rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7894

# **Abstract**

This paper investigates the impact of income diversification on farming households’ welfare in Nigeria on two rounds of the Nigeria General Household Survey-Panel, namely the 2010/2011 and 2012/2013. The study finds that income diversification is the norm in Nigeria, with about 60 percent of farmers diversifying away from subsistence farming into non-farm activities and cash crops. In addition, using the panel of farmers interviewed before and after a severe drought that hit Northern Nigeria in particular in 2011, the study finds that diversification increased throughout Nigeria from 60 to 64 percent and in the North from 58 to 63 percent. The study postulates the existence of heterogeneous returns on diversification as a consequence of the drought, and estimates the returns through a non-parametric selection model using a local instrumental variable. The choice of this model is dictated by the necessity to account for both heterogeneous effects of diversification and selection bias related to households’ decision to diversify. Overall, it is found that diversification positively affects consumption in Nigeria. However, who benefits the most is crucially determined by the initial conditions under which diversification is undertaken and the specific agro-climatic context in which households operate.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ebertoni@worldbankgroup.org,pcorralrodas@worldbank.org, vmolini@worldbank.org, and goseni@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
household budget surveys with detailed health budget components would greatly enhance the measurement of private out-of-pocket health expenditures.

So far, governments in the sub-region have managed to contain the increases in public spending on health within the rate of growth of their economies. At the same time, not only does the fiscal space to further increase public spending on health remain limited, but health insurance funds have generally failed to keep their expenditures in balance with their revenues, and the health system has accumulated substantial debt. If the desired health system goals are to be achieved, health system reforms will need to be sustained for extended periods of time. Thus, the Western Balkans no longer face the choice of either reforming their health sectors or leaving them unchanged. Fiscal pressures make sectoral change inevitable. The real question is whether policymakers will allow change to occur in a piecemeal fashion toward uncertain outcomes or will instead try to steer it along a sustainable path.

32
**Table** **<u>6: The effect of closures on labor market variables, further controls</u>**

||(1)<br>Emp|(2)<br>Emp|(3)<br>Wage|(4)<br>Wage|(5)<br>W_days|(6)<br>W_days|(7)<br>W_hours|(8)<br>W_hours|
|---|---|---|---|---|---|---|---|---|
|_CP_<sup>_30min_</sup>|-0.0043***|-0.0041***|-0.0598***|-0.0635***|-0.0217**|-0.0256***|0.0394***|0.0426***|
||(0.001)|(0.001)|(0.014)|(0.014)|(0.009)|(0.009)|(0.011)|(0.011)|
|_REG_<sup>_30min_</sup>|-0.0003|-0.0002|0.0009|0.0003|-0.0028|-0.0030|0.0004|0.0006|
||(0.000)|(0.000)|(0.003)|(0.003)|(0.002)|(0.002)|(0.003)|(0.003)|
|Fatalities|-0.0006***|-0.0005***|0.0017|0.0015|0.0012|0.0011|-0.0015|-0.0014|
|(q-1)|(0.000)|(0.000)|(0.002)|(0.002)|(0.001)|(0.001)|(0.002)|(0.002)|
|Wall (km)|0.0001**|0.0001**|0.0023|0.0025|-0.0012|-0.0008|-0.0033|-0.0036|
||(0.000)|(0.000)|(0.004)|(0.004)|(0.002)|(0.002)|(0.003)|(0.003)|
|Em Israel|-0.0002***|-0.0002***|0.0006**|0.0005**|0.0003**|0.0003*|-0.0005**|-0.0004*|
|p(q-1)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Sect-time eff.|NO|YES|NO|YES|NO|YES|NO|YES|
|Obs.|92,335|92,335|82,005|82,005|89,952|89,952|85,118|85,118|
|R-sq. within|0.080|0.109|0.028|0.034|0.015|0.021|0.008|0.011|
|Nr. Workers|44,172|44,172|41,278|41,278|43,152|43,152|42,414|42,414|

_Robust standard errors (Huber-White method) clustered at individual level in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. All regressions include individual fixed effects, time effects and a set of controls including years of schooling, age, age squared, marital status, tenure, urban area / refugee camp residence dummies, a set of work place dummies and Hebron districttime effects._ 40
- Umezaki, M. and R. Ohtsuka (1998), “Impact of Rural-Urban Migration on Fertility: A Population Ecology Analysis in the Kombio, Papua New Guinea.” Journal of Biosocial Science 30(3): 411-422.

