rafmacalaba/datause-encoder-data huggingface dataset:
OPTIMAL SIMPLE FISCAL RULES 29

**Optimal spending of SWF assets** ( _θA_ ) For most of this section, we fixed _θA_ = 0 _._ 1, which has almost no effect on the results with fixed capital (Panel A of Table 6) or when the fiscal rule responds to TFP shocks.<sup>33</sup> However, in the full RBC model (flexible capital) and when the fiscal rule is in terms of deviations in non-resource GDP, the optimal rule suggests the government should spend more than a third of SWF assets each year ( _θA_ = 0 _._ 36), mainly to keep public assets and interest rates near their steady state level (not reported). With less variation in public assets and interest rates, non-resource GDP becomes is a better proxy for transitory TFP shocks, and hence the coefficient on _θy_ becomes more countercyclical and similar to the value in the exogenous income model.<sup>34</sup> The optimal response to commodity price shocks remains strongly pro-cyclical _θP ≈_ 1.

<!-- Start of picture text --> 1<br>BBR<br>SSR<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 0.01 0.02 0.03<br>Debt-elasticity of interest rates ( ψ )<br>Correlation of nonres GDP and commodity prices<br><!-- End of picture text --> Figure 7. **Endogenous Correlation of Non-Resource GDP with Commodity Prices and the Debt-Elasticity of Interest Rates.**

# 8. Conclusions

In this paper, we re-evaluate the result that commodity exporters should save most commodity price windfalls using several simple models where a share of the population is unable to borrow or save, and fiscal policy takes the form of transfers directly to households. Unlike much of the literature, we find that the optimal fiscal rule is surprisingly procyclical, at least with respect to commodity revenues. Specifically we find that the optimal rule involves spending around two-thirds (or even up to 100%) of above-average oil revenues, though only around half of above-average gas or

> 33Specifically the optimal _θA_ in these cases is 0 _._ 09 _−_ 0 _._ 13 and the welfare loss is the same as optimal _θA_ to three significant figures.

> 34Although _θy_ = _−_ 0 _._ 36 is still much lower than _θy_ = _−_ 0 _._ 8 in the exogenous income models, recall that much of this is because the HtM HH is shielded from over 40% of variations in non-resource GDP due to capital income (which accrues to Ricardian HHs) as well as the tax system.
Policy ReseaRch WoRking PaPeR 4912

# **Abstract**

The paper’s objective is to explain factors underlying Africa’s weak infrastructure endowment and to identify suitable infrastructure goals for the region based on benchmarking against international peers. The authors use a dataset covering the stocks of key infrastructure— including information and communication technology (ICT), power, roads, and water—across 155 developing countries over the period 1960 to 2005. The paper also examines subregional differences within Africa. They make use of regression techniques to control for a comprehensive set of economic, demographic, geographic, and historic conditioning factors, as well as adjusting for potential endogeneities.

Results show that Africa lags behind all other regions of the developing world in its infrastructure endowment, except in ICT. By far the largest gaps arise in the power sector, with generating capacity and household access to electricity at half the levels observed in South Asia. While it is often assumed that Africa’s infrastructure deficit is largely a reflection of its relatively low income levels, the authors find that African countries have much more limited infrastructure than income peers in other parts of the developing world. Countries that face the most challenging environment, with low population density, weak governance, and history of conflict, have the poorest infrastructure endowments.

At the outset of the data series, Africa was doing significantly better than other developing regions for road density, generation capacity, and fixed-line telephones, but Africa’s relative position has deteriorated over time. The most dramatic loss of ground has come in electrical generating capacity, which has stagnated since 1980.

This paper—a product of the African Sustainable Development Front Office, Africa Region—is part of a larger effort in the region to gauge the status of public expenditure, investment needs, financing sources, and sector performance in the main infrastructure sectors for 24 African focus countries, including energy, information and communication technologies, irrigation, transport, and water and sanitation. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at jirving@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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._ Produced by the Research Support Team
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# b. Water supply

<!-- Start of picture text --> Ghana<br>South Africa<br>Congo, Rep. of<br>Ethiopia<br>Benin<br>Nigeria<br>Tanzania<br>0 0.2 0.4 0.6 0.8 1 1.2 1.4<br>% of GDP<br>unaccounted losses underpricing collection inefficiencies overmanning<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and others 2008.

