rafmacalaba/data-use-mentions-extended huggingface dataset:
|||T<br>|able 1: Summar<br>|yof data set<br>|||
|---|---|---|---|---|---|---|
||**Countries with**<br>**an autonomous**<br>**water regulatory**<br>**agency**|**Countries**<br>**with PPP**|**Countries with**<br>**both an**<br>**autonomous**<br>**regulator and**<br>**PPP**<br>**Full Sample(177**|**Countries with**<br>**PPP but no**<br>**autonomous**<br>**regulatory**<br>**agency**<br>**countries)**|**Countries with an**<br>**autonomous**<br>**regulatory agency**<br>**but no PPP**|**Countries with**<br>**no autonomous**<br>**regulatory**<br>**agency and no**<br>**PPP**|
|**Number**<br>|80<br>|157<br>|69<br>|89<br>|11<br>|8<br>|
|**Share(in %)**|45.2|88.7|39.0|50.3|6.2|4.5|
|||**Develo**<br>|**ping country sam**<br>|**ple
||(5.420)***|(8.892)***|(5.882)***|(9.728)***|
|---|---|---|---|---|
||[13.84]***|[14.70]***|[14.66]***|[16.35]***|
||{31.18}***|{43.40}***|{32.70}***|{45.84}***|
|No. of Children||-17.0||-21.6|
|||(3.393)***||(3.749)***|
|||[7.615]||[7.554]***|
|||{16.69}||{17.29}|
|No. Observations|37144|37144|32499|32499|

Notes: (1) The data used are IHDS 2005 and NSS 1995 respectively. (2) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at state-level in braces; *** p<0.01, ** p<0.05, * p<0.1.
only 6% in both periods. These adjustments suggest higher poverty rates when considering the imputed consumption data. Specifically, the poverty rate in 2016 would have been between 1.7 and 2.3 percentage points higher, and the extreme poverty rate between 3.5 and 3.7 points lower, had a comparable consumption aggregate been available. Similarly, the incidence of poverty in 2010 would have been between 5.5 and 5.8 percentage points higher, and the extreme poverty incidence between 5.1 and 5.4 points lower.

In conclusion, this study not only enhances the understanding of poverty and inequality trends in Bangladesh but also ensures these insights are consistent with established poverty profiles. This balance between novel methodologies and consistency with previous findings makes the study invaluable for policy makers and researchers, aiding significantly in efforts to reduce poverty and improve living standards in Bangladesh. The approach offers a model that could be replicated in similar socioeconomic studies globally, emphasizing the importance of accurate and comparable measurements in understanding poverty dynamics.

25
Health Service (NFZ), Ministry of Family, Labor and Social Policy (MRPiPS), Ministry of Health, and Ministry of Education. Finally, we use the 2010 Input-Output matrix to estimate the indirect effect of indirect taxes as described below and detailed in Appendix I.

# **Method and Approach**

To analyze the incidence of each fiscal intervention, and the impact of taxes and social spending on poverty and inequality, we follow Lustig and Higgins (2013) and measure per capita income before and after each fiscal intervention as described in Figure 1. In particular, for every household we define the following income concepts:

- _Market income_ includes pre-tax wages, salaries, and income earned from capital assets (rent, interest or dividends) and private transfers.

- _Disposable income_ is constructed by subtracting direct taxes and social contributions and adding direct transfers and social pensions to market income. Direct taxes include the personal income tax, capital tax, and farm taxes. Social contributions include those for disability and farmer pensions, health, accident and sickness insurance, as well as contributions to the labor fund, the fund of employee benefits, and to other farmer benefits. Direct cash transfers include housing benefits, child birth grant, nursing benefit, nursing allowance, social assistance, and family benefits. In addition, there are pre-retirement, family, disability, maternity, unemployment and farmer pensions which are treated as transfers.

- _Consumable income_ subtracts the impact of indirect taxes to disposable income. In Poland, indirect taxes included in this analysis include the VAT, excises on alcohol and tobacco, fuel and automobiles.

- _Final income_ adds in-kind benefits in the form of health and education to consumable income.

One area where there is no clear consensus in the literature is on how to treat contributory pensions and the related contributions. Arguments exist in favor of treating contributory pensions as individual savings or deferred income, while others argue that they should be treated as a government transfer, with the related contributions being treated as a direct tax.
|9.7|70.5|15.2|80.5|
|Second 10|9.0|33.5|8.1|46.3|
|Third 10|8.8|12.8|8.6|32.7|
|Fourth 10|8.7|21.3|6.8|43.1|
|Fifth 10|9.0|14.0|7.4|21.3|
|Sixth 10|8.6|15.1|9.8|21.4|
|Seventh 10|10.4|13.2|9.2|18.4|
|Eighth 10|14.1|9.1|13.7|18.4|
|Ninth 10|12.1|19.5|12.2|13.3|
|Top10|9.6|10.7|9.0|11.5|
||100.0||100.0||

Notes: All values based on observed – not predicted – data. Households ranked into decile groups on the basis of observed per capita household expenditure (excluding remittances). For those households receiving internal remittances (from Guatemala), column (2) shows the percent of total per capita household expenditure (including remittances) coming from internal remittances. For those households receiving international remittances (from USA), column (4) shows the percent of total per capita household expenditure (including remittances) coming from international remittances.

