rafmacalaba/datause-encoder-data huggingface dataset:
6

# I. Theoretical Framework and Motivation

Considerable debate exists on the question: Is the financial system important for economic growth? One line of research stresses the importance of the financial system in mobilizing savings, allocating capital, exerting corporate control, and easing risk management. A second branch of the literature argues that the financial system is unimportant for econoimlic growth.

Consider first the view that finance is unimportant. In a recent survey of development economics, Nicholas Stern (1989) does not mention the role of the financial system in economic growth. Furthermore, at the end of Professor Stern's review, he lists various issues that he did not have sufficient space to cover. Finance is not even included in the list of omitted topics. Similarly, a recent collection of essays by the 'pioneers of development economics,' including three Nobel Laureates, does not describe the role of the financial system in economic growth [Meir and Seers (1984)]. Clearly, according to these economists, the financial system plays an inconsequential role in economic development. Furthermore, the most recent Nobel Prize winner, Robert Lucas (1988), argues that economists frequently exaggerate the role of financial factors in economic development. Moreover, Joan Robinson (1952) argues that the financial system does not spur economic growth; financial development simply responds to developments in the real sector. Thus, many influential economists give a very minor, if any, role to the financial system in economic growth.' In contrast, a prominent line of research stresses the role of the financial system in economic growth. Bagehot (1873), Schumpeter (1911), Cameron, et. al. (1967), Goldsmith (1969), and McKinnon (1973) provide conceptual descriptions of how and empirical examples of when the financial system affects economic growth. Building on these semninal contributions, Gelb (1989), Ghani (1992), King and Levine (1993a,b), and DeGregorio and Giudotti (1995) show that measures of banking development are strongly correlated with economic growth in a broad crosssection of countries. According to this vein of research, a well-functioning financial system is critical for sustained economic growth. Thus, considerable debate exists on the importance of the

- Many of these references are from Chandavarkar's (1992) insightful discussion of financial and economic development.
percent, compared to an actual poverty headcount of 57 percent before the road project was implemented and 48 percent after the road was completed. Thus, the change in poverty incidence from 57 percent to 52 percent was due to road improvement and from 52 percent to 48 percent due to other factors. Similarly, predicted extreme poverty is 30 percent, compared to an actual extreme poverty rate of 35 percent in the base year and 29 percent in the year after the project completion. In other words, a road improvement project in RDP project villages has led to an approximate 5 percent reduction of moderate and extreme poverty; p-values for these reductions are also presented in Table 5, and the differences are statistically significant.

In RRMIMP project villages, improved roads have also modestly reduced the incidence of poverty. Predicted moderate poverty is 46 percent, against an actual poverty headcount of 52 percent in year 0, suggesting a 6 percent reduction in moderate poverty. Similarly, the predicted extreme poverty rate is 31 percent against an actual poverty rate of 38 percent in the base year, indicating an approximate 7 percent gain in extreme poverty reduction in RRMIMP villages that could be attributed to road improvement.<sup>30</sup> Again, the predicted reductions are statistically significant. Thus, overall we see a fairly substantial reduction in moderate and extreme poverty due to rural road development across both samples, with a slightly steeper decline in RRMIMP project villages.

# **7. Conclusions**

Road investment constitutes a major portfolio of public investment in rural areas, reinforcing the notion that rural income and productivity growth depend critically on roads and other public investments. Yet the traditional way of evaluating a rural road project has underestimated the impact of road projects through the use of transport surveys that simply determine the extent of transport cost savings for a road by the likely road users. While this way of estimating the project’s rate of return (the so-called internal rate of return) is a standard practice, roads are important public investments that have both short- and long-term effects that go beyond transport cost savings. Transport cost savings reflect the short-run and immediate impact of road investment; roads, however, also have short- and long-term effects on employment, income, and productivity as well as investment in the human capital of children. Furthermore, these household effects can be both direct and indirect results of road investment, and often cannot be captured through the traditional transport surveys. Household surveys are necessary to capture the full treatment effects of road development, and in particular, panel data for project and comparable

> 30 These estimates are likely to be an upper estimate, because of the assumption that each household on average gained the same way.  To the extent that gains accrued to households vary by household income distribution, the predicted impact of poverty could be lower than the reported estimates.

