rafmacalaba/datause-encoder-data huggingface dataset:
# **3.  Trend-based Projections**

Before conducting policy experiments with the model, we first project how the Ethiopian economy would evolve if recent economic trends were to continue into the future. Given that the model uses 2002 as its base period, this requires in turn making a series of assumptions for the policy and other exogenous variables, over the period 2003-2015. The assumptions underlying these projections remain, of course, somewhat arbitrary, but they are instructive to the extent that they serve to highlight the need for changes in the policy environment to stimulate growth and reduce poverty in the long term.

The stock of land is assumed constant and normalized to unity. The quality of public infrastructure is assumed to remain constant throughout. Population and the supply of raw labor are both assumed to grow at the constant rate of 2.9 percent. The shares of public investment in infrastructure, health and education are kept constant at their base period values (about 46 percent, for instance, for infrastructure). Domestic borrowing (which is negative in the base year) is assumed to increase to 2 percent of GDP in 2003 and to remain constant after that in proportion of GDP. Given the overall fiscal balance, we assume that Ethiopia borrows externally at concessional terms to close its budget gap. The interest rate on foreign public debt is assumed to be constant at 0.075 percent, whereas interest rates on private foreign borrowing and on domestic public debt are taken to be fixed at the level observed in 2002. Foreign aid (measured in domestic-currency terms) is kept constant in proportion of GDP at the 2002 level (about 11.6 percent). The allocation of foreign assistance between food and nonfood aid is done according to the constant shares observed in the base period (with food aid representing 37.5 percent of total aid). Public foreign borrowing is determined residually to balance the government budget (given the assumption of a constant domestic borrowing-GDP ratio), whereas private capital inflows are constant in per capita terms. Private unrequited transfers (measured in foreign-currency terms) are also assumed to grow in line with the population. The change in official reserves is assumed to be constant in proportion of total capital inflows (private and public). Prices of imports and exports are assumed 33
level. In fact, until one has reached that level, the marginal effect of education is small or zero. The skills learned during the first five years of school, at least those that are retained after one has spent several years in the labor force, are not sufficient to raise productivity in the sector. It is plausible, and ther?fore tempting, to suppose that literacy, and even more, numeracy, are valuable skills in this activity; and that among the people self-employed in retail trade, these skills are typically not consolidated until somewhere in secondary school (Catholic University (1988)]. The children of richer and better-educated parents, who come to school better prepared and may also attend better schools, may of course learn to read and cipher in fewer years, but those children are unlikely to become self-employed retailers.

In sum, one may conclude that educ&tion does have an impact on earnings in Peru, not only in the formal wage sector, but in small-scale selfemployment as well. Sometimes this impact is quite sizable. It is not, however, constant across all years of education, and the relative impact of different levels of education differs across sectors of employment, between urban and rural areas, and (to a lesser extent) between men and women. These findings are generally supportive of government policies that would encourage school attendance, on the part of men and women, and on the part of those who will become self-employed workers in small family enterprises. Education is not wasted on them, except as they acquire more schooling than is useful in a traditional occupation, and schooling may be their best opportunity to leave those occupations, which generally pay very little.

