rafmacalaba/data-use-mentions-extended huggingface dataset:
-31-

for inputs ("despesas diversas") into 20 categories is given only for the 24 major industry groups (see table 15 of the Censo Industrial). The information published in Producio Fisl.a refers to firms with 5 employees or more and/or a gross value of output that exceed 640 times the highest minimum wage in 1975. Only these firms were required to fill in census forms. However, in part 1 estimates are published for the smaller firms as well in separate tables so that the aggregate figures we use give a complete picture.

The information for Mexico was derived from the X Censo Industrial 1976 (1979a and 1979b), which refers to 1975. Here the CMAE-classification (Catalogo Mexicano de Actividades Economicas) was applied. It has a 4 digit classification for product groups; over 15,000 commodity items are specified but they share the generic number of the industry category into which they fall. There are two volumes, the Resumen General (1979a), containing general information for product categories, and the Desglose (1979b), in which detailed information on quantity and gross value of output at the product level is published. In the latter only the information on product groups which exceed 1 million pesos is published. Information on petroleum refining was not presented in the industrial census, but was derived from the detailed Mexican national accounts, Sistema de Cuentas Nacionales de Mexico (SPP, 1981). We derived information on indirect taxes and subsidies from this latter source in order to adjust the values to a producer price basis, which was the valuation basis used elsewhere in this study.

# Measurement of the Relative Level of Gross Output within Industries

The basic procedure involved weighting physical output of individual product items in 1975 by a common set of price weights. These "prices" were unit values, derived from production censuses by dividing gross value of output by the corresponding quantities. Two sets of binary comparisons were made, i.e. Brazil/USA and Mexico/USA. Each involved
Availability of ridesharing<br>has led to modest increases<br>in total vehicle miles<br>traveled and greenhouse<br>gas emissions|
|Tirazhini &<br>Gomez – Lobo<br>(2017)|Chile|January<br>11-20,<br>2017|Survey results on Uber|Monte Carlo Simulation|Ridesharing is like to<br>increase vehicle km<br>traveled|
|Hall et al<br>(2018)|United States|2004-2015|Transit ridership, Uber entry<br>and exit data|Difference-in-<br>differences|Uber complements public<br>transit by solving the last-<br>mile problem of public<br>transit|

54
- ii. Additionally, _η_  ̃ _c_ could be drawn from those estimated in the first stage (semi-parametric), this is possible to do in the parametric and bootstrapped simulation.

Under Henderson’s method III (H3) there is no defined distribution for the ˆ _ση_<sup>2parameter.Consequently,</sup> bootstrapped samples of the survey data are used to obtain _σ_ ˆ _η_<sup>2and all other parameters for every single</sup> simulation. This approach is also available under ELL methods. The bootstrap is also necessary for the instances when EB is chosen. When EB is chosen, the bootstrap produces a vector of _η_ s and of _σ_ ˆ _ηc_<sup>2</sup> ̃ ˆ for each of the clusters included in the survey and thus for every simulation we draw _ηc ∼ N_ � _ηc,_ ˆ _σηc_<sup>2</sup> �. For clusters not included in the survey the drawing is: _η_ ̃ _c ∼ N_ �0 _,_ ˆ _ση_<sup>2</sup> �.

3. _e_ ̃ _ch_ can be drawn from a normal distribution with variance such as the one specified in equation 8. This is done by:

- (a) Drawing _α ∼ N_ (ˆ _α,_ Var(ˆ _α_ )) (it may also be the product of the bootstrap process) and using it on _′_

- the census data, we obtain a new _D_ = exp� _Zch_<sup>_α_�</sup> , and using the new _D_ we obtain in conjunction with the first stage _A_ = 1 _._ 05 max � _e_ ˆ<sup>2</sup> _ch_ � and Var(<sup>�</sup> _r_ ) we get:

From this it is possible to draw _e_ ̃ _ch ∼ N_ �0 _,_ ̃ _σe,ch_<sup>2</sup> �.

