rafmacalaba/datause-encoder-data huggingface dataset:
(Behrman, Parker and Todd 2011) as well as analysis from other samples (Parker and Vogl, 2018) do not show gender differences in overall attainment. The sample we analyze in this paper is a national sample so that it is not necessarily surprising to have some differences between our results with those derived from a less representative evaluation sample.

Tables 7a through 7c present the impacts on the probability of attending secondary school by type of school. Note that we present unconditional results on type of school, so that changes in these variables include both the impact of more students enrolling in school and changes in the type of school conditional on enrollment. We present the impact of PROSPERA on enrolling in a tele-secondary school, a general secondary school and a technical school. Overall, the results suggest that the increase in secondary school enrollment we observed in Tables 6a to 6c leads to a larger proportional increase in enrollment in tele secondary schools. There is no effect on the probability of enrolling in general schools; this implies that the pool of students (now larger due to the enrollment effect) is just as likely to enroll in a general school with or without the program. There is however a reduction in the probability of attending a technical school as a result of the increase in the probability of attending a tele-secondary school. Thus, participating in PROSPERA is associated with an increase in enrollment and an increase in the probability of attending tele-secondary schools. The implications of this shift are important, as tele-secondary schools are generally considered to be an inferior option in the sense that these types of schools tend to be attended only when general and technical schools are not available.

|Table 7a: Impacts of PROSPERA on Type of Sec<br>All 6th Graders in Medium, High and Very Highly Ma<br>State Fixed Effects<br>|ondary School in 7<sup>th</sup><br>rginalized Commu<br>|grade<br>nities 2008<br>|
|---|---|---|
|Tele-sec|General|Technical|
|Variables<br>(1)|(2)|(3)|
|PROSPERA beneficiary 2008<br>0.099***|-0.008*|-0.091***|
|(0.006)|(0.005)|(0.006)|
|Female beneficiary<br>0.011|0.003|-0.005|
|(0.007)|(0.006)|(0.008)|
|Female<br>-0.004|-0.008*|0.006|
|(0.006)|(0.005)|(0.007)|
|Constant<br>0.605***|0.255***|0.163***|
|(0.035)|(0.029)|(0.039)|
|Observations<br>62,870|62,870|62,870|
|R-squared<br>0.215|0.2|0.144|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

21
# Intergovernmental Grants in Developing Countries

# by Larry Schroeder

## TABLE OF **CONTENTS**

|In<br>tro<br>d<br>ucon<br>.............................................<br>**1**|
|---|
|Concepts of and Empirical Evidence on Intergovemmental Grants .........................3|
|Measurement Issues .............................................<br>5|
|Importance of Grants to Granting Governments .............................................7|
|<br>Importance of Grants to Recipient Governments ............................................ 11|
|Objectives and Types of Grants ............................................<br>23|
|Objectives ............................................<br>23|
|<br>Types of Grants ............................................<br>33|
|Current Practices and Results of Grant Systems ............................................<br>46|
|Practices vis-a-vis Statutes ............................................<br>47|
|Practices vis-a-vis Principles ............................................<br>53|
|<br>Empirical Evidence ............................................<br>57|
|Conclusions: Lessons and Recommendations ............................................<br>67|
|llustrations|

|Table I ..........................................|9|
|---|---|
|Table2 ..........................................|13|
|Table 3 ..........................................|_15_|
|Table 4 ..........................................|36|
|Figure I ..........................................|40|
|<br>Figure 2 ..........................................|42|
|<br>Figure 3 ..........................................|44|
|Table 5 ..........................................|49|

Larry Schroeder is a Professor of Public Administration and Economics and Senior Research Fellow of the Metropolitan Studies Program at the Maxwell School of Citizenship and Public Affairs, Syracuse University. Vijay Rao, Graduate Assistant in **di** Metropolitan Studies Program, provided research assistance.
# **Dropout**

Table 5.5 and 5.6 show DiD results for dropout rates. **Table 5.5** focuses on overall dropout across grades; **Table 5.6** focuses on lower primary dropout rates. In both cases, we do not observe significant impacts in schools treated in either 2017 or 2018.

