rafmacalaba/datause-encoder-data huggingface dataset:
35

# **Table 6.  Expenditure Categories and Average Budget Shares, Indonesia, 2000 and 2007**

|**Expenditure category**|**Description**|**No remittanc**<br>**remittan**|**es in 2000, no**<br>**ces in 2007**|**Remittanc**<br>**remittan**|**es in 2000, no**<br>**ces in 2007**|**No remitta**<br>**remittan**|**nces in 2000,**<br>**ces in 2007**|**Remittances in 2000,**<br>**remittances in 2007**|
|---|---|---|---|---|---|---|---|---|
|||**2000**|**2007**|**2000**|**2007**|**2000**|**2007**|**2000 2007**|
|Food|Purchased food<br>Non-purchased food|0.600|0.551|0.609|0.535|0.615|0.550|0.627 0.563|
|Education|Educational expenses|0.049|0.051|0.049|0.051|0.045|0.056|0.029 0.049|
|Housing|Housingvalue|0.100|0.112|0.091|0.157|0.092|0.114|0.093 0.110|
|Health|Health expenses|0.018|0.020|0.024|0.030|0.010|0.020|0.017 0.052|
|Other|Household durables, Transport,<br>Communications,Legal|0.232|0.266|0.227|0.226|0.238|0.260|0.234 0.225|
|||1.000|1.000|1.000|1.000|1.000|1.000|1.000 1.000|

Notes: N=5301 households. All values are weighted. International remittances include remittances received from spouse, parents and children.

Source: Indonesia Family Life Survey (IFLS), 2000 and 2007.
Pritchett, Lant, Rukmini Banerji, and Charles Kenny. 2013. 'Schooling is not education! Using assessment to change the politics of non‐learning', _Center for Global Development Report_ .

- RTI International. 2009. "Early Grade Reading Assessment Toolkit." In.

- Snilstveit, Birte, Jennifer Stevenson, Daniel Phillips, Martina Vojtkova, Emma Gallagher, Tanja Schmidt, Hannah Jobse, Maisie Geelen, Maria Grazia Pastorello, and et al. 2015. 'Interventions for improving learning outcomes and access to education in low‐and middle‐ income countries: A systematic review', _Systematic Review 24, London: International Initiative for Impact Evaluation (3ie)_ .

- State of Texas Assessments of Academix Readiness. 2013. "Vertical Scale Technical Report " In.

- Strom, Bjarne. 2004. 'Student Achievement and Birthday Effects', _Working Paper. Norwegian University for Science and Technology._

- Utah Education Association. 2013. "Student Assessment of Growth and Excellence (SAGE): Overview " In.

- Valerio, Alexandria , María Laura Sánchez Puerta, Namrata Tognatta, and Sebastían Monroy‐Taborda. 2016a. 'Are there skills payoffs in low‐and middle‐income countries? Empirical evidence using STEP data', _Policy Research Working Paper Series 7879, The World Bank_ .

- ———. 2016b. 'Are there skills payoffs in low‐and middle‐income countries? Empirical evidence using STEP data', _Unpublished manuscript._

- Valerio, Alexandria, Maria Laura Sanchez‐Puerta, Namrata Tognatta, and Sebastian Monroy‐Taborda. 2016. "Are there skills payoffs in low‐and middle‐income countries? Empirical evidence using STEP data." In.

- World Bank. 2016a. "The STEP Skills Measurement Program " In.

- ———. 2016b. 'World Development Indicators '.

- ———. 2017. 'World Development Indicators '.

- ———. 2018. _World Development Report 2018: Learning to Realize Education's Promise_ (World Bank: Washington, DC).

