rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8387

# **Abstract**

This paper examines the agricultural productivity–farm size relationship in the context of Bangladesh. Features of Bangladesh’s agriculture help overcome several limitations in testing the inverse farm size–productivity relationship in other developing country settings. A stochastic production frontier model is applied using data from three rounds of a household panel survey to estimate simultaneously the production frontier and the technical inefficiency functions. The “correlated random effects” approach is used to control for unobserved heterogeneous household effects. Methodologically, the results suggest that the stochastic production frontier models that ignore the inefficiency function are likely mis-specified, and may result in misleading conclusions on the farm size–productivity relationship. Empirically, the findings confirm that the farm size and productivity relationship is negative, but with the inverse relationship diminishing over time. Total factor productivity growth, driven by technical change, is found to have been robust across the sample. Across farm size groups, the relatively larger farmers experienced faster technical change, which helped them to catch up and narrow the productivity gap with the smaller farmers.

This paper is a product of the Agriculture Global Practice. 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 mgautam@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
(and men) are asked whether they have a say in decisions about different issues, from large household expenses to personal health care, and mobility.<sup>19</sup> The top left graph in Figure 4 plots, for each birth cohort, the share of women who have a say in at least one of the three household decisions. The remaining group is thus that of women who have no say at all. Apart from Maldives, women born in recent years have less agency than women born in early years. This is especially the case for women in Afghanistan, Nepal, and Pakistan. To unpack the trends of this aggregate indicator, the separate trends of the three specific decision are shown in the Annex (Figure A2). In general, we observe near-identical trends of the three separate decisions to that of the aggregate agency shown in Figure 4.

Agency, like employment, has a life-cycle profile, as shown in Figure 8 for Nepal.<sup>20</sup> Not surprisingly women’s bargaining might increase due to having children, increased wealth, and other age-related factors (Mahmud et al. 2012; After and Chindarkar 2020). It is thus necessary to compare cohorts at the same age to be able to gauge more precisely the cohort effect. Apart from Bangladesh (Figure A5 in the Annex), there is no clear evidence that women of younger generations have higher agency than women of older generations at the same age. While in other countries such as Nepal, there is an initial rise for each cohort which then flattens out as the cohort ages, in Bangladesh (Figure A5 in the Annex ) the case is different. Women born in recent cohorts in Bangladesh have more agency, at a particular age, than women born in the preceding years. The gap is however almost negligible.

Agency, and more generally women’s empowerment, is influenced by age and education differentials among spouses.<sup>21</sup> The two middle panels of Figure 4 show these differentials, which are calculated by subtracting the years of schooling and age of a woman from the years of schooling and age of their spouse. In most countries, a positive gender gap in education remains between men and women across time but for more recent cohorts in Bangladesh and Maldives, the gap is negative. The reversing of the gap in Bangladesh is encouraging and may be a consequence of targeted policies for girls’ education. The

> 19 Questions included here are those referring to who has a say on (i) large household purchases, (ii) woman’s health care, and (iii) women’s visits to relatives and friends. Possible answers are: (a) respondent alone; (b) respondent and husband/partner jointly; (c) husband/partner alone; (d) someone else; and (e) other. Having a say is defined as choosing answers (a) or (b).

> 20 The age profiles of agency in Bangladesh and India show that women older than 30 years have higher agency within their households on average than women less than 30 (Figure A4 in the Annex).

> 21 See for example Carmichael, 2011 and Mogford, 2011, Nyyssola 2007 for different implications of the age and education gaps among spouses.

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where ZP<sup>i= 0fori = A, I,Gi= 0fori = I,andEi= 0fori = I, G.</sup> Actual investment adjusts to its desired level through a partial adjustment mechanism: where 0 < λPINV < 1.

The private capital stock depends on the flow level of investment and the depreciation rate of capital from the previous period, 0 < δP < 1:

# 2.8 Asset Allocation and the Credit Market

We consider in turn the determination of the portfolio structure of each category of households, the demand for credit by firms, and the behavior of commercial banks. The balance sheets of all agents (including the central bank and the consolidated public sector) are summarized in Table 1.

## 2.8.1 Households

Households’ financial wealth is allocated to five categories of assets: domestic money (cash holdings, which bear no interest), Hh, domestic currencydenominated bank deposits held at home, DDh, foreign currency-denominated deposits held domestically, FDh, foreign currency-denominated deposits held abroad, FDh<sup>W,and holdings ofgovernmentbonds,GBh.By allowing house-</sup> holds to hold foreign-currency denominated deposits in the domestic banking system, we therefore account for the high level of dollarized liabilities of the Turkish financial system. Indeed, as shown in Figure 1, such deposits continue to account for a sizable share of total bank deposits in Turkey.<sup>22</sup> Given liabilities of DLh, net financial wealth, WTh, is defined as As noted earlier, rural and informal sector households hold no foreigncurrency deposits, banks lend only to urban formal sector households (in

> 22See Civcir (2002) for a discussion of dollarization in Turkey. Note that our specification of dollarization on the asset side of banks’ balance sheets accounts only for foreign-currency denominated loans to firms, not households.

