rafmacalaba/data-use-mentions-extended huggingface dataset:
One difference is that private institutions may only be willing to share negative information voluntarily even though it may be in a wider social interest to share positive and negative information – see Japelli and Pagano (1993). The intuition is relatively simple. Sharing information has a cost and a benefit. The cost for a bank is that a competitor may learn something about that bank’s portfolio and the benefit is that the bank may be able to make more informed lending decisions and to predict default probabilities more accurately. It is more likely that the private benefits exceed the costs for negative information as revealing information about bad clients is not so costly. However, there may be net private costs to revealing positive information even though it may be beneficial to society to have that information shared.<sup>11</sup> And a regulator may compel regulated institutions to share both positive and negative information. In more than 80% of the PCRs surveyed, institutions are required to report on loans in good standing as well as loans in arrears.<sup>12</sup> While private credit bureaus may be able to develop some of this positive credit history data, it is often a slow process and they are unlikely to be able to achieve full reporting from supervised institutions as in a standard PCR.

A second important difference between PCRs and private credit bureaus is the type of information collected and the type of services offered. PCRs in general only supply very basic information and many do not supply a true credit history, but only a snapshot of the current status of a borrower with the financial system. Private credit bureaus tend to supply a greater quantity of information and finer information and also develop other products using that information, including, for example, credit-scoring products. One view is that PCRs should only provide very basic information and at very low cost while private credit bureaus can take this information, add to it, and develop value added information services. In this way a PCR may enhance the competitiveness of the
**Figure 1: Durban Dwell Time (2006-2010) at DCT Pier 2**

<!-- Start of picture text --> 18.0 Import<br>16.0<br>Export<br>14.0<br>12.0 Tranships<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2006-01 2006-03 2006-05 2006-07 2006-09 2006-11 2007-01 2007-03 2007-05 2007-07 2007-09 2007-11 2008-01 2008-03 2008-05 2008-07 2008-09 2008-11 2009-01 2009-03 2009-05 2009-07 2009-09 2009-11 2010-01 2010-03 2010-05 2010-07 2010-09 2010-11<br><!-- End of picture text --> _Source: Transnet Port Terminal_ Dwell time for transshipments is around 5 to 10 days with a few irregular peaks at around the 15 days mark, notably between July and September<sup>7</sup> . This is also related to the fact that “free time”<sup>8</sup> for transshipment is set at 7 days (with low charges below 15 days)<sup>9</sup> .

It should be noted that cargo is generally moved from the terminal to bonded warehouses before the expiry of the free storage period of 3 days offered by TPT. Therefore, the information provided does not capture all dwell time figures for the port. Additional data were also collected from those involved in cargo handling for the period from January to December 2010. The data are based on accounting information from arrival at port to the time cargo is passed on to the owner. The data shows that on average most cargo is held for less than 4 days (referred to as overstays).

According to the main shipping lines/warehouse owners, less than 10% of containers go beyond the 3 day period and less than 1% go beyond 28 days. Based on these multiple interviews, it was possible to reconstruct dwell time frequency (see Figure 2)
two tiers and divide by the total number of patients.<br>We then assign the resultingexpenditure to eachpatient.|
|8|The LCMS records information about health posts and<br>clinics separately, but the official public spending data<br>does not differentiate between them.|We aggregate health post and clinic data to match it with<br>the official spending reports.|
|9|Outlier observations.<br>(0.3% of sample)|Within each quintile we discard cases in which household<br>expenditures on hospitals or other facilities were larger<br>than the mean log-expenditure plus three times the<br>standard deviation (including only positive expenditure<br>values).|

_Source: Authors_

# **4.4. Allocation of Fertilizer Subsidies**

LCMS VI contains information about whether a household grew maize crops during the 2008/2009 period or not. The survey also asked if they received fertilizer (organic or inorganic) or seeds, who they received them from and how much they spent to get the inputs. A household is considered as beneficiary of FISP if they grew maize crops and received fertilizer from the government or from cooperatives (see Appendix 3).

Ideally, in order to identify the beneficiaries of FISP and the FSP, the LCMS VI should have included information about which households were participants in each of these programs, the amount of fertilizer and seeds they received from the programs, and the amount they paid in cash or in kind for these inputs. However, the data available are far less comprehensive and a complete reconstruction of participation in and benefits received from these programs is not possible. Table 10 summarizes the operational assumptions that were made.