Source: Calculated from Guatemala 2000 ENCOVI Survey.
- 2.2 Empirical studies comparing the experience of structural change across developing regions and countries

Using data from both the national accounts and Demographic and Health Surveys (DHS),<sup>3</sup> and covering 19 SSA countries, McMillan and Harttgen (2014) show that between 2000 and 2010, the share of the labor force employed in agriculture declined by roughly 10 percentage points. This decline has been accompanied by a systematic increase in the productivity of the labor force, as it has moved from low productivity agriculture to higher productivity manufacturing and services. The decline has been accompanied by higher economic growth rates and poverty reduction. Both the declines of poverty and agriculture’s labor share have been more rapid in countries where the initial share of the labor force engaged in agriculture is the highest and where commodity price increases have been accompanied by improvements in the quality of governance.

Likewise, Diao _et al_ . (2017) also use both data from the Groningen Growth and Development Center’s Africa Sector Database and the DHS to show that much of Africa’s recent growth and poverty reduction has been associated with a substantive decline in the share of the labor force engaged in agriculture over the period 1994-2008. This decline is most pronounced for rural females over the age of 25 who have a primary education; it has been accompanied by a systematic increase in the productivity of the labor force, as it has moved from low productivity agriculture to higher productivity services and manufacturing.

Haile and Moller (2018) examine the determinants of growth acceleration in West African Economic and Monetary Union (WAEMU) countries over the period 2011-17. Using the structural change lens as one of the methods of analysis, they find that growth was driven by static structural change and within-sector productivity growth. However, productivity growth was low in the subregion over the period of analysis.

de Vries _et al_ . (2015) analyze structural change in Africa and its implications for productivity growth during the past 50 years. According to this paper, during
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _26_

- Reinsdorf, M., Triplett, J. E., Reinsdorf, M., and Triplett, J. E. (2009). A Review of Reviews: Ninety Years of Professional Thinking About the Consumer Price Index. In _Price Index Concepts and Measurement_ , pages 17–83. National Bureau of Economic Research, Inc.

- Rubin, D. B. (1976). Inference and missing data. _Biometrika_ , 63(3):581–592.

- Rubin, D. B. (1996). Multiple Imputation After 18+ Years. _Journal of the American Statistical Association_ , 91(434):473.

- Sbahi, S., Ouazzani, N., Hejjaj, A., and Mandi, L. (2021). Neural network and cubist algorithms to predict fecal coliform content in treated wastewater by multi-soil-layering system for potential reuse. _Journal of Environmental Quality_ , 50(1):144–157.

- Seabold, S. and Coppola, A. (2015). Nowcasting prices using Google trends : an application to Central America. _Policy Research Working Paper Series_ .

- Seaman, J. and Holt, J. (1980). Markets and Famines in the Third World. _Disasters_ , 4(3):283–297.

- Shephard, N. (1994). Partial non-Gaussian state space. _Biometrika_ , 81(1):115– 131.

- The World Bank (2019). _Inflation in Emerging and Developing Economies: Evolution, Drivers, and Policies_ . The World Bank.

- van Buuren, S. (2012). Flexible Imputation of Missing Data. _Flexible Imputation of Missing Data_ .

- van Buuren, S. and Groothuis-Oudshoorn, K. (2011). Multivariate Imputation by Chained Equations in R. _Journal of Statistical Software_ , 45(3):1–67.

- Wan, E. A. and Van Der
Section A of Table 5 describes consumption shortfall for households who are vulnerable with a 50% threshold, Section B describes the additional households that are vulnerable when we lower the threshold to 40%, and Section C describes consumption shortfall for all households that are vulnerable with a 40% threshold. Using the 50% threshold, we by definition restrict to households whose expected level of consumption is below the poverty line. Thus, all households included in Section A experience a consumption shortfall, and the levels of shortfall are relatively large. The households in Section B all have expected consumption above the poverty line, and thus do not experience ‘shortfall.’ However, the percent above the poverty line in Section B is modest compared to the percent below the poverty line in Section A, except for households in Central where the difference between Section A and Section B is relatively small. When we combine the households from Sections A and B to create Section C, we see that the average shortfall is much smaller under the 40% threshold, and that the average gap is even positive.

