rafmacalaba/datause-encoder-data huggingface dataset:
arising from ownership of large holdings; and (iii) processing plants that transmit their economies of scale to farms, usually giving rise to wage plantations. A summary of the basic theoretical context is followed by a brief summary of these sources of economies of scale are followed by a brief description of international empirical findings and related issues. For a more detailed discussion on these, see Binswanger, Deininger and Feder (1993) and Johnson and Ruttan (1994).

In theory, economies of scale are defined by a production function which exhibits a more than proportional increase in output for a given increase in magnitude of all inputs. In practice, the concept provides problems as there rarely is a situation when an increase in magnitude of some inputs does not imply a change in the factors of production (Peterson and Kislev, 1991). The general consensus of researchers on economies of scale is that they do not exist, except under very special circumstances. Empirical studies typically find constant returns to scale (see, amongst others, Johnson and Ruttan, 1994; Peterson and Kislev, 1991), although lumpy inputs, credit and risk diffusion, and processing plants can be important sources of economies of scale.

# _Sources of Economies of Scale_

Lumpy inputs: Farm machinery -threshers, tractors and combine harvesters- are lumpy inputs, and reach their lowest cost of operation per unit at relatively large areas. With the advent of agricultural mechanization many people believed that the economies of scale associated with it are so large that it makes the family farm obsolete. Small owners would sell or lease their land to larger operators. However, it became quickly apparent that machine rental can permit small farmers to circumvent the economies of scale advantage associated with machines in all but the most time-bound of operations, such as ploughing and planting (seeding) in dry climates or harvesting where climatic risks are high. In those situations farmers compete for early service and therefore prefer to own their own machines. Thus, economies of scale associated with machines do increase the minimum efficient farm size, but by less than expected because of rental markets. The use of lumpy inputs leads to an initial segment of the production function that exhibits increasing returns with operational scale, but these technical economies vanish when farm size is increased beyond the optimal scale of lumpy inputs or when rental markets make the lumpiness of machines irrelevant.

Management skills, like machines, are an indivisible and lumpy inputs, so the better the manager, the larger the optimal farm size. Technical change strengthens this tendency. The use of fertilizers and pesticides, and arranging the finance to pay for them, require modern management skills. So does the marketing of highquality produce. In an environment of rapid technical change, acquiring and processing information becomes

> 3 These issues are also addressed in World Bank (q99$)
expected to be less productive (e.g., La Porta & Shleifer 2008), the actual dispersion is likely several times higher.<sup>1</sup> In this context, understanding mechanisms of expanding within productivity growth can be critical for Mozambique’s economic development, and a strategy that can complement well the continuation of policies to promote investment and economic transformation. Specifically, for policy, learning about the role firm capabilities can have in raising within sector productivity is important to potentially identify new drivers of growth for Mozambique.

Early studies showed that Mozambique scores low in measures of firm capabilities. It is ranked 137 out of 138 in the World Economic Forum’s Global Competitiveness Index (GCI), mostly due to low scores on weak higher education and training and limited business sophistication, and is ranked 148 out of 157 in the World Bank’s Human Capital Index, which measures the relative productivity of today’s generation based on their educational and health attainments. In an international comparison of management practices across 34 countries, firms in Mozambique reported the lowest adoption of business practices (Lemos & Scur 2014).

This paper uses new firm-level data on management and organizational practices, and firm-level skills, that was collected in 2018 as part of the World Bank’s Enterprise Survey to better understand the relationship between firm capabilities and firm performance in Mozambique. It expands from an early study about management practices (Lemos & Scur 2014) by covering also skills and training, and a wide range of sectors and locations in the country, in the context where Mozambique is facing a more difficult economic environment than in the high-growth period of 2000-2015. It also explores the importance of competition, contract enforcement and transaction costs in explaining differences in firm capabilities. Finally, it studies for the first time the relationship between firm capabilities and performance in Mozambique.

We find that larger firms, male-owned, with relationship with international markets (exporters, buying inputs from outside, with licensed technology), and those using internet, are more likely to have better firm capabilities. Measure of competition, captured by Herfindahl-Hirschman Index (HHI), however, does not have significant effect on firm capability in Mozambique, contrary to

> 1 On average, informal firms in Mozambique are 14 times less productive than micro formal firms. See also Aga et al. 2019.

