rafmacalaba/datause-encoder-data huggingface dataset:
# ALLOCATION AND MANAGEMENT OF PUBLIC SPENDING

## I. INTRODUCTION

1. This paper was commissioned by the World Bank as<sup>a contribution</sup> towards the preparation of the 1988 World Development Report (WDR). I was asked to comment on the points made in a preliminary outline of the proposed Public Expenditure chapter of the WDR and to illustrate my comments with specific examples of problems and attempted solutions.

2. The paper draws mainly on my own practical experience and that of Mokoro Limited in the field of national and public expenditure planning. As will be apparent, this experience is heavily concentrated in Anglophone sub-Saharan Africa (SSA). Readers familiar with other regions will<sup>be</sup> able to judge which comments may have a wider relevance.<sup>Annex A lists</sup> some of the published documents to which I referred in preparing this paper, but it is not exhaustive, and I also referred to a large number -f unpublished World Bank documents.

3. The paper began as a series of comments on an early<sup>outline of :'.e</sup> WDR. It retains its early structure in that it consists<sup>of a number of</sup> separate but related comments on aspects of planning<sup>and budgeting</sup> in Africa. I have arranged my observations under three main<sup>headings.</sup> Section II deals generally with the planning and<sup>budgeting</sup> process, Section III concentrates on the problems associated with the planning of recurrent expenditure, and Section IV addresses aid agency involvement in and technical assistance to planning and budgeting.

4. One theme of this paper is that many of the necessary reforms to planning and budgeting systems (PBSs) are very simple (which is not to<sup>say</sup> that they are easily accomplished). There is a tendency<sup>on the part of</sup> governments and external agencies alike to look for more sophisticated solutions than are actually required. It is not sophisticated economic analytical techniques that are most lacking but simple organisational ones. However, the basic elements of an effective PBS are interdependent; in countries where the PBS is in disarray, isolated improvements to elements of the PBS may be ineffectual.

5. Opinions expLessed are, of course, my own and not necessarily shared by the World Bank or any other body.

Page 1
Rainfalls are found to have long term benefits for women, partly because they lead to better harvests. In Indonesia, women who experienced a positive weather shock around the time of their birth reported better health and were taller as adults, while no difference was reported by men. The strong relationship found between birth year rainfall and adult height suggests that nutrition in infancy varies with early-life rainfall, especially for girls, as rainfall’s effects on crop output—and thus household income and food availability—lead to variations in parents’ abilities to provide nutrition, medical inputs, and generally the nurturing environments for infant girls (Maccini and Yang 2009). In Nepal, increased rainfall led to lower weight and height in the short term due to more diseases but had a positive effect in the medium run due to the favorable economic opportunities from better harvests during wet years. No significant differences between boys and girls were detected (Tiwari, Jacoby, and Skoufias 2017).

Due to their higher sensibility to shocks in uterus, boys may develop more disabilities due to weather shocks. In South Africa, droughts in early life led to an increase in physical and mental disabilities, particularly in men (Dinkelman 2017). Similarly, in Colombia, higher prenatal floods were associated with an increase in mental disability rates in adults aged 25-65 in 2005, with a greater impact on men (Carrillo 2020).

In addition, women in many countries often bear the responsibility of collecting water, food, and firewood, which can increase their exposure to diseases such as malaria from contaminated water sources following extreme weather shocks. This has been observed in Cambodia, South Africa, and Nigeria, as highlighted by studies conducted by Davies et al. (2015), Meyiwa et al. (2014), and Adebola (2021), respectively.

Lastly, weather shocks can also lead to increased childbearing among women as the value of their agricultural labor lowers during droughts. In Madagascar, higher fertility in young women living in agricultural households was observed even four years after a drought occurred in the agricultural season (Dessy et al. 2020). The drought did not affect marriage timing but mainly reduced the number of women’s working hours and their agricultural income. The drought had no effect on the fertility of young women living in non-agricultural households or non-agrarian communities, nor did it impact fertility if it occurred during the non-agricultural season. These findings suggest that droughts, by reducing the value of women's agricultural labor, lowered the marginal cost of a child, thus increasing fertility.

