rafmacalaba/datause-encoder-data huggingface dataset:
WPS4125

# Incentives, Supervision, and Sharecropper Productivity

Hanan G. Jacoby* Ghazala Mansuri*

## **Abstract**

Although sharecropping has long fascinated economists, the determinants of this contractual form are still poorly understood and the debate over the extent of moral hazard is far from settled. We address both issues by emphasizing the role of landlord supervision. When tenant effort is observable, but at a cost to the landlord, otherwise identical share-tenants can receive different levels of supervision and have different productivity. Unique data on monitoring frequency collected from sharetenants in rural Pakistan confirm that, controlling for selection, "supervised" tenants are significantly more productive than "unsupervised" ones. Also, landlords’ decisions regarding the intensity of supervision and the type of incentive contract to offer depend importantly on the cost of supervising tenants.

## World Bank Policy Research Working Paper 4125, February 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Development Research Group, World Bank. Contact Information: Jacoby: email: hjacoby@worldbank.org; Mansuri: email: gmansuri@worldbank.org.
# **Table 2: The effects of temperature on subnational inequality**

|Inequality:|Gi|ni|The|il|
|---|---|---|---|---|
||Panel FE –<br>all countries|Long<br>differences|Panel FE –<br>all countries|Long<br>differences|
||(1)|(2)|(3)|(4)|
|**_Panel A: Country-level_**|**_analysis_**||||
|Temperature|0.154***|0.171***|0.253***|0.275***|
||(0.029)|(0.040)|(0.049)|(0.063)|
|Precipitation|0.002|0.025**|-0.002|0.031|
||(0.007)|(0.012)|(0.012)|(0.021)|
|Country FE|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|
|Mean dependent var.|34.406|34.406|23.417|23.417|
|Observations|423|90|423|90|
|Equality test (Panel vs.<br>long differences)|p = 0|.726|p = 0.|604|
|**_Panel B: Subnation-lev_**|**_el analysis_**||||
|Temperature|0.285***|0.349***|0.350***|0.592***|
||(0.086)|(0.049)|(0.032)|(0.082)|
|Precipitation|-0.156**|0.474***|0.267**|0.699***|
||(0.072)|(0.100)|(0.110)|(0.170)|
|Subnational FE|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|
|Mean dependent var.|35.605|35.605|25.383|25.383|
|Observations|4,129|1,019|4,129|1,019|
|Equality test (Panel vs.<br>long differences)|p = 0|.383|p = 0.|029|
|Equality test (country<br>vs. subnational)|p = 0.000|p = 0.000|p = 0.000|p = 0.000|
|Number of countries|128|90|128|90|
|Number of regions|1,484|1,019|1,484|1,019|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level in Panel A and subnational level in Panel B. Regressions are weighted by region population. Inequality data are taken from the GSAP. Inequality and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. The data for the countrylevel analysis and the subnational-level analysis comes from our newly constructed database. The equality test p-values show the t-test between the panel FE results vs. the long differences results, and the country analysis results vs. the subnational analysis results. *** p<0.01, ** p<0.05, * p<0.1.

32
also suggest that the limited reductions in transport costs, despite an increase in entry, are attributable to the legal privileges granted to shipping company agreements.<sup>22</sup>

# _11.3 Restrictions on port and auxiliary services_

Both port and auxiliary services, particularly cargo handling, have tended to be monopolized. There are two aspects to the liberalization of these services. One is to allow foreign ships serving the domestic market non-discriminatory access to such services. The second is to allow foreign competition in the supply of the service itself. Some progress was made in the GATS in ensuring access to and use of port services, and some progress also in allowing foreign entry into the supply of auxiliary services.

Seaports are coordinated by public or, in fewer cases, private organizations called port authorities. Depending on the role assumed by these institutions, seaports can be classified into different categories. First, there are _landlord ports,_ in which the port authority owns and manages port infrastructure, while private firms provide the rest of port and maritimne auxiliary services; private firms are able to own superstructure, and operate assets pertaining to infrastructure by concession or licensing (ex. Buenos Aires, Argentina). Then there are _tool ports,_ where the port authority owns both infra and superstructure, but private firms provide services by renting port assets, through concessions or licenses (ex. Antwerp, Belgium). Finally, in _service ports,_ the port authority owns assets and supplies services by directly hiring employees. It has been argued that the landlord port is the most desirable category from the efficiency point of view, since it allows private enterprise and market forces to play a role in the supply of services, while preventing monopolization of essential assets by private firms.<sup>23</sup> With this broad benchmark in mind, we shall seek to capture some of the restrictions in place on port and auxiliary services.