3
35 Table 2. Descriptive Statistics: pre-determined municipal controls

|Variable|Year|Obs.|Mean|Standard Deviation|Category|
|---|---|---|---|---|---|
|Per capita GDP (Millions)|2005|1,097|6.381|6.632|Economic Growth|
|Night Light Density|1995|1,048|3.968|7.466|Economic Growth|
|GINI|1993|1,043|0.456|0.0378|Poverty and Inequality|
|Subsidized Health System Cov. (%Pop.with UBN)|1998|1,136|0.716|0.411|Poverty and Inequality|
|Unsatisfed Basic Needs (UBN, % Households )|1993|1,035|52.98|19.21|Poverty and Inequality|
|Number of Financial Institutions|1995|1,046|1.754|8.922|Institutions|
|Number of Tax Collection Ofces|1995|1,046|36.05|182.4|Institutions|
|Informal Labor* (% Household)|2005|1,114|0.949|0.0571|Labor Market|
|Municipal Tax Income (Millions)|1995|1,098|1,033|16,066|Government Finance|
|Mun. Public Expenditure (Thousands)|1995|1,098|2,909|28,866|Government Finance|
|Central Gov.Transfers (Millions)|1995|1,098|1,168|5,348|Government Finance|
|Homicide Rate (per 100,000 Indv.)|1995|1,048|52.92|66.89|Confict and Violence|
|Hectares of Coca Crops|1999|1,124|142.5|960.2|Confict and Violence|
|N. of Terrorist Attacks|1993|1,124|0.657|2.628|Confict and Violence|

Notes: *Informal Labor is a dummy variable equal to one if less than 100% of the economically active population within a household does not contribute to the pension system.
RELPOL ranges between 0.32 and 0.59 while in the non-MENA cases, RELPOL needs to be higher, above 0.6 for external intervention to have a statistically significant effect on conflict incidence (Figure 3). At the averages for RELPOL in the data, the estimated marginal effect of intervention in the case of MENA is about 0.25 compared to 0.07 in the non-MENA case.

**Table 4 Logit Regressions for the Incidence of Civil Wars (PRIOCW) in the Presence of Regional Dummies (1965-1999)**

||**(1)**<sup>**1**</sup>|**(2)**<sup>**2**</sup>|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|---|---|---|---|---|---|---|---|---|
|LGDPPC|-0.41*|-0.41|-0.35|-0.40|-0.60*|-0.60*|-0.46|-0.50**|
|LPOP|0.38***|0.45***|0.46***|0.46***|0.54***|0.50***|0.49***|0.39**|
|PRIMEXP|-1.15|-2.19|-2.54|-2.55|-2.67|-2.61|-2.81|-2.22|
|MOUNTAINS|-0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|NONCONT|0.09|0.44|0.52|0.42|0.45|0.33|0.39|0.48|
|DEMOCRACY|0.09|0.62|0.56|0.66|0.66|0.64|0.72|0.38|
|ETHFRAC|0.26|0.63|0.41|0.12|0.01|0.21|0.08|0.05|
|ETHPOL|**2.35*****|**1.96***|**2.49****|**2.21***|**2.64****|**2.18****|**2.44****|**2.59****|
|RELFRAC||||-2.81|-1.29|-1.89|-2.37||
|RELPOL||||1.91|-0.88|-0.31|1.30||
|Int_nh|||1.28***|**-0.02**|**0.31**|**0.16**|0.25||
|MENA|**Excluded**|2.44**|2.09**|2.22**|0.31|0.74|1.75*||
|SAFRICA|Included|1.03|1.03|0.81|0.11|-0.59|0.90||
|LAAM|Included|0.49|0.51|0.40|-0.19|-2.45|0.40||
|ASIAE|Included|1.00|0.90|1.27|1.18|1.84|1.57*||
|Int_nh˟RELPOL||||2.50***|2.04*|2.11**|1.69|**1.04**|
|Int_nh˟ETHPOL||||-0.37|-0.59|-0.41|-0.82||
|RELPOL˟MENA|||||**5.55****|**5.17***|||
|RELPOL˟SAFRICA|||||1.84|1.69|||
|RELPOL˟LAAM|||||1.89|0.78|||
|RELPOL˟ASIAE|||||0.49|0.54|||
|ETHPOL˟MENA||||||-0.61|||
|ETHPOL˟SAFRICA||||||0.98|||
|ETHPOL˟LAAM||||||3.72|||
|ETHPOL˟ASIAE||||||-1.22|||
|Int_nh˟RELPOL˟MENA|||||||**3.53***|**5.43*****|
|Int_nh˟RELPOL˟AFRICA|||||||**0.71***|**1.04**|
|Int_nh˟RELPOL˟LAAM|||||||**1.00**||
|Int_nh˟RELPOL˟ASIAE|||||||**-3.08**||
|Intercept|-6.07**|-8.40**|-9.35**|-8.06**|-8.06*|-7.23*|-8.70**|-6.14**|
|<br>N|846|859|859|859|859|859|859|859|
|McFadden’s R<sup>2</sup>|**0.127**|**0.169**|0.203|0.240|0.240|0.245|0.237|0.208|
|McFadden’s Adjusted R<sup>2</sup>|**0.093**|**0.133**|0.165|0.179|0.179|0.173|0.176|**0.175**|

