rafmacalaba/data-use-mentions-extended huggingface dataset:
major manufacturing economies: from relatively high-productivity Vietnam to relatively low-productivity Bangladesh.

I focus the analysis on manufacturing firms (two-digit ISIC codes 15-37). I also report data specific to the garment sector (ISIC codes 17-19). The key variables I extract from the data are number of workers (defined as the sum of employees on permanent and temporary contracts), total sales, total labor costs (including wages, payroll taxes, benefit payments, etc..), total cost of materials (the cost of physical inputs in production), and total value of physical capital. I measure productivity as sales per worker. Further, I calculate a measure of values added that takes into account rental payments for capital and land. All figures are converted to 2010 USD by using exchanges rate from the relevant year and US CPI data.<sup>2</sup> I use sampling weights to compute averages representative of the underlying population.<sup>3</sup> Missing data is not common, with a few exceptions – in particular for material costs. All variables are truncated at the 95th percentile of the country-specific distribution. I report all key descriptives in Tables A.1-A.5.

I also use data from two other sources to complement the analysis. First, I use original survey data collected for manufacturing firms in Addis Ababa by Abebe et al. (2017) to check the robustness of the sales figures reported for Ethiopia. Second, I use data from the _World Management Survey_ to compare the quality of management across the countries in my sample (with the exception of Bangladesh, which is not included in the World Management Survey). This survey studies management practices in manufacturing firms in 34 countries.<sup>4</sup> Firm managers are asked to describe several management practices in the firms over a phone interview and are then scored by a trained enumerator. The instrument produces an overall management score, as well as specific scores for the quality of operations management, monitoring of
.601|
|Togo|0.399<br>0.652|0.388|0.645|
|Uganda|0.406<br>0.636|0.380|0.629|
|Zambia|0.325<br>0.549|0.331|0.573|
|Zimbabwe|0.343<br>0.585|0.432|0.635|
|Mean|0.322<br>0.562|0.369|0.611|

Note: Data are drawn from DHSs. Means are population weighted. The table lists the proportion of underweight women who fall into the bottom 20<sup>th</sup> and 40<sup>th</sup> percentiles of predicted values from the regressions with log BMI as the dependent variable. For example, 27.1 percent of underweight women in Benin have predicted BMI values that fall into the bottom 20 percent of all predicted values for women.

49
~~~~~~~~~~~~~~~~~~~~~~2- 9<br>4 ~~~~~~~~~~~~~~~~~-92<br>10- -9<br>2 I~~~~~~~~~~~~~~~~~-1<br>e vibity ofhen<br>EE -~~~~~~~~~~~~5- -s0<br>1 999 1 990 1 992 1 994 1996 1790 1999<br>- domestic deposit rates<br>MEM ditferential between domestic deposit rates and US LIBOR<br>ladjusted for en post exchange rate movement.)<br><!-- End of picture text -->

The variability of the unpredictable component is given by the estimation of an ARCH model of the nominal exchange rate on a constant and a time trend. Nominal exchange rate index 1990=100.

29
-> <!-- Start of picture text --> Industrial Production and Export Growth<br>12 30<br>10 25<br>Real growth y/y, %<br>8 20<br>6 15<br>4 10<br>2 5<br>0 0<br>-2 -5<br>Industrial Output (RHS) Export index growth (LHS)<br>1999 06 1999 09 1999 12 2000 03 2000 06 2000 09 2000 12 2001 03 2001 06 2001 09 2001 12 2002 03 2002 06 2002 09 2002 12 2003 03<br> Export grpowth<br><!-- End of picture text --> _Source_ : Staff calculations based on GUS data.

The macroeconomic environment deteriorated progressively at the end of the last decade. While monetary policy tightened during the second half of 2000, fiscal policy eased considerably during 2001. Not only did the general government balance widen from –3.0 percent for 2000 to -5.9 percent for 2002, but the structural deficit (i.e., corrected for business cycle effects) worsened by over 2 percent to over 5 percent of GDP by 2002 and 2003 (see Figure 3). This “tight monetary and loose fiscal” policy mix has additionally crowded out investment and moderated the potentially expansionary effect of fiscal policy.

