rafmacalaba/datause-encoder-data huggingface dataset:
- 35 -

# **VII. CONCLUSION**

The livestock industry is one of the important sectors for economic growth and poverty reduction, particularly in rural areas. However, it seems far from conclusive how to promote livestock productivity in developing countries.

The paper investigates the effects of public infrastructure and international trade preferences on efficiency in livestock production and trade. It is commonly accepted that rural infrastructure is essential to stimulate the livestock industry. The paper questions whether infrastructure really matters and what type of infrastructure is most important in this area. On the other hand, there is also an expectation that the provision of trade preferences could facilitate trade integration involving developing countries and promote their economic development. The paper examines to what extent trade preferential arrangements could contribute to achieving higher production efficiency for beneficiary countries.

Using trade from the European Union, the paper focuses on the EU beef import market where several African countries have been granted considerable trade preferences under the Cotonou Agreement. The system of supply and demand equations is jointly estimated by the three-stage least squares estimator. The traditional endogeneity problem between (export) price and quantity is taken into account. In addition, the simultaneity of infrastructure stocks and national income representing the reservation price of labor is also controlled for.

The estimated demand equation shows that the demand elasticity is relatively high and the EU beef import market is competitive in spite of the large degree of distortion caused by trade preferences. It means that less competitive beef producers would easily be excluded from the market.

The estimated supply function reveals that infrastructure, particularly quality roads and electricity, could significantly reduce beef export prices. Telecommunications and education may be important but not conclusive. Public capital expenditure, of which a certain fraction is supposed to be spent on agricultural infrastructure, has a significant impact on export
26

**Table 5: Effect of Treatments on Average Post-Treatment Water Consumption, by subgroup**

|**Outcome Variable:**<br>**Average of August and September 2014**<br>**Water Consumption,**<br>**cubic meters, m**<sup>**3**</sup>|**Full**<br>**Sample**<br>**(I)**|**Below**<br>**Median**<br>**Consumption**<br>**(II)**|**Above**<br>**Median**<br>**Consumption**<br>**(III)**|**First**<br>**Quintile**<br>**(IV)**|**Second**<br>**Quintile**<br>**(V)**|**Third**<br>**Quintile**<br>**(VI)**|**Fourth**<br>**Quintile**<br>**(VII)**|**Fifth**<br>**Quintile**<br>**(VIII)**|
|---|---|---|---|---|---|---|---|---|
|**Treatment 1: Neighborhood Comparison**|-1.28**|-0.38|-2.03*|-0.92|-0.46|-0.65|-0.80|-3.25|
|**(SE)**|(0.60)|(0.54)|(1.06)|(0.87)|(0.88)|(0.82)|(1.30)|(2.33)|
|**Treatment 2: Municipality Comparison**|-0.81|-0.45|-1.15|-1.30|-0.31|0.14|0.14|-2.62|
|**(SE)**|(0.58)|(0.52)|(1.03)|(0.85)|(0.84)|(0.78)|(1.40)|(2.09)|
|**Treatment 3: Planning Postcard**|-1.23**|-0.97*|-1.47|-1.53*|-1.23|-0.21|-1.22|-1.88|
|**(SE)**|(0.59)|(0.48)|(1.06)|(0.78)|(0.80)|(0.76)|(1.31)|(2.26)|
|**Constant**|7.12***|4.59***|10.73***|5.39***|3.51***|-0.21|6.80|12.42|
|**(SE)**|(1.89)|(0.59)|(4.61)|(0.94)|(2.19)|(3.04)|(5.28)|(8.75)|
|**Observations**|5061|2534|2527|1004|1024|1032|1004|997|
|**R-squared**|0.52|0.24|0.40|0.10|0.04|0.06|0.03|0.37|
|_Notes:_|||||||||

_1. Numbers in parentheses are standard errors 2. Stars indicate statistical significance: *=p<0.10, **=p<0.05, ***=p<0.01 3. All columns include control for Rainy Season 2013 Total Consumption (May 2013- November 2013)._
<u>Table 3: School Variables Correlated with Tracking</u>

|Variable|Desc. of Association with<br>Tracking|Proportion<br>of sample<br>true (%)|
|---|---|---|
|Public|positive|48|
|autonomy – hiring teachers|negative|33|
|autonomy – teacher salary increases|negative|90|
|autonomy – course offering|positive|96|
|student teacher ratio|positive|-|
|locatedin metropolitanstratum relative torural|positive|-|
|Source: Korea PISA 2003|||

The variables measuring autonomy over hiring teachers and teacher salary increases are positively correlated with each other and negatively correlated with tracking. Since autonomy over hiring teachers displays more variation, it can serve as a proxy for autonomy over salary increases and any other unobserved characteristics that both might reflect. Autonomy over course offering has very little variation since 96 percent of schools reported this characteristic; this variable was tested for correlation with achievement, and the results were not statistically significant; for this reason, it will be excluded from the models.