21
|S/N|**Table A3 continued**<br>Data dimension|<br>Data category|Representative indicators|Categorical disaggregation|Coverage element 1 scoring guidelines*|
|---|---|---|---|---|---|
|12|Economic statistics|Price Indexes|(12.1) Consumer price index<br>(CPI);<br>(12.2) Producers price index<br>(PPI)|(12.1, 12.2) None|To receive full point: Must have all indicators. Any data in the<br>most recent 5 years (2013 onward) must be presented on at<br>least quarterly basis to receive a full point.<br>To receive half point:Must have at least one indicator. Data can<br>bepresented on a monthly, quarterly,or annual basis.|
|13||Government Finance|(13.1) Actual revenues;<br>(13.2) Actual expenditures.|(13.1) Revenue source;<br>(13.2) Administrative<br>classification; economic<br>classification; functional<br>classification.|To receive full point:Must have all indicators with one<br>disaggregation each.<br>To receive half point: Must have one indicator with one<br>disaggregation.|
|14||Money and Banking|(14.1) Money supply;<br>(14.2) Interest rates.|(14.1) M1; M2; M3;<br>(14.2) Rate type.|To receive full point: Must have all indicators with one<br>disaggregation. Must have at least three rates.<br>To receive half point:Must have one disaggregated indicator. If<br>indicator is(14
# **3 Data and district inequality estimation**

# **3.1 Data**

We combine an array of data sources to construct a data set with the relevant important variables for estimating the effects of mining activities on district inequality: the National Oceanic and Atmospheric Administration, National Geophysical Data Center (NOAANGDC)<sup>9</sup> for data on night-time lights intensity; the United States Geological Survey (USGS)<sup>10</sup> for data on the spatial location of mineral deposits and mining activities in Africa and historical US mineral prices; the United States Global Land Cover Facility (GLCF)<sup>11</sup> for the Normalized Difference Vegetation Index (NDVI)—a proxy for agricultural productivity. We also use Tropical Applications of Meteorology using Satellite Data (TAMSAT)<sup>12</sup> to extract data on rainfall—a proxy for climatic shocks. Finally, we extract population data from the Gridded Population of the World (GPW v4).<sup>13</sup> Since our units of analysis are districts, the combined data set constitutes three clustered levels—districts, regions, and countries.

NOAA-NGDC data include annual time-series night-time lights intensity data, which are globally recorded across countries daily from 20:30 to 22:00 local time by a satellite orbiting the earth. The use of this data set follows recent literature that attempts to circumvent the absence of reliable and consistent subnational data (for a detailed discussion, see Chen and Nordhaus 2011; Henderson et al. 2012), particularly in Africa.<sup>14</sup> These data come in three main formats: cloud-free, average visible, and stable light composites. We use the stable light composites, which are clear of ephemeral events, background noise, summer light, and auroral activities. Moreover, we remove gas flares to eliminate potential bias in our estimates.

USGS data provide spatial locations of mineral deposits, and details of
, 2008b), firm financing patterns (e.g. Beck, Demirguc-Kunt, andMaksimovic, 2008; Cull and Xu, 2005, Ayyagari, Demirguc-Kunt, and Maksimovic, 2010) and dispute resolution via courts (e.g. Djankov, La Porta, Lopez-de-Silanes, and Shleifer, 2003).

> 6 The master list of firms is sometimes obtained from other government agencies such as tax or business licensing authorities. In some cases, the sampling universe is generated from lists maintained by the Chamber of Commerce and business associations or marketing databases where registration is voluntary. In a few cases, the sample frame is created via block enumeration.

> 7 We have a few firms in the sample that report having less than 5 employees. All our results are robust to dropping these firms. Our results are also robust to dropping firms that started out as informal enterprises and were formally registered after starting operations.

6
OPTIONS AND RECOMMENDATIONS FOR REFORM

# Natonal Social Protection Policy

Zambia has a strong tradition of mutual support based on family and community ties. Although this has been disrupted by the consequences of the economic crisis, inter- and intra-family transfers are still relatively important in alleviating hardship and maintaining living standards. Any public social protection system should seek to complement and strengthen these family and community ties rather than replace them. A recent World Bank Poverty Assessment<sup>4</sup> has concluded that 69 percent of all Zambians live in households with expenditures per adult below a poverty line based on nutritional and other basic needs, and 55 percent do not have sufficient income to meet their basic nutritional needs. While poverty is both more prevalent and more severe in rural areas there has been a dramatic increase in urban poverty - only 4 percent in 1975 but now approximately 50 percent. Similarly there has been a decline in social indicators with a downturn since 1985 in primary school enrollment rates, increased infant and child mortality, and increased levels of malnourishment among children.

The state has a basic obligation to adopt policies aimed at alleviating poverty and improving living standards. Given the narrow tax base and high levels of evasion tax revenues are insufficient to meet social protection needs. Inevitably, these scarce resources must be targeted at the poorest and, in general, at meeting basic needs for health, education, housing, sanitation, clean water, and for improving services and infrastructure which will provide opportunities for income generating activities.