22 See, for example, "Obstacles lie ahead", _1999 Year-end Economic Review,_ Bangkok Post, 1999.

23 Trujillo and Nombela (1999).

11
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_16_ **_PoUsh Fiscal Crisi_** Flgure 3 <!-- Start of picture text --> 19 91<br><!-- End of picture text --> <!-- Start of picture text --> lisle sage (31<br>Paifr,I taxes (2213<br>___ l~~~~~~~ite taxt 2s<br>lilt estate tax (2S1) 1D41d 0<br>Tura*vef tax (143 ilat tax (121]<br><!-- End of picture text --> We have the following relationships (we ignore wage bonuses and, as before, we normalize by sales): CF= HCA+A-IB-IA **RP** = CF - PTAX - DIV - TIP PTAX = t,HCA where PTAX is profit tax, levied at a rate of t,, RP is retained profit, DIV is the dividend tax, _TIP_ is the tax-based incomes policy tax (the "popiwek"). Set RP=O, substitute and rearrange and we get the following expression for HCA profit:

# _HCA -_ 11 _[IB+l-A-+DIV+T7P_

The average profit tax rate in 1990 was 37%. In 1991, a significant number of firms were lossmakers and so their marginal tax rate was zero. For our comparative statics exercise we take 30% as the marginal tax rate over 1990-91.1' This gives us a value for the "multiplier" 1/(l-t,) of 1.43.

Table 7 summarizes the direct and indirect impact of changes in the variables on enterprise profitability. The cumulative impact of 1990-91 changes in all the variables is a drop of 18% in HCA profitability, which is about what actually happened."' The changes in the inflation bias and in inventory accumulation cannot be separately identified very accurately, but this is not a problem with the cumulative

- **is. Gross** losses **were** between 1/4 and **1/3** of gross profits in 1991; **gross losses were** virtually nil in 1990. So we **take** as the marginal tax rate **_.5_** * 37% + **_S5_ *** (1/3 * 0 + 2/3 * 37%) **m** 30%.

16. This is not a coincidence, since our behavioral assumption of constant (small) retained profit wu roughly **what was** actually observed.
Olson, Mancur, and Richard Zeckhauser. 1966. "An Economic Theory of Alliances." _Review of Economics and Statistics_ XLVIII (August), pp. 266-279.

Paul, Samuel. 1995. "Evaluating Public Services: A Case Study on Bangalore, India." _New Directions for Evaluation,_ American Evaluation Association, no. 67 (Fall). Pharr, Susan J., Robert D. Putnam. 2000. _Disaffected Democracies._ Princeton, NJ: Princeton University Press.

Putnam, Robert D. 2000. _Bowling Alone: The Collapse and Revival of American Community._ New York: Simon and Schuster.

Riordon, William L. 1963 ed. _Plunkitt of Tamanny Hall._ New York: E. P. Dutton.

Roberts, Clayton. 1980 ed. _The Growth of Responsible Government in Stuart England._ Cambridge: Cambridge University Press.

Rose-Ackerman, Susan. 1999. _Corruption and Government: Causes, Consequences, and Reform._ Cambridge: Cambridge University Press. (see esp. Ch. 11).

Rounds, David. 1970. _Coalitions_ . New York: Dutton.

Ruzindana, Augustine. 1997. "The Importance of Leadership in Fighting Corruption in Uganda." In Kimberly A. Elliott, ed., _Corruption and the Global Economy_ . Washington, D.C.: Institute for International Economics.

Stapenhurst, Frederick, and Sahr J. Kpundeh. 1998. "Public Participation in the Fight Against Corruption." _Canadian Journal of Development Studies_ XIX:3, pp. 491-508.

Tendler, Judith. 1979. "Rural Works Programs in Bangladesh: Community, Technology and Graft". Transportation Department, The World Bank, Washington, D.C.

Uslaner, Eric M. 2001. "Trust and Corruption." Paper presented at the Conference on Political Scandals Past and Present, University of Salford, England (June 21-23).

Wilson, James Q. 1973. _Political Organizations._ New York: Basic Books.