Low capital budget execution in the infrastructure sectors results in losses of around $52 million per year. From 2004 to 2007, the government allocated every year on average $157 million, or about 3 percent of GDP, to support infrastructure service delivery in the four sectors. However, on average only 67 percent of budget allocations were executed. The capital budget execution ratio has been particularly low for power (41 percent of budget allocations). Improving budget execution efficiencies could therefore make a further $52 million available for infrastructure spending each year.

By reallocating resources within and across sectors, Congo could gain US$ 38 million and US$ 74 million, respectively, toward funding its infrastructure needs (table 14).3 First, the country spends much more on maintenance than it will need to once its road infrastructure and hydropower portfolio are further developed. In the power sector, about $18 million per year could be reallocated from operations and maintenance to capital investment. A further $20 million per year could be reallocated toward capital spending within the transport sector. Second, the country’s large road investment program is an effort to catch up with a backlog of underinvestment in rehabilitating and extending the road network. In the long run, this level of spending goes beyond what will be needed, and $74 million could be reallocated from the transport sector to other sectors with greater spending needs. In total, approximately $112 million in spending could be recovered each year by reallocating it within and across infrastructure sectors (table 14).

> 3 This refers to spending beyond the capital or operational spending needs estimated for achieving infrastructure development targets. In this context, total spending resources could be split in three categories, including spending traced to support needs, spending with potential for reallocation within sectors, and spending with potential for reallocation across sectors. The potential reallocation within sectors corresponds to spending that can be reallocated to a better economic use, for example from operations and maintenance to capital investment. The potential reallocation across sectors corresponds to spending that can produce higher economic returns if allocated to those sectors with the greatest needs.

34
not associated with a positive change in generalized trust. This appears to rule out concerns about mood effects.

Another potential benefit of better market information is to increase the likelihood that farmers will make better choices on target traders/markets. To test this, we assess if mobile phone ownership is associated with an increase in the frequency with which they go to municipal markets. Indeed, under the likely assumption that price uncertainty prevents farmers from travelling to markets to sell their goods, we should expect that better price information would lead farmers to travel to markets more frequently. As before, we re-estimate equation (1) but substitute our consumption variable with the number of trips to the municipal market taken during the month prior to the survey. Results are available in Panel C of Table 6. Owning a mobile phone is associated with an increase in the number of trips taken to the municipal market. Further, households able to pay for transporting their goods to market might be better able to take advantage of those new opportunities (Fafchamps and Vargas Hill 2005). As a result, we interact our mobile phone dummy with a dummy equal to one if the household owned a motorcycle in 2003. The interaction term is positive and significant. This is consistent with the argument that access to better information allows households with access to transportation to increase the range of markets at which they sell their produce.

# **_7. Conclusion_**

This paper analyzed the role of information on farmers’ consumption. Combining spatially coded data on mobile phone coverage with panel data from farmers in rural areas of the Philippines, we show that purchasing a mobile phone has a large positive impact on the growth rate of per capita consumption. Estimates range from 11 percent to 17 percent depending on the sample and the specification chosen. This result is robust to the inclusion of a number of control variables and to allowing for selection on unobservables. We also include independent variables capturing both positive and negative shocks. Further, we present evidence consistent with the argument that access 14
<!-- Start of picture text --> Brasil | 0.207<br>Chile | 0.162<br>Colombia | 0.122<br>México | 0.109<br>Costa Rica | 0.106<br>Peru | | 0.099<br><!-- End of picture text -->

<!-- Start of picture text --> Panama (2014) 0.083<br>Paraguay | | 0.080<br>DR — 0.074<br>Ecuador | 0.071<br>Guatemala | 0.050<br>Average excl. DR 0.108<br><!-- End of picture text -->
also be correlated with smoking cessation, so we estimate treatment effects with the full set of baseline covariates as control variables.

