rafmacalaba/datause-encoder-data huggingface dataset:
# 4. Conclusion

The purpose of this paper was to investigate the relationship between the composition of pubic expenditure and economic growth. Using a simple, analytical model, we showed how a change in the mix of public spending in favor of productive activities could lead to a higher steady-state growth rate for the economy. The empirical implementation of the model, however, yielded some surprising results. **All** of the standard candidates for productive expenditure **--** capital, transport and communication, health and education **--** had either a negative or insignificant relationship with economic growth. The only broad category which was associated with higher economic growth was current expenditure. Finally, some expenditures within the health and education sectors **--** preventive care and "other education" **--** had a positive coefficient in the regression with economic growth.

At least two interpretations suggest themselves. One is that our model is misspecified, or our data inaccurate, so that we are not capturing the "true" link between these components of public expenditure and growth. However, we have attempted to control for several of the factors which may affect economic growth: external shocks, policy distortions, region-specific effects, and development index. We also report a nonlinear specification for the expenditure variables. Thus, the charge of model misspecification rests on the existence of some other factor which both affects long-term economic growth and is systematically related to public expenditure composition. Similarly, while public expenditure data are notoriously poor, we have no reason to believe there are any systematic biases in them which would yield the above results. The one exception is the importance of sub-national government spending in education and health, in particular, in some of the larger, federal countries. We addressed this issue **by** examining the

**23**
typically prudential in their nature,<sup>14</sup> or the discretionary warnings on the elevation of risk articulated in NBRM speeches or publications (e.g. Financial Stability Reports) are excluded from the focus of the following analysis.

# <u>Regulation on FX lending</u>

As with many other emerging markets and small economies, the presence of foreign currency in the everyday operation of individual business entities is considerably high. The banking system is not an exception to this pattern. The lending and deposit-taking in foreign currency are highly present in the banks’ balance sheets. Looking at the data from 2004 to 2006, the share of FX lending<sup>15</sup> has grown by 15.7 percentage points (Chart 12).<sup>16</sup> At the end of 2006, for the first time the share of these loans was higher than the share of the denar loans.

**Chart 12. Share of foreign currency (FX) lending in the total amount of loans**

<!-- Start of picture text --> 25.9%<br>26.3%<br>26.1%<br>29.4%<br>25.4%<br>23.4% 26.4%<br>20.0% 23.5%<br>16.1%<br>20.6% 22.1% 23.8%<br>21.0%<br>44.7%<br>50.4% 47.3%<br>62.9% 59.4% 54.5% 50.8%<br><!-- End of picture text --> Source: NBRM Despite the growth of the FX lending, the banking system was still among the systems with moderate participation in FX lending.<sup>17</sup> However, the recorded growth triggered the socalled induced credit risk, a credit risk arising from the clients’ imbalances in the currency structure of their inflows and outflows. In order to address this growing risk, in 2006 the NBRM issued a decision on the conditions and the manner of extending FX loans and denar loans with FX clauses.<sup>18</sup> The decision regulates the conditions under which banks can extend FX loans and

> 14 In January 2009, in light of unstable developments on the international financial markets and increasing risks, the NBRM launched auctions of FX deposits of the banks. The implementation of this measure started in February 2009 through auctions conducted by applying volume tenders, for unlimited amounts of FX deposits with maturity of 1, 3, 6, and 12 months. The interest rates of the FX deposits are equal to the interest rates of the funds placed on the international financial markets.

> 15 The FX lending encompasses both FX loans and denar loans with FX clauses.

> 16 The liberalization of FX legislation in 2003 supported the rise of the FX lending.

> 17 As a comparison, the share of FX lending in some of the Baltic countries in 2005 was above 70 percent.

> 18 The decision was not the first regulation of this kind. In 2003 the NBRM had adopted a similar FX regulation, which encompassed only the FX loans.