38
|Rural Roads Improvement Project|
|---|
|Rural Roads Improvement Project|
|Port Project(03)|
|Port Project(03)|
|Urban Development Project(03)|
|Urban Development Project(03)|
|Urban Development Project(03)|
|Urban Development Project(03)|
|Manila Sanitation and Sewerage Project|
|Manila Sanitation and Sewerage Project|
|Manila Sanitation and Sewerage Project|
|Manila Sanitation and Sewerage Project|
|Manila Sanitation and Sewerage Project|
|Livestock - Fisheries Project(03)|
|Livestock - Fisheries Project(03)|
|Watershed Management and Erosion Control Project|
|Watershed Management and Erosion Control Project|
|Watershed Management and Erosion Control Project|
|Watershed Management and Erosion Control Project|
|Watershed Management and Erosion Control Project|
|Industrial Finance Project|
|Industrial Finance Project|
|Industrial Finance Project|
|Industrial Finance Project|
|Education Project(07)|
|Education Project(07)|
|Education Project(07)|
|Education Project(07)|
|Education Project(07)|
|Structural Adjustment Loan - SAL Project(01)|
|National Fisheries Project|
|National Fisheries Project|
|National Fisheries Project|
|National Fisheries Project|
|National Fisheries Project|
|National Fisheries Project|
|Communal Irrigation Project|
|Communal Irrigation Project|
|Communal Irrigation Project|
|Communal Irrigation Project|
|Communal Irrigation Project|
|Communal Irrigation Project|
|Communal Irrigation Project|
|Agricultural Support Services Project|
|Agricultural Support Services Project|
|Agricultural Support Services Project|
|Agricultural Support Services Project|
|teristics. Across all regressions, the dependent<br>ipbursements from the World Bank expressed<br>heterogeneity. At the bottom of the table we<br>, as well as the associated p-value. Column 1<br>he World Governance Indicators (with higher<br>_closure requirements_ as measured by Djankov<br>_enness_ as reported by Chinn and Ito (2006).<br> polity2 from the Polity IV Project. Column<br>ed in the World Development Indicators. All<br>d time fxed efects. Percentage changes are<br>nd 99th percentile. Standard errors clustered|**credit**<br>**markets**<br>(5)|3.7033*<br>(2.0589)<br>2.0798*<br>(1.1430)<br>1,435<br>0.0963<br>YES<br>YES<br>-1.624<br>.477|
|---|---|---|
|diferent charac<br> quarterly aid d<br>e dimension of <br>low the median<br>ruption from t<br> presence of _dis_<br>_ital account op_<br>y the variable <br>GDP as report<br>ude country an<br>zed at the 1st a|**democratic**<br>**institutions**<br>(4)|3.3812**<br>(1.4936)<br>1.8541<br>(1.8224)<br>1,582<br>0.1131<br>YES<br>YES<br>-1.527<br>0.554|
|ountries with<br>n we interact <br>edian in som<br>ove versus be<br> control of cor<br> based on the <br>s value of _cap_<br>as captured b<br>r relative to <br>ted) and incl<br>s are winsori|**capital**<br>**account**<br>(3)|1.7202<br>(1.0980)<br>4.4568*<br>(2.4139)<br>1,606<br>0.1091<br>YES<br>YES<br>2.737<br>.288|
|vary across c<br> In each colum<br> the sample m<br>es that are ab<br>high focus on <br>ded countries <br>temporaneou<br>c institutions <br>fnancial secto<br>DP (not repor<br>d aid variable<br>5, * p_<_0.1)|**disclosure**<br>**rules**<br>(2)|2.7571**<br>(1.0606)<br>4.4350*<br>(2.3600)<br>1,648<br>0.1009<br>YES<br>YES<br>1.678<br>.497|
|**Table 4: Heterogeneity.** The table shows how the results<br>variable is the quarterly percentage change in haven deposits. <br>as a share of GDP with indicators for being above and below <br>show case the diference in coefcient estimates across countri<br>distinguishes between countries based on whether they have <br>control of corruption implying less corruption). Column 2 divi<br>et al. (2010). Column 3 divides countries based on their con<br>Column 4 examines heterogeneity associated with democrati<br>5 divided countries on the basis of _domestic credit_ from the <br>regressions control for the quarterly percentage change in G<br>approximated with the diference in log-levels. The deposit an<br>at the country-level are in parentheses (*** p_<_0.01, ** p_<_0.0|**control over**<br>**corruption**<br>(1)|Aid*(Control of corruption=low)<br>4.4482**<br>(1.7478)<br>Aid*(Control of corruption=high)<br>2.1635*<br>(1.0512)<br>Aid*(Disclosure required=0)<br>Aid*(Disclosure required=1)<br>Aid*(Capital account openness=low)<br>Aid*(Capital account opennes=high)<br>Aid*(Polity low)<br>Aid*(Polity high)<br>Aid*(Credit=low)<br>Aid*(Credit=high)<br>Observations<br>1,648<br>R-squared<br>0.1013<br>country FE<br>YES<br>time FE<br>YES<br>Aid disbur*(high-low)<br>-2.285<br>p-value<br>.247|

26
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# **2. Data and Methods**

# **2.1 Data**

This study makes use of two data sets: the 1998 Vietnam Living Standards Survey (VLSS) and the 1999 Population and Housing Census. The VLSS was implemented by the General Statistics Office (GSO) of Vietnam with funding from the Swedish International Development Agency and the United Nations Development Program and with technical assistance from the World Bank. The sample included 6000 households (4270 in rural areas and 1730 in urban areas), in Vietnam, selected using a stratified random sample.