- (b
headline inequality measures to examine patterns of upward and downward relative mobility, and to construct profiles of the upwardly and downwardly mobile.

Our focus in this paper is to enquire into income mobility _within_ generations (intragenerational mobility). Here one is asking whether long-term incomes are possibly distributed more equally than current incomes, and whether the process of inequality change reflects the experience of winners and losers (see further discussion in Jantti and Jenkins 2015).<sup>5</sup> We ask whether there is much mobility across our survey years, and whether there has been an increasing tendency for households to see their relative positions in the consumption distribution change over the quarter-century since the late 1980s. We further explore whether we can identify population characteristics that are more commonly aligned with upward or downward movement in the consumption distribution, and whether there is evidence of change over time in the pertinence of those population characteristics.

As noted above, studies of intragenerational mobility at the all-India level have relied on IHDS panel data to describe mobility in terms of income. The IHDS survey was conducted in 2004/05 and again in 2011/12. It is a nationally representative survey with a sample size of just over 40,000 households, and it features a panel structure that allows one to track households across this interval. Moreover, the IHDS rural sample can also be linked to a sample surveyed in 1993 by the NCAER in their Human Development Profile of India (HDPI). Thus, there is scope for some assessment of mobility patterns between 1993/94 and 2004/05. Key issues that arise with respect to the analysis of mobility based on the IHDS (and HDPI) are, first, the analysis is based on income rather than consumption (even though most discussion about

> 5 Another direction is to study mobility across generations: asking questions about the extent to which children’s living standards are associated with those of their parents, and often interpreting findings as offering a window on the extent of equality of opportunity in society
dominated), and MJ/kg 1.32 (100% biomass); CK: 1.64 MJ/kg (low capacity), 1.51 MJ/kg (medium capacity), and 1.07 MJ/kg (high capacity). The SEC for average internal fuels (boiler ash and agriculture waste) is 0.48 MJ/kg. d Nepal-specific data sources.

e For BTK: 1.17 MJ/kg (90% coal and 10% rice husk), 1.04 MJ/kg (70% coal and sawdust, and 30% rice husk), 1.19 MJ/kg (100% bagasse briquette), and 0.99 MJ/kg (40% coal and 60% bagasse briquette.; For ZZK: 0.60 MJ/kg (100% coal), 0.91 MJ/kg (100% coal), and 1.06 MJ/kg (43% coal, 32% coal and rice husk and 25% rice husk as internal fuel).

f For ID ZZK, the value is for 25% perforation brick; and for TK, the value is for 60% perforation hollow brick. g All values are 100% coal-based. Electric SEC is excluded. BTK with perforated brick production is 1.06-1.16 MJ/kg. h Based on the combination of coal, sawdust, and bagasse. Values reported are in MJ/brick.

i Values for CK (100% fuelwood); BTK (100% coal), VSBK (100% coal as external fuel; boiler ash/wheat straw as internal fuels) j Based on a combination of coal and fuelwood. k (MinErgy and FNBI, 2015). 100% coal.

l All is 100% coal-based. Also reported is 100% biomass-based for BTK as 1.33-1.95 MJ/kg of fired brick. m For BTK: 1.46 MJ/kg (coal, wood and tire
compared the costs of fired and non-fired brick production [ ] based on the cost of building BB one square meter wall ( _Equation 9_ ). These costs include material (brick) cost [mm ], labor CC (tt) BB cost [ ], and raw material costs [ r ]. The information needed for estimating bbrriicckk CC (tt) BB BB these costs is collected from the available literature. ccrrbbccrr ww mmr eerriirrcc CC (tt) CC (tt) r (9) BB BB BB BB where mm bbrriicckk ccrrbbccrr ww mmr eerriirrcc CC (tt) = CC (tt) + CC (tt) + CC (tt) BB = {ddbbw ii bbwwbbcckk, eeEEee−ddbbw ii bbwwbbcckkee (CCSSOOBB CCeeii HHCCBB)}

# **4. Data**

Several different types of data exist that are relevant to this study, such as process-specific data, average data, and generic data. Each type of data is collected either from a primary or secondary source. For this study, the data and information are collected from two main sources. The first and primary source of data and information collected is from the field-visit rapid survey of 22 brick kilns selected across the country. The details of the survey, including the survey instruments and analysis, are available at (Timilsina and Malla, 2023).