15
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|---|---|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|Shire River Basin Management Program(GEF)|Josses Mugabi|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|China:Qinghai XiningUrban Transport Project|Jin Wang|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|SERBIA ROAD REHABILITATION AND SAFETY PROJECT|Murad Gurmeric|
|BR FosteringShort Sea Shipping|Gregoire Francois Gauthier|
|BR FosteringShort Sea Shipping|Gregoire Francois Gauthier|
|BR FosteringShort Sea Shipping|Gregoire Francois Gauthier|
|AFCC2/RI Horn of Africa EmergencyHealth and Nutrition Project|Sheila Dutta|
|AFCC2/RI Horn of Africa EmergencyHealth and Nutrition Project|Sheila Dutta|
|AFCC2/RI Horn of Africa EmergencyHealth and Nutrition Project|Sheila Dutta|
|Solid Waste Sector DPL3|Jaafar Sadok Friaa|
|MA-Inclusive Green Growth DPL|Andrea Liverani|
|Access to technical assistance|1.819<sup>a,b</sup>|0.372|
|---|---|---|
|Returns per unit of household labor|1.819<sup>a,b</sup><br>|0.333|
|Access to inputs|1.894<sup>b</sup>|0.356|
|Long‐term agricultural income|1.894<sup>b</sup>|0.418|
|Access to finance|1.906<sup>b,c</sup>|0.352|
|Business management skills|1.906<sup>c</sup>|0.352|
|Economic impacts on wider community|2.025<sup>c.d</sup>|0.404|
|Ability to adapt to changes in market conditions|2.034<sup>d</sup>|0.480|
|Short‐term agricultural income|2.063<sup>d</sup>|0.406|
|Ability to be involved in viable farmer‐based organizations|2.106<sup>d</sup>|0.306|
|Social standing in the community|2.238<sup>e</sup>|0.389|
|Stability/predictability of income|2.275<sup>e</sup>|0.483|
|Participation of women|2.301<sup>e</sup>|0.467|
|Food security|2.319<sup>e</sup>|0.421|
|Environmental sustainability of farming enterprise|2.469<sup>f</sup>|0.475|
|Environmental sustainability of the wider community|2.547<sup>f</sup>|0.509|
|Health risks to farm workers|2.699|0.360|
|Level of financial risk|2.965|0.401|

_Note: Mean scores denoted by the same letter are not significantly different from each other at the 5% level on the basis of a Wilcoxon signed rank test._ In order to make further sense of the individual item scores, the Likert scale data were subject to principle components analysis (PCA). A total of three underlying factors were identified that collectively accounted for 67.9 percent of the variation in scores (See Annex Table 1). The Kaiser‐Meyer‐Olkin measure of sampling adequacy was 0.752 and Bartlett's test of sphericity was significant (χ2 =1763.25 (66 df); p=.000), suggesting that the individual item scores factor well and that the factor solution is rigorous (Hair _et al_ ., 1998; McDonald, 1985; Jackson, 1991).

Loadings on the first principal component (PC1) posit an association between participation in higher‐value supply chains and enhanced **access to resources and knowledge** . This involves a cluster of impacts related to: ‘understanding of market requirements’, ‘level of technical/agronomic learning’, ‘access to improved technologies’, ‘business managements skills’, ‘access to technical skills’, ‘enhanced ability to adapt to the changes in the markets’ and ‘access to finance and inputs’. This factor accounted for 36.4 percent of the variation of the sample.

Loadings for the second principal component (PC2) suggest an association between participation in higher‐value supply chains and **economic and social gains.** This involves the cluster of impacts that includes: ‘short‐term agriculture income’, ‘long‐term agricultural income’, ‘ability to be involved in viable farmer‐based organizations’, ‘economic impacts on wider community’, ‘food security’ and ‘stability/predictability of income’. Some 21.5 percent of the variation of the sample is accounted for by PC2. The 9
studies are critical to institutional reform of the WSS sector and for the privatization process, particularly in the development of policy instruments to address accessibility and affordability for poor households.

Past microeconomic studies have focused almost exclusively on costs as being the primary concern of water and sanitation customers and the major constraint to demand. However, a review of other service industries (Eto et al., 2001) and intuition suggest that consumers place value on multiple service attributes, not just costs. Recently, Pattanayak et al. (2001, 2004) and Hensher et al. (2004) have used more flexible approaches such as conjoint methods to show that customers do value multiple attributes of piped water supply.

Stated preference approaches allow the researcher to evaluate new goods, services or policies for which no observational data exists. Conjoint analysis represents a type of field experiment in which the researcher manipulates the WSS service features offered to respondents to create options that may not currently exist (Harrison and List 2004). Conjoint analysis offers a compromise between a controlled experiment in which WSS services are randomly varied across the population and consumer data, which has too little variation for analysis. For example, if everybody in the sample pays exactly the same price, it will be impossible to trace out a demand curve using consumer data.

Information from conjoint analysis can be used in service delivery programs to design appropriate service levels and tariffs, balance costs with service attributes, and increase consumer demand. By estimating the relative value placed on different service attributes, along with costs, customer preferences for different attributes can be ranked. Conjoint analysis can also be used to estimate willingness to pay for the sample as a whole and across sub-populations.