Six months and 12 months after the initial marketing, the bank attempted to administer the urine test to _all_ study subjects (testing procedures are detailed in Section II). CARES clients had to take the six-month test or automatically forfeit their deposit balance. Non-clients (including those assigned to the cues and control groups) were paid 30 pesos (60 cents US) for taking the six-month test, and everyone in the sample frame was paid 30 pesos for taking the 12-month test.

Table 1a Panel B shows that the bank reached 63% of those in the baseline for the six-month urine test, with no difference in contact rate across the three treatment and control groups). Of those contacted 95% agreed to take the test. Since we find lower agreement in the CARES group (93% vs. 97% in the control) we report six-month treatment effects under alternative assumptions about the smoking status of those who refused to take the test.

Table 1a Panel C shows that the bank reached 60% of those in the baseline for the 12month urine test, with no difference in contact rate across the three treatment and control groups). Of those contacted 95% agreed to take the test, again with no differences across groups.

# **V. Results**

# _A. CARES Takeup_

In total, 83 out of 781 (11%) individuals offered CARES signed a contract. Table 1b Columns 7-9 shows univariate analysis of the takeup decision from data on the limited set of characteristics marketers collected in the quick baseline survey administered prior to treatment assignment and marketing.<sup>17</sup> The following baseline characteristics were positively correlated with taking up CARES: wanting to quit (at some point in life, or now), optimism about quitting (as indicated by responding yes to “will you quit smoking in the next year?”), and pre-existing strategic behavior in managing one’s cravings (as indicated by responding yes to “do you try to avoid areas or situations that make you 17 Only a handful of the 2,000 subjects were existing Green Bank clients. Marketers did not elicit income directly, but their observation of subject appearance and work activity indicated that average subject income was substantially lower than that of typical Green Bank clients.

11
# **Tables and Figures**

Table 1: Norm-related variables from DHS surveys by domain (top panel) and WVS attitudes (bottom panel)

|**Gender norm-influenced**<br>|**outcomes**<br>||
|---|---|---|
|**Do**|**main**|**Measure**|
|Economic<br>empowerment<br>Human Capital|Economic empowerment<br>Human Capital|Labor force participation<br>Years of education*|
|Marriage and children|Human capital|Age at marriage|
|||Age at first birth|
|Intrahousehold|Decision|Say in HH decisions – mobility,|
|bargaining and agency|making/autonomy|householdpurchase and health^|
||Intrahouseholdpower|Educationgapwith spouse|
|||Agegapwith spouse|
||Control over own|Marriage to first birth interval|
||body/aspirations re<br>children|Son preference (difference in ideal<br>number of boyandgirl children)|
|||Difference between desired fertility and<br>realized fertility **<br>IPV experience ^^|
|**Gender norm-influenced**<br>|**attitudes**<br>||
|**Do**|**main**|**Measure**|
|Human capital||University is more important for a boy<br>than agirl *|
|Voice and agency||Both the husband and wife should<br>contribute to household income ^|
|||Justification for IPV ^^|
|Fertility||A woman has to have children to be<br>fulfilled******|
|Economic empowerment||When jobs are scarce, men should have<br>more right to ajob than women**#**|

The symbols *, ^, **, ^^, and # are used to link gender outcomes variable of the top panel of the table with gender norm-influenced attitudes of the bottom panel. For instance, “*” links years of education to the attitude towards university education for boys vis-à-vis girls.