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19

-

-

Tabie 4: Results of the Achievement Equation, Holding Student Characteristics Constant

|||School Type||
|---|---|---|---|
|Variables|Public|private 0|Private F|
|Intercept|10.50***|9.53***|10.10***|
||(1.01)|(2.11)|(2.88)|
|Pretest score|0.41***<br>|<br>0.38***<br>|<br>0.81***<br>|
||(0.03)|(0.06)|(0.04)|
|Mother's education|-0.05<br>|-0.20*<br>|-0.14|
||(0.03)|(0.09)|(0.09)|
|Age in months|-0.02***|0.01|0.01|
||(0.01)|(0.02)|(0.02)|
|Block house|-0.04|-0.53|-1.03|
||( 0.19)|( 0.53)|( 0.65)|
|Female|-0.55***|-1.14***|0.22|
||' 0.16|( 0.32)|( 0.37)|
|Full-time working|0.16|1.01*<br>|-0.07|
|mother|( 0.22)|(0.42)|(0.43)|
|Father's occupation||||
|White-collar|-0.33|-1.13|-1.03|
||C 0.25)|(0.66)|(0.83)|
|Agricultural|-0.47*<br>|-0.28|-0.43|
||(0.21)|(0.73)|(0.79)|
|Unclassified|-0.35<br>|-0.06|0.14|
||(0.31)|( 0.66)|(0.95)|
|Commuting time||||
|About 30 minutes|0.06<br>|0.60|0.02|
||(0.18)|(0.41)|(0.45)|
|More than 45 minutes|-0.51|-2.14*|-1.04|
||( 0.27)|(0.88)|(0.97)|
|No response|0.79|-2.19|2.19|
||(0.93)|( 3.43)|( 2.09)|
|Days absent||||
|More than 5|0.02|0.22|-0.58|
||(0.32)|(0.72)|(0.79)|
|Fewer than 5|-0.36|-0.06|1.11|
||(0.29)|( 0.64)|(0.80)|
|Fewer than 3|-0.01|0.53|-0.74|
||(0.18)|(0.41)|(0.43)|
|No response|-2.46**|<br>-0.76*|<br>-2.27|
||(0.77)|(1.54)|(2.32)|
|Lambda|-1.06|-2.52**|-3.86**|
||( 0.62)|(0.94)|(1.28)|
|N|1619|402|<br>453|
|R<sup>2</sup>|0.21|0.23|0.60|

Note: The numbers are the regression coefficients; the standard errors are in parentheses. ***p < .001, ** p < .01, p < .05.
non-health related country specific factors; **_B-a_** vector of coefficients of X; and E-an error term. In order to capture the direct effects that governance may have on health status, the variable **_GC_** is included as an independent term in the equation.

# _4.2 Data and choice of variables_

Our empirical analysis uses annual data for 1990 and 1997 (see Appendix A for more information on data including sources). These two years are chosen because we have, for them, the necessary information on a fairly decent sample of countries. Moreover, we wanted to compare our results with those of Filmer and Pritchett (1999), who have also used 1990 as the year for their cross-national study.

We study the impact of public health spending on child (under 5) and infant mortality. However, unlike previous researchers, we model the interaction between public spending and governance indicators in assessing this impact. Like Filmer and Pritchett (1999), the variable on public health spending that we use is available for a large cross-section of countries from the World Development Indicators that are put together by the World Bank. This variable measures total public spending on health; it is used because data on its composition across different health inputs are not available. We use two measures of governance-corruption and bureaucratic quality-to interact with public health spending. These indicators-measured on a scale of 0 to 6 (for corruption) and 1 to 6 (for bureaucratic quality) and available on a monthly basis-are put together by the U.S.-based Political Risk Services Group, which provides information on a regular basis for international businesses. The index of corruption measures corruption within the political system, which among other things reduces the effectiveness of government. The indicator of bureaucratic quality measures institutional strength and quality of the civil service. It assesses how much strength and expertise bureaucrats have. Other non-health related variables that we use in the regressions are standard in the literature; they include, among others, ethnolinguistic fractionalization, percentage of population that is Muslim, percentage of all females aged 15 and above that have attained primary education, percentage of population in urban areas, and the Gini coefficient which is a measure of income inequality. We also include a demographic variable-the percentage of population aged under-5 for the child mortality regressions and under-I for the infant mortality regressions, respectively.

