rafmacalaba/datause-encoder-data huggingface dataset:
- Kruger, D. (2007). Coffee production effects on child labor and schooling in rural Brazil, _Journal of Development Economics_ **82** , pp. 448--463.

- Levison, D., K. Moe, and F. Knaul (2001). Youth education and work in Mexico, _World Development_ **29** , pp. 167--188.

- López-Calva, L.F. (2002). A social stigma model of child labor, _Estudios Economicos_ 17. López-Calva, L.F. (2003). Social norms, coordination, and policy issues in the fight

- against child labor, in _International Labor Standards_ (Eds.) K. Basu, H. Horn, L. Roman, and J. Shapiro, Oxford, Blackwell Publishing.

- Patrinos, H.A. and G. Psacharopoulos (1997). Family size, schooling and child labor in Peru - An empirical analysis, _Journal of Population Economics_ **10** , pp. 387--405.

- Psacharopoulos, G. (1997). Child labor versus educational attainment: Some evidence from Latin America, _Journal of Population Economics_ **10** , pp. 377--386.

- Psacharopoulos, G. and H.A. Patrinos (1994). _Indigenous People and Poverty in Latin America: An Empirical Analysis_ . Washington: The World Bank.

- Rosati, F. and M. Rossi (2003). Children's working hours and school enrollment: Evidence from Pakistan and Nicaragua, _World Bank Economic Review_ **17** , pp. 283--295.

- Schultz, T. P. (2004). School subsidies for the poor: evaluating the Mexican Progresa poverty program, _Journal of Development Economics_ **74** , pp. 199--250.

- Skoufias, E. (2005). PROGRESA and Its Impacts on the Welfare of Rural Households in Mexico, IFPRI Research Report No. 139, International Food Policy Research Institute, Washington, D.C.

- Skoufias, E. and S. Parker (2006). Job loss and family adjustments in work and schooling during the Mexican peso crisis, _Journal of Population Economics_ **19** , pp. 163-181.

- Wydick, B. (1999). The effect of microenterprise lending on child schooling in Guatemala, _Economic Development and Cultural Change_ **47** , pp. 853--869.

9
# **Abstract**

This paper examines the importance of fiscal autonomy in the analysis of decentralization. Using new data published by the OECD (2001, 2002), it reproduces several indicators and proposes new measures of decentralization that take into consideration subnational governments’ autonomy over their revenues. Two models are reproduced: Davoodi and Zou (1998) on decentralization and economic growth, and Oates (1985), on decentralization and public sector size. Some evidence suggests that fiscal autonomy positively affects economic growth. Also, it seems to affect the size of the state, but evidence on this relation is limited. Despite some statistical weaknesses, there are sufficient indications to argue that subnational governments’ fiscal autonomy should be a major concern when measuring decentralization.
# **1. INTRODUCTION**

For most of the population of Sub-Saharan Africa, farming is a main source of both food and household income. Analysis of the Rural Income Generating Activities (RIGA) study shows that, while non-farm activities are increasing in importance, the vast majority of rural African households remain heavily dependent on agriculture, with on-farm sources of income ranging from 59 to 78 percent of total income (Davis et al., 2010). The comparatively higher importance of agriculture for the African countries included in the RIGA study confirms the critical role that agriculture plays in the economic development and improvement of living standards in the region. This relationship has also been the focus of vast quantities of other literature, including the World Bank’s 2008 World Development Report, _Agriculture for Development_ (World Bank, 2008). However, only rarely is there a focus on issues of underlying data quality and data collection methods. Historically, the data on rural household farming are perceived to be of poor quality, particularly when collected outside the domain of specialized farm surveys, but recently the greater demand for household data on both farming decisions and non-farm activities has led to the expansion of general household surveys to include extensive agricultural modules to capture agricultural production.