Additionally, several variables are of interest in the Korean context. Whether a particular school is subject to the equalization policy or not can not be observed in the 2003 PISA sample, but the importance a school places on a student’s academic record for his or her admission can be observed. Consequently, this variable is included. Also, some schools are co-educational and some are strictly male or female. Schools report the number of female students and the number of male students which can be used to construct a binary variable of whether the school is co-ed or not. Table 4 lists all the variables used in this analysis with some descriptive statistics.

9
# **Table 2:  Determinants of Fiscal Revenue Capacity**

_<u>Panel A (population growth used as proxy for demographic characteristic)</u>_

|**Revenue/GDP**||**1994-2003**|||**1994-1998**|||**1999-2003**||
|---|---|---|---|---|---|---|---|---|---|
||(Eq. 1)|(Eq. 2)|(Eq. 3)|(Eq. 1)|(Eq. 2)|(Eq. 3)|(Eq. 1)|(Eq. 2)|(Eq. 3)|
|GDP per capita1|2.157|1.555|1.624|0.359|-0.510|-0.984|2.966|2.251|2.666|
||(5.39)**|(3.36)**|(3.48)**|(0.45)|(0.55)|(1.01)|(6.00)**|(3.47)**|(4.33)**|
|Population Growth|-1.246|-1.605|-1.504|-1.508|-1.590|-1.691|-1.635|-2.133|-2.054|
||(5.64)**|(6.65)**|(6.36)**|(4.41)**|(3.16)**|(3.28)**|(4.41)**|(5.60)**|(5.60)**|
|Trade Openness|0.070|0.057|0.057|0.077|0.051|0.050|0.054|0.046|0.047|
||(10.02)**|(9.26)**|(9.08)**|(5.83)**|(4.52)**|(4.41)**|(5.69)**|(5.12)**|(4.96)**|
|Agriculture Value<br>Added|-0.285|-0.268|-0.264|-0.289|-0.298|-0.294|-0.309|-0.264|-0.266|
||(13.18)**|(11.97)**|(11.37)**|(8.72)**|(7.33)**|(7.28)**|(9.51)**|(8.26)**|(8.12)**|
|Corruption||-0.545|||-0.545|||-0.603||
|||(3.29)**|||(1.90)|||(2.25)*||
|Bureaucracy|||-0.392|||-0.587|||-0.260|
||||(3.27)**|||(2.79)**|||(1.41)|
|Constant|23.278|21.704|22.079|24.332|23.236|23.501|24.337|22.434|23.449|
||(24.92)**|(18.93)**|(20.50)**|(13.82)**|(9.06)**|(11.27)**|(19.05)**|(13.62)**|(15.30)**|
|Observations|886|725|725|330|252|252|381|334|334|
|R-squared|0.47|0.50|0.50|0.41|0.39|0.39|0.54|0.57|0.57|

_<u>Panel B (Age dependency ratio used as proxy for demographic characteristic)</u>_