4
</mark>

<mark>Breiman, Leo. 2001a. "Random Forests."</mark> _<mark>Machine Learning</mark>_ <mark>45 (1): 5–32.</mark>

- <mark>___________. 2001b. "Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)."</mark> _<mark>Statistical Science</mark>_ <mark>16 (3): 199–231.</mark>

<mark>Brookings Institution. 2015.</mark> _<mark>Global Metro Monitor 2014.</mark>_ <mark>Washington, DC: Brookings.</mark>

- <mark>Brueckner, J. K. 1987. “The Structure of Urban Equilibria: A Unified Treatment of the Muth‐Mills Model.”</mark> _<mark>Handbook of Regional and Urban Economics,</mark>_ <mark>Vol. 2, edited by E. S. Mills, 821–45. Amsterdam: North‐Holland.</mark>

- <mark>Burchfield, M., H. G. Overman, D. Puga, and M. A. Turner. 2006. “Causes of Sprawl: A Portrait from Space.”</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>121 (2): 587–633.</mark>

- <mark>Cameron, A. Colin, and Pravin K. Trivedi. 2005.</mark> _<mark>Microeconometrics: Methods and Applications</mark>_ <mark>. Cambridge, U.K.: Cambridge University Press.</mark>

- <mark>Combes, Pierre‐Philippe, Gilles Duranton, Laurent Gobillon, and Sébastien Roux. 2010. “Estimating Agglomeration Economies with History, Geology, and
6 GIZ conducts a global survey of gasoline and diesel prices in November every other year, the most recent having been completed in November 2012. It is also setting up a database of fuel pricing policy fact sheets, currently covering 136 countries and regions (Energypedia 2013). GIZ recently issued a sourcebook for policymakers on how to increase the energy efficiency of urban transport, one outcome of which would be a reduction in fuel consumption (GIZ 2012). YépezGarcía and Dana (2012) reviewed how the power sector in oil-importing countries in Latin America and the Caribbean had handled exposure to oil price risk and propose a menu of options that include hedging, diversification from oil, energy efficiency improvement, and regional integration. Given the recent oil price levels, many reports on fuel price subsidies have been issued. They include a joint report by four international organizations on fossil fuel subsidies (IEA et al. 2011), various reports including case studies of fossil fuel subsidies published by the Global Subsidies Initiative (GSI), evidence on energy subsidies in developing countries (Vagliasindi 2012 and 2013), and papers by the staff of the International Monetary Fund (IMF), including IMF (2013a, 2013b), Arze del Granado, Coady, and Gillingham (2012), Coady, Flamini, and Antonio (2012), Guillaume, Zytek, and Farzin (2011), and Coady et al. (2010).

This paper complements the above work by including more recent developments, the impact of interlinkages among different sectors on supply and prices, the impact of government policies on supply conditions, and unintended consequences of government policies. It also includes two specific mitigation policies to reduce demand for petroleum products: diversification and energy efficiency improvement. It addresses gasoline, diesel, kerosene, and liquefied petroleum gas (LPG), the last of which is widely used for cooking and heating in many countries and less covered in other studies. The paper provides the policy context for a recently published study on vulnerability to oil price risks and
**Table 3: Summary statistics for nutritional indicators using LSMS**

||Underweight<br>women|Stunted<br>children|Wasted<br>children|
|---|---|---|---|
|Burkina Faso|n.a.|0.342|0.110|
|Ethiopia|n.a.|0.406|0.121|
|Ghana|0.081|0.385|0.202|
|Malawi|n.a.|0.260|0.079|
|Nigeria|n.a.|0.234|0.106|
|Tanzania|0.095|0.120|0.048|
|Uganda|n.a.|0.280|0.036|
|Mean|n.a.|0.288|0.093|

Note: Data are drawn from LSMS surveys. Observations with missing values have been dropped. Means are population weighted. Women between 15 and 49 years of age and children between 0 and 5 years of age are included in the sample. A woman is underweight if she has a BMI less than or equal to 18.5. A child is stunted if she is two standard deviations below median height-for-age and wasted if she is two standard deviations below median weight-for-height.