Because of the complexity of farming, where salient actions on the farm take place over several months of a season (plot preparation, input application, plot maintenance, harvest and selling), agricultural information would ideally be collected through multiple visits over a farming season to facilitate accurate recall of events. Specialized farm surveys are often designed to visit the household at multiple times, particularly those utilizing resident enumerators (e.g. agricultural extension agents or other Ministry of Agriculture staff). Such specialized surveys, however, offer limited scope to analyze the links between agriculture and non-farm income activities, as well as other socioeconomic outcomes. To meet the demand for integrated data, multi-topic/multipurpose household surveys (such as a Living Standards Measurement Study [LSMS] survey) are often extended to cover agricultural issues. Yet, cost and logistical considerations usually dictate that the data in these surveys be collected during a single visit to the household. In this case, the household/farmer is asked to report information relating to farming by recalling the details of past events for the last completed agricultural season, often including two or more separate harvests. Depending on the timing of the survey, the interview itself will not necessarily be the most propitious timing for a single visit (i.e. immediately after the main harvest).

2
the tax benets that lobbying rms are obtaining from the government. Consequently, I target the eective tax rate of public lobbying rms, which in the data is equal to 18.8 % . Finally, the parameter _μ_ is the scale parameter of the tax benet policy, which controls the amount of resources the government is losing due to tax benets tied to capital. Based on statistics from the IRS, 33% of all the corporate tax revenue is lost in tax benets. I calibrate _μ_ such that in the pre-crisis steady state that relationship holds. The values used for these parameters can be found in table (8).

Institutional Parameters There are 3 parameters to calibrate. I set the tax rate for rms to be equal to 35% in the benchmark economy. The parameter _⊖_ determines the fraction of the rm level tax collection from the government that a rm can claim on tax benets. In the benchmark calibration I set this to 1 . The parameter that measures the degree of credit depth of the economy, _λt_ , governs the aggregate ratio of external nance to capital. To measure this statistic in the data, I take the ratio of the stock of credit market liabilities to non-nancial assets of the non-nancial corporate sector. The numerator corresponds to credit market liabilities of the non-nancial corporate sector, line 5 from table _D._ 3 from the ow of funds coming from the Federal Reserve.<sup>45</sup> The stock of non-nancial assets is constructed using the net stock of xed assets for the corporate non-nancial sector from the U.S. Bureau of Economic Analysis (BEA). I adjust the value to level it to 2007 using current values. To calibrate _λ_ in steady state (pre-crisis), I target a ratio of external nance to non-nancial assets of 0.65, which is the value at the peak of the pre-crisis period in 2007. The values of these parameters and moments can be found in table (9).

To sum up, there are 11 parameters that are jointly calibrated, given that _ρ_ is solved in general equilibrium for a given value of _ε_ :<sup>46</sup> Despite solving a rather complicated multidimensional mapping, the model targets all moments quite closely. The only two moments that the model nds diculty to match are the eective tax rate paid by lobbying rms, which is lower than the same moment in the data, and the right tail of the distribution of wealth that I am targeting with _σ._

# 5.3 Model Testing

We have seen that the model hits the proposed targets quite closely after the calibration. Here, I evaluate the performance of the model using additional moments that were not targeted during the calibration. Table (10) shows some selected moments. Overall, the model behaves extremely well in matching the targeted moments and the non-targeted moments. For example, the model

> 45Line 5 is the total credit market liabilities of the non-nancial corporate business (series LA144104005.Q). It includes the stock of bank loans, and the stock of commercial papers, municipal securities and corporate bonds of the corporate sector.

> 46As a reminder, this parameter has been estimated using a reduced form equation coming from the rst order conditions of the model.

29
# **SURVEYING MIGRANT HOUSEHOLDS: A COMPARISON OF CENSUS-BASED, SNOWBALL, AND INTERCEPT POINT SURVEYS**<sup>**#**</sup>

David J. McKenzie<sup>*</sup> and Johan Mistiaen _Development Research Group, World Bank_

# The survey on which this research is based was funded by ABN-Amro through the World Bank’s Knowledge for Change Program. We thank Ricardo Guedes and the survey team from _Sensus_ for their hardwork in implementing the survey, and Alejandrina Salcedo for research assistance. The survey would not have been possible without the collaboration of Yoko Niimi, Claudia Romano, who coordinated activities in Brazil, Kaizô Beltrão and Sonoe Pinheiro who carried out the sampling, and provided valuable expertise and insight on the Nikkei community in Brazil, and Omar Noryo Okino and his team at _Sudameris,_ who provided contacts with Nikkei associations. We also thank the Nikkei associations for their support, and especially the survey participants. A preliminary version of this paper was presented at the PAA Meetings 2007. The views expressed here are those of the authors alone, and do not necessarily reflect those of the World Bank Group, its Executive Directors, or the countries they represent.