To supplement data collected from primary sources through the rapid survey, several secondary sources were used. The data and information from the secondary sources are collected and compiled through comprehensive desk study and review of relevant peer-reviewed articles, and government and non-government published documents, reports, and articles. The key parameters considered in the techno-economic assessment and the cost estimation are summarized in Table 2. Selected information on key data collected from the survey is provided in Annex I and the detailed benchmark dataset description and sources are presented in Annex II.

**Table 2: List of key parameters and data considered in the study**

|Study period|Base year: 2021|
|---|-
# **1 Introduction**

Investment in research and development (henceforth R&D) as well as employment in the R&D sector exhibit substantial fluctuations relative to those of aggregate production and aggregate employment. Moreover, contrary to the Schumpeterian view, R&D appears to be procyclical in the data. These facts raise interesting questions regarding the sources of the excessive volatility and the nature of the relation between the R&D sector and aggregate fluctuations. The purpose of this paper is to examine the impact of shocks on the R&D sector as well as the contribution of the sector to annual fluctuations. Specifically, we identify sectoral productivity shocks as well as capital investment-specific shocks by employing a Vector Autoregression (VAR) whose shock structure is disciplined by a stochastic general equilibrium model.

Using annual data from the US for the period prior to 2008, we find that capital investment-specific shocks play the largest role in driving the fluctuations of R&D investment while R&D productivity shocks a↵ect considerably the fluctuations of output in the non-R&D sector. Our analysis suggests that not only sources listed under R&D expenditures contribute to the stock of R&D. While there can be direct additions to the stock of R&D within the R&D sector (identified from R&D expenditures), there can also be costly transfers from the non-R&D sector contributing to the stock of R&D. We show that the cost of the transfer is inversely related to positive R&D shocks. Thus, an improvement in R&D productivity may induce a transfer of sources from the non-R&D sector as investment in the stock of R&D which then augments the production of the non-R&D output. Our calibration suggests that at the steady state such transfers are positive. Consequently, despite the fact 2
reduction. In particular, if the UNHS data are correct, very little has changed in the last decade.<sup>9</sup>

## **Table 1:  Sectoral composition of primary employment, 1992/93 - 2009/10 show sluggish movement out of agriculture**

|||**Ye**|**ar**||
|---|---|---|---|---|
||**1992/93**|**2002/03**|**2005/06**|**2009/10**|
|Labor force|100.0|100.0|100.0|100.0|
|Agriculture|82.6|70.2|74.9|70.0|
|Industry|4.5|7.2|5.4|8.0|
|<br>Services|12.9|22.6|19.7|22.0|

**Source:** Authors calculations based on the IHS 1992/93, UNHS 2002/03, UNHS 2005/06 and UNHS 2009/10

# **5. Uganda’s structural transformation and its impact on poverty**

# **5.1 Nature of the transformation**

If the movement of labor from the low productivity sector (agriculture) to the higher productivity sectors was limited, then what were the drivers of the extensive poverty reduction? Our analysis suggests three channels:

- (i) transformation in the type of employment in each sector;

- (ii) transformation in household livelihood portfolios; and

(iii) transformation within the agricultural sector, from ―traditional subsistence‖ to new, higher value export crops, the use of livestock, and modern marketing channels.

To see the first transformation, the change in the structure of primary <u>employment by sector</u
quickly in the subsequent years. The absolute rating of the Russian Federation collapsed following its sovereign debt default in 1998. During the subsequent period, other countries (such as Croatia, Jordan and South Africa) had fairly stable absolute ratings but their relative ratings improved (Figure 2).