# **6. Conclusions**

Considerations of risk and vulnerability are critical to understanding the dynamics of poverty in Malawi, where the majority of rural households are poor rain‐fed farmers. This study assessed the vulnerability to consumption shortfalls of rural households in Malawi using a two‐period panel, drawn from the 2010 IHS3 and 2013 IHPS.

The results show that many households in Malawi are vulnerable to poverty, though as with many other studies of rural areas in other countries, much of vulnerability is due to chronic poverty. In terms of empirical evidence, most studies find that poverty is the most important component of vulnerability. Nonetheless, risks – particularly rainfall and loss of off‐farm employment shocks – are also important in explaining why poor households remain poor, and why some non‐poor households are more likely to fall into poverty in the next period.

The calculation of expected shocks matters quite a bit in the assessment of vulnerability
agricultural inputs, mirroring the push for the adoption of objective survey methods for the measurement of land areas (via GPS) and crop productivity (via crop cutting) in household and farm surveys. Finally, these results raise important questions about whether and how corrective information - about plot size or other inputs – to correct farmers' mistaken beliefs improves input allocation and related decisions, thereby addressing apparent allocative inefficiencies that are strongly associated with productivity and welfare indicators. Longitudinal survey data collection on treatment and control farmers in subsequent agricultural seasons may help shed light on this hypothesis.

31
Table A.3).

## _Male- versus female-headed households_

A second relevant comparison to make at the outset is between gender differences at baseline: mainly differences between male- and female-headed households since most of our regressions are at the household level. Anecdotal evidence suggests that the default head of household is a man, with the implication that female-headed households are likely those where women are widowed, separated, or divorced. In contrast, a very small percentage of male-headed households are unmarried. Therefore, it is likely that female-headed households have one working-age adult less on average, compared to maleheaded households. This indeed appears to be the case in our sample. As Table A.4a shows, there appear to be only small differences between male- and female-headed households in VARHS 2008 in the education or literacy levels of the household heads, but statistically significant differences in the size of the household. Male-headed households are larger on average than female-headed households by close to one adult per household.

> 15 Another reason these samples could differ is that the sampling unit that we draw data from is the commune whereas VARHS is intended to be representative at the province level. However, nothing in the sampling strategy of VARHS (described in Section 2) gives us a reason why we should expect the systematic differences we observe in Table A.3 based on the fact that the data are not representative at the commune level. Therefore, we interpret this largely as a result of RTP3 targeting.

13
for upstream suppliers would be lower than the average for that upstream industry across countries.

We convert a 1996 U.S. Census Bureau input-output table to use ISIC industry classifications. As shown in Table 6, the manufacturing inputs of a firm represent for most industries 90% or more of the total inputs to production. As a result of the aggregation at the 3- digit ISIC level, the largest suppliers for a firm in many industries are other firms in the same industry. In order to compute upstream inventories, we take a weighted average of all the finished goods inventory levels of upstream suppliers, where the weights are given by the percentages in the input-output table.

We find that the increases in raw materials inventories due to poor infrastructure and poor functioning markets is not offset by a decrease in upstream finished goods inventories. Regressions (4), (5), and (6) show that infrastructure does not affect finished goods inventories and that poor markets increase finished goods inventories by 10 to 32%. Real interest rates are significant and of the expected sign here, but the effect is small, about 7% of inventories per standard deviation change. The regressions thus far suggest that poor infrastructure should lead the distribution of inventories to shift downstream. Regressions (7) and (8) examine raw materials inventories as a percentage of total inventories in the chain to confirm this shift.

This check on shifting of inventories is incomplete in several ways. First, we do not have inventory data on non-manufacturing industries and therefore cannot check for the existence of shifting from upstream suppliers such as agriculture, mining, or forestry, or to downstream customers such as the wholesale and retail sectors. Second, we do not have data on the inventories of upstream suppliers and downstream customers abroad, and, for this reason, cannot test for shifting across country borders. Third, the input-output tables do not allow us to 22
that taken in García (2022) and relies on the fact that MNREGA implementation in non-Star states was limited compared to Star states to estimate the impact of MNREGA by comparing outcomes in Star and non-Star states after implementation.<sup>10</sup> This approach has the advantage of allowing for comparisons between Treated and Never Treated districts, which alleviates many of the most significant challenges with estimation in the staggered difference-in-differences approach (Goodman-Bacon, 2018; Callaway and Sant’Anna, 2021).

In Figure 1 we show the average number of water projects of various types completed in Star and non-Star states. Star states complete substantially more conservation, irrigation, and microirrigation projects than non-Star states.<sup>11</sup> They also complete slightly more drought mitigation projects and slightly fewer flood mitigation projects. Collectively Star states complete almost five times as many projects on average as non-Star states. These differences in the number of completed projects lend support to our choice to define treatment based on both the timing of MNREGA implementation and location in a Star state.