33
# **Table 3.  Summary Statistics by Firm Size**

||**S**|**mall**<br>|**Pooled**<br>**Me**|**Sample**<br>**dium**<br>||**Large**<br>|
|---|---|---|---|---|---|---|
||mean|sd|mean|sd|mean|Sd|
|I/lagged K|0.527|1.315|0.295|0.747|0.239|0.469|
|Ln(GDP per capita)|8.877|0.631|8.930|0.623|9.231|0.659|
|Firm age|9.716|8.294|13.179|12.284|18.563|<br>17.997|
|Non-state|0.913|0.281|0.794|0.404|0.614|0.487|
|Foreign|0.045|0.208|0.114|0.318|0.211|0.408|
|Loan|0.367|0.482|0.614|0.487|0.785|0.411|
|Tradecredit|0.076|0.179|0.082|0.185|0.106|0.225|
|UCA|0.019|2.191|0.100|1.559|0.214|1.056|
|Sales/lagged K|14.708|22.729|4.619|8.135|2.836|4.783|
|CF/lagged K|1.106|2.227|0.508|1.153|0.342|0.602|
|Lagged sales growth|0.640|1.538|0.506|1.199|0.480|1.137|
|Tobin’s Q|1.206|0.147|1.206|0.138|1.204|0.129|
|||**No**|**n-state dom**|**estic firms**|**only**||
||**S**|**mall**|**Me**|**dium**||**Large**|
||mean|sd|mean|sd|mean|Sd|
|I/lagged K|0.515|1.291|0.305|0.769|0.246|0.450|
|Ln(GDP per capita)|8.848|0.613|8.857|0.586|9.112|0.617|
|Firm age|9.333|7.681|12.069|11.273|16.579|<br>16.156|
|Loan|0.372|0.483|0.645|0.479|0.848|0.359|
|Tradecredit|0.073|0.175|0.075|0.168|0.088|0.183|
|UCA|0.020|2.148|0.030|1.628|0.099|1.016|
|Sales/lagged K|14.270|22.086|4.479|7.447|2.461|3.193|
|CF/lagged K|1.080|2.137|0.486|1.042|0.314|0.413|
|Lagged sales growth|0.625|1.498|0.546|1.257|0.462|1.026|
|Tobin’s Q|1.201|0.139|1.198|0.130|1.191|0.121|

Note. Small and large firms are firms with capital stock in the bottom and top quartiles. Medium-sized firms are those in the middle two quartiles in capital stock.

44
pregnancies was collected in two or more waves; for some pregnancies, the order of the pregnancy and its outcome were coded differently across waves. Duplication of pregnancies and inconsistencies in coding meant it was necessary to check all pregnancies reported in the pooled dataset individually before dropping duplicate observations and correcting birth order and outcomes data.

Pooling observations made it possible to check the consistency of schooling data reported by panel individuals across the three waves. Years of schooling reported in IFLS were generally consistent between one wave and the next. Where there were inconsistencies, detailed education modules from each wave were compared in order to identify the correct level of schooling for each individual. Information on the highest level of schooling achieved was collected in the household roster, as well as the education module in each wave. This provided an additional level of verification for schooling data.

Summary statistics are provided in Table 1. On average, women had 6.2 years of education, which is slightly above primary school level. At least one ante-natal care visit occurred during the first trimester for just over 70% of all observed pregnancies; 17% of all pregnancies resulted in hospital deliveries. Nearly half of all observed deliveries were assisted by a doctor, nurse of skilled midwife at home or in hospital; the remainder were assisted by traditional birth attendants or family members, generally at home.

14
jobs would predict that rates of formality increase with age as individuals repeatedly attempt to access good jobs and never leave them. In such a model, the age-formality gradient reflects the arrival rate of formal jobs, i.e. the degree of rationing. In contrast, other theories of informal that or non- employment emphasize comparative advantage pecuniary benefits do not structurally produce life cycle dynamics (Clark et al. (2017)).

We find that cohorts of Egyptian men who exit school when labor market conditions are bad do not exhibit the same persistent scarring effects identified in rich countries (e.g. Kahn (2010b), Oreopoulos et al. (2012)). Instead, they are worse off for at most a few years and only in terms of the quantity but not the quality of employment. More puzzling, good initial conditions in fact are associated with negative outcomes after 5 or 6 years of potential experience. For example, those who faced a better labor market when they exited school (as measured by high employment rates) are less likely to be employed, to have social security coverage, to have a full time job, to have a contract, and to be wage workers, and they work fewer hours, especially towards the end of their first decade of labor market experience.

We that these results that variation in labor market con- hypothesize reflect cyclical ditions dominates effects. Cohorts that enter into a bad labor market end scarring up profiting from eventual economic recoveries, and are not strongly penalized by having a bad start. The negative correlation between initial employment rates and eventual employment quality could also be consistent with a model of queuing in which some cohorts forgo initial employment opportunities in order to secure rationed quality jobs down the road (and vice versa). We concluded that data sets spanning longer periods are needed to confirm either hypothesis.