9
**<u>Table 1.11. Description of Labor Transition Variables</u>**

|**_Variable_**|**_Description_**|**_Data available_**|
|---|---|---|
|**Full-time/part-time**<br>**employment**|Workers are considered as having full-time employment if their<br>actual work hours in the main job are more than 120 hours in the<br>reference month. They are considered as having part-time<br>employment otherwise.|1994-2015|
|**Public/private sector**|Workers are considered as employed in the public sector if they<br>do not work in a foreign-owned enterprise and are employed in<br>a state budgetary organization in education, health and social<br>work, housing or communal services, or the public<br>administration.|2004-2015|
|**Formal/informal**<br>**sector**|Workers are considered as employed in the formal sector if<br>they are employed officially or with a contract in the main job.<br>They as considered as employed in the informal sector<br>otherwise.|1998-2015|
|**Occupational skills**|All major professional groups are divided between four skill<br>levels, where the 1<sup>st</sup>skill level is related to simple and routine<br>work and the 4<sup>th</sup>skill level involves problem-solving and<br>decision-making tasks according to ISCO-08 definition<br>(International Standard Classification of Occupations in 2008,<br>International Labour Organization, 2012).|1994-2015|

59
**Table 2** : Producer subsidy equivalent for OECD and member economies, selected years 1996-2011.

||**1996**|**2001**|**2006**|**2011**|
|---|---|---|---|---|
|OECD average|29.61|28.78|26.43|18.83|
|New Zealand|0.78|0.58|0.89|0.79|
|Australia|6.33|3.26|4.51|2.98|
|Chile|7.34|6.18|4.23|3.51|
|United States|13.25|22.10|11.23|7.66|
|Mexico|5.66|18.23|13.22|11.56|
|Israel|18.93|20.46|7.91|14.03|
|Canada|15.82|15.47|20.86|14.20|
|European Union|33.83|30.18|28.97|17.54|
|Turkey|23.11|14.29|33.39|20.16|
|Iceland|58.24|62.58|64.49|43.68|
|Japan|57.95|56.33|51.56|51.63|
|Korea, Rep.|64.83|57.71|58.62|53.26|
|Switzerland|68.60|67.31|65.28|54.43|
|Norway|66.25|65.31|64.10|57.69|

Source: OECD (2012)

**Table 3: Free Trade Agreements reached by the EU and Turkey and new agreements under consideration**

|Existing Free Trade Agreements|Third Parties|
|---|---|
|Reached by both Turkey and EU|Albania, Bosnia-Herzegovina, Chile, Egypt, Iceland, Israel, Jordan, the<br>Republic of Korea, Liechtenstein, Macedonia, Mauritius, Montenegro,<br>Morocco, Norway, Palestinian Authority, Serbia, Switzerland, Syria, and<br>Tunisia|
|Reached by EU, under consideration by Turkey|Algeria, Colombia, Costa Rica, El Salvador, Faroe Islands, Guatemala,<br>Honduras, Mexico, Nicaragua, Panama, Peru, Seychelles, and South Africa|
|Reached by Turkey, under consideration by EU<br>New Free Trade Agreements|Georgia|
|Under consideration by the EU and Turkey|African Caribbean and Pacific Group of States, Canada, Gulf Cooperation<br>Council countries, India, Japan, Malaysia, Mercosur, Moldova, Thailand,<br>Ukraine, and Vietnam|
|Under consideration by the EU alone|Armenia, Association of Southeast Asian Nations, Singapore, and the United<br>States|
|Under consideration by Turkey alone|Cameroon, Democratic Republic of the Congo, Ecuador, Ghana, Indonesia,<br>and Libya|

Sources: European Commission (2013); World Bank (2014a) 25
# **5. Policy Implications**

Although there are numerous caveats in emerging Asia’s growing interest in the Sub-Saharan Africa region, this paper has tried to focus on the opportunities for African countries to take advantage of the growing Asian giants in trying to leap into the global trade networks. It should be clear from the above analysis that a potential market in Asia does exist and will only grow in importance over the years to come. Over the last 15 years, Asia has already been Africa’s fastest growing export market, is much more open to trade than Europe and America, and there is no evidence to suggest that this trend will not continue in the near future.

The five market-related success factors that were identified for effectively participating in global network trade – _price, speed-to-market, labor productivity, flexibility, and product quality_ – are highlighted in the value chain studies. The underlying policies are even more critical: efficient institutions and the right infrastructure will allow countries to actually experience the success factors or “outcomes.” It is vital for developing countries to get a supportive policy and institutional regime in place along with the appropriate infrastructure, in order to be able to improve these critical factors.