- White, M.L., L. Moreno, and S. Guo (1995), “The Interrelation of Fertility and Geographic Mobility in Peru: a Hazards Model Analysis.” International Migration Review. 29(2): 492-514.

38
At the county level the dependence on agricultural taxes varies even more widely. In Table 26 we present summary data from rural counties in Yunnan and Henan provinces. The average share of the five agricultural taxes was 32 percent among counties in Yunnan and 37 percent in Henan, but the maximum share was around three-quarters in both provinces. The 2005 data reflects the effect of abolishing agriculture taxes while retaining the tax on tobacco, which remains important in some counties, albeit a much smaller number.

**Table 26.  The Share of Agricultural Taxes in County Revenues, 2003 and 2005**

||20<br>|03<br>|20<br>|05<br>|
|---|---|---|---|---|
||Yunnan|Henan|Yunnan|Henan|
|Maximum|77%|75%|50%|26%|
|Minimum|1%|5%|0%|0%|
|Average|32%|37%|19%|5%|
|Standard deviation|14%|16%|14%|5%|
|Coefficient of variation|0.46|0.44|0.74|1.16|

Note: This is the sum of five taxes: the agricultural tax, animal husbandry tax, agricultural special products tax, farmland occupation tax, and title tax. The slaughter tax is also often included, but this tax generates insufficient revenues to make a difference.

Source: Based on Compendium of Fiscal Statistics for All Prefectures, Cities, and Counties, 2003, 2005.

The fact that counties vary so widely in their dependence on agricultural taxes means the RTFR may have potentially large distributional impact across localities depending on how provinces pass through the revenue replacement subsidies from the central government. For provinces such as Henan, which receives 100 percent replacement for revenue losses, the impact could still be differentiated across affected counties depending on the choices made by the province. Agricultural counties in provinces that receive only partial subsidies may be more likely to suffer net revenue losses.

Finally, the impact of the RTFR falls overwhelmingly at the township level **.** This is not only because townships and villages had been so reliant on fees and levies, but also because agricultural taxes had accrued mostly to the township level, and townships were in turn the most dependent on agricultural tax revenues. As shown in Table 27, in 2003, while agricultural taxes constituted 8.7 percent of total revenues for subnational governments, they were less than 2 percent of provincial revenues but 31 percent of township revenues. The fact 54
creation of a bu¤er stock of ...nances to smooth out the volatility. Increases in the required o¤setting ...nancing could make the country acknowledge the need for a debt management strategy. On the other hand, if the volatility in foreign aid is disrupting the ...nancing plans of the government at some point it could undermine the e¢ cient continuation of a debt management strategy. The series of net o¢ cial transfers was taken from the GDF & WDI Central database of the World Bank.

Regional dummy variables - we include also regional dummies into the regression to explore the possibility that the existence of a strategy is dependent on the region a country belongs to. The regional classi...cation corresponds to that used throughout the graphical analysis in section 2.<sup>16</sup> When constructing the regional dummy variables we take as a base the LAC region due to the highest number of available observations.

All indicators were calculated using available annual data covering the period from 1990 to 2006.

# 4.2 Estimation Results

This section reports and discusses the results of PROBIT model estimation. Recall that by using the PROBIT model we try to explain the probability of a country having a public debt management strategy using selected economic indicators discussed in section 4.1. The maximization of the log-likelihood of the PROBIT model<sup>17</sup> is carried out using the Berndt-Hall-Hall-Hausman algorithm. The inference is based on the quasi-maximum likelihood (QML) standard errors due to Huber and White<sup>18</sup>

> 16We have tried to insert a regional dummy taking 1 if the country was an OECD country and 0 otherwise, but this dummy appeared to be insigni...cant and was dropped from the estimation. This is most likely due to its colinearity of 0:87 with the income levels of the countries.