> 8Data available from ICRISAT: http://data.icrisat.org/dld/src/irrigation.html

> 9Data available here: https://esa-soilmoisture-cci.org/ We use data from ESA CCI SM v06.1

> 10In A1 we present results that use only the staggered roll-out of MNREGA for identification, an approach that is more standard in the literature on MNREGA. This approach has the disadvantage of not allowing for comparisons to a Never Treated control group because all districts eventually receive MNREGA. Broadly our results are consistent between the two approaches.

> 11Table A2 formally tests whether Star states complete more projects than non-Star states and confirms that they do.

7
**Figure 1. Sectoral CO2 and non-CO2 Emissions per Value Added across Sectors (metric/million USD).**

<!-- Start of picture text --> 4,000 CO2 Emissions per VA 20,000 Non-CO2 Emissions per VA<br>High Income 15,000<br> 2,000 Lower Middle Income 10,000<br>Low<br> 5,000<br>Upper Middle<br> - -<br><!-- End of picture text --> Source: Authors’ computations using GTAP data. Utilities are removed from Figure 1 for CO2 emissions for presentational purpose, as they have nearly five times higher emissions per value added (on average) compared to transportation and communication.

Figure 1 clearly shows that transportation and communication, mining, and manufacturing are among the highest contributors to CO2 emissions per value added, while other services, finance, and trade have the lowest levels of CO2 emissions per value added. It is important to note that utilities are intentionally removed from the graph as they have nearly five times higher emissions per value added (on average) compared to transportation and communication. There is also a large dispersion within these broad sectors indicating that production structures within industries could be diverse differing among developing countries relative to the different stages of sophistication and the choice of technology. At the same time, the CO2 emissions per value added vary substantially across the different country income groups. Finally, non-CO2 emissions in terms of CO2 equivalent are multiple times higher for agriculture, mining, and utilities with a large variation across income groups. These non-CO2 emissions include methane (CH4), nitrous oxide (N2O) and the group of fluorinated gases (F-gases).

# **5. Estimation Results**

The paper estimates the impact of the share of informal workers on emissions per value added using cross country and industry data in the following regressions: OLS, OLS with control variables, industry fixed effects, country fixed effects, and
in the score (7.25 points).

These health service delivery indicators are the best measures we have available in our study context to capture the productivity of the district bureaucracy as a complex organization. Rasul and Rogger (2016) and Rasul, Rogger and Williams (2017) review the available measures of productivity of national bureaucracies and focus on completion rates of projects undertaken. The advantage of the setting we examine–of district-level bureaucracies–is that productivity can be measured in terms of services that are directly linked to development outcomes, such as health in poor communities.

# **3 Empirical Specification**

The empirical analysis in this paper is not positioned to causally identify the effect of any variable on health service delivery performance. Rather, we examine what are the robust correlates, if any, of variation in performance across districts using all available data on a variety of potentially significant correlates for this purpose. The main question guiding the analysis is whether (which) selection traits of bureaucrats and politicians are significant in explaining district performance. In order to control for the role of other district socio-economic and political characteristics in driving any correlation, by simultaneously influencing both selection traits and health service delivery, we use other District level administrative data on these district characteristics.

Specifically, we estimate variants of the following equation: where Yij will often be the standardized health score in the District League Table for district _i_ in region _j_ . In some instances, Yij will instead be specific components of the DLT score. The vector X<sup> ̄</sup> i includes our main variables of interest: the average trait of politicians and/or bureaucrats in District _i_ ; different specifications will use different subsets of traits. Zi is a vector including a series of socio-economic and political characteristics of districts 16
1.47)|(-1.55)|(-1.59)|(-0.65)|(-0.42)|
|**Firm Performance**||||||
|Firm has no need for external financing||||-0.121|-0.223|
|(dummy)||||(-1.32)|(-1.46)|
|Firm has ISO certification||||-0.197**|-0.327**|
|(dummy)||||(-2.00)|(-2.03)|
|Firm Growth 2005-2007||||0.003|0.001|
|(employment growth, %)||||(0.78)|(0.29)|
|<br>Firm invested in 2007||||-0.031|0.036|
|(dummy)||||(-0.33)|(0.24)|
|<br>Return on Sales in 2007|||||-0.002|
|(percent)|||||(-0.72)|
|**Joint test for significance of overdraft and loan variables**|||6.91|||
|**P-value**|||0.03|||
|**Pseudo R-Squared**|0.06|0.06|0.06|0.07|0.12|

***,**, * Significant at 1,5, and 10 percent level.  T-statistics in parentheses. Source: Authors‘ calculations based upon World Bank Enterprise Surveys

Note: Since dependent variables are dummy variables, probit regressions are used. Percent private-owned is omitted due to co-linearity. Country dummies are used rather than country controls because of the