First of all, the example of Ghana’s pineapple industry illustrates the importance of product **price** in European markets. The only reason Ghana is even able to stay in the European pineapple market along competitors Costa Rica and Cote d’Ivoire is because of its significantly lower price, which it is forced to accept because of the lower quality of its product. However, Ghana faces a significant risk in this low price/low margin market unless it can ramp up quality as well as increase scale through appropriate policy support. On the other hand, Kenya’s apparel industry is in jeopardy because of its relatively high price compared to its major competitors from Asia and Central America. The high cost is driven mainly by raw material prices which are caused mainly by the “rule of origin” clause in the AGOA agreement.

The second critical factor, **speed-to-market** , is crucial in the success of the Kenyan cut flower industry. The fact that there are several planes leaving Nairobi every day for its main markets in the European Union not only makes for fast delivery – an obvious competitive advantage – but also its high responsiveness, which directly relates to **flexibility** (the fourth factor). Kenya was one of the first African countries to privatize its airline industry in 1996; and more recently, with the signing of the Air Services Agreement between China and Kenya, Kenya Airways has been granted landing rights in several cities in China, which also agreed to cooperate in areas of information and telecommunications. Kenya Airways is now operating direct flights to Hong Kong and Guangzhou in southern China from Nairobi. Since Kenya was granted the Preferred Tourist Destination Status in 2004, arrivals from China have more than doubled and are expected to 25
**Table 27. Governance-related drivers of legal and regulatory obstacles: Quality of laws**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0527|0.0436|||0.101|0.0750|||
||(0.0570)|(0.0545)|||(0.109)|(0.112)|||
|Export % of sales|-0.00168**|-0.000278|-0.00161**|-0.000233|-0.00310**|-0.000475|-0.00290**|-0.000380|
||(0.000754)|(0.000744)|(0.000762)|(0.000750)|(0.00143)|(0.00150)|(0.00147)|(0.00154)|
|Share of sourcing via imports|0.00219***|0.00105|0.00194**|0.000702|0.00427***|0.00281*|0.00397**|0.00226|
||(0.000806)|(0.000780)|(0.000836)|(0.000805)|(0.00154)|(0.00159)|(0.00166)|(0.00169)|
|High-income source country|-0.0808|-0.0140|-0.0863|-0.0157|-0.177|-0.0330|-0.211|-0.0563|
||(0.0830)|(0.0823)|(0.0835)|(0.0828)|(0.160)|(0.170)|(0.166)|(0.176)|
|>250 employees|0.0369|0.0549|0.0189|0.0361|0.0628|0.118|0.0157|0.0761|
||(0.0706)|(0.0720)|(0.0717)|(0.0730)|(0.132)|(0.144)|(0.139)|(0.150)|
|Cumulative inv. >US$10M|0.177***|0.0763|0.163**|0.0656|0.339***|0.129|0.322**|0.110|
||(0.0676)|(0.0667)|(0.0673)|(0.0664)|(0.128)|(0.135)|(0.131)|(0.137)|
|Years in country|-0.00261|-0.00278|-0.00221|-0.00234|-0.00552|-0.00702|-0.00503|-0.00614|
||(0.00218)|(0.00222)|(0.00213)|(0.00218)|(0.00399)|(0.00461)|(0.00402)|(0.00466)|
|Level of foreign ownership (%)|0.000241|0.00115|0.000295|0.00140|0.000595|0.00221|0.000729|0.00265|
||(0.00116)|(0.00117)|(0.00115)|(0.00115)|(0.00215)|(0.00233)|(0.00219)|(0.00233)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.017|0.115|0.042|0.136|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Quality of laws and regulations]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

44
tetanus case out of 175 births was reported, a rate of 6 per 1000 live births.

Further conclusive evidence on the efficacy of tetanus immunization in reducing neonatal tetanus deaths is found in the study by Newell, et al. (1966) in Colombia during the 1961-1966 period. This study consisted of a double-blind experiment in which all registered women were randomly assigned to one of two groups and offered either tetanus toxoid or influenza-virus vaccine. Subsequent births to these women during 1961-66, as well as deaths to these births and specific cause of death, were then followed. The results demonstrated no neonatal tetanus deaths during this period among births to women who had received 2-3 tetanus immunizations, compared to a rate of 78 ner 1000 live - births among the control group whose mothers r ;ved 2-3 injections of the influenza vaccine.