1 Column (1) here is column (2) in Table (5) of Montalvo and Reynal-Querol (2005), who do not have a dummy variable for MENA and include the MENA countries into the reference group.

2 Column (2) here is column (2) in Table (5) of Montalvo and Reynal-Querol (2005), with the addition of MENA dummy to the regression.

27
Overall, the government-guaranteed debt for the entities at the close of 2002 totals DKK 77.8 billion.<sup>85</sup>

# **5.1.4 The Danish Mortgage backed bond market**

With a market value of DKK1.298 billion (equal to 96.5% of Danish GDP) and a turnover of DKK 3.148 billion during 2001, the Danish mortgage bond market is the second largest in Europe (second only to the German Pfandbriefe market) and the largest in the world in per capita terms.<sup>86</sup> The first Danish mortgage bank was set up in 1797 as a direct consequence of the need to finance the rebuilding of Copenhagen after a great fire in 1795 (see also Chapter 5.3.1.3 - Box 8). The fire resulted in the creation of a system for investment in real property with an intermediary – a private mortgage credit institution (MCI) - between investors and lenders. Since the mid-19th century, the MCIs have taken up a predominant position in the financing of real property in Denmark.<sup>87</sup> MCIs normally offer property funding through callable bonds. These mortgage bonds usually have an initial maturity of 30 years. In recent years, MCIs have issued adjustable rate mortgage loans, which are based on non-callable mortgage bonds. These mortgage bonds have a shorter maturity and the interest rates may change over the term of the loan. The Danish mortgage credit bond market is also characterized by a very large number of series (see Table 3).<sup>88</sup> The market concentration is relatively high, partly due to a de facto prohibition by government for new banks to enter into the mortgage market from 1970 to 1989, when free access to the market was introduced via the adoption of a new, more liberal mortgage credit act (see Chapter 5.3.1.3 – Box 8).

> 85 In addition to the above entities, also another entity, Ørestadsselskabet, is subject to the guidelines for government-guaranteed entities.  However, since the entity is a general partnership of which the central government is a co-owner no government guarantee is provided for the entity's borrowing.  Ørestadsselskabet has had access to re-lending for a number of years.  The extent of re-lending to the entity was DKK 9.5 billion at the end of 2002.  In 2002, A/S Storebælt and A/S Øresund gained access to borrow via re-lending.  In 2002, these two government-guaranteed entities took up re-lending for a total of DKK 3.0 billion.

> 86 Source: The Danish Mortgage Credit Association.

> 87 Until mid-1980s, mortgage bonds were considered the Danish benchmark bonds.  During the 1980s, the Danish government bond market expanded due to several years of very large public deficits.  Today, with public surplus for several years and subsequently pay-back of government bonds, the situation is the opposite, and this may ultimately lead to a situation when mortgage bonds may regain their position as Danish benchmark bonds in some market segments.

88 The very high number of series also gives Copenhagen Stock Exchange (CSE - the domestic stock exchange) a special function in the Danish fixed income market compared to other stock exchanges where domestic fixed income markets are often based on a few, highly liquid government bond issuances.

70
The potential effect of re-raking is illustrated by Table 7. It represents the share of the population belonging to households with different types of welfare dynamics: chronically poor (below low poverty line in both periods),<sup>18</sup> those who have experienced sharp and slight falls in living standards, and those who moved up a ladder of welfare. Table 7 suggests that while 36 percent of the population (22+14 percent) have experienced improvement in their position, and a further 16 percent managed to gain in line with the average, thus preserving their position as non-poor, as many as 48 percent of the population have either stayed in poverty or experienced losses – and for 17 percent there were very deep losses (movement down by more than two deciles).