_8_
**Table 4: Triple difference estimates of impacts from program participation**

||**PHASE 1**|**DDD**<br>**DISTRICT**|**S**|**DDD**|**and PSM **||
|---|---|---|---|---|---|---|
|Consumption (Rs/year)|1261|(428)|***|943|(377)|**|
|Energy intake (Kcal/day)|252|(79)|***|152|(94)||
|Protein intake (g/day)|3.25|(1.44)|**|2.07|(1.54)||
|Nonfinancial assets (Rs/year)|623|(443)||772|(403)|*|
|Consumption (log)|0.107|(0.039)|***|0.068|(0.042)||
|Energy intake (log)|0.094|(0.036)|***|0.056|(0.036)||
|Protein intake (log)|0.060|(0.032)|*|0.034|(0.037)||
|Nonfinancial assets (log)|0.405|(0.111)|***|0.359|(0.118)|***|
|Number of observations|1,017+1,4|10=2,427||1,000 +|1,345 = 2,345||
||**PHASE 2 an**|**d 3 DISTRI**|**CTS**||||
|Consumption (Rs/year)|-24|(594)||822|(621)||
|Energy intake (Kcal/day)|100|(112)||253|(116)|**|
|Protein intake (g/day)|2.47|(2.11)||5.80|(2.07)|***|
|Nonfinancial assets (Rs/year)|-412|(507)||113|(481)||
|Consumption (log)|-0.032|(0.059)||0.072|(0.062)||
|Energy intake (log)|0.043|(0.051)||0.108|(0.049)|**|
|Protein intake (log)|0.060|(0.048)||0.127|(0.050)|**|
|Nonfinancial assets (log)|-0.144|(0.229)||-0.113|(0.241)||
|Number of observations|439+ 1,|345 = 1,784||424 +|1,165 = 1,589||

_Source:_ Authors’ computation from household survey and administrative data. _Notes:_ All figures in per capita terms using the 2004, 2006, and 2008 panel data. DDD = triple difference estimation; PSM = propensity score matching. Robust standard errors are in parentheses. Significance level: *: 10%, **: 5%, ***: 1%.

19
Figure 3. Impacts of AGOA on apparel across years and sub-regions in Africa <!-- Start of picture text --> East Africa Central and West Africa Southern Africa<br>Year Year Year<br>2000200220042006200820102012201420162018 2000200220042006200820102012201420162018 2000200220042006200820102012201420162018<br>1.5 1.5 1.5<br>1 1 1<br>.5 .5 .5<br>0 0 0<br>-.5 -.5 -.5<br><!-- End of picture text --> Notes: figures show coefficients and 95 percent confidence intervals based on robust standard errors, clustered by HS 6-digit product obtained by estimating Eq. (1) allowing for separate coefficients in each year after 2001 for AGOA and for separate coefficients for each sub-region in Africa.

Figure 4: Apparel export growth decomposition <!-- Start of picture text --> Decomposition of Export Growth between 2010 and 2016<br>129.1<br>ETH 20.6 109.7<br>-1.2<br>47.5<br>KEN -0.5 88.2<br>-40.2<br>95.6<br>MDG 63.6 74.5<br>-42.5<br>29.0<br>MUS 2.8 30.6<br>-4.5<br>-50 0 50 100 150<br>Export Growth<br>Agg. Export Growth Continuing Exporters New Exporters Exiting Exporters<br><!-- End of picture text --> Notes: the figure shows long-term growth decomposition between 2010 and 2016 of apparel exports to the US for Ethiopia (ETH), Kenya (KEN), Madagascar (MDG) and Mauritius (MUS). The formula for the decomposition is provided in the Online Appendix.

43
<!-- Start of picture text --> A. Marginal impact of smoke<br>High omits day H<br>'1 —.&aLow smoke day Soybeans<br>'<br>High & day 'H<br>ta Corn<br>-0.2 -0.1 0.0 0.1 0.2<br>Percent change in yield from one additional smoke day<br>B. Average impact 2008-2020, corn C. Average impact 2008-2020, soybean<br>umes<br>pati4<br>a<itieeccaeneepoSSAa wieae = Pars]Siac’.;<br>SoNeWgaceesstgeemcasa’ (seneey__», coeteeWr/ue)<br>- Spergercataealage =| SBiinetSEirealtesiecic (oP sepaengaey‘Se ee,<br>r 4 Re eaea<br>* i fAelie Bie on<br>. ahh. “<br>ee<br>Se<br>-0.5 0.0 0.5 1.0 1 2 3<br>i<br>) 1 2 3<br>Percentage point change in the impact of smoke on yields<br><!-- End of picture text -->
- <mark>Costa, P., and R. McCrae. 1992. Revised NEO Personality Inventory (NEO-PI-R) and NEO FiveFactor Inventory (NEO-FFI) professional manual. Odessa, FL: Psychological Assessment Resources.</mark>

- <mark>de la Torre, A., M. Martínez Pería, a</mark> n <mark>d S. Schmukler. 2009. “Drivers and Obstacles to Banking SMEs: The Role of Competition and the Institutional Framework.” CESifo Working Paper No. 2651.</mark> Munich, <mark>Germany: Munich Society for the Promotion of Economic Research.</mark>

- <mark>De Mel, S., D. McKenzie, and C. Woodruff. 2008, “Returns to Capital in Microenterprises: Evidence from a Field Experiment.”</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>, 1</mark> 23 (4): 1329-72.