17
were for the full period, 1990-95, and for each individual year. Table 4 shows the result of Polish wheat prices against CBOT and LIFFE wheat prices. The results were most promising for the deseasonalized prices in 1993 (especially for the LIFFE prices) and for the 1993/94, 1994/1995 marketing years. While these results are more promising than the previous correlations, the results are still very inconsistent from one year to the next.

In general, there was poor correlation arnong the price series examined in this section. This could be explained by:

- a) International and Polish wheat prices have increased over the past two years, generally rising above government support levels.

- b) In the 1994/95 crop year Poland was a significant importer of wheat. This activity would create greater links between the Polish wheat market and international wheat markets.

- c) Due to budgetary constraints, the ARR was less active in interventions in the Polish wheat market during the 1994 harvest. This apparently enabled Polish prices to better reflect conditions in the open market.

More data available only with the passage of time will be needed in order to asses whether this stronger positive correlation can be sustained.

# **3.1.2. Correlation of the Lagged Prices in** US **Dollar Terms**

Tables _5_ and 6 show the results of correlations of Polish wheat prices in US dollar terms lagged against CBOT and LIFFE (US dollar terms) prices. The lagging process improves the correlation results for CBOT futures prices, especially when prices are lagged three or four months. This suggests that there could be inefficiencies related to the transmission of information to the Polish grain markets. In other words, it takes up to three or four months for factors readily apparent to those trading at the CBOT to be fully discounted into the Polish wheat market. Lagging Polish wheat prices against LIFFE prices did not improve the correlation results.

# **3.1.3. Cointegration analysis of Polish and CBOT and LIFFE prices**

Cointegration analysis indicates whether there is a stable long-term relationship between two (or more) price series. Thus, prices can show short-run deviations, but in the long-run they move together. We tested for pairwise cointegration between the Polish and CBOT prices and between the Polish and LIFFE prices. The test used is the ADF test and its results are shown below."

||**Polish-CBOT**|**Polish-LIFFE**|
|---|---|---|
|**Nominal**Prices|-2.71|-2.23|
|**Deseasonalized Prices**|-2.65|-2.21|

The ADF statistics are calculated for the period 1990 to 1995 using monthly data, for a total of 72 observations. The ADF statistics are below the 90% critical level (-2.84) thus the hypothesis of cointegration is rejected. This means that according to the ADF test and for the period tested,

**"' ADF is theAugmnented** Dickey Fuller **test** (see Dickey and Fuller, 1979).

_9_
The survey also includes a self-reported measure of income shocks.<sup>8</sup> Individuals are asked, ― _Did you (your family) experience an unexpected significant reduction of your income over the past 12 months,_ ‖ and we define a dummy ―Income shocks‖ for those who answer yes to this question. The summary statistics in Table 1 show that 35.9% of the sample reported the experience of a negative income shock during 2009, showing that the crisis and associated decline in income affected a large share of the Russian population.

# **[Insert Table 2 about here]**

# **4.2** **_Use of Formal and Informal Credit_**

Our next set of variables measures financial inclusion, which includes variables related to respondent affiliation with financial institutions and borrowing behavior.<sup>9</sup> Our first variable is ― _Bank Account,_ ‖ which indicates whether an individual uses a bank account (which includes the use of debit cards). In Russia it is common practice for an employer to provide employees with an account and associated debit card, so-called salary or ―plastic‖ cards, at a bank chosen by the employer, and salaries are paid to these accounts only. However, the employee can use this account only to withdraw salary, and cannot make deposits to the account; thus, this may overestimate the actual voluntary

> < US$833; 2<sup>nd</sup> quartile: $833 ≤ monthly income < $1,433; 3<sup>rd</sup> quartile: $1,433 ≤ monthly income < $2,084; 4<sup>th</sup> quartile: monthly income ≥ $ 2,084.