The summary statistics for the absolute and relative ratings for our sample of emerging markets and developing economies are provided in Table 2. We include countries with at least 8 quarters of data on all variables in both the pre- and post-financial crisis periods. Our final sample contains 26 countries comprising 17 emerging market economies and 9 frontier market economies. There are on average 64.5 quarterly observations (about 16.1 years) per country ranging from 45 to 77 observations with information on all the variables in our baseline model. The average sovereign rating among all countries is 33 (around BBB- stable) on our rating scale of 0-60, ranging from an average rating of 14.49 (B- negative) for Ecuador to 47.96 (AAnegative) for Chile. Relative ratings range from -13.11 on average for Indonesia to 18.64 for Botswana.<sup>6</sup>

# _3.2 Capital flows and control variables_

A comprehensive database on net and gross capital flows is compiled at a quarterly frequency for emerging and frontier market economies with available quarterly data. The main source of capital flows data is the International Monetary Fund’s Balance of Payments Statistics, supplemented with country-sourced data from Haver Analytics.<sup>7</sup> Net private capital inflows are the difference between gross private inflows and gross private outflows. Gross private inflows are the sum of foreign direct investment, portfolio investment, and other investments (comprising largely bank lending) by non-residents net of their own disinvestment in the reporting economy.<sup>8</sup> Similarly, gross private outflows refer to flows by residents, net of their own disinvestment. We use quarterly net capital inflows as our main measure
enterprises show that enterprises in developing countries are badly managed (Bloom et al. 2010). This study provides the answers to the following questions. How critical are business services and management innovation for rural entrepreneurs? How could business services and management innovation promote business entry and performance if they are critical?

# **3. Assessment of the Rural Investment Climate**

The RIC surveys were conducted in three Governorates. However, sampling data are representative of each Governorate with sampling weights. Sample sizes of household enterprises in industrial sectors are listed in Table 3.1.

|**Table **<br>|**3.1 Samp**<br>|**le Size**<br>|**of Hou**<br>|**sehold **<br>|**Enterp**<br>|**rises**<br>||||
|---|---|---|---|---|---|---|---|---|---|
|**Governorate**|**San**|**aa**|**Lah**|**j**|**Dha**|**mar**|**Tot**|**al**||
|**Male or Female Manager**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|**F**||
|Agriculture|42|3|30|1|24|0|96|4|100|
|Mining & Construction|8|0|8|0|21|4|37|4|41|
|Food Processing|
adopt the policy or have recently adopted it and are still experimenting with its configuration. During 1989–2016, 138 municipalities in our data set adopted a participatory budgeting program for at least four years, although some adopted PB for the entire 20-year period. Accounting for this variation allows us to determine whether a longer experience with PB influences any relationship it has with tax outcomes. We recorded a 0 for municipalities that had not adopted PB during each four-year timeframe between 1989 and 2016 and a 1 for those that had. Some cities in our sample adopted PB and then eliminated it after a few years. These cities’ scores reverted to 0 and began anew with 1, in the event they re-adopted PB as a policy later in the timeframe our data cover.<sup>6</sup> We select municipalities with more than 100,000 residents because they are representative of the average Brazilian citizen’s experience with governance. By 2010, 85% of Brazil’s population lived in urban areas and the overwhelming majority 5 The source surveys we use do not always include the same question for each year. We assume municipalities maintained their policy councils through the mayoral administration when the survey question was originally asked in the absence of countervailing evidence. This assumption aligns with survey responses in our data set where only 3% of municipalities eliminated a policy council during the same mayoral administration.

6 There are approximately 3% of cases in our data set where a municipality adopted participatory budgeting, abandoned it and then adopted it again.