|**Revenue/GDP**||**1994-2003**|||**1994-1998**|||**1999-2003**||
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita1|(Eq. 1)<br>2.488|(Eq. 2)<br>2.094|(Eq. 3)<br>1.970|(Eq. 1)<br>1.098|(Eq. 2)<br>0.559|(Eq. 3)<br>0.233|(Eq. 1)<br>2.984|(Eq. 2)<br>2.286|(Eq. 3)<br>2.523|
||(6.14)**|(4.37)**|(4.17)**|(1.31)|(0.57)|(0.25)|(5.92)**|(3.37)**|(3.82)**|
|Age Dependency Ratio|1.488|-1.200|-0.224|0.830|0.407|0.725|-2.061|-7.569|-6.309|
||(0.75)|(0.57)|(0.11)|(0.24)|(0.11)|(0.19)|(0.77)|(2.70)**|(2.25)*|
|Trade Openness|0.066|0.055|0.055|0.071|0.042|0.041|0.051|0.047|0.048|
||(9.01)**|(8.13)**|(8.11)**|(5.13)**|(3.66)**|(3.54)**|(5.09)**|(4.70)**|(4.62)**|
|Agriculture Value<br>Added|-0.315|-0.310|-0.308|-0.290|-0.309|-0.307|-0.345|-0.289|-0.294|
||(12.68)**|(10.36)**|(10.39)**|(7.92)**|(6.22)**|(6.08)**|(10.25)**|(7.65)**|(7.84)**|
|Corruption||-0.336|||-0.454|||-0.490||
|||(1.95)|||(1.50)|||(1.79)||
|Bureaucracy|||-0.357|||-0.494|||-0.283|
||||(2.95)**|||(2.56)*|||(1.35)|
|Constant|21.214|21.874|20.963|21.525|21.217|21.062|24.658|25.693|25.489|
||(13.72)**|(14.94)**|(13.52)**|(7.13)**|(6.71)**|(6.93)**|(12.23)**|(11.94)**|(10.58)**|
|Observations|871|726|726|324|252|252|375|334|334|
|R-squared|0.44|0.48|0.48|0.36|0.36|0.36|0.52|0.54|0.54|

Robust t statistics in parentheses 1GDP per capita*10,000 Note: The dependent variable is Taxable capacity. Estimation technique is OLS.

- 12 -
# **TABLE OF CONTENTS**

|**1.**<br>**INTRODUCTION ................................................................................................................... 3**|
|---|
|**2.**<br>**MEXICO’S ENVIRONMENTAL CONDITIONAL CASH TRANSFER PROGRAM ............................... 6**|
|**3.**<br>**DATA ................................................................................................................................... 9**|
|**3.1 LAND-COVERDATA**......................................................................................................................... 10|
|**3.2 LAND-COVERMANAGEMENT ANDSOCIALOUTCOMESDATA**................................................................ 11|
|**4.**<br>**EMPIRICAL STRATEGY ........................................................................................................ 13**|
|**5.**<br>**RESULTS ............................................................................................................................ 16**|
|**5.1 AVOIDEDDEFORESTATION**............................................................................................................... 16|
|**5.2 LAND-COVERMANAGEMENTACTIVITIES**............................................................................................ 18|
|**5.3 HOUSEHOLDSOCIO-ECONOMICINDICATORS**...................................................................................... 19|
|**5.4 SOCIALCAPITAL**.............................................................................................................................. 21|
|**6.**<br>**DISCUSSION ....................................................................................................................... 22**|
|**FIGURES AND TABLES ................................................................................................................................. 25**|
|**REFERENCES ............................................................................................................................................... 43**|
|**APPENDIX: Sample calculations and additional figures and tables..................................................... 48**|

2
off-trend fluctuations in NO2, which we now show are indicative of business cycles.

# **3.2 Time Series Characteristics**

Figure 1 presents the example of China, to provide some basic intuition regarding NO2’s superior ability to capture cyclical movements. In the left-hand side panels are NO2, while the right are lights. In the top row are raw data, while the bottom is the cyclical component. All panels cover the same period, from January 1, 2015 to January 1, 2021, which includes two significant downturns: August 24, 2015’s “Black Monday”, when the Shanghai main share index lost 8.49% of its value, and January 1, 2020, a rough estimate for the beginning of the COVID-19 pandemic. Both downturns are clearly evident in the raw data and the cyclical component of NO2, which fell significantly in their aftermath. However, the same may not be said for lights; whereas Black Monday is evident in the cyclical component, the pandemic is not. Thus, regardless of the fact that NO2 is overall downward-trending, the cyclical component seems to contain more information regarding changes in economic activity in this case.

Figure 2 changes the perspective to average world NO2, and back to the 2009 Financial Crisis. From this perspective as well, the potential for NO2 to indicate the timing of global business cycles is evident; for instance, the ramifications of the 2015 episode in China are also visible in world data, as are both the 2009 and 2020 crises.<sup>9</sup> Thus, a hypothesis we wish to test is whether NO2 reflects cyclical economic activity better than lights globally.

# **3.3 Cross-Sectional Characteristics**

Thus far we have emphasized time series fluctuations. But NO2 also contains important crosssectional information. While NO2 has broader applications than just the COVID-19 pandemic, this is a nonetheless an example of a particularly stark reduction in economic activity where we may see geographic heterogeneity clearly.