> 8 This is a categorical variable: the first category is individuals who report that they do not have enough money, even for food (7%); the next category is individuals who report they can buy food, but cannot buy clothes (23%); the third category is individuals who report they can buy food and clothes, but not durable goods (e.g., a television or refrigerator) (52%); finally, individuals who report they can buy durable goods (16%).

> 9 An important feature to note about Russia is the absolute lack of and relatively low levels of trust in the banking sector, which is potentially an important factor in explaining the low level of use of banking products. Remarkably, only 28% of surveyed individuals in Russia report confidence in banks, the second to lowest score in the region (EBRD, 2006).

12
duration of stay at the destination. The bottom set of rows in Table 4 reports summary statistics on the origin distribution of migrants across four intervals of duration of stay at their destinations. The data suggest that most migrants (i.e. about 50 percent) have lived at their destination for over 10 years, although this is driven by female migrants. Regardless of the duration of stay considered, there is very little variation in the distribution of migrants by origin (e.g. inter-state versus intra-state), especially among males.

# **2.2 Migration measures and other controls**

The key dependent variables, bilateral migration stocks, are based on the Census data described above. In addition, we construct several explanatory variables needed for the gravity estimation. These are standard bilateral distance, linguistic overlap and other geographic proximity variables. They are described and discussed in detail below.

## _Bilateral Migration Stocks_

We define _mij_ as the stock of migrants who moved from origin (or previous) district _i_ to destination (or current) district _j_ as of 2001.<sup>8</sup> We also amalgamate intra-district migrants with non-migrants in the empirical analysis. Lastly, we disaggregate the migrant numbers also by education, age, reason for migration and duration in later sections and _mij_ represents the relevant bilateral migrant stock in each regression.

Following the approach in gravity models of international migration, we control for dyadic factors that influence migration costs: physical distance, linguistic proximity,<sup>9</sup> contiguity, and state borders.<sup>10</sup> The construction of these control variables is explained below.

## _State Borders and Contiguity_

Borders, either physical or institutional, could impose costs on mobility. To cap-

> 8Since we only measure the migrant stock at 2001, we do not observe return or circular migration.

> 9We include cultural proximity variables using caste information in a robustness check.

> 10We should note that origin and destination specific factors are not included since we control for them with origin and destination fixed effects in our empirical analysis.

11
tically significant (Table 2 Panel A and B column 4).

> 51This result is driven by only 26 respondents. When women were provided an option to take up or refuse the vaccine at the clinic, only 26 refused it. We observe some, but not too stark, differences in characteristics between these 26 women and 585 women who accepted the vaccine under Clinic condition (Appendix S5). These 26 women are on average younger, more likely to be pregnant, and less likely to have visited the clinic before than others. They also perceive higher likelihood of contracting tetanus and are more worried about tetanus, but less likely to feel scared about the needle. These differences in perceptions seemingly contrary to their behaviors provides additional evidence for the limitation of observational data discussed below.

52 When we repeat all analyses with interviewer fixed effects additionally controlled for, all results are almost the same.

53It should be noted that our experiment cannot detect women who have significant psychic costs of vaccination but also have positive perceived net benefit of vaccination, Bv in the model, i.e., those who would take up the vaccine despite the positive psychic costs. In such a case, the observational data is likely to indicate that they have psychic costs of vaccination, but our experiment would not detect this case. In other words, our experiment only captures the psychic costs of vaccination that are large enough to alter their vaccination behaviors.

> 54The analysis excluding women under Clinic condition yields results very similar to those reported here.

> 55Both perception and objective measures increased on average after the intervention, regardless of the treatment status. This may be because of the basic information about tetanus and tetanus vaccination provided to all respondents.

> 56Specifically, respondents who were shown the “scared straight” flipchart were likely to believe that 2.52 more people would die from tetanus out of hypothetical 100 people than respondents who were shown the control flipchart. The “scared straight” flipchart also increased the probability that respondents felt very worried about tetanus, felt that tetanus is very bad, and felt that it is very important to be protected from tetanus by 16.0, 13.8, and 11.1 percentage points, respectively.