18
Table 7 Aggregate Expected Loss from the Credit Allocation

||(1)<br>Total Credit<br>Program<br>(Million US$)|(2)<br>Total Credit<br>Program/GDP<br>(%)|(3)<br>Share of<br>Credit<br>Program<br>(%)|(4)<br>Default<br>Probability<br>(%)|(5)<br>Expected<br>Loss/GDP<br>(=(2)x(4))<br>(%)|(6)<br>_Ex Post_<br>Default<br>(%)|(7)<br>_Ex Post_<br>Loss/GDP<br>(=(2)x(6))<br>(%)|
|---|---|---|---|---|---|---|---|
|_Panel A: Risk Groups, Formal Firms_||||||||
|High Risk|607|0.2|7|18.2|0.04|17.1|0.04|
|Medium Risk|1,087|0.4|12|9.9|0.04|12.2|0.05|
|Medium-Low Risk|1,863|0.8|21|5.7|0.04|11.2|0.08|
|Low Risk|3,972|1.6|44|2.1|0.03|6.2|0.10|
|No Risk Data|1,411|0.6|16|18.2|0.10|17.1|0.10|
|Total: Formal Firms|8,941|3.6|100|7.4|0.27|10.4|0.38|
|_Panel B: Formal Firms + Natural Persons_||||||||
|Formal Firms|8,941|3.6|78|7.4|0.27|10.4|0.38|
|Natural Persons|2,563|1.0|22|18.2|0.19|17.1|0.18|
|Total: Formal Firms + Natural Persons|11,504|4.6|100|9.8|0.45|11.9|0.55|

This table shows the distribution of the aggregate of the credit allocation, for natural persons and the formal firms sample. Panel A reports statistics across the firms’ risk distribution. Panel B reports statistics separately for formal firms and natural persons. Column 1 shows the total amount of guaranteed credit in dollar terms, and Column 2 normalizes Column 1 by GDP. Column 3 shows the share of guaranteed loans for each category. Column 4 shows the default probability of each category, using the model in Table 1, Columns 4 and 8. Column 5 shows the total expected loss as a share of GDP (Column 2 times Column 4). Column 6 shows the _ex post_ default rate of each category, using a dummy equal to one if the firm defaults on a loan after May 2020 (has payment past due over 90 days) and zero otherwise. Column 7 shows the total _ex post_ expected loss as a share of GDP. Values in Columns 4 and 6 are weighted by the total amount of guaranteed credit granted to each firm as a share of the total guaranteed credit granted to all the firms within its category. Totals by panel reported in Column 4 are calculated as the sum of the product of each category’s statistic by its relative weight (Column 3). Firms are classified into risk categories based on the distribution quartiles of the fitted values of the regression specifications reported in Table 1, Columns 4 and 8. Firms with a missing risk category are assigned the risk from the high-risk category.

49
country as per our computerized map. In other words, we do not calculate the cost of upgrading the entire road network in each country, but only those parts of it connecting cities with year 2000 population above 300,000 people.

Results are presented in Table 14. Since our range of unit costs covers a wide variety of work types, the total cost estimates cover a correspondingly broad range. Focusing on mean unit costs for the region, we can see that a full upgrade (i.e., 13 countries) would involve a total up front cost of the order of US$8 billion. By contrast, focusing on three countries only would reduce that cost very considerably, to just over US$3 billion. Comparing these numbers with Table 12 shows that even once the costs of an upgrade are netted out, the trade benefits to the region from a road upgrade are very substantial: of the order of $45 billion for a region wide program, and $30 billion for a three country program, without allowing for any amortization of the cost of the upgraded road network over its expected lifespan.<sup>18</sup>

# **7 Conclusions and Directions for Further Research**

In this paper, we have built on and extended recent work by Buys et al. (2006) to show that an ambitious but feasible road upgrade program in ECA has great potential to boost intra-regional trade—by as much as 50%. Moreover, it is possible for the region to reap a large proportion of the overall gains by focusing attention on just three countries which are important transit corridors but exhibit significant limitations in terms of infrastructure quality: Albania, Hungary and Romania. Such a concentrated program of road upgrading would come at significantly

> 18 Even using the extreme upper tail of ROCKS unit costs results in net benefits of around $35 billion and $27 billion for the 13 and 3 country programs respectively.