40
interview.<sup>12</sup> Lau et al. (2019) report slightly different conclusions, with IVR at 43 percent of the cost of CATI and SMS at 24 percent of the cost of CATI. Each of these studies suggests that phone surveys are substantially less costly than in-person survey implementation, and that CATI is among the most expensive modes among the options for phone-based survey implementation. However, with a suggested length of 5 to 10 items and a maximum length of 15 items, IVR and SMS surveys cannot accommodate the more complex interviews that are made possible in a 20 to 30 minutes conversation in CATI (Glazerman et al., 2020). In sum, given literacy and non-response considerations discussed previously, and the complexity of most agricultural surveys that cannot be easily accommodated by IVR and SMS surveys, CATI remains the preferred mode of phone surveys for agricultural data collection.

The structure of survey costs differs greatly between phone and in-person surveys, and specifically between CATI and CAPI. The main budget lines for in-person surveys are transportation, especially in agricultural surveys where teams travel to sparsely populated rural areas, and per diems for long fieldwork periods. These lines are mostly eliminated in phone surveys, for which total costs are heavily driven by fixed costs including personnel and the acquisition of tablets or computers.<sup>13</sup> Costs for the establishment of call centers, where they do not already exist, such as rental fees for the space, need be considered. Variable costs for phone surveys, primarily in the form of airtime, are marginal.

For reference, in the LSMS-supported phone surveys, incentives made up between 6 and 17 percent of the per-interview cost. In addition to incentives, it may be necessary to provide mobile phones to respondents in the sample who do not already have one. This is not possible if using RDD, or using a register of existing numbers, but could be a possibility in phone surveys that are using an in-person survey as a baseline. This decision
maps of the region differ significantly for the three poverty definitions that we have employed for this exercise. By extension, our pollution impact maps also vary by poverty indicator.

Using our integrated spatial database, we also consider the implications for targeted measures to reduce traffic congestion in Dar es Salaam. For each grid cell, we calculate a composite traffic impact indicator from three elements: traffic volume in the focal cell, the vulnerability of all other cells, and the distance of the focal cell to each of those cells. Then we map the results for each poverty indicator. We find more spatial stability here than in the distribution of pollution impacts: The maps for all three poverty indicators identify the same core areas where congestion reduction would yield the greatest estimated reductions in impact.

To conclude, we should stress that our assessment for Dar es Salaam is inevitably a pilot exercise because, to our knowledge, these high-resolution information sources have not previously been combined for an integrated regional assessment of any city. As more cases are studied, we will undoubtedly gain further insight into the key factors that link traffic, air pollution and distributional impacts in metropolitan regions. In closing, we should note one particularly beneficial side of this new direction for research: It depends only on free global information sources, so it can be undertaken as readily in developing-country cities like Dar es Salaam as in developed-country cities where such research has traditionally been conducted. And even those cities will benefit from the new information sources, because their pollution and distributional impact issues can be revisited with data that simultaneously offer more granularity and broader spatial coverage than has previously been available.

21
0.029|
||0.46|1.22||||0.36|
|Drought this year*ln(hours) from<br>500k city|‐0.0006|‐0.0923*|549|‐128|4192**|0.0058|
||‐0.06|‐1.95|1.56|‐1.07|2.28|1.03|
|Drought last year|‐0.0008|‐1.2525*|‐288|‐2208|17699|0.0538|
||‐0.01|‐1.68|‐0.05|‐1.49E+00|0.75|0.67|
|Drought last year*drought<br>frequency|‐0.1257|‐1.0411||||‐0.1084|
||‐0.45|‐0.85||||‐0.94|
|Drought last year*ln(hours) from<br>500k city|0.0111|0.3263**|‐388|313|‐4247|‐0.0053|
||0.31|2.24|‐0.28|0.87|‐0.72|‐0.3|
|Drought frequency (proportion)|‐1.2609***|‐3.4800**|11703|10822***|64532|‐0.3115**|
||‐4.25|‐2.26|1.11|2.98|1.18|‐2.15|
|Drought frequency*ln(hours) from<br>500k city|0.2845***|1.0100**|‐3830|‐2761***|‐19709|0.0738*|
||3.72
2020). All summary statistics were taken over March 2020, except for land cover, which pertains to the period between January and December 2019.