**Table 7: Distribution of Population by Welfare Dynamics, 2005-2008**

**Percent of population (Total=100)**

|**Category**|**Percent of**<br>**population**|
|---|---|
|Chronic poor*|10|
|Deep falls in welfare (down by >2 deciles)|17|
|Slight falls in welfare (a fall to a next decile)|21|
|Preserving ranks as non-poor|16|
|Slight improvement in welfare over 2005-08|22|
|Big jump ahead in welfare (up by >2 deciles)|14|

_*Note: Lower poverty line. Source: Authors estimates based on panel HIECPS survey 2005-2008._ Moreover, when only one third of those belonging to the lowest decile in the initial period preserve their ranks three years later, the meaning of ‘the poor’ in the definition of pro-poor growth from the cross – section perspective simply does not make sense: these are predominantly different people in 2008 compared to 2005. Hence, other measures are required to capture the effect of growth on the non-poor in terms of degree of their vulnerability to become poor. Growth may have dramatically different effects on poverty depending not only how it (positively) affects the poor, but also how it may (negatively) affect the non-poor. Further analysis is warranted to decompose the degree of “pure mobility” (i.e. whether mobility was greater the poorer the individual) from the “re-ranking” effect that we can observe only in the panel. A potential approach is the one proposed by Nissanov and Silber (2009), which tries to

> 18 The analysis of poverty in Egypt is based on household consumption (and not on expenditures or income), and due to “smoothing” of consumption by households in the face of income fluctuations, it is believed to be the most stable measure of household welfare. It is therefore justifiable to assume that if a household is observed to be in poverty at both observation points – 2005 and 2008 – this household was also likely to have stayed in poverty between these points, and will remain poor for some time.  Such a household is called “chronically poor”, even though just two observations three years apart are available.

19
Figure 2: Migration outflows and forced displacement from Donbas, annual averages (a) Pre-conflict migration outflows (2008-12) (b) IDP outflows after the conflict (2014-19) <!-- Start of picture text --> Kharkiv (5,540)<br>Donetsk (11,227)<br>Kyiv City (3,197)<br>Luhansk (6,720) Dnipropetrovsk (1,918) Kyiv City (30,743)<br>Zaporizzhia (1,139)<br>Kyiv (1,038)<br>Odesa (825)<br>Poltava (636)<br>Sumy (432)<br>Cherkasy (394) Donetsk (84,006)<br>Vinnytsia (349) Kharkiv (26,354)<br>Chernihiv (349)<br>Kherson (325)<br>Zhytomyr (254)<br>Khmelnytsky (250)<br>Mykolaiv (245)<br>Kirovohrad (231) Dnipropetrovsk (14,003)<br>Lviv (213)<br>Ivano-Frankivsk (116)<br>Volyn (112)<br>Rivne (109)<br>Zakarpattia (101) Kyiv (11,935)<br>Chernivtsi (95)<br>Ternopil (79)<br>Zaporizzhia (11,064)<br>Luhansk (43,912)<br>Odesa (7,261)<br>Poltava (4,492)<br>Kherson (2,764)<br>Sumy (2,229)<br>Vinnytsia (2,203)<br>Cherkasy (2,173)<br>Lviv (2,148)<br>Mykolaiv (1,631)<br>Chernihiv (1,454)<br>Zhytomyr (1,404)<br>Kirovohrad (1,297)<br>Khmelnytsky (1,223)<br>Ivano-Frankivsk (743)<br>Zakarpattia (664)<br>Volyn (613)<br>Rivne (612)<br>Chernivtsi (489)<br>Ternopil (419)<br><!-- End of picture text --> _Notes:_ Panel 2a shows gross migration outflows from Donetsk and Luhansk oblasts between 2008 and 2012 by destination, with average annual numbers in parentheses. Panel 2b does the same by using annualized official internal displacement numbers (IDPs) between 2014 and 2019.