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# **2. Methodology**

The objective was to apply a hydrological model across the whole continent of Africa rather than to any particular catchment or water resource system. The model inputs were the climate variables of the 1961–1990 climatology and physiological parameters (e.g. soil properties and land use) derived from global datasets for each of the 0.5<sup>o</sup> latitude/longitude cells across the continent. The primary model output comprised a time series (monthly time step) of simulated runoff for all the grid cells. For each of the districts in the countries of interest the runoff was computed as the areal weighted average of the runoff generated on all the cells that lay within that district. When a district lay entirely within a cell the runoff was assumed to be the same as that across the cell. No attempt was made to disaggregate data within cells. The runoff was also accumulated and ‘routed’ via a drainage network to simulate flow. This enabled flow into and out of each district to be estimated.

# **_2.1 Model description_**

To address scientific issues at the continental scale, hydrological models must characterize the dispersed nature of climate and hydrology over space and time while avoiding excessive complexity. In the current study, simplification was required not only to ensure reasonable computing time, but also to develop a generic form of the model applicable to a wide range of conditions across the continent.

The model used is a version of a conceptual rainfall-runoff model called WatBal (Yates 1996), which can be applied to gridded data. The model simulates changes in soil moisture and runoff. It is essentially an accounting scheme based on a conceptualized, onedimensional bucket that lumps together both the root and upper soil layer. The model comprises two elements. The first is a water balance component that describes water movement into and out of a conceptualized basin (Figure 2). The second is the calculation of potential evapotranspiration, which, in the gridded version of the model, is computed using the FAO Penman-Monteith approach (Monteith 1965). The simplified representation of soil moisture dynamics has been shown to adequately represent runoff changes due to climate fluctuations (Yates & Strzepek 1994; Yates1997).

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25 Table 3: Descriptive statistics for household outcomes, 2003

||NCMS hou<br>|seholds<br>|Non-N<br>househo<br>NCMS co<br>|CMS<br>lds in<br>unties<br>|Household<br>NCMS c<br>|s in non-<br>ounties<br>|
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD|
|Doctor visit last 2 weeks|7%|0.25|7%|0.25|8%|0.27|
|Inpatient last 12 months|3%|0.18|2%|0.16|3%|0.16|
|No. inpatient spells last 12<br>months|0.04|0.22|0.03|0.20|0.03|0.21|
|Outpatient visit village clinic|4%|0.19|3%|0.18|4%|0.19|
|Outpatient visit THC|2%|0.13|2%|0.13|2%|0.14|
|Outpatient visit county hospital|1%|0.12|2%|0.13|2%|0.14|
|Inpatient THC|1%|0.09|1%|0.08|1%|0.08|
|Inpatient county hospital|2%|0.15|2%|0.13|2%|0.14|
|Household health care expenses<br>last 12 months|260|597|246|543|221|913|
|Catastrophic payments (> 10% of<br>income)|37%|0.48|32%|0.47|45%|0.50|
|Catastrophic payments (> 20% of<br>income)|17%|0.38|15%|0.35|21%|0.41|
|Catastrophic payments (> 40% of<br>income)|6%|0.23|3%|0.18|8%|0.27|
|Net out-of-pocket payments per<br>outpatient visit|10|153|8|78|13|191|
|Net out-of-pocket payments per<br>hospital stay|100|918|73|767|81|1107|
|Net out-of-pocket payments for<br>deliveries|65|451|55|373|16|150|
|Out-of-pocket payments for self-<br>treatment|3|60|5|93|4|71|
|Net out-of-pocket payments<br>associated with ambulatory care|56|834|42|370|63|749|
|from formal providers|||||||
|Net out-of-pocket payments for<br>inpatient care|100|918|73|767|81|1107|
|Additional hospital expenditures|12|117|8|93|9|102|
|Total out-of-pocket payments<br>reported by individual|236|1417|185|1065|172|1419|
|Insurance contributions|46|291|30|212|4|38|
|Total health expenses reported by<br>individual|284|873|215|665|176|872|
between various firm characteristics such as size, age, export status, ownership and so on with the observed management score. Section 4 presents a simple regression framework for assessing the link of management practices with firm performance. Section 5 explores the drivers of better management practices. The last section concludes with policy recommendations.