The Matlab study area is the source of much of the conclusive evidence on the effectiveness of maternal tetanus immunization in reducing mortality from neonatal tetanus. As part of a large-scale cholera vaccine trial in the Matlab study area in 1974, non-pregnant adult women and <5 year children received tetanus toxoid as a placebo. A total of 93,000 individuals participated in this trial, which was randomized on an individual basis, with participants receiving either 1 or 2 injections of cholera toxoid or 1 or 2 doses of tetanus toxoid, all on a double-blind basis. Black et al. (1980) analyzed the mortality impact of tetanus toxoi6 placebo in this trial upon two cohorts of children born during 1975-77 to mothers who participated in the 1974 vaccine trial. A recent study by Koenig, et al. (1991b) extended the original study through the 1975-89 period in the comparison area only, which has remained a population largely unimmunized against tetanus until recently., Finally, Rahman, et al. (1982) analyzed the mortality experience of children born during 1978-79 in the Matlab study area. Two groups of births to mothers immunized against tetanus were evaluated: those who had received tetanus toxoid while not pregnant as part of the 1974 vaccine trial described above, and those who received tetanus toxoid while pregnant in 1978-79, as part of the expanded MCH-family planning program introduced in half of the Matlab study area.

In the absence of conclusive diagnosis of neonatal tetanus deaths, the Bangladesh studies used mortality during days 4-14 of life as a proxy for neonatal tetanus deaths. For the two birth cohorts combined, the study by Black, et al. showed that two injections of tetanus toxoid resulted in a reduction in 4-14 day mortality by 68 percent relative to the control group who received cholera vaccine (from 25 to 8 per 1000), a highly significant effect. The study by Rahnan, et al. found that relative to births to unimmunized mothers, two tetanus toxoid injections during pregnancy resulted in statistically significant 20
Table 5: Estimations by manufacturing <u>production functions and local unorganized sector shares</u>

|||<br>In|DV: Log outpu<br>dia 2000 Sam|t in manufactur<br>ple|ing establishme|nt<br>U.S. 199|7 Sample|
|---|---|---|---|---|---|---|---|
||Full|Full|Full|Traditional|Modern|Full|Full|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Log employment in establishment|0.514+++|0.511+++|0.526+++|0.512+++|0.449+++|0.318+++|0.312+++|
||(0.027)|(0.025)|(0.024)|(0.026)|(0.054)|(0.003)|(0.004)|
|Log capital in establishment|0.061+++|0.061+++|0.057+++|0.061+++|0.050+++|0.123+++|0.127+++|
||(0.006)|(0.006)|(0.006)|(0.007)|(0.014)|(0.002)|(0.002)|
|Log materials in establishment|0.508+++|0.509+++|0.504+++|0.505+++|0.565+++|0.607+++|0.610+++|
||(0.013)|(0.013)|(0.013)|(0.013)|(0.017)|(0.003)|(0.003)|
|Urbanization premium: log manufacturing|0.031++|0.021|0.011|0.022|-0.001|0.018+++|0.013+++|
|employment density in district or county|(0.013)|(0.016)|(0.016)|(0.017)|(0.026)|(0.001)|(0.001)|
|Log share of local manufacturing|-0.328+++|-0.298++|-0.104|-0.182++|-0.280+|0.026+++|0.019+++|
|employment in unorganized sector|(0.114)|(0.119)|(0.166)|(0.075)|(0.146)|(0.002)|(0.003)|
|Log share of local manufacturing|||-0.266|||||
|employment that is unpaid workers|||(0.212)|||||
|Agglomeration premium: log employment||0.012|0.031|0.010|0.082+||0.006+++|
|density in district- or county-industry||(0.020)|(0.022)|(0.021)|(0.047)||(0.001)|
|Log share of local industry employment||-0.149+|-0.049|-0.088|0.015||0.007+++|
|in unorganized sector||(0.089)|(0.102)|(0.065)|(0.082)||(0.001)|
|Log share of local industry employment|||-0.301++|||||
|that is unpaid workers|||(0.140)|||||
|Observations|215,957|215,957|215,957|192,622|23,335|367,802|341,926|
|Adjusted R-squared|0.852|0.852|0.854|0.837|0.955|0.975|0.973|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Estimations consider simple production functions for manufacturing establishments in India and the United States, with country and time period indicated by column headers. Indian data are taken from Annual Survey of Industries and National Sample Statistics; U.S. data are from 1997 Census of Manufacturers. Metrics for Urbanization and Agglomeration Premiums are built upon the local density of manufacturing and own-industry employment, respectively. Metrics for shares of local activity in the unorganized sector use definitions shown in Table 1a; the U.S. estimations use the establishment size distribution to mimic the unorganized sector definitions for India. Estimations report standard errors clustered by district or county, include industry fixed effects, and weight Indian observations by sample weights. + significant at 10% level; ++ significant at 5% level; +++ significant at 1% level.
about the value of an activity. Our paper examines how investment choices are affected by biases in information transmission.