> 17The log-likelihood function is of the form:

where � is standard Normal cumulative distribution and � is the standard deviation of the unobserved shock in the regression underlying the PROBIT model, see e.g. Johnston and Dinardo for further details.

> 18The QML variance covariance matrix is computed as

20
# Figure II: Impact of Desk Audits

## A: Income Tax

<!-- Start of picture text --> Share of revisers: 19%<br>Initial tax declared: 759 mio CRC<br>Increase post revision: 1335 mio CRC (+.12% of total revenue)<br>Cost: 5138*36,700 = 188 mio CRC<br>0 100 200 300 400 500<br>Revenue change (mio CRC)<br>Taxpayer-Year 45 degree line Linear fit<br>B: Sales Tax<br>Share of revisers: 16%<br>Initial tax declared: 165 mio CRC<br>Increase post revision: 224 mio CRC (+.02% of total revenue)<br>Cost: 2000*36,700 = 73 mio CRC<br>0 1 2 3 4 5<br>Change in Tax Collected (mio CRC)<br>Taxpayer-Month 45 degree line Linear fit<br>500<br>400<br>300<br>200<br>Cost change (mio CRC)<br>100<br>0<br>5<br>4<br>3<br>2<br>1<br>Change in Input Tax Credit (mio CRC)<br>0<br><!-- End of picture text --> Notes: The gure shows the revenue and cost adjustments made by rms contacted via phone calls by tax ocers, after a desk audit uncovered a discrepancy between self-reported and third-party reported sales. The desk audits are for income tax returns for 2013 and 2014, and for sales tax returns for 2013. The gures focus on rms submitting a revised tax return, and display the change in revenue and costs (sales tax collected and input tax credit deducted in panel B), comparing the initial return with the revised return. The text displays the share of revisers, the initial tax declared by revisers, the dierence between their nal tax paid after revision and the initial tax declared, this dierence as a share of initial total tax revenue for the relevant tax and scal year, and the cost of the desk audit intervention. The cost is calculated by multiplying the number of cases by the daily wage of a tax ocer, accounting for the fact that tax ocers are asked to handle one case per day.

39
Table 2. Presence of roads in a cell

|Road quality|1976|1986|2008|2014|
|---|---|---|---|---|
|Class 1 road|0.61%|0.67%|||
|Class 2? road|0.83%|0.98%|||
|Class 3 road|1.03%|1.15%|||
|Track|0.55%|0.60%|||
|Unknown|0.01%|0.00%|||
|Any road|2.99%|3.35%|||
|Intersection|0.03%|0.04%|||
|Administrative road|definition||||
|Highways|||n.a.|0.00%|
|Primary|||0.63%|0.25%|
|Secondary|||0.36%|1.05%|
|Tertiary|||0.82%|1.66%|
|Local road|||n.a.|0.28%|
|Any road|||1.80%|3.12%|
|Intersection||n|.a.|0.23%|

Note: Number of observations: 3,880,380. Unknown roads in 1976 and 1986 denote a very small proportion of roads of unknown quality. Road quality in 1976 and 1986 does not align with administrative road definitions in 2008 and 2014, which themselves differ as well in spite of the partial similarity in names. Highways in 2014 include a single motorway/freeway from Accra to the nearby city of Tema and is present in 113 cells only. Averages for road availability in the 8 (N1) and 16 (N2) neighboring cells are not shown here to save space. They are essentially identical to the averages reported here since they are averages over essentially the same cells (except far small differences at the edges). Intersections cover all classes of road available in the data.
_Tigt_ = _β_ 0+ _β_ 1 _MarketLaborigt_ + _β_ 2 _MarketLaborigt⇥Ageig_ 1+ _β_ 3 _Ageigt_ + _β_ 4 _Age_<sup>2</sup> _igt_<sup>+</sup><sup>_✓i_+</sup><sup>_λt_+</sup><sup>_γg_+</sup><sup>_✏igt_</sup> (2) _Tigt_ = _β_ 0+ _β_ 1 _MarketLaborigt_ + _β_ 2 _MarketLaborigt⇥Tig_ 1+ _β_ 3 _Ageigt_ + _β_ 4 _Age_<sup>2</sup> _igt_<sup>+</sup><sup>_✓i_+</sup><sup>_λt_+</sup><sup>_γg_+</sup><sup>_✏igt_</sup> (3) where _MarketLaborigt ⇥Ageig_ 1 and _MarketLaborigt ⇥Tig_ 1 are interaction terms between child labor status and age and test score at the first year the student is observed in the sample, respectively. A negative coefficient estimate associated with the interaction between child labor and age suggests that child labor e↵ects younger students more than older students; while a negative coefficient associated with the interaction between test score indicates that higher scoring students are more harmed by working.