**China.** Figure 3 depicts the change in NO2 in China, provinces outlined, between the first quarter of 2019 and 2020. Within the area of most significant downturns (blue) is Hubei province, containing Wuhan. Amongst all world countries, China is notable for how uniform the reduction in NO2 was in its populated regions reflective of the efficacy of lockdowns in spurring collective action. For reference, China had some of the highest global concentrations of NO2 before the crisis. World maps of NO2 in 2019 previous to the pandemic, and lockdown effects in 2020, are depicted in Online Appendix A.3.

**Europe.** Figure 4 depicts the change in NO2 in Europe between the second quarter of 2019 and

> 9NO2 was already below trend before the pandemic due in part to the 2019 manufacturing recession, which is more clearly seen in U.S.-specific data in Online Appendix A.2.

8
Policy ReseaRch WoRking PaPeR 4873

# **Abstract**

The authors use firm-level survey data on 998 small and medium enterprises registered for tax in South Africa regarding tax compliance costs to investigate the use of outsourcing to complete tax compliance tasks. Overall, about 43 percent of the enterprises do all their tax compliance work in-house, 11 percent outsource all their tax compliance work, and the remaining 46 percent use a combination of both (“partial outsourcing”).

The data display an inverted-U shape for outsourcing of tax compliance tasks: the smallest firms (those under R 300,000 turnover or well under US$50,000) tend not to outsource, due to a combination of relatively higher cost-burden and less complexity. Relatively larger firms (those with more than R 14 million turnover or about US$2 million) report that they have sufficient in-house capacity and therefore do not need to outsource. Those in the middle are most likely to outsource at least some of their tax compliance work, mostly because tax is a specialist field and they presumably lack sufficient capacity in-house. The survey data show that the costs of tax compliance are clearly the highest for those who engage in partial outsourcing, as it appears there is likely duplication of effort. Most such firms could reduce their tax compliance costs (and probably minimize the incidence of post-filing problems) by moving from partial to full outsourcing of all tax compliance work.

This paper—a product of the Regulatory Simplification Division, Investment Climate Department—is part of a larger effort in the department to support business tax reform, improve the efficiency of business tax administration and reduce tax compliance costs for businesses. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at gkisunko@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
POLICY RESEARCH WORKING PAPER 1357

# Summary findings

Development practitioners are coming to a consensus that participation by the intended beneficiaries improves project performance. But is there convincing evidence that this is true? Skeptics hay.. three objections:

* 'Participation" is not objective: project rankings are subjective.

-* This subjectivity leads to "halo effects."

* Better project performance may have increased beneficiary participation rather than the other way around; a statistical association is not proof of cause and effect.

Isham, Narayan, and Pritchett show methodologically how to answer each of these objections. Subicctivity does not preclude reliable cardinal measurement. Halo effects do not appear to induce a strong upward bias in estimating the effect of participation. Finally, instrumental variables estimation can help establish a structural cause and effect relationship between participation and project performance - at least in the rural water supply projects they studied.

- - This paper a product of the Office of the Vice President, Development Economics is one in a series of background papers prepared for _World Development Report 1994_ on infrastructure. Copies of this paper are available free from the World Bank, 1818 H StreetNW, Washington DC 20433. Please contact Michael Geller, room T7-079, extension 31393 (38 pages)- September 1994.

**7be** **_Potiq Rteseareb 'Workig_** _Paper S_ **_d bi_** y _hemii cy fmdRegs o_ **_ork_** _iss_ **_in pra&tio cncurage the exc6ang of idCas atnte demlopmecnt issues. An_** _obrcaiive of_ **_theseriesis togct he findingsout quidkJy,_** _cucn if_ **_the presentations are kss tha full polWd Tt, prpers arry the nams cof Me authors andlshould be zsed and cited accordingly. Tbc fuiigs. iaterpretafionss ad conclusions are the aubors' oam and should not_** _be_ **_attributed to the World Bank, irs_** _Executive_ **_Board of Directors or axy_** _of_ **_its mEmber countris_**

Produced by the Policy Research Dissemination **Center**
- 38 -

- GDP(t-l): Real GDP 5 years before current 5-year period. This variable is used here as an substitution for the initial GDP level in Mankiw's single period regression model. Source: Summers and Heston (1991).

