghum tend to have lower poverty rates, but this relationship again reverses for millet (Figure 36). The positive trend for sorghum and the negative trend for millet are explained by the more universal cultivation of sorghum, as opposed to millet, which is grown primarily 37
# **<u>Table 3 Outcomes of Peru’s Antidumping Investigations 1993–June 2004</u>**

||1993–2000|2001–June 2004|
|---|---|---|
|Number of investigations completed|32|25|
|Number of restrictive measures|11|18|
|Number closed without restrictive measures|21|4|
|Restrictive measures as percentage of|||
|investigations completed|34|82|
|Average number of definitive measures per<br>year|1.4|4.5|

**Source: WPS3658**

46