17
of poverty in each location. As a benchmark case with which to compare our results, we make the extreme assumption of no knowledge whatsoever about the spatial distribution of poverty – in which case our given budget is distributed uniformly to the entire population. We set up a series of comparisons to this benchmark, where we assume knowledge about poverty levels for administrative units with progressively smaller populations. For a given level of disaggregation, we ask how knowledge about poverty outcomes across regions can be incorporated into the design of a transfer scheme so as to improve the overall targeting performance relative to the benchmark case. We consider a variety of transfer schemes that make use of this knowledge in different ways. The schemes range from simple, intuitive transfer schemes to more sophisticated ones, in the latter of which expected poverty at the national level is minimized given the information and budget constraints. We are interested in comparing performance across schemes, as well as the relative performance of each scheme at alternative levels of disaggregation. We consider performance in terms of the squared poverty gap – a measure of poverty that increases not only with the number of people below the poverty line but is also particularly sensitive to the distance between a poor person’s income level and the poverty line. To avoid making strong assumptions about the exact value of the poverty line or the size of the budget, we also show results for two poverty lines and two hypothetical budgets.<sup>6</sup> Finally, we report how close “optimal geographic targeting” in combination with our poverty maps, comes in terms of poverty reduction to the hypothetical scenario of “perfect targeting”. From this we can get a sense of the potential benefit from combining detailed geographic targeting with additional targeting mechanisms such as

> http://econ.worldbank.org/programs/2473/topic/14460/.

> 5 For a more detailed discussion, see Demombynes et al. (2002).

> 6 We have tried more poverty lines and budget sizes, but
population shocks across rural and urban areas. Population levels in areas with initially high population densities experience further agglomeration, i.e. exogenous immigration leads to population growth. Only in rural areas do we continue to find path dependence.<sup>2</sup> This suggests that a positive exogenous population shock generates a ‘Matthew effect’ (‘those who have will be given’), as densely populated areas gain population relative to sparsely populated areas. We further investigate this heterogeneity by examining how the effect varies with both initial population density and the reduced form magnitude of the shock. For a given initial density, a larger shock leads to higher endogenous population growth. This is consistent with the idea that a significant shock is required to push a locality from its current equilibrium onto a new trajectory. These results imply that policies aiming to foster migration can further trigger urban agglomeration forces in high density areas.

South Africa’s history lends itself to studying our research question and the country maintains excellent census data, both before and after Apartheid. One important limitation of the census data however, is that after apartheid many changes were made to various geographical and other definitions, which limits the comparability of our data before and after 1994, although the population data can be matched with some confidence on a level as fine as wards. Another drawback of our data on the regional level is that it does not identify internal migrants explicitly, so we have to infer differences in migration as differences in population growth conditional on covariates that account for differences in fertility and mortality. A third shortcoming is that no reliable information for population in homelands is available. For our purposes,

> 2While standard models of trade and urbanization typically do not predict path dependence, recent studies that have found path-dependent behavior for small and medium sized towns include Bleakley and Lin (2012) and Michaels and Rauch (2018).

4
Policy Research Working Paper 10778

# **Abstract**

This study examines the impact of farm input subsidies, food and cash transfers, and irrigation investments on the dietary diversity, food consumption scores, and coping strategy index in Malawi. Despite the potential for synergies to address a range of vulnerabilities affecting food consumption, very few studies focus on combined program effects. The analysis employs three-waves of integrated household panel surveys for Malawi from 2013, 2016, and 2019, and uses instrumental variable Poisson and Tobit regression to address endogeneity. The findings show weak joint program participation effects, which may be due to program design or data limitations in this evaluation. Households that receive food and cash transfers showed improvements in diet diversity and the food consumption score. Input subsidies were less effective in helping households cope with food insecurity and reduced diet diversity and the food consumption score. This suggests that overreliance on agricultural input subsidies may lead to reduced variety in food consumption. Policies that are aimed at more linkages between programs should also diversity and rebalance public spending to reduce food and nutrition insecurity.

This paper is a product of the Agriculture and Food 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://www.worldbank.org/prwp. The authors may be contacted at mirriammatita@gmail.com.

_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