# **2 Data**

## **2.1 Measures of informality**

Three measures of informal employment are most often used in the literature, see Wahba and Assaad (2017). One is the type of contract (officially written or not), another is 3
# The Impact of EC-92 on Developing Countries' Trade: A Dissenting View

by A. J. Hughes Hallett (University of Strathclyde, Glasgow and Centre for Economic Policy Research, London)

# Table of Contents

|.|Intr|oduction|I|
|---|---|---|---|
|2.|EC-|92: General Implications|4|
||2.1|The Development of Prices and Incomes after EC-92|5|
||2.2|Trade Creation and Trade Diversion in Other Countries|6|
||2.3|Investment Diversion|7|
||2A|The Influence of Commercial Policy on EC-92 Outcomes|8|
||<br>_2.5_|<br>The Scope for Trade Creation and Trade Diversion afLer EC-92|10|
||2.6|<br>The GATT as a Cooperative Game and the EC's Trade Policy|13|
|3.|EC-|92 and the Developing Countries|17|
||3.1|What the Developing Countries Might Fear|17|
||<br>3.2|<br>A Sectoral Analysis|18|
|4.|Emp|irical Estimates of the Impact of EC-92 on Developing Country and World||
||<br>Trad|<br>e Manufactures|23|
||4.1|Trade Creation Outside the EC|24|
||4.2|Trade Diversion OuLside the EC|25|
||4.3|Investment Diversion Outside the EC|26|
||4.4|Trade Creation and Diversion in Imperfectly Competitive Markets|26|
||<br>4.5|<br>Dissenting Estimates: Will Anything Significant Happen at All?|2|
|5.|The|Impact of EC-92 on Manufacturing vs. Commodity Trade in the Developing||
||<br>Cou|<br>ntries|29|
||5.1|Aggregation Issues|29|
||<br>5.2|<br>Trade Patterns after EC-92|30|
||5.3|The Impact of EC-92 on Developing Countries Manufacturing Output||
|||<br>by Country Groups|31|
||5.4|<br>The Impact of EC-92 on Commodity Producers|34|
|Footno|tes||36|
|Tables|||37|
|Referen|ces||40|
in both the treatment and the control villages contributed to the public good. Again, the point estimate of the ATT is negative, very close to zero and far from statistically significant. Columns (3) and (4) in the upper panel show the estimates from the trust game measures.<sup>13</sup> The dependent variable in column (3) is the amount sent by the sender in the first round, which is a measure of generalized trust. On average subjects in both the treatment and control communities sent about 1.4 Sudanese pounds, about 47 percent their endowment.<sup>14</sup> The ATT is, again, negative, very close to zero and not at all statistically significant. The dependent variable in column (4) is the amount returned to the sender by the receiver as a percentage of the total amount available to the receiver, which is our laboratory measure of trustworthiness. On average, subjects in both the treatment and control communities returned about one-third of the amount available to them to their sender.<sup>15</sup> As in the other cases the point estimate ATT is negative, but statistically it is a precisely estimated zero. The lower panel of Table 4 presents the mean effect of the program across all four of the 13The number of senders and receivers is unequal because on a few occasions an odd number of subjects arrived for the games due to attrition. Rather than turn away a sure-to-be disappointed subject who had traveled through the dessert, often on foot, to attend our games session, we randomly matched two receivers to one sender in the trust game in these sessions. In those cases receivers received the payoff consistent with their actions and the relevant senders received the payoff decided by the first receiver with whom they were randomly paired.

> 14This amount is close to the average amount sent in the Johnson and Mislin (2011) meta analysis of trust games. The found that subjects sent about 50 percent of the endowment and that African subjects sent significantly less than did subjects from Western countries. Our results are consistent with their findings.

> 15Again these results mirror the general findings reported in the meta analysis of Johnson and Mislin (2011) who calculated that receivers sent about 37 pecent of their total pot back to their sender, and that African subjects tended to return less than subjects from western countries.

25