- 24 -
TABLE OF CONTENT.

|I.<br>*|INT|RODUCTI|ON ......................................................<br>1|
|---|---|---|---|
||A.|Backgr|ound ....................................................<br>1|
||B.|Impact|of Macro Developments on Public Sector Finances ........2|
|II.|<br>Pub|lic Sec|tor Finances ............................................<br>2|
||A.|The|Tax System ............................................<br>..<br>2|
|||A.1|Income Tax .............................................<br>4|
|||A.2|Property Taxes .<br>.<br>....................................<br>4|
|||A.3|Sales Tax ..............................................<br>4|
|||A.4|Excise Taxes<br>,<br>.......<br>5|
|||A.5|Taxes on Imports .<br>.<br>..................................<br>5|
|||A .6|Export Taxes ...........................................<br>8|
|||A.7|Petroleum Differential Tax<br>.<br>.<br>.<br>8|
|||A.8|Tax Administration .<br>.<br>.................................<br>9|
|||A.9|Customs .............................................<br>9|
|||A.10|Overall Conclusions on the Tax System<br>..<br>9|
||B.|<br>Cent|ral Government Expenditures<br>.<br>.<br>.<br>10|
|||B.1|Introduction ..........................................<br>10|
|||B.2|Education Expenditures ................................<br>12|
|||B.3|Health Expenditures ...................................<br>13|
||C.|<br>Cent|ral Government Finances ................................<br>13|
||D.|<br>Fina|ncing of the Central Government Deficit ................ 14|
||E.|<br>Budg|et for 1988 ............................................<br>16|
||F.|<br>Publ|ic Investment Program ..................................<br>18|
|||F.1|Public<br>Investment Expenditures ........................<br>18|
|||F.2|<br>Public Investment Program for 1987-90 ................. 19|
|||F.3|The Institutional Framework for the Preparation of|
||||Public Investment Programs ............................<br>21|
||G.|Public|<br>Sector Enterprises ............................ .....<br>22|
|||G.1|Introduction ..........................................<br>22|
|||G.2|Institutional Links among the Central Govtrnment and|
||||Public<br>Sector Enterprises .............................<br>24|
|||G.3|Privatization<br>..<br>25|
|||G.4|National Electric Energy Company (ENEE)<br>.<br>26|
|||G.5|National Agricultural Development Bank (BANADESA)<br>..... 27|
|||G.6|National Industrial Development Corporation (CONADI)..27|
|||<br>G.7|<br>Honduran Telecommunications Company (HONDUTEL)<br>........ 28|
|||G.8|National Water and Sewerage Service Company (SANAA) ...<br>28|
|||G.9|National Port Authority (ENP)<br>......................... 29|
|||G.10|National Railway of Honduras (FNH) ....................<br>30|
|||G.ll|Honduras Agricultural Marketing Institute (IHMA) .. 30|
# **Table 5a—Trade Flows by Trading Partner of Kenya (in percentage)**