* Corresponding author: dmckenzie@worldbank.org
A third group of evaluations examined the effectiveness of Community-Driven Development projects to strengthen social cohesion. A commonly proposed theory is that of the importance of social cohesion, or the (re)building of interpersonal or intergroup networks, trust, and reciprocity, as a crucial factor for peacebuilding and conflict prevention. In a recent talk at the launching conference for the High Commissioner on National Minorities (HCNM) Guidelines on Integration in Diverse Societies, Stefan Wolff answered his rhetorical question about what it is about social cohesion that is so important for successful conflict prevention in the following way: “One of the fundamental ideas underlying the notion of conflict prevention in diverse societies is that different population segments can resolve any differences by recourse to institutional processes rather than violence. For such institutional processes to be effective, a viable and resilient state is required whose fundamental constitutional principles are broadly accepted and respected across all segments of society. If this is the case, societies may well be diverse across any number of indicators, including, ethnicity, language, and religion, but they will also be characterized by a sufficient level of social cohesion.” (Wolff, 2012). Efforts to strengthen social cohesion have increased among development organizations, most often operationalized through Community-Driven Development (CDD) or Community Driven Reconstruction (CDR) projects. In a systematic review of interventions to promote social cohesion in Sub-Saharan Africa, including in several conflict-affected countries (King et al. 2010), the authors outlined the theory underlying CDD interventions: “projects promote social cohesion by supporting and building community capacity for decision-making and collective action through a process of participation. The hypothesis is that, by handing over control of decisions and resources to the community, the sub-projects will better meet communities’ needs and enhance ownership, and that the experience of being involved in this participatory process will empower communities, improve capacity for local development and improve social cohesion.” (King et al. 2010, p. 347) Drawing upon the available evidence from impact evaluations that fulfilled a set of quality criteria, the review finds that the evidence of pro-social effects from Community-Driven Development (CDD) type interventions is weak. More surprisingly, a negative effect on individuals’ perceptions of inter-group relations is found across the three studies that measured this factor.<sup>6</sup> The preceding examples of how impact evaluations have been used as tools to test and critically examine commonly-held assumptions about how development interventions affect change were all based on large _n_ impact evaluations. But what happens when we have a question about

> 6 The review indicates that this finding may be partly explained by the fact that broad and substantive participation, including in actual decision-making, was often lacking and suggests that the implementation of the CDD interventions may have been flawed.

16
vey (NLSS), the most recent official survey for measuring welfare and poverty in Nigeria. The NLSS sample comprises approximately 22,000 households and is representative of Nigeria’s 36 states and the Federal Capital Territory (FCT), aside from the state of Borno.<sup>18</sup><sup>_,_19</sup> The 2018/19 NLSS is a multipurpose household survey that allows us to compare monetary poverty and overall expenditure to poor food access using either the FIES or FCS for each household.

The consumption aggregate used to identify monetary poverty includes information on consumption of food and non-food items, and expenditures on health, education, housing, and meals consumed outside the home.<sup>20</sup> The consumption aggregate is deflated spatially and temporally using unit values from the food consumption module, and can then be compared with the national poverty line of 137,430 naira per person per year to calculate poverty statistics.

We further follow FAO guidance on the construction of the FIES, which suggests that — under certain conditions it is possible to use the “raw score” the sum of the eight — dummy variables that comprise the FIES module to classify households’ food access status. These conditions are met using the NLSS 2018/19 and, following other analysis on Sub-Saharan Africa (e.g., Wambogo 2018), we classify those households with a raw score of 7 or 8 as severely food insecure and those with a raw score of 4, 5, or 6 as moderately food insecure.<sup>21</sup><sup>_,_22</sup> Furthermore, the 2018/19 NLSS contains the full FCS module. The FCS is calculated in the typical way, providing a direct measure of dietary diversity and food access at the household level. Each food group is given a score from zero to seven, depending on the

> 18Following the guidance of Nigeria’s National Bureau of Statistics (NBS), we dropped Borno state from the analysis. The 2018/19 NLSS is not representative of Borno state because violence prevented the survey field teams from accessing all of the sampled enumeration areas. For further details see NBS (2020).