We begin in Section 2 with a description of conjoint surveys, showing how they can serve as a tool for field experiments and source of valuable empirical data. We emphasize the design and logistical challenges associated with applying the conjoint method in the field. In Section 3 we use data from our recent conjoint survey conducted in Sri Lanka to evaluate WSS preferences for poor and non-poor households. Through the conjoint survey data, we can determine what service attributes affect WSS demand and whether these attributes vary across poor and non-poor households. We find that households care about service quality (not just price) and the households have diverse preferences in terms of 3
# **Appendix Table I. 13: Organized Sector Log (Employment) of Small, Medium-sized and Large Plants**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Small Organ|ized Plants|Medium-sized|Organized|Large Orga|nized Plants|
|||_Other-_|_sector Employ_|_ment Spillov_|_ers_||
|Horizontal Employment Spillovers|||||||
|from Unorganized Sector|0.1189***<br>(0.0140)|0.0565***<br>(0.0123)|0.0833***<br>(0.0176)|0.0287*<br>(0.0169)|0.1028***<br>(0.0192)|0.0594***<br>(0.0191)|
|Forward Employment Spillovers from|||||||
|Unorganized Sector|-0.0539|0.2559|0.2663++|0.8187+|0.1909|1.6859***|
||(0.1500)|(0.4258)|(0.2031)|(0.5672)|(0.2257)|(0.6285)|
|Backward Employment Spillovers|||||||
|from Unorganized Sector|0.3262***|-1.2318***|0.4813***|-0.5764|0.3081**|1.1280+|
||(0.0926)|(0.4320)|(0.1302)|(0.6365)|(0.1411)|(0.7467)|
|||_Other-_|_sector Product_|_ivity Spillov_|_ers_||
|Average TFP of Horizontal-linked|||||||
|Unorganized Plants|0.0482+|0.0420++|0.0370|0.0054|0.0288|-0.0054|
||(0.0333)|(0.0319)|(0.0565)|(0.0571)|(0.0635)|(0.0621)|
|Average TFP of Backward-linked|||||||
|Unorganized Plants|0.0612|0.2782|-0.0049|0.4300|0.0346|-0.9826*|
||(0.0679)|(0.3545)|(0.1104)|(0.5051)|(0.1223)|(0.5505)|
|Average TFP of Forward-linked|||||||
|Unorganized Plants|-0.1025|0.6258**|-0.0235|0.5412|-0.0888|1.5090***|
||(0.1005)|(0.3061)|(0.1459)|(0.5202)|(0.1592)|(0.5051)|
|||_Own-_|_sector Employm_|_ent Spillov_|_ers_||
|Forward Employment Spillovers from|||||||
|Organized Sector|1.3223***|1.4528**|1.1988***|0.9818|1.1194***|-3.0681***|
||(0.1980)|(0.5832)|(0.2571)|(0.7712)|(0.3318)|(0.8613)|
|Backward Employment Spillovers|||||||
|from Organized Sector|0.7021***|-0.2855|1.0177***|0.3201|1.5039***|1.7375***|
||(0.1043)|(0.3352)|(0.1559)|(0.6101)|(0.1975)|(0.5842)|
|Constant|-21.5475***|-0.0616|-29.5700***|-15.9440|-30.9233***|-16.5117|
||(3.3318)|(9.3018)|(4.2033)|(13.8352)|(5.2646)|(14.2225)|
|Observations|5888|5888|5888|5888|5888|5888|
|R-squared|0.1111|0.2859|0.0555|0.1845|0.0626|0.1965|

_Note: The dependent variables are constructed using the total employment by organized plants in a state-industry cell. Small plants are defined as those with less than 50 employees, medium-sized plants have 51-150 employees and large plants are defined as those with more than 151 employees. All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2, 4 and 6 contains state-year and industry-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 66
fluctuated around 1.5 tons per hectare mark from 1995 to 2007, but has been above 2.3 tons per hectare in the period of 2008-2014. The latest national yield estimate in 2014 was 2.5 tons per hectare. Recent FAOSTAT yield estimates, however, are markedly higher than those computed from household survey data, including from the methodological experiment informing this study, which employs objective measurement methods. These discrepancies in yield estimates call into question, at least for Uganda, the validity of FAOSTAT figures.

# **4. Data**

# **4.1. Overview**

MAPS: Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety is a two-round household panel survey that was conducted in Eastern Uganda to test the relative accuracy of subjective approaches to data collection vis-à-vis objective survey methods for maize yield measurement, soil fertility assessment, and maize variety identification. The survey has been implemented by the Uganda Bureau of Statistics, with technical and financial assistance provided by an inter-agency partnership that is led by the World Bank Living Standards Measurement Study (LSMS), using the Survey Solutions Computer Assisted-Personal Interviewing (CAPI) platform.<sup>9</sup>

# **4.2. Sampling Design**

In Round I, the MAPS fieldwork was conducted during the first rainy season of 2015, from April to October 2015, in Eastern Uganda, the top maize-producing region of the country. The sample was composed of 75 enumeration areas (EAs) that were selected from the 2014 Population and Household Census (PHC) EA frame and that were distributed across 3 strata, namely (1) Sironko district (15 EAs), (2) Serere district (15 EAs), and (3) a 400 km<sup>2</sup> remote sensing tasking area spanning Iganga and Mayuge districts (45 EAs). In each stratum, the EAs were sampled with probability proportional to size, in accordance with the pre-dissemination 2014 PHC EA-level household counts.