**Table 5.5. Impact of PqTR reduction on dropout rates**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.031***|0.040***|
||(0.004)|(0.003)|
|Time|0.001|-0.003|
||(0.006)|(0.004)|
|Treatment|0.020***|0.003|
||(0.006)|(0.006)|
|DiD (Treatment and Time)|-0.006|0.005|
||(0.008)|(0.008)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019_ _Note: Robust standard errors in parentheses. *p<0.10, ** p<0.05, *** p<0.01_ _2017/18 compares dropout rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares dropout rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._

**Table 5.6. Impact of PqTR reduction on dropout rates in lower primary**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.029***|0.037***|
||(0.005)|(0.004)|
|Time|-0.001|-0.003|
||(0.007)|(0.005)|
|Treatment|0.020***|0.002|
||(0.006)|(0.006)|
|DiD (Treatment and Time)|-0.004|0.009|
||(0.009)|(0.009)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019_ _Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01_ _2017/18 compares lower primary dropout rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares lower primary dropout rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._ 20
Figure 4: Plants and vertebrates: global cell totals for model-predicted threat probabilities, all GBIF Species <!-- Start of picture text --> t — )0=—tiéd an<br>% EN , w Pa y<br>Biodiversity Index Value<br>1st dec. 3rd dec. 5th dec. [| 7th dec. [| 9th dec.<br>2nd dec. Ath dec. a 6th dec. [| 8th dec. [| 10th dec.<br><!-- End of picture text -->
|1956-58|78|93|84|
|---|---|---|---|
|1959-61|85|91|93|
|1962-64|93|89|104|

Source: Diaz-Alejandro (1970) Between 1925 and 1949, when the overwhelming majority of Argentina' s exports were agricultural products, the internal terms of trade of agricultural goods deteriorated relative to Argentina's external terms of trade as a result of domestic policies. In the period from 1930 to 1939, Diaz-Alejandro calculated the average index value as 105.5 The second period, from 1945 to 1955, covered the Peron years, during which policies were severely biased against agriculture. This period was characterized by extensive importsubstituting industrialization, and the index average dropped to 55. During the final period, from 1955 to 1964, the index averaged 94, as the bias against rural goods diminished.

# _The Effective Protection Approach_

The concept of effective protection, developed by Corden (1966) and Balassa (1965), was used in cross-country studies of the manufacturing sector by Balassa (1965), and by Little, Scitovsky, and Scott (1970). In their analysis of seven developing countries in the 1950s and 1960s, Little et al. (1970) compared domestic value added to value added at world prices. They found extremely high levels of protection for manufacturing in several of the countries, as high as 313 percent in India in 1961 (the lowest being 27 percent in Mexico in 1960). In four of the seven cases, nominal protection exceeded 100 percent, far higher than could be justified by those who argued in favor of protection of infant industries.

The protection of manufacturing worked against agriculture by reducing both the relative incentive (or demand) to invest in the production of agricultural goods, and the resources available (supply) for this investment. This was especially true of agricultural tradables, since protection (through quotas, tariffs, and other controls) enabled overvalued currencies to persist. Little et. al. found nominal protection for agricultural exports ranging from -10 percent to zero in their sample. Considering that the domestic prices of manufactured agricultural inputs exceeded world prices in general, effective protection of agriculture was even lower, and lower still when compared with the high levels of protection afforded manufacturing.

# _The "True Protection" Approach_

> 5 Diaz-Alejandro excluded the World War II period, 1940-44.