> 19The sample is also representative at the national and zonal level.

> 20Further details on the construction of the consumption aggregate with the 2018/19 NLSS can be found in World Bank (2020b).

> 21Specifically, the infit statistics should range between 0.7 and 1.3; in the 2018/19 NLSS data they range from 0.85 to 1.15 for each of the eight FIES components. Similarly, the Rasch reliability statistic is 0.77, placing the 2018/19 NLSS data within the range of most other datasets considered in FAO’s cross-country analysis (e.g., FAO 2016). For more details, see FAO (2016).

> 22As a robustness check, we also calculate moderate and severe food insecurity at the zone level by directly applying the full Rasch model to FIES module in the 2018/19 NLSS to better account for zone differences. This is implemented using the RM.weights package in R (Cafiero et al. 2018b).

13
sector, which is geared toward middle-income groups (see below), may have caused some increase in the reported average income of public tenants.

**Table 7. Tenure and Household Income in Studied Countries**

|**Households**||**Armenia**|||**Lithuania***||
|---|---|---|---|---|---|---|
|Quintile|**Owners**|**Public**<br>**rental**|**Private**<br>**rental**|**Owners**|**Public**<br>**rental**|**Private**<br>**rental**|
|I|20.8|28.3|20.0|n/a|n/a|n/a|
|II|18.9|16.5|18.9|n/a|n/a|n/a|
|III|18.9|23.7|22.3|n/a|n/a|n/a|
|IV|19.2|15.3|13.1|n/a|n/a|n/a|
|V|22.2|16.3|25.7|n/a|n/a|n/a|
|||**Poland**|||**Romania**||
|I|9.9|16.4|21.2|8.9|32.9|9.2|
|II|15.5|21.1|20.6|14.3|25.1|9.8|
|III|19.5|21.8|21|20|18.4|16.4|
|IV|24.7|21.3|19.6|25|11.5|21.6|
|V|30.3|19.4|17.7|31.9|12.2|43.1|
|||**Russia**||**Serb**|**ia and Monten**|**egro**|
|I|10.9|13.1|17.6|21.5|14.6|27.5|
|II|17.6|17.8|15|19.2|20.5|20.2|
|III|20.8|20.9|16|19|24|14.6|
|IV|23.1|24.1|17.9|19.1|26.6|21.1|
|V|27.7|24.1|33.6|21.3|14.3|16.6|

_Source_ : Household surveys (Table 1). _Note_ : Urban apartment sector, income proxies (for definitions, see data descriptors in Table 1).

In contrast, low-income households in Armenia, Romania, and also Lithuania<sup>55</sup> are strongly over-represented in the urban public rental sector (see also Figure 8 for Romania). However, this empirical result reflects the sector’s marginalization after the almost completed massive privatization rather than an outcome of purposeful public policy. Still, even in these countries some income-rich tenants remain in the public stock and continue to pay very low “token” rents.

> 55  Data from the Lithuanian Household Survey in 2002 suggest an overrepresentation of both very poor and very rich tenants in the public stock. This is partly due to the relatively uniform distribution of public housing among urban and rural locations. However, anecdotal evidence for Vilnius suggests a concentration of poor clients even in a high-income region.

31
**Policy, Psearch, and External Affairs**

<!-- Start of picture text --> Pubilc Economics<br>WPS 703<br><!-- End of picture text --> This paper-a product of the Public Economics Division, Country Economics Department-is part of a larger effort in PRE to study the political economy of fiiscal policies in developing countries. Copies are available frec from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Ann Bhalla, room N10-061, extension 37699 (80 pages, with tables).

Most economists treat fiscal policy as exogenous and consider. licymakers as machines to be programmed. Rarely do they seek to determine why, for instance, some countries rely on the inflation tax while others use direct taxation, let alone what political factors affect such decisions. Yet without a theory of how fiscal policymakers behave, at both the revenue and the expenditure levels, there is no guarantee that policy advice will turn out to be sound.

policymakers to borrow too heavily and to leave the bills to their successors.