- De Mel, S., D. McKenzie, and C. Woodruff. 2010, “Who are the Microenterprise Owners?: Evidence from Sri Lanka on Tokman v. de Soto.” In: J. Lerner, and A. Schoar, editors. _International Differences in Entrepreneurship_ . Chicago and London: University of Chicago Press.

- Djankov, S., C. McLiesh, and A. Shleifer. 2007. “Private Credit in 129 Countries.” _Journal of Financial Economics_ 12 (2): 77–99.

- Ferraro, C., and G. Stumpo. 2010. _<mark>Política de Apoyo a las PYME en América Latina: entre avances innovadores y desafíos institucionales</mark>_ <mark>. Santiago, Chile: CEPAL.</mark>

- Hall, G. 1989. “Lack of Finance as a Constraint on the Expansion of Innovatory Small Firms.” In: J. Barber, J. Metcalfe, and M. Porteous, editors. _Barriers to Growth in Small Firms_ . London and New York: Routledge.

<mark>Haltiwanger, J., and C. J. Krizan. 1999. “Small Business and Job Creation in the United States: The Role of New and Young Businesses.” In S. Ackermann, editor.</mark> _<mark>Are Small Firms Important? Their Role and Impact</mark>_ <mark>, 79–97. United States: Springer.</mark>

- <mark>Haltiwanger, J., R. S. Jarmin,</mark> an <mark>d J. Miranda. 2013. “Who Creates Jobs? Small Versus Large Versus Young.”</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>95 (2): 347–61.</mark>

- <mark>Hijzen, A., R. Upward,</mark> an <mark>d P. W. Wright. 2010. “Job Creation, Job Destruction, and the Role of Small Firms: Firm‐level Evidence for the UK.”</mark> _<mark>Oxford Bulletin of Economics and Statistics</mark>_ <mark>72 (5): 621–47.</mark>

- <mark>Horrace, W., and R. Oaxaca. 2006. “Results on the Bias and Inconsistency of Ordinary Least Squares for the Linear Probability Model.”</mark> _<mark>Economics Letters</mark>_ <mark>90 (3): 321–27.</mark>

<mark>Ibarraran, P., A. Maffioli,</mark> and <mark>R. Stucchi, 2010. “Big Questions about Small Firms.” Cap. 9 In: C. Pages, editor.</mark> _<mark>The Age of Productivity: Transforming Economies from the Bottom Up</mark>_ <mark>. New York, United States: Palgrave Macmillan.</mark> 27
Between them the two demand‐side factors ‐‐‐that is, market size and transport costs‐‐‐ are stronger and more consistently observed drivers of revenue productivity across the 11 industries than the only supply‐side driver on which we have observations, namely, the cost of entry licensing.

# _I. Costs of utilities and financial services as supply‐side factors_

In this section, we report parameter estimates of a more general specification of the productivity equation explicitly controlling for supply‐side factors beyond those spelled out in the Meltiz and Ottaviano model. The extension leads to important nuances to our estimates of the effects of market size, trade costs and sunk costs of entry on aggregate productivity. Specifically, controlling for the cost of power and financial services in the productivity equations shows that indicators for sunk costs proxy as much for entry barriers as they would do for recurrent costs of utilities and tradable service input. This is particularly the case in food processing, textiles, garments and the cement industry. The reason is that the costs of power and financial services are both major factors in productivity in those industries.

The share of the cost of power and financial services is also comparatively high in the cost structure of in the wood work and metal work industries. Because firms of both industries tend to operate in larger towns and cities, the effects of local market size on aggregate productivity cannot be identified separately from the costs of power and financial services in those industries.

# _J.  “Other” Industries_

# **Food processing and the cement industry**

The first of these results comes out from the comparison of the estimates in Table 6 with those of Table 6B and those of Table 10 with those in Table 10B. Starting with the first pair, Table 6B shows that increases in the cost of power or in the cost of financial services would lead to significant decline in revenue productivity (TFPR) when we pool data across the four industries. The same effect is also observed on data within each of three of the four industries, namely, grain 23