- SCHOOL: Primary school enrollment as percentage of age group. Source: same as above.

- EXPORIT: Average annual growth rate of export, weighted by the share of export in GDP.

Source: Summers and Heston.
# Figure 6. Historical contributions of global shocks to global inflation around global recessions

## A. 1975 global recession

## B. 1982 global recession

<!-- Start of picture text --> Percentage points Percentage points<br>0.8 Oil price 0.8 Oil price<br>0.6 Global supply 0.6 Global supply<br>Global demand Global demand<br>0.4 0.4<br>Interest rate Interest rate<br>0.2 0.2<br>0 0<br>-0.2 -0.2<br>-0.4 -0.4<br>-0.6 -0.6<br>-0.8 -0.8<br>-3 -2 -1 t=0 1 2 3 -3 -2 -1 t=0 1 2 3<br><!-- End of picture text -->

## C. 1991 global recession

## D. 2009 global recession

<!-- Start of picture text --> Percentage points Percentage points<br>0.8 Oil price 0.8 Oil price<br>0.6 Global supply 0.6 Global supply<br>Global demand Global demand<br>0.4 0.4<br>Interest rate Interest rate<br>0.2 0.2<br>0 0<br>-0.2 -0.2<br>-0.4 -0.4<br>-0.6 -0.6<br>-0.8 -0.8<br>-3 -2 -1 t=0 1 2 3 -3 -2 -1 t=0 1 2 3<br>E. 2020 global recession<br>Percentage points<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2 Oil price<br>-0.4 Global supply<br>Global demand<br>-0.6<br>Interest rate<br>-0.8<br>-3 -2 -1 t=0 1 2 3<br><!-- End of picture text --> Note: The historical contributions of the structural shocks to global inflation are estimated using global FAVAR model. The troughs of the global recessions—1975 Q1, 1982 Q4, 1991 Q1, 2009 Q1, and 2020 Q2—were identified using global per capita GDP and the algorithm in Harding and Pagan (2002). Horizontal axes indicate years before and after the troughs of global recessions (shaded area, t=0).

29
|Table 3. Conditi|onal Diff<br>E|erences in<br>stimates f|Differenc<br>or cultivati|es Estima<br>on|te of Be<br>E|ingin Spr<br>stimates|ayed Reg<br>for sprayi|ion.[RS6]<br>ng|
|---|---|---|---|---|---|---|---|---|
|||Control|ling for pr<br>score|opensity||Controll|ing for pr<br>score|opensity|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Observations per<br>year|10880|10880|10880|10880|10880|10880|10880|10880|
|_Notes:_The table p<br>in the sprayed reg<br>and 5 present line<br>propensity score.<br>columns 4 and 8|resents co<br>ion (relati<br>ar regress<br>In column<br>we matc|nditional<br>ve to the<br>ions. In c<br>s 3 and 7<br>h observa|difference<br>exclusion<br>olumns 2<br>we stratif<br>tions on t|s in diffe<br>areas) on<br>and 6 we<br>y on the e<br>he prope|rences e<br>cultivat<br>reweigh<br>stimated<br>nsity sc|stimates o<br>ion and s<br>the data<br>propensi<br>ore. The|f the effec<br>praying. C<br>using the<br>ty score.<br>propensit|t of being<br>olumns 1<br>estimated<br>Finally, in<br>y score is|
|estimated with a<br>explanatory varia<br>bottom panel pres<br>and serial correlat|probit mo<br>bles. The<br>ents year-<br>ion within|del using<br>top panel<br>by-year es<br>cells are|data for c<br>presents re<br>timates. S<br>reported in|ultivatio<br>sults poo<br>tandard e<br>parenth|n and sp<br>ling sev<br>rrors rob<br>esis. Esti|raying fro<br>eral years<br>ust agains<br>mates wit|m 2000 t<br>together;<br>t heterosk<br>h<sup>***</sup>are s|o 2005 as<br>while the<br>edasticity<br>ignificant|
|at the 1%, those w|ith<sup>**</sup>are|significa|nt at the 5|%, and th|ose with|<sup>*</sup>are sig|nificant a|t the 10%.|
|_Source: Authors’_<br>_UNODC._|_analysis_|_based on_|_data from_|_United_|_Nations_|_Office of_|_Drugs a_|_nd Crime,_|

*********