- Governments tend to implement adjustment - policies -including major devaluations early in their tenure in office, when they command political authority. But if political conflict arises, they may lack the strength to change the macroeconomic status quo and will resort instead to inflation and deficits.

Edwards and Tabellini present the results of an empirical analysis of the political economy of fiscal policy for a group of developing countries. They look at alternative ways of incorporating political variables into the explanation of government policy actions. Dividing their results into three sections, one each for inflation, budget deficits, and devaluations, they find that: The equilibrium inflation rate is higher the more citizens disagree about which party should hold office, and the more unlikely it is that the government currently in office will be reappointed.

Edwards and Tabellini argue that their results have important implications for the design of adjustment and stabilization programs. Institutional reforms that make it hardtr for a government to reverse course without warning will increase the credibility of the reforms, thereby reducing political instability - **and** the equilibrium level of inflation. The creation of independent central banks should also be a priority. This and other teforms .hat take money creation out of the hands of governments will boost macroeconomic stability Their results serve as a general endorsement of World Bank conditionality.

* Political instability and polarization lead to a collective myopia that sometimes tempts

The PRE Working Paper Series disseminates the findings of work under way in thc Bank's Policy, Research. and Extemal I Affairs Complex. An objective of the series isto get these findings out quickly, even if presentations are less than fully polished. i The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the PRE Dissemination Center
# Planted area losses vs. Plant productivity losses

<!-- Start of picture text --> Plots with any loss Plots only with partial losses<br>OoNNOo<br>==<br>< ra<br>8 8<br>oo oo<br>oa oa<br>o | | all. | I il o | | alla | . 1 TL<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Planted area lost [%] or Productivity reduction [%] Planted area lost [%] or Productivity reduction [%]<br><!-- End of picture text -->

Excluding those w/o any losses
- 27 -

Output-based aid (OBA) is one application of the above principle. In OBA, unlike traditional approaches, the payments of aid are linked to the delivery of specific services or outputs. Given the pre-agreed specific policy targets, a significant part of the payments of aid, which aim to subsidize the cost of particularly noncommercial development activities, would be disbursed only after those outcomes have been proven accomplished (GPOBA, 2008). In a community water project in Andhra Pradesh, India, for instance, three outputs are set out: (i) installing an ultraviolet filter water purification plants in each of 25 villages, (ii) registering at least 500 households with water subscription fees paid, and (iii) continuing to operate water supply services with customers paying their bills. The official subsidy covers investment, community awareness and running costs. Twenty percent of it is designed to be disbursed to operators once the first target is achieved. Sixty percent will be paid after the second target is met. The rest will be paid after the third output is confirmed (Mandri-Perrott, 2008). Therefore, it still follows a traditional contractual method, but the authorities can infer the quality of outputs from the actual accomplishment and thereby incentivize contractors to make an effort to achieve the better quality of projects. See more examples in Table 9.

_Proposition 14. Some of the performance-related proxies can be used to complement highpowered incentive mechanisms particularly if quantity and quality are net complements, even though ex post quality per se is not verifiable._ <u>Table 9. Selected example of output-based aid</u>

|Country|Project description|Output target(s)|Verified outputs<br>(as of June,2008)|Number of<br>beneficiaries|
|---|---|---|---|---|
|Armenia|Connect 17,000 low-income households to<br>public gas and heating systems|Households with access to heating<br>through individual connections or local<br>boiler systems|2,344|9,517|
|Colombia|Connect 35,000 families to the natural gas<br>distribution network|Households with gas connections|30,412|182,472|
|India|Develop safe drinking water systems in rural<br>communities|Villagers with ultraviolet water<br>purification systems|4|12,000|
|Mongolia|Provide telephony and Internet services to<br>header communities and soum areas|Public access telephone network<br>Wireless network for soum|1<br>2|22,300|
|Morocco|Expand water and sanitation connections to<br>11,300 low-income households in peri-urban<br>and rural areas|Households with water and sanitation<br>connections|1,764|8,820|

Source: GPOBA (2008).