> 57It is noted that the perceived risk of tetanus is extremely high among respondents. For example, the average perceived likelihood of contracting tetanus is 45.9 percent, though it is actually less than 1 percent of total newborns; at most 35,000 in Nigeria (UNICEF). This extremely high perceived risk might explain why priming does not alter the vaccination behavior.

> 58There are several other potential reasons why the priming intervention did not alter women’s vaccination behavior. First, the “scared straight” message may have increased the perceived severity of disease only among respondents who would have received the vaccine even without that message. A piece of counterevidence against this is that the priming increased perceived severity of disease especially among women with low perceived severity

27
camps. But 1-5 km away from campsites, forest losses are driven by settlement expansion, most likely for Bangladeshis moving towards the refugee camps.

Policy actions and project interventions could make significant differences in natural resource management, including reducing deforestation rates. Before delivering 1,242,995 liquefied petroleum gas (LPG) to the Rohingya refugee and some host households by UNHCR and partners, refugee dependence on fuelwood for cooking was high. Recently, fuelwood demand has gradually fallen as a direct outcome of the LGP program’s rollout. However, refugee reliance on bamboo for shelter construction, renovation, and improvement remain exceptionally high. According to a recent survey, nearly 99 percent of all refugee shelters required both “muli” and “borak” bamboo types for shelter improvement (UNHCR, 2020a). Bamboo extraction is one of several reasons for deforestation and pressures for natural resource exploitation in Cox’s Bazar. One of the approaches to lessen deforestation and increase bamboo supply for local uses is to explore the potentials of bamboo farming as a sustainable economic enterprise with livelihood benefits for residents with land ownership or access rights (mostly Bangladeshis). However, access to land and labor market participation constraints faced by the Rohingya refugees could severely limit the positive impact of such activities. Where possible, bamboo farming enterprise could reduce economic vulnerability and prevent environmental degradation in Cox’s Bazar. More specifically, a bamboo farm will generate sustainable solutions for meeting the refugees' shelter needs, generating income for Bangladeshis, and significantly contributing to climate change mitigation through the incredible carbon sequestration and storage capacities of treated bamboo materials used in building construction (Correal, 2019; Huang, Ji, & Yu, 2019; Silva, Farbiarz, & Silvasy, 2019; Trujillo & López, 2019; Yuen, Fung, & Ziegler, 2017). To assess the complexities of this sustainable enterprise, we recommend a follow-up cost-benefit analysis study to evaluate the net benefit of bamboo for sustainable shelter and climate change mitigation in Cox’s Bazar. For example, the land-cover type to be substituted with bamboos could be studied in detail using high resolution remotely sensed data.

Another critical policy question is whether there would be significant forest cover loss if refugee hosting in Bangladesh took a more integrated approach. Past studies suggest that integrated refugee populations are less likely to rely heavily on natural resources, more likely to find alternative sources of income and livelihood strategies, more likely to get assistance from members 34
<!-- Start of picture text --> Gross national saving rate’ Global saving and interest rates Global saving and GDP growth<br>36 30 7 8 25 8<br>=== Global =— Global savings rate (Ihs) == Global savings rate (Ihs)'<br>—L . =— US term premium (rhs)* ==— Global real GDP growth<br>==== AdvancedEmerging oemarkets 32 28} Eyro area term premium (rhs) 6 24 rate (rhs)* 6<br>28 V =— OECD interest i<br>26 rate (rhs) 4 33 4<br>\ 24 24 a 2 221 |.<br>LY IVa<br>16 20 —2 20 -2<br>85 90 95 00 05 g2 96 00 04 08 90 95 00 05 10<br>‘Asa percentage of GDP. ? Nominal 10-year term premia based on zero-coupon real and nominal yields calculated based on<br>estimates from a modified version of the term structure model in P Hérdahl and O Tristani, “Inflation risk premia in the term structure of<br>interest rates”, BIS Working Papers, no 228, May 2007. * 2005 GDP PPP-weighted average of real long-term (mainly 10-year) interest<br>rates for Australia, Canada, Denmark, the euro area, Japan, New Zealand, Norway, Switzerland, the United Kingdom and the United<br>States. * Year-on-year growth rates.<br><!-- End of picture text -->
# TABLE OF CONTENTS