The 1999 Census was carried out by the GSO and refers to the situation as of April 1, 1999. It was conducted with the financial and technical support of the United Nations Family Planning Association and the United Nations Development Program. As the full results of the Census have not yet been released, this analysis is based on a 3 percent sample of the Census. The 3 percent sample was selected by GSO using a stratified random sample of 5287 enumeration units and 534,139 households. The 3 percent sample of the Census was designed to be representative at the provincial level.

There are a number of variables which are common to both the VLSS and the Census, and which allow household level expenditures to be predicted and disaggregated poverty estimates produced. Table 1 summarizes the 17 variables that were selected for inclusion in our poverty mapping exercise.

_5_
22

# _(1) Governmen: expenditure_

The coefficients for composition of government expenditure and for total government expenditure are the most robust both in sign, and significance. They are significant (at less than 10% level)24 and have the expected sign in six out of seven equations. The coefficients on both the level and composition of government expenditure are n significantly different across the equations and between the two models estimated. For expenditure composition the estimated coefficient from reduced form in Table 3 is lower and less precisely estimated than those in Table 2, but boch the estimates are not significantly different from unity.<sup>25</sup> As for total government expenditure (GEXPG), again their reduced form estimates are not significantly different from those estimated in the basic model and the precision level is the same.

Increases in the level of total government expenditure and in the share of capital (ADP' expenditure in total expenditure depreciates the real exchange rate. For any given total public expenditure, a shift in its composition away from capital or ADP expenditure apgreciates the real exchange rate. Thus the effect of a given reduction in total expenditure on RER will be different if it is accompanied by a change in composition of such expenditure, than if it is not. This suggests that specifications of RER models for Bangladesh that ignore 24Even in the RERT equation (column 1, table 4 and 6) the coefficient for total goverrnment expenditure is significant 3nd of the right sign; that for composition is not significant.

25T-tests confirm that they are not significantly different from one.
# **1. Introduction**

Intensification of peasant agriculture, by increasing the use of modern inputs such as fertilizer and high yielding varieties, through centralized extension services that focuses on technological packages and government-guaranteed credits has been a key policy emphasized by Ethiopia’s overall development strategy of agricultural-led growth (Dercon and Christiaensen 2011; World Bank 2007). In a setting where high exposure to risk and meager surpluses from subsistence agriculture limit opportunities for self-insurance and savings, provision of credit is often seen as a key element to increase productivity through more intensive use of fertilizer and seed and to facilitate consumption smoothing. However, the potential impact of such a strategy and the extent to which it should be complemented with other efforts to improve access to technology have not been subject to rigorous analysis.

To fill the gap, this study investigates the productivity impact of being credit constrained in the semiformal sector of two regions in Ethiopia, one of them, Gojam, surplus producing and another one, Wollo, with more limited agricultural potential. We use a survey-based direct elicitation approach to identify credit constrained and unconstrained households. As disequilibrium is likely in credit markets, an endogenous switching regression model is used to explore the returns on productive endowments for constrained and unconstrained households. Findings suggest that political connections matter in determining access to semi-formal sector loans. Being credit constrained affects productivity in more productive villages where loans were used to buy agricultural inputs, with an estimated productivity loss of 11.4 percentage points due to being credit constrained. While this supports the notion of credit access being important for productivity of land use in the villages with potential, we find no evidence of an appreciable effect of being credit constrained in less productive areas where loans were largely used for purposes of non-farm activities and consumption smoothing, implying that allocation of credit might be governed by non-economic criteria. Before putting in place efforts to improve credit access, careful analysis of reasons for credit rationing and the impact of alternative policies will be appropriate.

The paper is organized as follows. Section 2 provides the conceptual framework discussing the potential impacts of credit market imperfections. It also discusses ways to obtain information on households’ credit constrained status and, on this basis presents the direct elicitation approach and elaborates the estimation strategy used. Section three introduces the data and presents descriptive statistics both on credit access and use, the incidence and reasons for credit rationing, and the characteristics of credit constrained vs. unconstrained households in the two regions. Section four provides empirical results on determinants of credit access as well as, using the endogenous switching model, the impact of credit access on productivity of resource use. Section five concludes with a number of policy implications.