|||||Im|ports|||||||||Exports|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Tanzania|Uganda|Rwanda|COMESA|SADC|USA|EUR|China|ROW|Tanzania|Uganda|Rwanda|COMESA|SADC|USA|EUR|China|ROW|
|**Business Services**|||||||||||||||||||
|Air Transport|0.0|0.0|0.0|0.3|0.4|1.0|58.2|0.3|40.2|0.0|0.1|0.0|0.4|1.3|20.7|39.1|2.1|36.4|
|Communication|0.1|0.0|0.0|1.3|0.2|13.7|53.6|1.9|29.3|0.0|0.0|0.0|0.7|0.0|11.6|60.7|1.6|25.4|
|Insurance|0.0|0.0|0.0|0.2|0.0|18.6|49.5|1.4|30.3|0.0|0.0|0.0|0.0|0.0|20.6|36.8|4.5|38.1|
|Business Services nec|0.0|0.0|0.0|0.2|0.3|8.6|53.8|1.6|35.5|0.0|0.0|0.0|0.6|1.1|12.3|33.7|2.0|50.3|
|Financial Services nec|0.0|0.0|0.0|0.3|0.6|25.2|38.6|1.2|34.0|0.1|0.0|0.0|0.5|0.8|12.5|41.6|3.2|41.3|
|Transport nec|0.0|0.0|0.0|0.0|1.2|6.0|39.5|4.4|48.9|0.0|0.0|0.0|0.5|0.7|12.4|45.2|3.7|37.5|
|Water Transport|0.0|0.0|0.0|0.0|0.5|1.8|47.4|0.6|49.7|0.0|0.0|0.0|0.2|0.2|0.8|45.9|0.2|52.7|
|**Dixit-Stigliz Goods**|||||||||||||||||||
|Chemicals Mineral and Metal Products|0.3|0.2|0.0|1.2|21.7|2.2|21.2|10.6|42.5|12.8|24.0|4.2|13.1|7.6|0.8|2.8|0.8|33.9|
|Energy and Minerals|0.1|0.1|0.0|0.1|1.4|0.1|0.1|0.0|98.1|11.4|25.9|8.6|1.8|0.6|0.3|9.6|9.3|32.5|
|Food Products|4.7|5.5|3.0|14.1|13.2|8.2|11.1|0.9|39.4|6.4|11.7|2.1|13.2|13.0|3.3|31.6|0.7|18.0|
|Petroleum and Coal Products|0.0|0.0|0.0|1.7|1.3|0.2|2.8|0.1|94.0|7.1|42.0|11.8|8.1|2.1|3.0|12.3|0.8|12.9|
|Other Manufacturing|0.3|0.1|0.0|0.4|3.2|14.5|35.2|12.2|34.2|12.3|17.5|4.2|25.6|4.0|4.6|13.6|0.3|18.0|
|Textile and Apparel|2.9|0.7|0.0|1.1|3.2|0.9|4.9|51.2|35.2|3.9|6.4|1.0|3.4|2.1|50.6|13.9|1.6|17.1|
|Wood and Paper Products|8.0|0.3|0.0|8.1|12.3|1.7|29.5|9.7|30.4|14.3|28.8|6.4|13.3|6.1|2.4|6.4|0.4|22.0|
|**CRTS**|||||||||||||||||||
|Agriculture and Forestry|11.3|18.7|0.3|2.4|4.7|14.1|6.7|0.5|41.3|0.6|0.6|0.1|13.4|0.9|2.8|48.1|0.5|32.9|
|Other Services|0.0|0.1|0.0|0.8|0.9|38.8|30.1|2.0|27.3|0.1|0.0|0.0|0.9|0.9|33.7|26.0|1.7|36.6|
|Trade|0.1|0.0|0.0|0.5|0.3|11.0|37.9|3.0|47.2|0.0|0.0|0.0|0.6|1.2|12.9|29.7|1.8|53.8|
|Utilities|0.2|19.1|0.0|0.2|1.8|7.0|41.4|3.2|27.2|0.0|0.1|0.0|0.4|1.9|9.4|47.9|3.4|36.9|

# **Source:** GTAP 8.1 data set.

59
## **2.5 Ethics Statement**

Our study was reviewed and approved by the DRC’s National Institute of Statistics (#0340/INS/DG/fau/2023) and the University of California, Berkeley’s Institutional Review Board (Protocol ID: 2023-0516343). The National Institute of Statistics validated the methodology and tools used to carry out our study and granted us the necessary statistical approval for our survey. Prior to initiating any part of the research, informed consent was obtained from all subjects as well as from the participating restaurants. We anonymized all subject data.

We implemented distinct randomization procedures for our demand-side and supply-side experiments. In the demand-side experiment, we utilized SurveyCTO’s programming features to assign subjects to treatment or control groups based on a randomly generated number. This process occurred automatically upon survey initiation, with the assignment concealed from enumerators, subjects, and restaurants. For the supply-side experiment, we used R version 4.1.2 to randomly select one or two restaurants for treatment on specific days from a vector containing all four participating restaurants (R Core Team, 2024). Each evening, our field team informed restaurants of their next day’s treatment status.