|I.|Introduction<br>...............<br>1|
|---|---|
|II.|Booming Sector Economics and the Dutch Disease<br>.<br>4|
|III.|Distinguishing<br>Permanent and Cyclical Shocks.<br>9|
||Limitations of the Permanent Verses Cyclical Distinction...<br>12|
|IV.|Country Experience with Booms ...................................<br>15|
||The Effect of External Shocks on the Public Sector<br>.....<br>22<br>Kenya: The Distribution of the Windfall and Fiscal Control.27<br>The Nigerian Experience<br>.....<br>28<br>Jamaica: Booms and Economic Stagnation<br>.....<br>30<br>Cameroon: A Case of AdeptBoomManagement<br>.....<br>33|
|V.|Appropriate Policy Responses<br>.....<br>37|
||Limiting Increases in Consumption Expenditure.<br>.....<br>38<br>The Distribution of Boom Income andits<br>Effect on Spending.44<br>Producer Prices and Comodity Stabilization<br>Funds<br>.....<br>48<br>The Allocation of Increased Saving .....<br>49<br>Foreign Reserve Accumulation<br>and Debt Management<br>...................................<br>51<br>Foreign Asset Accumulation<br>.52<br>Domestic Capital Investment<br>.55<br>Monetary Aspects of Coffee Booms .58|
|VI.|Conclusions<br>.62|
|REFF|RENCES<br>..<br>63|
|APPE|NDICES|
|A.|The Impact of Capital Controls on the Management of Export Booms.66|
|B.|Macro Effects of the 1976-80 Coffee Export Boom in Colombia<br>......<br>68<br>Inflation and the Real Exchange Rate<br>........................<br>68<br>The Monetary Policy Response<br>................................<br>69<br>Foreign Reserve Inflows, Exchange Race Policy,<br>and Capital Controls<br>...................................<br>73<br>The Fiscal Policy Response<br>..................................<br>74|
|C.|Data on Individual Country Experiences<br>with Booms<br>.78|
performance. These students received the program in varying degrees, but every year for 3 years. Since we did not have student-level administrative data from the beginning, we first used schoollevel administrative data and propensity score matching to create a comparable group of schools. We then designed a pre-analysis plan using survey data we collected in all program and control schools, including on student performance on a test we designed. The survey data allowed us to build a second control group from within program schools. We consider the latter as our preferred control group because it addresses potential additional biases stemming from the differences in the quality of schools after the matching at the school level. We later used students’ high-school exit exam performance to replicate the results and control for student-level characteristics. Figure A2 in the appendix shows the steps of the study design timeline.

# 3.1. Data and Descriptive Statistics

We used three main data sets for the analysis in this paper. The first source is the Gambia’s Education Management Information System (EMIS) data as of 2014. EMIS is a systemwide data set collected by MoBSE annually and covers all schools from early childhood development (ECD) to senior secondary education across the country, both public and private. EMIS data aim at facilitating better education system planning and policy dialogue. We used the EMIS data as the main data source for information about school characteristics at the baseline.

The second data source is the 2014 and 2018 WASSCE data, obtained from the West African Examinations Council (WAEC). WASSCE is a type of standardized test in West Africa. It is administered by the WAEC. It is only offered to candidates residing in Anglophone West African countries (Nigeria, Ghana, Sierra Leone, Liberia and The Gambia). Admission to university education is based on student WASSCE exam scores. The WASSCE score is graded on a 9-point scale with 1 being the best score while 9 is a fail. A satisfactory grade locally known as “credit” is a score between 1 and 6, inclusive. Scoring “credit” in both Math and English” is a pre-requisite for entering university in The Gambia. We used the 2014 WASSCE data along with the EMIS data 11