In order to account for the possibility of heterogeneous transition e↵ects, we estimate specification (1) separately for those entering and those exiting the labor market using the paired sample. Thus our third test of heterogeneous e↵ects is on those who enter into child labor compared to those who never worked, and our fourth test is on those who exit out of child labor compared to those who work in both periods. Notice that this is a di↵erent comparison to the benchmark test which compares those who transit into or out of market labor to those who not change their status (both not working in all periods and working in all periods of observation).

Next, we ask whether child labor has cumulative and lingering e↵ects. To test for these, we analyze whether the impact of work on learning depends on the length of time spent working. For these exercises, we use the 3 period sample restricted to students that were 15
the fund assets. In Latin America, funds mostly invest in seven countries. On average they hold the following portfolio: Brazil **(38** percent), Mexico **(30),** Argentina **(13),** Chile **(11),** Peru **(3),** Colombia (2), and Venezuela (2). The two largest countries-Brazil and Mexico-account on average for **68** percent of the funds' holdings; while the four largest countries-Argentina, Brazil, Chile, and Mexico-account for **93** percent of the assets. Funds hold large positions in ADRs/GDRs (American and Global Depository Receipts), traded on the New York Stock Exchange. Therefore, they often do not trade in the local stock markets when investing abroad. Within the sample period, the share for Mexico grew from **26** to 34 percent.'<sup>5</sup> In the case of transition economies, mutual fund investment is concentrated on five countries. On average, they hold assets in the Czech Republic **(13** percent), Hungary **(21),** Poland (24), Russia **& CIS** (40), and the Slovak Republic (2). The shares are volatile, for example, with Russia raging from **25** to **59** percent.

# **IV. The Behavior of Mutual Funds during Crises**

Crises in the 1990s have not been country-specific, or even region-specific. Indeed, there is consensus that crises have a contagious nature, with currency turmoil spreading to countries as far apart as Argentina, the Czech Republic, and South Africa. Crises before **1990** also had a contagious nature, witness the debt crisis in **1982.** But until recently, contagion tended to be regional. The 1990s changed that. While the Asian flu was mostly confined to south East Asia, it also triggered currency turmoil in Argentina, Mexico, and Chile. The speculative attack on the Hong Kong dollar in October **1997** also traveled the

> **15** Data from Morningstar and **SEC** for Latin American funds show an almost identical picture.

14
9

**Table** 2. Population Grouth **by eroraphic** Region, **1985-2025**

||Popul<br>**(milli**|ation<br>**ons)**|Growt<br>(perc|h rate<br>ent)|Birth<br>(per|rate<br>1,000)|Death<br>(per|rate<br>**1,000)**|
|---|---|---|---|---|---|---|---|---|
|Region|1985|2025|1985-90|2020-25|1985-90|2020-25|1985-90|2020-25|
|**World**|4,843|8,262|**1.73**|0.96|27.1|17.8|9.8|8.2|
|Africa|560|**1.508**|3.06|1.76|**44.8**|24.3|14.2|6.6|
|America|667|1.040|1.55|**0.72**|23.2|15.3|7.9|8.1|
|Asia|**2.821**|4,815|1.84|0.91|27.7|17.2|9.2|8.0|
|Etrope, U.S.S.R.|771|864|0.48|0.14|15.0|12.9|10.3|11.5|
|**Oceania**|25|.35|1.41|0.56|19.9|14.7|8.1|9.1|

**Note:** In **all** tables, figures **may** not **add up** to given totals because **of rousding.**

More people are now being added to the planet every year than ever before, even though the peak population growth rate (2.1 percent in 1965-70) is past. In 1987, about 88 million people were added, and this number will increase for another decade, eventually reaching about 92 million.