9 The technical assistance to MAPS I (2015) and MAPS II (2016) was provided through the World Bank LSMS – Minding the (Agricultural) Data Gap Research Program, funded by UK Aid. In MAPS I, MAPS implementation was financed by the World Bank LSMS – Minding the (Agricultural) Data Gap Research Program, the Global Strategy to Improve Agricultural and Rural Statistics, led by the Food and Agriculture Organization of the United Nations (FAO), and the CGIAR Standing Panel on Impact Assessment. In MAPS II, MAPS implementation was financed by the World Bank Innovations in Big Data Analytics Program, the World Bank Trust Fund for Statistical Capacity Building – Innovations in Development Data Window, and the CGIAR Standing Panel on Impact Assessment. In MAPS I and MAPS II, Terra Bella provided free high-resolution satellite imagery for the MAPS remote sensing tasking area for research purposes. The technical partners included (i) the World Agroforestry Center on soil fertility measurement, (ii) the CGIAR Standing Panel on Impact Assessment on maize variety identification, and (iii) Stanford University Center on Food Security and Environment on remote sensing.

7
report cross-national carbon dioxide Engel curves. We next present data from California and Europe to document that these wealthy areas feature significant numbers of consumers creating a large average carbon footprint. In our last piece of empirical work, we turn to micro survey data to estimate the association between education and support for environmental protection. Together, these various pieces of evidence allow us to provide a consistent explanation for the slow progress in negotiating sharp greenhouse gas emissions reductions.

# **Estimating Engel Curves**

Table 1 lists the nations for which the World Bank has assembled data on household durables ownership. The table also reports the first year the survey data are available. Data on asset ownership come from the World Bank’s Global Monitoring Database (GMD). The original source of the data are country-level household survey data that measure household income or consumption in each country. World Bank teams work closely with National Statistical Offices (NSOs) to ensure that household survey data are of good quality and that technical calculations are robust and aligned with international best practices. However, data on durables ownership are not collected in all national household surveys. Further, the World Bank does not have access to micro data from China’s National Bureau of Statistics; thus, China does not appear in the data set. For each of these nation/year pairs we have five data points for each durable good. These five data points correspond to the quintiles of the income distribution in that nation/year. The World Bank data also provides a measure for each nation/year/income quintile of either the average daily personal consumption or average daily income measured in PPP $ 2011.

For 66% of the sample, we observe the income and for 33% we observe the consumption measure. This variable is the key explanatory variable in our regressions below. In results available on request, we have run our Engel curve estimates separately (so using log(consumption) for 33% of the sample and log(income) for the other 66% of the sample). The estimated income effects are quite similar so we pool our results below but the separate regressions are available on request.

Our econometric specification is presented in equation (1). The dependent variable is the share of households in income quintile i in nation j at time t that owns a specific durable.

8
economic volatility.

Dynamic considerations are extensively analyzed in a series of recent papers on temporary and return migration (see Dustmann and G ̈orlach (2016) for a recent survey). As several papers illustrate, return migration levels have always been quite high. Bandiera, Rasul and Viarengo (2013) show out-migration rates from the United States were over 60% during the age of mass migration at the turn of the 20th Century. Bijwaard, Schluterare and Wahba (2014) use administrative data from the Netherlands and Bratsberg, Raaum and Sorlie (2007) use register data from Norway and Sweden to explore more recent patterns. Several papers on return and circular migration have used more formal and explicit dynamic models. Among the first examples, Kirdar (2012) develops a dynamic stochastic model to jointly explore return migration and savings decisions of migrants and estimates it using panel data from Germany. Among more recent and prominent examples are Thom (2015) and Lessem (2015) who use data from the Mexican Migration Project on detailed migration histories of individuals. Among their findings is the importance of border enforcement (similar to our simulations of blocked corridors) to circular and return migration decisions. Using multiple data sources from Mexico, G ̈orlach (2016) develops a comprehensive dynamic life cycle model to analyze the role of financial constraints on emigration, return migration and re-emigration decisions. In an another lifecycle model, Adda, Dustmann and G ̈orlach (2015) explore human capital accumulation and migration duration decisions using data on Turkish migrants in Germany. All of these papers rely on detailed panel data from a single origin or destination country (or a single corridor) as the data requirements of such dynamic models are quite demanding. One of our main contributions is to analyze a model of global migration patterns and find empirical solutions to address some of the data constraints.

The next section presents the dynamic structural model. Then, we discuss the data used in the paper, followed by the estimation algorithm. We next present the estimation results and the simulation exercises. We conclude with a discussion of future research paths and policy analysis.

7