# **<u>Section 2: Critical Firm Capabilities: Management in Focus</u>**

# _2.1 Why do we need a management survey?_

Although managerial and organizational practices have been conceived to be critical in affecting firm outcome since the time of Adam Smith’s pin factory and Walker’s 1887 paper on business performance, these practices are not well measured in administrative data on firms or the commercial data on companies’ accounts. Some studies have used information on publicly traded firms to link managerial style of senior managers and company performance (e.g., Bertrand and Schoar, 2003), this data is rather limited to a country such as the United States and there is evidence that suggests a company is far more than simply the identity of its most senior employee (Bender et al., 2015). Furthermore, this type of data on its own does not tell much about how firms are managed or organized. Therefore, over the last decade Bloom and his co-authors have attempted to fill this gap in data by collecting comprehensive information on management practices (see Bloom et al., 2014).

In general, three broad management and organizational capabilities that are generally recognized to be important for firm productivity (Bloom et al., 2016b): (i) performance monitoring: collecting and analyzing information, on daily activities of the firm for continuous improvement, (ii) target setting: using and stretching short and long run targets, tracking outcomes, and taking appropriate action, and (iii) performance incentives: rewarding high-performing employees, and retraining or moving underperformers and careful hiring. To collect information on these practices, two alternative strategies could be deployed: (i) Open Ended questions (e.g. world management survey, WMS)<sup>11</sup> and (ii) Closed Ended questions (e.g. Management and Organizational Practices Surveys, MOPS).<sup>12</sup> **Table 2** summarizes the comparison of the two approaches on a number of dimensions.

> 11 Designing these surveys take some expertise in terms of selecting questions and response grids and hence intense training and monitoring of the interview team. The full questionnaire is available on <u>www.worldmanagementsurvey.com.</u> 12 The full questionnaire is available on http://bhs.econ.census.gov/bhs/mops/form.html
# **I. Introduction**

In Western developed countries, modernization and economic development have been commonly associated with the availability of a pool of commercial, entrepreneurial, professional, and technical abilities which generally tend to be drawn from those occupational groups often referred to as _middle class_ (Geithman, 1974). The importance of this rather heterogeneous social group has spurred an intense debate on its definition. Back in the 19<sup>th</sup> century, the list of political scientists, sociologists, philosophers and economists that analyzed the phenomenon is long: Marx, Weber, Sombart, Shumpeter and many others.

While in the past definitions have focused on the functional role of the middle class, occupational composition (e.g. coincidence with the white collar group) or on its specific system of values,<sup>1</sup> more contemporary analysis of middle class has taken advantage of available information on household welfare characteristics; the middle class definition has, thus, progressively shifted to a quantitative measurement problem. Economists have started to take the lead in framing the issue in terms of income, or income boundaries within which households or individuals can be categorized as middle class (Ferreira et al., 2013).

The increasing availability of household level data in developing countries has opened the opportunity to conduct middle class analysis there also. It is important, however, to bear in mind one key point. Despite the usefulness of the theories and methods so far refined for developed countries, one might argue that the dynamic forces of change and modernization in many developing countries may be radically different from those that operated in the presently developed countries. The very notion of middle class, thus, can assume a different meaning in the two contexts.

An historical overview can shed light on this. The concept of middle class is rooted in the rise of 19<sup>th</sup> century capitalism in Europe and the United States. The development of the capitalist system reshaped Western societies by creating new classes such as the proletariat, the urban manufacturing workers that became, in more advanced Western countries, the most numerous class at the bottom of the income distribution. The bourgeoisie, already existing in pre-industrial Europe, also experienced sharp modifications. Within this class, the least affluent part represented by clerks, small entrepreneurs, shop keepers, artisans was increasingly identified as petty bourgeoisie (Hobsbawm, 1975). Poorer than the core bourgeoisie ( _haute bourgeoisie_ ) but aspiring to become that, the petty bourgeoisie was very

> 119th century French literature is a particularly rich source on the middle class system of values. Above all, Maupassant and Balzac are sharp observers, though very critical, of the rising French middle class.