Like in other conflict situations, Ukraine faced major data constraints after the onset of the conflict. Most importantly, a complete record of the gross bilateral migration flows among Ukrainian regions is not available in the post-conflict period. Instead, we use information on the IDPs from Donbas as an indicator of the conflict-driven out-migration. By October 2019, the total number of officially registered IDPs were reported at 1,412,589 (21 percent of the combined population of Donetsk and Luhansk oblasts before the conflict). About 55 percent of all IDPs were registered within Donbas, while the rest was registered elsewhere in Ukraine. This outflow exhibited a pattern similar to the pre-conflict migration outflows, 12
# WPS4088

## The Impact of Commodity Price Changes on Rural Households:

## The Case of Coffee in Uganda<sup>♦</sup>

Maurizio Bussolo*, Olivier Godart**, Jann Lay*** and Rainer Thiele***

**Abstract:** Policies and external shocks affecting agriculture, the main source of income for rural households, can be expected to have a significant impact on poverty. This paper studies the case of Uganda. Throughout the 1990s, more than 90 percent of its poor lived in rural areas and, during the same period, large international price fluctuations as well as an extensive domestic deregulation affected the coffee sector, its main source of export revenues. Using data from three household surveys covering the 1990s, this paper confirms a strong correlation between changes in coffee prices (in a liberalized market) and poverty reduction. This is clearly highlighted by comparing the performance of different households grouped according to their dependence on coffee farming. Regression analysis (based on pooled data from the three surveys) of consumption expenditure on coffee-related variables, other controls and time fixed effects, corroborates that the mentioned correlation is not spurious. We also find that while both poor and rich farmers enter the coffee sector, the price boom benefits relatively more the poorer households, whereas the liberalization seems to create more opportunities for richer farmers. Finally, notwithstanding the importance of the coffee price boom, the agricultural policy framework and the thorough structural reforms in which the coffee market liberalization was embedded have certainly played a role in triggering overall agricultural growth. These factors appear to matter especially in the second half of the 1990s when prices went down but poverty reduction continued.

### World Bank Policy Research Working Paper 4088, December 2006

_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._

> ♦ We would like to thank Simon Appleton and Aylin Isik-Dikmelik for help with the household surveys and Ataman Aksoy and two anonymous referees for comments and suggestions on earlier drafts of the paper. This research was funded by the Dutch Government under the BNNP program “Agricultural Trade Reforms and Their Impact on Rural Poverty”.

* World Bank ** University of Nottingham, UK *** Kiel Institute for the World Economy, Germany
constitutional autonomy is constrained by the Indian civil service traditions under which courts operate, as well as the social dependence of judges on the Bar and on political and economic elites. “Leaders of the Indian Bar,” writes Baxi, “whether elected or otherwise, have always wielded historically disproportionate influence over the Justices.” (U Baxi, 2006) In addition to the embedding of judges in their social and political milieu, strategic factors also limit judicial autonomy: courts in every country depend on the other branches of government for their political survival, so they will, by and large, take care that their decisions garner sufficient support from key political actors (Epstein & Knight, 1998; Gauri & Brinks, 2008b). In short, because ideological orientations in the larger Indian political economy have drifted rightward over the past two decades or so, it is to be expected that the courts have followed. Assessments of the extent to which PIL supports the poor, then, need to develop an implicit benchmark of what can be expected of courts in the specific contexts in which they operate.<sup>6</sup> There are a set of empirical questions on PIL answers to which might guide these assessments: 4. What share of PIL cases have claimants who are not poor, and has this share changed over time?

5. How many PIL cases have NGOs or CSOs as counsel or supporters, and has this changed over time? This is important because NGO- or CSO-led cases typically involve collective claims that have a greater likelihood of generalizing benefits to an entire class of similarly situated people, such as the poor or scheduled castes, scheduled tribes, and other backward classes (SC/ST/OBC).

6. What is the win-loss rate for PILs, and for PILs of different types? Do these winloss rates vary with the social class of the claimants?

To fill in _some_ of the information needed for a more complete assessment of PIL in India, four different samples of PIL and Fundamental Rights cases at the Supreme Court level were examined: (i) cases that, according to the Supreme Court registrar’s office, the Court has itself classified as PIL from 1988-2007 (some 2800 “cases” overall); (ii) all Supreme Court cases in the Manupatra database that involved Fundamental Rights and that addressed concerns regarding women and children rights, whether or not explicitly admitted as PILs (86 cases); (iii) all Supreme Court cases in the Manupatra database that involved Fundamental Rights and were related to issues regarding SC/ST/OBCs, whether or not explicitly admitted as PILs (180 cases); (iv) all Supreme Court cases in the Manupatra database that the Supreme Court explicitly called a PIL (44 cases).

> 6 For an extended discussion of inequality in social and economic rights litigation, see (Brinks & Gauri, 2008)

9