From Open StreetMap, indicators were obtained representing the total length and number of highways in each AGEB. Two measures of road length were calculated: The first is the total length of the portions of highways contained within the AGEB, and the second is the total length of all highways that intersect the AGEB, including the portions of the road outside the AGEBs. In addition, the total counts of open StreetMap amenities and points of interest, such as hospitals, offices, schools, churches, and so on, were calculated for each AGEB. A final auxiliary variable, besides the Open StreetMap indicators, is the total area of the AGEB of the AGEB in sq km, which was calculated directly from the AGEB level shapefile provided publicly by INEGI. For each auxiliary variable, municipal variables were constructed by taking either population-weighted averages or simple sums (for counts and area) of the AGEB values.

# **c. Model selection**

Variables for prediction were selected using the LASSO (Least Absolute Selection and Shrinkage Operator), with the penalty chosen to minimize the Bayesian Information Criteria (BIC) (Zhang et 7
This is available for 139 countries of 168 for all years and 120 countries for the period 2015present. There are some countries that are missing data on water access but that have achieved universal access to at least a limited standard of water as per the latest data available in World Development Indicators.<sup>18</sup> For these countries we assume no household is vulnerable on the water dimension. This

> 18 Here we define universal coverage as above 98 percent of the population having access.

19
Against the backdrop of the existing literature discussed above, the main contribution of our paper is in terms of testing the validity of an _ex ante_ tool for evaluating the impacts of CCT programs (the BFL model) that is operationally useful, both in terms of ease of implementation and data requirements. Compared to the dynamic _ex ante_ models suggested by Attanasio et al and Todd and Wolpin (see above), the BFL model makes fewer demands on data requirements and do not rely on the availability of panel data, as it employs a reduced-form approach that imposes simplifying assumptions on household behavior. If the predictions of the BFL model with all its simplicity were to compare well with the impacts measured by rigorous _ex post_ evaluations, it would be a practical and credible _ex ante_ tool for policymakers to employ during the design and planning phase of CCT programs.

# **III.** **_Ex Ante_ Simulation with the BFL Model**<sup>**9**</sup>

This section presents a summary description of the BFL model, used as the _ex ante_ evaluation tool in this paper (a more detailed discussion of the model can be found in the original BFL paper). Rather than constructing a complete structural model of demand for schooling and intra-household labor allocation, the BFL model aims to obtain reasonable orders of magnitude for the likely effects of cash transfers that are conditional on school attendance. The structural aspects of modeling are thus kept to the minimum necessary to capture the main effects of the program. There are four key underlying assumptions. Firstly, occupational choice is assumed to reflect (in reduced form) the end result of whatever decision-making process about a child’s time allocation unfolds in a household, regardless of _how_ the decision is made. Secondly, the decision to send a child to school is assumed to be made _after_ all occupational decisions by adults within the household have been made, so that the schooling decision does not affect those earlier decisions. Thirdly, the model does not allow
households with no female adults.<sup>9</sup> This method of classifying households is useful because women living in a household with no male adults are likely to face different challenges than female household heads whose adult sons live with them. This is particularly relevant in Ethiopia, where gender norms limit women’s role in agriculture and the presence of an adult son has implications for access to land (Bezu and Holden 2014b; Kosec et al. 2018).

- We disaggregate the individual-level analysis by sex of the respondent. This is valuable because it reveals the landownership status of women in male-headed households, who are otherwise hidden in the household-level analysis.