12
# 2 A Long-Term Model to Investigate the Macro-Fiscal Implications of Aging

This section introduces a simple modeling framework where demographic changes aect the scal balances through both revenue and expenditure channels. On the revenue side, aging and shrinking population change the scale and composition of the labor force. As a result, the supply of eciency labor changes over time, which has a direct impact on output, thus, on tax revenues. On the expenditure side, public expenditures on education, healthcare and long term care adjust to accommodate the changing needs of an aging society. Moreover, the benet-contribution decits in the Pay-as-You-Go (PAYG) pension system, which emerge from rising imbalances between number of beneciaries and contributors, is also nanced by transfers from the scal budget. These transfers, therefore, also adjust as the relative sizes of working age population and dependent population changes.

One of the objectives of this projection model is to bring together the independent estimations of population, labor force, pension system and scal aspects of the economy in a consistent manner. Figure 2 shows the overall structure of the model and its sub-modules which serve this purpose. In order to facilitate consistency, the model incorporates a number of assumptions that aim to simplify this integration. First, we assume that the labor supply is exogenously determined by the forces of demographics. An increasing rate of labor force participation over time can be imposed, however, this does not arise endogenously as a reaction to wages or lifetime income expectations. Policies designed to provide further labor force participation (LFP), such as changes in the statutory retirement age and/or establishment of daycare facilities, have an impact on the aggregate supply of labor, which are incorporated through dierent scenarios. However, we abstract from analyzing the optimization problem at the individual level, which would involve decision making on the basis of labor-leisure choice. As the share of population in the older age cohorts increase, the aggregate LFP decreases. This structure enables us to use the demographic projections that are prepared outside the model in the simulations, such as the UN population projections or the projections by the authorities in the country.

The second simplication is about the public services. The model abstracts from characterizing the impact of government expenditures on welfare of the households. Provisions of public services are determined by a simple projection methodology, e.g. indexed to per capita income through income elasticities, which may be unitary, or greater. A unitary elasticity case can be considered as a result of an underlying structure with homothetic household preferences and responsive government policies. However, we do not characterize them for5
and carbon constraint policies (SR & CEC) in the absence of cross-border trade. Case 1 will be used as the benchmark to estimate the benefits of cross-border electricity trade (CBT) in the absence of subsidy removal. It will also be used as the benchmark to estimate the impacts of subsidy removal and carbon constraint policies together in the presence of cross-border trade. Case 2 will be used as the benchmark to measure the impacts of cross-border trade and the impacts of carbon constraints in the absence of subsidies. While Case 3 will serve as the benchmark to measure the impacts of carbon constraints in the presence of cross-border electricity trade, Case 4 will do the same for measuring the impacts of cross-border in the presence of carbon constraints. The types of impacts assessed and the simulations used to measure these impacts are provided in Table 6.

**Table 6. Matrix to measure the impacts**

|**Impacts Na**|**me**|**Condition**|**Formula**|
|---|---|---|---|
|Subsidy removal (SR)|impacts|No CBT,no CEC|Case 2 - Case 0|
|||CBT, no CEC|Case 3-Case 1|
|Cross-border trade (C|BT) impacts|SUB, no CEC|Case 1-Case 0|
|||No SUB,no CEC|Case 3-Case2|
|||No SUB, CEC|Case 5-Case 4|
|Carbon emission const|raint (CEC)|No SUB,no CBT|Case4 -Case2|
|impacts||No SUB, CBT|Case 5-Case 3|
|d<br> <br>|SR & CBT|No CEC|Case 3-Case 0|
|ine<br>cts<br>|SR & CEC|No CBT|Case 4-Case 0|
|mb<br>pa||CBT|Case 5-Case 1|
|Co<br>im<br>|CBT & CEC|No SUB|Case 5-Case 2|
|<br>|ALL THREE||Case 5-Case 0|

SUB – Subsidy; CBT – Cross-border trading; CEC – Carbon emission constraining

# **6. Results and Discussion**

This section offers the key results of the model simulations and their interpretations. We start with economic implications of regional electricity trade in the region and how the impacts under different situations, such as with and without fuel subsidies, the imposition of carbon constraints to meet the economies’ NDCs. Economic effects also include changes in electricity prices and investment requirements for capacity expansion. This would be followed by physical 18
# **Use of Imported Inputs and the Cost of Importing: Evidence from Developing Countries**

Mohammad Amin* and Asif Islam** _Keywords_ : Imports, Trade Barriers, Cost of Importing, Trade Policy _JEL_ : F14, F16, O14, O57 _______________________________

*Corresponding author, Enterprise Analysis Unit, DECEA, World Bank, Washington DC, 20433. Email: mamin@worldbank.org.