In the next century, the projections indicate that world population will continue to grow, though more slowly. Not until the 22nd century do the projections show population stabilizing. At that point, world population will 'probably be well above 10 billion.

# Changes in the grojections

-

Table 3 indicates that these projections involve important changes since the 1980 projections, but only minor changes since last year.

Since 1980, the world population for the year 2000 has been revised upward by 2 percent, because of a substantial upward revision for Africa and a smaller upward revision for Asia, only partly balanced by downward revisions for America and Europe. The current projections give a population for the entire continent of Africa in the year 2000 that is 8 percent larger than in the 1980 projections. This is based on ample recent evidence that fertility levels are more resistant to change than had earlier been assumed. For Asia, the change in the projections mainly concerns China. A recent upswing in the birth rate makes it less likely that Chinese fertility will soon dip below replacement and stay below for an appreciable period. In contrast to Africa, Latin America has shown larger fertility declines than previously projected. For Europe and other developed countries, the revision has mainly involved stretching out the period of below-replacement fertility.
**Table A.1.3. Co-insurance, payment coverage type, and coverage value distribution**

|**Country name**|**Co-**<br>**insurance**<br>yes=1 no=0|<br>**Co-**<br>**insurance**<br>**percentage**|**Payment**<br>per depositor=1<br>per deposit=0|<br> <br>**Percentage of**<br>**deposit value**<br>**covered**|**Information source**<br>**for coverage**<br>**distribution**|**Reference date of**<br>**data on coverage**<br>**distribution**|
|---|---|---|---|---|---|---|
|Albania|1|15|1||||
|Algeria|0|0|0||||
|Argentina|0|0|0|40|Garcia|2000|
|Austria|1|10|0||||
|Bahamas|0|0|0|39|IADI|2001|
|Bahrain|0|0|0||||
|Bangladesh|0|0|0|31|Garcia|2000|
|Belarus|1|20|0||||
|Belgium|1|10|0||||
|Bolivia|1|50|0||||
|Bosnia-Herzegovina|0|0|0||||
|Brazil|0|0|0|23.6|FGC|2003|
|Bulgaria|0|0|1|71|DIF|2003|
|Canada|0|0|0|34.3|CDIC|2002|
|Chile|1|10|1|30|Demirguc-Kunt and|2003|
||||||Levine||
|Colombia|1|25|1|30.8|IADI|2002|
|Croatia|0|0|1|68|Garcia|2000|
|Cyprus|1|10|1||||
|<br>Czech Rep.|1|10|1|86.2|FPV|2003|
|Denmark|0|0|1|44.6|IADI|2001|
|Dominican Republic||||100||2003|
|<br>Ecuador|0|0|1||||
|El Salvador|0|0|1||||
|Estonia|1|10|1||||
|Finland|0|0|1|40|Garcia|2000|
|France|0|0|1||||
|Germany|1|10|1||||
|<br>Gibraltar|1|10|1||||
|Greece|0|0|1||||
|Guatemala|0|0|1||||
|Honduras|0|0|1||||
|Hungary|0|0|1|86.8|NDIF|2003|
|Iceland|0|0|1||||
|India|0|0|1|72|Garcia|2000|
|Indonesia|||||||
|Ireland|1|10|1||||
|Isle of Man|1|25|1||||
|Italy|0|0|1|62|Garcia|2000|
|<br>Jamaica|0|0|1|33.5|Garcia|2000|
|Japan|0|0|1|87.9|DIC|2002|
|<br>Jordan|0|0|1||||
|Kazakhstan|0|0|1||||
|Kenya|0|0|1|16|Garcia|2000|
|Korea|0|0|1|80.8|KDIC|2003|