2
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 22. Funding gaps by sector**

|$ million per year|||||||
|---|---|---|---|---|---|---|
||ICT|Irrigation|Power|Transport|WSS|Total|
|Spending needs|(86)|(1)|(55)|(160)|(47)|(349)|
|Existing spending*|10|0|20|88|11|129|
|Efficiency gains|0|0|22|10|5|37|
|Funding gap|(76)|(1)|(13)|(62)|(31)|(183)|
|Reallocation potential across sectors|0|—|0|0|0|0|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it will be applied toward other infrastructure sectors. — = Not available. *traced to needs. **Assuming complete fungibility across sectors.

As can be seen in table 22, the size of the efficiency gains are equivalent to only about 17 percent of the total funding gap, suggesting that to meet infrastructure targets the country needs to scale up the size of its infrastructure to boost economic growth and provide basic services to its population. This finding concurs with the earlier discussion of the potential contributions of infrastructure to annual per capita growth, where it was established that most of the growth gains would be due to faster accumulation of infrastructure assets.This point also coincides with one of the pillars of the CAR’s poverty reduction strategy, which places the emphasis on building new assets.

The infrastructure funding gap is almost equally divided between capital investment and O&M (table 23). The water supply and sanitation sectors, and— to a lesser extent—the ICT and transport sector have important gaps in funding capital expenditure. The largest funding gap for O&M is found in the ICT sector ($50 million), followed by the transport sector ($39 million).

**Table 23. The size and the composition of the funding gap by sector, O&M and capital**

$ million per year

||Capital expenditure<br>gap|Operation and<br>maintenance gap|Total funding<br>gap|
|---|---|---|---|
|ICT|23|50|73|
|Irrigation|—|—|0|
|Power|18|0|18|
|Transport|22|39|62|
|WSS|25|6|31|
|Total|88|96|183|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ WSS = water supply and sanitation; ICT = information and communication technology; O&M = operations and maintenance.

# What else can be done?

Extending the time horizon, prioritizing spending, and tapping resources outside the budget are alternatives to the poor budgetary situation of the government.

## **More time**

Extending the time horizon for meeting the infrastructure targets beyond the illustrative 10-year period is considered here. Simulations suggest that in a scenario in which a country is unable to raise additional finance, the identified infrastructure targets could be achieved within a 40-year horizon 47
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67
ment arm in the intervention and then we cross randomized and assigned the participants into two groups in the midline survey - see Table A1.<sup>15</sup> Each of these groups was asked to complete the survey through an alternative mode, as follows:

- **Group 1 — CATI — 300 individuals:** This group received a call from a live person asking them to complete a phone-based survey (CATI). At the outset, they were informed about the monetary incentive (US$ 2.50 in Tigo Money) to complete the survey. On average, participants received 3,7 (3,4) calls per day over three days – including the picked up survey call. Enumerators were trained to conduct surveys on sensitive topics. Following recommendations by Glazerman et al. (2020), we developed a contact-attempt tracking log for each participant.

- **Group 2 — Self-completion — 299 individuals:** This group received WhatsApp messages<sup>16</sup> inviting them to complete an online survey. The message included a link to the self-completion survey with a note on the incentive to complete the survey (US$ 2.50 in Tigo Money) and the deadline for completion (three days). Tigo provided free access to the survey. On average, participants received 2.9 (2.6) calls per day over three days from trained enumerators who reminded them to complete the self-completion survey. Thus we made the same effort to reach participants in each group. These contact attempts were tracked in the same individual log as Group 1.

To credibly identify the relative convenience of the two modes, we not only randomly assigned individuals to one of the two groups but also ensured that aside from the survey mode itself, both groups were treated equally. The instrument was the same, all individuals received the same monetary incentive to complete the survey, both groups had three days to complete it, there were no costs of completing the survey by either method (such as data plan usage), and the maximum number of reminders was similar across groups. These features are non-trivial and allow for a causal interpretation of the effect of the two survey modes without confounding with other potential aspects of survey design.

> 15While the initial sample was 600 participants, one person dropped out of the study, so we ended up with 299 individuals from the treatment group and 300 from the control group.

> 16Throughout this paper, we use the terms “CATI” and “phone-based survey” to refer to the first treatment arm, and “self-completion” and “WhatsApp” to refer to the second group.

9