** Enterprise Analysis Unit, DECEA, World Bank, Washington DC, 20433. Email: asif.m.islam@gmail.com.
However, after closely examining the data, we find that this is not the case for many countries. Furthermore, changes in livestock used for slaughter are included as “increases in stocks,” not as fixed capital. Thus we construct a separate data series on livestock capital, recognizing that there may be some overlap, but also that livestock accounts for a considerable share of agricultural capital and should not be ignored. We return to this issue in Appendix A.1.

Conceptually, the calculation of the livestock is fairly straightforward. The Food and Agricultural Organization (FAO) reports the quantities of all farm animals -- cattle, sheep, pigs, poultry, and so on. We aggregate the value of these individual components to obtain the livestock. Ideally, we would use market prices of live animals to value local herds, but these data were not consistently available. In their place, we use regional export unit values, based on FAO trade data, to value domestic herds. We calculate separate prices for each region by dividing regional dollar export values by regional export quantities. These unit prices are then applied to national herd statistics for each category of livestock. We convert the aggregate values to constant (1990) dollars by using the U.S. gross domestic product (GDP) deflator for the agricultural sector.

# **Treestock**

Standing orchards, plantations, and smallholder trees represent another important category of investment in agriculture. According to United Nations accounting practices, the value of investments in treestock should be included along with other land improvements in national accounting systems. However, a close examination of country data suggests that, in practice, such stocks may go unaccounted (see Appendix A.1). We therefore construct a direct estimate of the value of treestock.

The available information for constructing the treestock consists of FAO data on the area harvested by crops, production, and output prices. We begin by using the condition for long-run equilibrium, in which the cost of investment in the orchard equals the present value of the expected future income generated by the orchard. The income from the orchard is value of output less the costs of production. There are no published data on production costs; thus we construct our estimates under the assumption that production costs account for 80% of gross revenues.

We derive the yield (output per hectare) from the data on output and area. The yield depends on the age of the trees, but the necessary information for estimating the yield curve is not

> crop when they are harvested cannot be fixed assets.” In the most recent (2008) version of the SNA, the terminology changes from “cultivated assets” to “cultivated biological resources,” while the description remains similar.

7
<!-- Start of picture text --> Median age of employees in each sector<br>8 en<br>ie<br>e er<br>@CAN OSM KAZ<br>ie}SoL @cEG @GEO<br>a ree Scene@cea @71,8@ P60Nace nemcee PE<br>@ Rw<br>Pr<br>reyN xtes<br>25 30 35 40 45 50<br>Public sector<br>@ East Asia and Pacific e Eastern Europe and Central Asia @ Latin Americaand Caribbean<br>@ Middle East and North Africa e Western Europe and North America @ South Asia<br>© Sub-Saharan Africa 45 degree line<br>Proportion of employees with secondary education<br>or above in each sector<br>= Cd<br>FS © ecm e Ma oo R4 PeCeNi<br>a en<br>£ ese? YD. «.<br>Sa err Poe ger<br>a e aw - ABE,<br>Oo<br>4 6 8 4<br>Public sector<br>e East Asia and Pacific @ Eastern Europeand Central Asia @ Latin America and Caribbean<br>e Middle East and North Africa @ South Asia ©@ Sub-Saharan Africa<br>————= 45 degree line<br><!-- End of picture text -->
effect of a boom-bust cycle is typically adverse. The asymmetry does not show up in the simulations which allows for smooth adjustment, both up and down the cycle.