Using these household typologies by gender and definitions of land ownership, in the second part of the analysis in sections 4.3 and 4.4, we discuss the prevalence, tax payments, and incidence of land taxation and its implications for Ethiopia. The central question in assessing vertical and horizontal equity is how to rank households and individuals by their ability to pay—a proxy measure for the “same” circumstances. Household expenditures should be adjusted by some measure of the household size, such as per capita or adult equivalence scales, because poverty could be underestimated if poorer households are larger than wealthier households (Deaton and Zaidi 2002; Lustig 2018). However, the per capita approach has been criticized for assuming the consumption needs of adults and children are the same, which overestimates the incidence of poverty when there are many young children in large households (Lustig 2018; World Bank 2018). We therefore order households by spatially-adjusted adult equivalent consumption into four quartiles to take into account household size and the demographic composition of household members.<sup>10</sup> Households are ranked according to their positions in the expenditure distribution of all rural and urban households and individuals by their positions in the expenditure distribution of all individuals. As a welfare measure, we use annual household expenditure consumption, which is the sum of the annual value of food
estimates of returns to investment in the microenterprise sector using productivity measures. Section five presents estimates of marginal returns to investment using a profit function approach. Both approaches show essentially higher rates of return to investment in microenterprise sector. Section six discusses the constraints faced by microenterprises and presents estimates of the effects of constraints, especially lack of access to finance, on profit margin and profitability. Section seven analyzes the extent of cost-effectiveness of microenterprise investment supported under financial institutions, especially microcredit agencies. Section eight concludes.

# <u>2. The role of microenterprise in rural economy and its trend and determinants</u>

This section examines the role of the microenterprise sector in rural income and productivity growth and the underlying characteristics and determinants of microenterprises in Bangladesh. Using the data sets of three rounds of the Household Income and Expenditure Surveys (HIES) conducted by the Bangladesh Bureau of Statistics (BBS), we considered the rates of income growth and poverty reduction over time as a pretext to understand the scope of microenterprise growth in overall rural income growth (Table 1). Overall income grew at a rate of 1.2 percent per year during the period of 2000 and 2005 and 7.5 percent per year during 2005 and 2010 with an overall growth rate of 4.5 percent per year. Interestingly, while the share of nonfarm income in total rural income has increased from 36 percent to 38 percent over this period, moderate and extreme poverty in rural areas have substantially declined. While rural moderate poverty was 56 percent in 2000, it dropped to 33 percent in 2010, implying an average of 2.3 percent reduction of poverty per year over this period. During the same period, extreme poverty declined by about the same rate (2.2 percent per year). Hence, expansion in both farm and non-farm income over this period is likely to have played a role in reducing rural poverty.

6
0.3|-0.2|-0.3|-0.5|-0.6|0.0|-0.1|0.0|
|**Overall fiscal balance (cash basis, before**<br>**interest rescheduling)**|**-2.0**|**-2.5**|**-1.7**|**-1.7**|**3.4**|**-1.4**|**0.3**|**-2.0**|
|Domestic fiscal balance|-0.7|-0.1|0.4|-1.5|1.1|-1.1|1.2|0.2|

Sources: Congolese authorities and IMF staff estimates 1Exceptional expenditure includes spending for the Demobilization, Disarmament, and Reintegration (DDR) program, cost of the elections, payments for retirement allowances, repayments of domestic arrears, and payments for bank restructuring.

# **3.2 Data**

Data come from the 2012 wave of the 1-2-3 survey, representative of the DRC civilian non-institutionalized population. The data, containing anonymous information on household expenditure and composition, are obtained from the DRC National Institute of Statistics. They are organized to reflect annual expenditures. The 1-2-3 survey provides rich socioeconomic data along with large sample size, which allows detailed analysis by area of residence, poverty status, and quintile. The 1-2-3 survey further provides data on quantities and prices of food commodities; but there is some concern about pricing the food commodities, as some households engage in selfconsumption. Good practice in the analysis of welfare data recommends the use of farm-gate prices among households that consume their own production (A. Deaton and S. Zaidi, 2002). Unfortunately, our data do not include enough information to compute reliably such prices.