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# **Table 8.1: Determinants of Lending to SMEs (OLS) Dependent Variable: Share of SME Loans in Total Bank Loans**

Regressions are estimated via OLS at bank level for the year 2007 to 2009. Robust t statistics in brackets and (bank level) clustered t statistics in parenthesis.* significant at 10%; ** significant at 5%; *** significant at 1%

||1|2|3|4|5|6|7|8|9|10|11|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Log Total loans|**-1.47**<br>[3.81]***<br>(2.50)**|**-2.53**<br>[4.60]***<br>(3.15)***|**-2.53**<br>[4.59]***<br>(3.14)***|**-2.26**<br>[4.08]***<br>(2.78)***|**-1.33**<br>[2.48]**<br>(1.70)*|**-1.09**<br>[2.28]**<br>(1.52)|**-2.44**<br>[6.33]***<br>(4.18)***|**-2.53**<br>[6.51]***<br>(4.32)***|**-2.14**<br>[5.36]***<br>(3.49)***|**-1.72**<br>[4.22]***<br>(2.73)***|**-1.52**<br>[3.80]***<br>(2.57)**|
|GCC Dummy|**-9.36**<br>[7.08]***|**-4.13**<br>[2.13]**|**-6.14**<br>[2.80]***|**-7.44**<br>[3.46]***|**-11.71**<br>[4.98]***|**-5.41**<br>[3.11]***|**-5.19**<br>[3.20]***|**-6.21**<br>[3.61]***|**-8.14**<br>[4.23]***|**-9.79**<br>[4.75]***|**-5.36**<br>[3.60]***|
||(4.30)***|(1.34)|(1.74)*|(2.19)**|(3.15)***|(1.98)*|(2.03)**|(2.26)**|(2.67)***|(2.98)***|(2.30)**|
|State Ownership Dummy|**0.59**|**0.86**|**2.00**|**0.96**|**1.54**|**1.24**|**1.50**|**2.23**|**1.65**|**1.75**|**1.29**|
||[0.40]|[0.52]|[1.18]|[0.60]|[0.96]|[0.73]|[0.97]|[1.39]|[1.10]|[1.12]|[0.84]|
||(0.25)|(0.31)|(0.71)|(0.36)|(0.57)|(0.44)|(0.58)|(0.83)|(0.66)|(0.66)|(0.50)|
|Log Number of Branches||**1.58**|**1.20**|**1.72**|**0.69**|**0.17**||||||
|||[1.95]*|[1.43]|[2.16]**|[0.94]|[0.24]||||||
|||(1.27)|(0.92)|(1.42)|(0.62)|(0.15)||||||
|Separate Unit for SME Clients Dummy|||||||**6.39**|**5.25**|**6.12**|**4.45**|**2.86**|
||||||||[3.12]***<br>(1.96)*|[2.56]**<br>(1.62)|[3.05]***<br>(1.93)*|[2.18]**<br>(1.38)|[1.24]<br>(0.75)|
|Maximum Coverage Registry or Bureau||**0.00**|**-0.01**|**0.08**|**0.03**|**-0.08**|**-0.01**|**-0.02**|**0.06**|**0.01**|**-0.03**|
|||[0.04]|[0.22]|[1.69]*|[0.61]|[1.63]|[0.21]|[0.32]|[1.12]|[0.21]|[0.56]|
|||(0.03)|(0.17)|(1.37)|(0.43)|(1.28)|(0.16)|(0.25)|(0.94)|(0.15)|(0.43)|
|Legal Rights Index|||**1.92**|||||**1.53**||||
||||[2.27]**|||||[1.79]*||||
||||(1.36)|||||(1.09)||||
|Time to Register Property||||**-0.07**|||||**-0.06**|||
|||||[4.27]***<br>(2.96)***|||||[3.78]***<br>(2.58)**|||
|Time to Enforce a Contract|||||**-0.03**<br>[5.91]***|||||**-0.02**<br>[4.17]***||
||||||(3.77)***|||||(2.73)***||
|Time to Close a Business||||||**-2.48**|||||**-1.70**|
|||||||[3.67]***<br>(2.43)**|||||[2.62]***<br>(1.74)*|
|Partial Credit Guarantee % of GDP||**10.16**<br>[3.87]***|**10.31**<br>[3.95]***|**8.51**<br>[3.09]***|**7.19**<br>[2.66]***|<br>**11.91**<br>[4.32]***|**12.37**<br>[4.75]***|**12.47**<br>[4.92]***|**10.98**<br>[4.02]***|**10.12**<br>[3.91]***|<br>**13.58**<br>[5.06]***|
|||(2.49)**|(2.52)**|(1.98)*|(1.71)*|(2.74)***|(3.20)***|(3.32)***|(2.68)***|(2.63)**|(3.37)***|
|Observations|245|239|239|239|239|203|220|220|220|220|184|
|R-squared|0.32|0.35|0.37|0.37|0.42|0.39|0.39|0.41|0.41|0.42|0.39|
|Number of Countries|15|15|15|15|15|13|15|15|15|15|13|
|Number of Banks|96|96|96|96|96|82|88|88|88|88|74|
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