But there is also need for social protection of workers who are temporarily or permanently deprived of their earnings through sickness, injury or retirement. Here, income related benefits are required. There are two possible sources for social protection in this respect - either employers as part of the conditions of service or some form of social security scheme. Zambia has already established the basis for a social security system with the ZNPF and the pension schemes for public sector employees. Many employers have established supplementary pension schemes or entered into collective agreements with their
method if the cost of cashing it out is high for villagers in remote areas with poor road conditions. Third, the selection of food items matters. If the in-kind food items are _inframarginal_ (the consumption quantity even without transfer is greater than the quantity provided), in-kind will have little to no distortion effects. On the other hand, if there is a household surplus of some food items, then in-kind transfers can be distortionary in the sense that it might induce

> 23 A good example of collecting costing data is the World Bank’s <u>Partnership for Economic Inclusion’s Costing Dashboard, which reports and compares costs of economic inclusion policies. This type of tool could be used as a</u> framework to collect costing data for cash and in-kind programs in humanitarian settings. Hendren and Sprung-Keyser (2020) also developed a unifying framework to compare the effectiveness of different policies and interventions, which relies mainly on understanding the long-term impact of policies as well as knowing the implementation cost.

29
WPS3728

# Local Economic Structure and Growth

## Rita Almeida∗ World Bank Research Department

#### Abstract

This paper tests how the local economic structure - measured by local sector specialization, competition and diversity - affects the technological growth of manufacturing sectors. Most of the empirical literature on this topic assumed that in the long run more productive regions will attract more workers and use employment growth as a measure of local productivity growth. However, this approach is based on strong assumptions as that of national labor markets. In this paper I show that if we relax this assumption, regional adjusted wage growth is a better measure of regional productivity growth than employment growth. I compare the two measures using data for Portugal between 1985 and 1994. With the regional adjusted wage growth I find evidence of MAR externalities in some sectors and no evidence of Jacobs or Porter externalities in most of the manufacturing sectors. These results are at odds with my findings for employment based regressions which show that concentration and region size has a negative and significant effect in most of the manufacturing sectors. These employment based results are in line with most of the existing literature, which suggests that using employment growth to proxy for productivity growth leads to misleading results.

Keywords: Local Growth, Knowledge spillovers and Productivity. JEL Classification codes: R11, R12, O47.

### World Bank Policy Research Working Paper 3728, October 2005

_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._

>
# **Figure 2.8. Labor force participation has remained high and stable.**

Labor Force Participation Rate (Percent of population aged 15-64), 2009–2020, Women, Men and Total.

<!-- Start of picture text --> 95<br>Male Female<br>Cambodia Urban<br>Rural<br>90<br>85<br>80<br>75<br>70<br>2009 2014 2019<br>Labor force participation<br><!-- End of picture text -->

**Source:** Authors’ elaboration using the CSES household survey.

**Figure 2.9. Non-farm employment has pushed participation in urban areas.**

Labor Force Participation Rate (Percent of population aged 15-64 Urban vs Rural by Gender), 2009–2020 <!-- Start of picture text --> Urban Male<br>95<br>Rural Male<br>90<br>85<br>80<br>75<br>70<br>65<br>2009 2014 2019<br>Source: Authors’ elaboration using the<br>CSES household survey.<br>Labor force participation<br><!-- End of picture text --> The massive structural transformation Cambodia experienced between 2009 and 2019 explains the urban-rural labor dynamics (Karamba et al., 2022). During these years, the agriculture sector lost approximately 1,100,000 million jobs, which is the main employment sector in rural areas. At the same time, more than 2 million jobs were created in industries such as sales, apparel, construction, and other services that are more prominent in urban areas.

FLFP, among the highest in the region, remained stable between 2009 and 2019, a result of Cambodia’s organized structural transformation. While rural FLFP remained stable, urban areas attracted female workers to the labor force.

Cambodia has also
|_87%_|_0%_|_80%_|_73%_|_93%_|_67%_|_93%_|_93%_|71%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform

> 14 Because BENECO is a non-stock cooperative there are no shareholders. The consumer owners pay equal membership fees but there are no capital shares.

> 15 For details on the corporate governance index, see the annex.

> 16 For details on the utility management index, see the annex.

38
# **_Choosing sectoral price deflators_**

An official price deflator for sectoral gross fixed capital formation is not available for the whole of the period since 1952. Even at the aggregate level, a deflator is only available for the period 1952 to 2004. Since then the NBS has only published a deflator for the sum of fixed capital and inventory investment. On the other hand, it has published a national price index for fixed asset investment continuously since 1989. This series has sub-indices for three separate assets: construction, equipment and other assets. Given the difficultly in interpreting whether such investment should be included in GFCF, the price index for _other assets_ has been ignored.