Using extractive industries revenue for transfers to household would have a greater impact on poverty reduction in the short and medium term, as investments in physical capital would primarily benefit the owners of capital and investment in human capital have a longer gestation period. However, in the long run, the trickle down effects from accelerated economic growth as a result of investment in human and physical capital are likely to offset the initial lack of pro-poor bias. Nonetheless, this may imply that considering distributional aspects in the use of government revenue from the extractive industries sector also need to be taken into account. A mixed strategy where revenue is used to enhance public infrastructure, investment in human capital, and transfers through social protection programs may thus be preferable to a strategy that focusses only on one of these elements.

# **_Future Work_**

In this analysis, we use a recursive dynamic model to analyze the effects of an exogenous increase in oil revenue. For future work, we will optimize over timing and spending source of exogenous oil revenues.

To further analyze development strategies for Niger, we can investigate the links between spending on sectors such as health and education and their impact on labor productivity. An analysis using a MAMS (Maquette for MDG Simulations) approach would provide a better explanation of total factor productivity growth.<sup>26</sup> Finally, in this analysis, we treat export revenue from the sale of a natural resource (oil) as an exogenous increase in income – we are interested in how that income is spent in the economy. This is a reasonable description of the oil sector in Niger. China is providing the labor and capital to extract crude oil in Niger; the impact on Niger is via the revenue from oil sales. For other natural resources, a change in output to increase exports can impact the economy as resources are used in that natural resource sector. Mining is labor intensive, using unskilled labor in Niger that could also be used in other sectors. When uranium production expands, other sectors will see increased input costs as the uranium production competes with other sectors for factor inputs. We will expand our analysis to include the structural changes that occur as the expanding natural resource sector competes for domestic inputs to production.

> 26 See Lofgren et al. (2013).

35
cated by the household, _Cari,c,t_ the percentage owning a car, _Motorcyclei,c,t_ the percentage owning a motorcycle, _Land Phonei,c,t_ the percentage owning a land phone line, _Mobile Phonei,c,t_ the percentage owning a mobile phone, and _Use of interneti,c,t_ the percentage using internet. _Employmenti,c,t_ can be either the sectoral share or the labor force participation share.

We identify several identification challenges. Infrastructure investments are likely endogenously allocated with respect to the outcomes of interest. Given the high cost of infrastructure investments, conscious allocation decisions are to be expected (by, for example, targeting high growth potential locations or politically demanded locations). Measurement error in the righthand side variables may lead to attenuation bias (from, for example, inaccurate information on the timing of infrastructure expansion or imprecise historic road and grid maps). Measurement errors, which are expected to be large in this case, lead to an OLS estimate that is biased toward zero. We consider these issues by providing the results of an IV strategy in the next section.

We first report results for the unbalanced panel of districts that include at least one EA. We estimate local average associations of the three infrastructure investments—roads, electricity, and Internet—and the interaction between these investments on sectoral employment at the district-year level. Then we analyze heterogenous impacts on structural transformation.

# **3.1.2 OLS results**

We start with a regression that includes all countries, Cameroon, Nigeria, and Chad. We then compare the results by country and discuss the existence of heterogeneous effects across subnational locations. Throughout, standard errors are clustered at the district level, the level of treatment.

**All countries** A better access to infrastructure is associated with a transformation away from agriculture. Table 2 reports the results of pooling together data from Nigeria, Cameroon, and Chad. Having access to paved roads but no electricity at the district level is significantly associated with a 2 percentage-point increase in the employment share of manufacturing. Having access to Internet at the district level is associated with a 4.6 percentage-point reduction in the employment share of agriculture and a 4.8 percentage-point increase in the employment share of services. Combined investments in paved roads and electrification is associated with larger impacts, a 8.5 percentage-point reduction in the employment share of agriculture, a 5 percentage-point increase in the employment share of manufacturing, and a 4 percentage-point increase in the employment share of services. Table 3 presents the results using the dummy variable that captures electricity access based on the grid expansion for validity check. The results are similar showing a significant association between roads and Internet infrastructure investments and sectoral shares, and the role of complementary electricity investments.

13