34
Table 4: Estimation of propensity score, using pretreament data (2007)

||Panel A:|Fifth-grade schools|Panel B: N|inth-grade schools|
|---|---|---|---|---|
||Coefficient|_P_value|Coefficient|_P_value|
||(1)|(2)|(3)|(4)|
|∆ Portuguese grade|-0.02|0.00|-0.02|0.00|
|∆ Mathematics grade|0.04|0.00|0.02|0.00|
|Portuguese grade|0.03|0.00|0.04|0.00|
|Mathematics grade|-0.05|0.00|-0.05|0.00|
|Dropout rate|0.04|0.00|0.00|0.73|
|IDEB Index|-1.02|0.00|-1.34|0.00|
|∆ IDEB|0.48|0.00|1.09|0.00|
|Municipal population|0.00|0.00|0.00|0.00|
|Students' enrollments|0.00|0.00|0.00|0.00|
|IDHM|19.11|0.00|17.32|0.00|
|Urban|0.33|0.58|-0.65|0.06|
|Basic sanitation|-1.10|0.00|-1.13|0.01|
|Library|0.16|0.02|0.29|0.00|
|Sports court|-0.06|0.30|-0.17|0.02|
|Number of classrooms|-0.05|0.00|-0.04|0.00|
|Number of computers|0.19|0.07|0.08|0.60|
|Science lab|0.12|0.17|0.10|0.19|
|Computer lab|-0.15|0.01|-0.24|0.00|
|Teachers room|0.29|0.00|0.23|0.16|
|DVD|0.12|0.24|-0.09|0.44|
|Overhead projector|-0.01|0.92|0.03|0.76|
|Number of employees|0.00|0.45|0.00|0.35|
|School meals|-0.21|0.06|-0.02|0.89|
|Number of classrooms used|0.03|0.00|0.00|0.66|
|Morning lessons|-0.60|0.00|-0.50|0.02|
|Child care|0.14|0.33|0.67|0.00|
|Kindergarten|-0.11|0.06|-0.14|0.11|
|State schools|-0.27|0.00|-0.23|0.01|
|High school|-0.48|0.00|-0.43|0.00|
|Share classes in school with full-time day (7 or more hours)|2.51|0.00|3.77|0.00|
|Total duration of school day (hours)|-0.38|0.00|-0.67|0.00|
|Intercept|-5.80|0.00|-2.24|0.02|
|Number of observations||17874||13959|
|Pseudo-_R_ <sup>2</sup>||0.48||0.53|

_Sources:_ MEC/FNDE, _Prova Brasil,_ School Census, and INEP.

_Note:_ Propensity score was computed by estimating a Probit model (equation (4) in the paper) where the dependent variable is a dummy variable for schools that registered in _Mais Educação_ in 2008 and remained until 2011. Our control group is the schools that, by 2011, had not yet joined _Mais Educação._ Schools that joined the program in 2009, 2010, and 2011 were excluded from the sample to define the control group. Table reports the estimated coefficients (columns (1) and (3)) and _p_ values (columns (2) and (4)). Panel A reports these statistics for schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
# Table 1: Health coverage of the Thai population in 2000

|Type of Health Coverage|Number<br>(millionpersons)|Percentage of<br>Population|
|---|---|---|
|Medical Welfare Scheme (WHS)|20|33%|
|Health Card Scheme (HCS)|7-8|12%|
|Civil Servant Medical Benefit Scheme (CSMBS)|~7|11%|
|Social Security Scheme (SSS)|6|10%|
|Private Health Insurance|~5.9|~10%|
|No coverage|>15.5|>25%|
|Eligible for more than 1 type of coverage*|?|?|
|Total|61.5|100%|

Source: Na Ranong et al. (2004).

* For example, those who covered by CSMBS from being a parent or a child of a civil servant would be covered by CSMBS but also automatically covered by WHS.