# **3 Results**

## **3.1 Demographic Characteristics of Subjects in Demand-Side Experiment**

We begin by comparing the characteristics of treatment and control subjects in the demand-side experiment. Each row of Table 1 considers one of the eight individual subject characteristics that are included in the matrix _Xi_ in Equation 1. The first three columns respectively display the mean value of a characteristic among subjects in the control group, the mean difference between the treatment and control groups, and the standard error of this difference. All observed 18
# **4. Results of the Main Experiment**

The central purpose of the main experiment was to test different policy options for the Zambian maize market with robust replicable data. The game theoretic analysis of the model suggests the rules-based policy, in which the government precommits to its decisions, to be strongly superior to the discretionary regime. However, whether this advice is empirically valid is another matter. The theoretical inferiority of the discretionary policy stems from the social dilemma, i.e. the conflict between individual and social rationality, present in the maize market. Numerous experimental studies, however, have shown that subjects are frequently able to overcome such dilemmas and reach stable optimal outcomes through trust and reciprocity (e.g. Fehr, Kirchsteiger, and Riedl (1993), Berg, Dickhaut, and McCabe (1995), Dufwenberg and Gneezy (2000), Abbink, Irlenbusch, and Renner (2000), Fershtman and Gneezy (2001), Gächter and Falk (2002); for an experiment in an African context see Haile, Sadrieh, and Verbon (2006)).

## **4.1. The discretionary treatment**

In the discretionary game government players have a strong incentive to choose a high quantity because it is the dominant strategy. In order to reach a Pareto-superior cooperative arrangement, traders must trust that the governments can resist this temptation. At the same time the government also needs to trust the traders. If traders supply low quantities, then the government’s payoff is very small if it also chooses _low_ – an outcome corresponding to a food shortage situation.

Figure 1 shows the average total quantity supplied by the traders (left axis) and the percentage of _high_ choices by the government (right axis). The figure illustrates that cooperation is frequently attempted in early rounds, but it is very short-lived. Over time, _high_ choices from the government become increasingly dominant. By the end of the experiment, cooperation collapsed in all but one market. In accordance with the rising frequency of _high_ choices, quantities supplied by the traders decrease from the third round onwards. There is some evidence to suggest that it is the governments which first cease to cooperate and that the traders respond to this. In the disaggregated data, however, no predominant response pattern is evident.<sup>15</sup>

> 15 In 13 of the 16 markets _high_ frequencies rise from the first to the second half of the game, whereas in the remaining three markets this frequency remains unchanged.  The binomial test rejects the null hypothesis of equal likelihood of rising and falling frequencies at p=0.0001 (one-sided).  Trader quantities fall in 10 of the 12 markets in which there is a change and this fall is significant (p=0.0193, one-sided). In the six rounds of play trader quantities do not fully converge to the noncooperative equilibrium, but the trend points toward that outcome.  Notably government _high_ frequencies rise from the third round on; trader quantities typically follow with a one-round lag.

13
the LBS only provide the breakdown between bank and non-bank debtors (where non-bank captures both the non-bank private and the public sector).<sup>77</sup> We focus on cross-border bank lending in the LBS in the form of loans, for which we have data starting in 1996. However, our methodology described below can also be applied to total cross-border bank claims (in all instruments).<sup>78</sup> Next, we describe how we use the sectoral split information contained in the CBS/IC data in order to divide the Non-Bank sector in the LBS data into Non-Bank Public sector and Non-Bank Private sector. This is described next. First, we go over our methodology for constructing the split for the outstanding stocks of LBS cross-border bank loans. Then, we describe our methodology for constructing the split for exchange rate adjusted changes, which relies on currency composition information available in the LBS.

# **B.3.1 Borrowing Sector Splits for Outstanding Stocks**

For outstanding stocks, we use the share of international bank debt for each sector from the CBS to estimate the split of the Non-Bank LBS data into Public and Private components.<sup>79</sup> We calculate that as follows: 77The enhanced BIS data, available from 2013 on, splits the non-bank sector into public and private sub-sectors. Note that the LBS include central banks with banks instead of public, but central banks tend to compose a very small portion of cross-border bank claims in the BIS data.

> 78Starting in 1984, we have data for total bank cross-border credit (in all instruments). We don’t use this in our initial analysis in order to avoid double counting external bond flows. In practice, the difference between total bank credit and bank credit in just the loan and deposit instruments tends to be small.

> 79This estimation is also used in Arslanalp and Tsuda (2014a) and Arslanalp and Tsuda (2014b).