Our analysis relies on a natural experiment involving exogenously assigned peer groups. Starting in 2008, the Colombian Stock Exchange (CSE) launched a series of professional courses on financial topics (discussed in detail in Section 3),<sup>5</sup> with the majority focusing on equity strategies. Registered individuals were assigned to small sections that studied stock trading in a classroom setting with 16 students per class, on average. Each group was formed based on availability, and the CSE did not use nor it verified past trading experience as a prerequisite to enroll in the program. Hence, the setting resembles a random assignment. We combine class records with administrative microdata of stock transactions to distinguish students with trading experience from those with no such background. In other words, we observe the trades of students who were active in the stock market before participating in one of the CSE’s financial courses. We also observe the trades and performance of students who began trading only after completing a course; that is, after interacting with experienced classmates in their artificially formed group. Overall, our novel data set combines the official class records and trading activity of 13,730 students from over 1,100 courses between 2008 and 2016.

While we accurately observe the stock trades of experienced students, we do not know the specific information that was shared among classmates. To formalize the role of selective communication in our setting, we introduce information-transmission bias in a model of social learning. In the model, potential investors with different trading skills are uncertain about the expected payoffs from trading stocks. These individuals update their beliefs after observing public information in stock prices and signals from experienced investors in their classroom. The theoretical model allows us to identify a set of testable predictions that are unique to the story of selective communication, relative to predictions from alternative or complementary explanations about investment choices under social interactions. For example, we

> 5 Most courses were scheduled on weeknights or Saturdays to accommodate working professionals. However, there was no educational background requirement or age restriction that constrained student participation.

3
|**Table 5A: Im**|**pact of SOF**<br>|**ES on jobs on**<br>|**the side, Bache**<br>|**lor students, SO**|**FES data**<br>||
|---|---|---|---|---|---|---|
||N|<RD|>RD||PROBIT||
|DS=0.08|(1)|(2)|(3)|(4)|(5)|(6)|
|±0.005|253|0.237|0.226|-0.010<br>(0.056)|-0.011<br>(0.057)|-0.030<br>(0.054)|
|±0.010|733|0.238|0.252|0.015<br>(0.032)|0.047<br>(0.034)|0.046<br>(0.034)|
|±0.015|1,051|0.249|0.235|-0.014|0.018|0.013|
|||||(0.026)|(0.028)|(0.028)|
|±0.020|1,292|0.245|0.246|0.001<br>(0.024)|0.026<br>(0.025)|0.023<br>(0.025)|
|DS=0.15|||||||
|±0.005|125|0.221|0.196|-0.010|0.084|0.127|
|||||(0.074)|(0.081)|(0.082)|
|±0.010|571|0.225|0.223|0.004<br>(0.041)|0.023<br>(0.044)|0.051<br>(0.046)|
|±0.015|683|0.230|0.209|-0.018<br>(0.035)|0.016<br>(0.039)|0.030<br>(0.040)|
|±0.020|802|0.222|0.224|0.005<br>(0.033)|0.020<br>(0.035)|0.030<br>(0.036)|
|DS=0.30|||||||
|±0.02|270|0.236|0.130|-0.106<sup>*</sup>|-0.096|-0.115<sup>*</sup>|
|||||(0.054)<br>|(0.056)|(0.049)<br>|
|±0.03|328|0.243|0.136|-0.107<sup>**</sup>|-0.078|-0.093<sup>*</sup>|
|||||(0.047)|(0.052)|(0.049)|
|±0.04|395|0.234|0.177|-0.057<br>(0.044)|-0.058<br>(0.047)|-0.075<br>(0.045)|
|_Controls_|||||||
|Socio-ec.<br>background||||No|Yes|Yes|
|Educational<br>background||||No|No|Yes|

Note: Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates significance at the 5%-level, and *** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’s gender, the student’s age, and a dummy indicating whether the student’s family owns a real estate property. Controls denoted by “educational background” include the student’s grade point average at the highest three completed education levels, dummies indicating the type of school attended (public or private), the term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional 10% of tuition.</u> 25