The price trends for equipment and construction have differed markedly. Between 1995 and 2016, equipment prices fell by 1.1% annually, while construction prices rose by 2.6%. Such large differences in the movement of prices mean that a price index using the base-period expenditure shares for equipment and construction will gradually resemble an index based only on the price of construction expenditure. It will thus understate the extent of differences in the movement of prices between the four sectors of capital formation that should reflect the different shares of construction and equipment in each sector.

There are several methods available to overcome this so-called substitution bias. One method is to change the weights annually, another is to use a Törnqvist price index, which is calculated as the weighted geometric average of the price relatives using arithmetic averages of the value shares in adjacent years. Data for the share of construction and equipment in fixed asset investment exist since 1981. They show a stable trend in the share of construction in national total fixed asset investment over the period 1981 to 2007. After that date, the share of construction reflects the increase in the share of construction intensive sectors in total FAI, as the shares by sector have remained stable. It is only possible to calculate the asset structure of investment by sector in urban areas (which account for almost all FAI) since 2004.
?|No|exceed strength|
|Does the law mandate paid or unpaid maternity leave?|Yes||
|Who pays maternity leave benefits?|Employer<br>100%||

_Source: The World Bank Women Business and the Law_

# **3. Data and descriptive statistics**

The data used in the analysis are nationally representative firm-level data from the World Bank Enterprise Survey (ES) for the DRC; data were collected between August 2013 and May 2014. The ES have high coverage in terms of countries and topics including access to finance, corruption, infrastructure, crime,

> 12 According to the ILO definitions, "“Industrial undertakings” include “(a) mines and extractive industries of any kind (mining); (b) undertakings in which goods are manufactured (manufacturing), modified, cleaned, repaired, decorated, finished, prepared for sale, destroyed or demolished or in which the materials undergo a transformation, including shipbuilding, production, transmission of electricity (energy) and motive power in general; construction (construction) and civil engineering companies, including construction, repair, maintenance, processing and demolition.”

7
Where Y = performance TF= the sum of the formal measures (mean= 7.38, s.d. = 3.10) TI = the sum of the informal measures (mean = 6.38, s.d. = 3.25) ui = a normally distributed error term (mean of zero, standard error 2.5) In the Stern-Holder data set, as shown in Table 3 below, the measures of formal and informal regulatory quality are highly correlated with each other. In particular the correlation coefficient between the formal measures and the total index is 0.96.

# **Table 3:  Correlation between Formal and Informal Measures of Regulatory Quality**

Correlation

||_TF_|_TI_|_OT _|
|---|---|---|---|
|TF|1|||
|TI|0.852|1||
|OT|0.960|0.964|1|

Note: OT = TF + TI Source: Stern and Holder (1999) _The Specification of the Problem_ Unless data have been specifically collected on the informal characteristics of regulatory quality, we are left only with the more easily obtained formal characteristics. To investigate the impact of this, we carried out illustrative simulations see the effect of using only formal measures of regulatory effectiveness when the true data generating process involves both the formal and informal measures.

The results are summarised in Table 4, which lists the results of the estimated coefficients for 12 simulation runs 31
types of response error not investigated here. Two examples are given below. We refer interested readers to a more comprehensive treatment of this issue in Smith, Dupriez, and Troubat (2014).

First, intentional error could also stem from interviewers subtly guiding respondents to give answers that minimize interview length or who rush to complete the questionnaire. We can assume that such errors become more likely as questionnaires get longer and if supervision is limited. This type of error has also been observed in high-frequency panels where follow-up survey rounds are likely less accurate than earlier rounds (Halpern-Manners and Warren 2012). Extensive enumerator training and active field supervision that emphasizes adherence to study design can minimize these errors.

Second, in most of the developing world, households do not typically purchase, harvest, or consume their food in standard units (kilograms or liters). Some surveys force reporting in standardized units, and there are doubts about the accuracy of these reports if they are made by people who rarely transact in metric units. Alternatively, consumption surveys can allow the respondent to report in local units, such as bunches, heaps, tins, buckets, or bundles. However, to aggregate food consumption these local units must be converted into standard units. If common foods are more likely to be reported in nonstandard units, that is, pieces of cassava and bunches of bananas, and conversion factors are inadequate, unit conversion error could significantly distort the resulting value estimates. A final relatively low-cost addition to existing consumption surveys would be to ensure that such conversion factors, which are often geographically specific, are locally relevant, well specified, and systematically collected by survey teams.

18