87
country and year are 147 million US$, which is 17%<sup>21</sup> of the average development aid (880 million US$) received by the countries in our sample in 2009.<sup>22</sup> For scenario 2 (providing all households with high-end water and sanitation technologies) the estimated average costs amount up to 347 million US$ or about 40% of current development aid received per country and year.

It is worth highlighting that all of the cost-benefit ratios presented here are likely overstating the true cost, and understating the true benefit of water and sanitation investment for two main reasons: First, as we have made clear in the preceding section, we have selected the upperbound cost estimates from Hutton and Haller (2004) to avoid underestimating the true cost of providing infrastructure.<sup>23</sup> Second, and more importantly, our mortality estimates only capture the direct child mortality effects of water and sanitation infrastructure, that is, the private mortality benefits that accrue at the child or household level, without taking into account any positive externalities, and without taking into account morbidity effects. Water, and in particular sanitation, can be presumed to have substantial positive spillovers within clusters and neighborhoods (see also Günther and Fink, 2010). Improved water and sanitation can also be assumed to be associated with considerable improvements in morbidity and overall wellbeing. The cost-benefit ratios presented in this paper should thus be viewed as a lower bound, since the benefit estimates shown are likely to underestimate the true social benefits of water and sanitation, while the cost estimates are likely to overstate the true financial commitment required for their provision.

# **5. Discussion and Conclusion**

In this paper we have used micro-data from 38 developing countries to estimate the mortality effects of water and sanitation, and to quantify the degree to which improvements in water and sanitation infrastructure could contribute to achieving the mortality objectives established in the Millennium Development Goals. We have shown that the mortality gains from improved water and sanitation are large: on average, access to improved water and sanitation lowers the odds of a child dying before the age of five by about 5 to 8 percent, while private access to piped water and toilets connected to a septic tank or sewage system can reduce the odds of early child mortality by about 16 percent.

> 21 This figure excludes India, Nigeria, Bangladesh and Indonesia; given that these countries receive very low development aid per capita, their inclusion would have led to misleadingly high ratios of water and sanitation investment costs relative to international development aid.

> 22 OECD Statistics: http://stats.oecd.org/Index.aspx?DatasetCode=ODA_RECIP#

> 23 Note that the cost data from Hutton and Haller (2004) reflect estimates from 2000.

28
# **Rural Non-Farm Employment and Household Welfare: Evidence from Malawi**

Guigonan Serge Adjognon<sup>1</sup> , Saweda Lenis Liverpool-Tasie<sup>2</sup> , Alejandro de la Fuente<sup>1</sup> , Rui Benfica<sup>3</sup> 1 The World Bank Group (WBG) 2 Michigan State University (MSU) 3 International Fund for Agricultural Development (IFAD) _Keywords_ : Poverty, Rural Non-Farm Employment, Household Enterprises, Sub Saharan Africa, Malawi.

JEL Classification: J43, J71, O13, O55, Q12 _Acknowledgements_ : This paper builds on and expands analysis done for the Malawi Poverty Assessment report (World Bank, 2016). The authors wish to thank comments received throughout the review process of the Poverty Assessment. The findings, interpretations, and conclusions of this paper are those of the authors and should not be attributed to the World Bank Group or its member countries.
2012a and 2012b, Ball, Leigh and Loungani 2013). The changes are most pronounced for the EU10 and Balkan and Turkey, while the employment elasticity remained low in the CIS even during the global financial crisis. In addition, we provide some evidence that employment growth increases with reforms of labor and product markets, stronger macroeconomic policy frameworks, better governance, and more economic integration and economic diversification (Crivelli, Furceri and Toujas-Bernate 2012). However, in many instances, structural factors appear to have no significant impact on employment growth. This suggests that job dynamics depend on the interplay of a number of factors, and reforms in one area might not improve labor market outcomes due to the interaction with other areas (Blanchard, Jaumotte and Loungani 2013; World Development Report 2012).

These findings imply two policy conclusions. First, the state of the business cycle, including the strength of aggregate demand, matters for labor market outcomes in many ECA countries. This suggests that weak labor market trends are foremost a result of weak growth, especially in the short term. Second, structural reforms are likely to improve employment growth although the impact might often be visible only over time.

11