rafmacalaba/data-use-mentions-extended huggingface dataset:
and give weekly and monthly average prices in the major markets for long periods going back to the 1960s and earlier. An alternative would be to use statistics on farm harvest prices, but these are usually averages with unknown weights of different varieties, the coverage is uneven and uncertain, and it is difficult to obtain reasonably lengthy time series. Experiments using farm harvest prices were tried in the detailed studies of coarse grains and of pulses, and in an adjunct study of wheat and rice, but were judged to be unsatisfactory.

A fixed proportion ( and in some cases 100 percent) of some commodities has been acquired by the government at "levy prices " which are lower than open market wholesale prices. In these cases we have calculated and used the weighted average of the levy price and the free market wholesale price in estimating nominal protection, where the weights are the mandated share of levy sales in production and the free market share. This is important for sugar, for which the government has varied the levy proportion of mill sales quite substantially from year to year, and also for molasses where the entire production of each sugar mill has been acquired at very low levy prices. 2 The domestic prices used in the estimation of nominal protection are intended to approximate as closely as possible the prices that the farmers (or mills in the case of estimates done at that level) receive during the harvest. Prices during the rest of the year reflect a variety of other factors including storage costs of traders, and using them to measure incentives would include the incentives for post harvest storage and distribution functions as well as farming. The principle we have followed has therefore been to calculate average prices during the periods during which the bulk of each crop is harvested. These periods are indicated in the individual studies. But it should be noted that they differ considerably as between commodities e.g. most of the wheat crop in the north-west is harvested and sold during a short six week period, whereas rubber and coconut trees in the south produce
(Appendix Tables 3 and 4). Across the board, hospitals are more likely to have all pieces of equipment, regardless of urban/rural location.

**Figure 8** : Availability of at least one functioning thermometer, stethoscope, and sphygmomanometer by facility type (hospital vs. non-hospital) and urban/rural location* in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Hospitals Non-hospitals<br>Kenya - Rural<br>Kenya - Urban<br>Sierra Leone - Rural<br>Sierra Leone - Urban<br>Tanzania - Rural<br>Tanzania - Urban<br>0 10 20 30 40 50 60 70 80 90 100<br>Facilities with all basic equipment (%)<br><!-- End of picture text -->

> * Sierra Leone sample did not have any rural hospitals, and therefore a single dot for the “Sierra Leone – Rural”

Beyond the availability of basic equipment, the SDI also reports on facility capacity for sterilization, which is needed to ensure that equipment and reusable supplies are clean and do not spread infection. For instance, in Kenya, sterilization<sup>18</sup> is available in 87% of hospitals but only 72% of non-hospitals.

# Availability of treatment supplies

Indicators from the SDI survey can paint a broad picture of availability of treatment supplies for a pandemic. While there are no widely recommended treatments for COVID-19, some emerging evidence suggests that chloroquine and azithromycin may promote virologic cure.<sup>19</sup> The three SDI surveys analyzed for this exercise did not collect information on chloroquine availability at facilities (though older surveys did, when chloroquine was more commonly used for malaria treatment) but did collect information on availability of azithromycin. The SDI surveys are unique in that they require that enumerators verify the availability of unexpired medicines by visual inspection rather than asking facility employees to simply report availability. Azithromycin was available and unexpired in
- **Rossin-Slater, Maya, Christopher J. Ruhm, and Jane Waldfogel.** 2013. “The Effects of California’s Paid Family Leave Program on Mothers’ Leave-Taking and Subsequent Labor Market Outcomes.” _Journal of Policy Analysis and Management_ , 32(2): 224–245.

- **Rovigatti, Gabriele, and Vincenzo Mollisi.** 2020. “PRODEST: Stata Module for Production Function Estimation Based on the Control Function Approach.” _Statistical Software Components, Boston College Department of Economics_ .

- **Seb ̋ok, Anna.** 2019. “The Panel of Linked Administrative Data of CERS Databank.” Centre for Economic and Regional Studies Institute of Economics Budapest Working Papers on the Labour Market 2019/2.

- **Stearns, Jenna.** 2015. “The Effects of Paid Maternity Leave: Evidence from Temporary Disability Insurance.” _Journal of Health Economics_ , 43: 85–102.

- **Stearns, Jenna.** 2018. “The Long-Run Effects of Wage Replacement and Job Protection: Evidence from Two Maternity Leave Reforms in Great Britain.” _Mimeo_ .

- **Sun, Liyang.** 2021. “EVENTSTUDYINTERACT: Interaction Weighted Estimator for Event Study.”

- **Sun, Liyang, and Sarah Abraham.** 2021. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.” _Journal of Econometrics_ , 225(2): 175–199.

- **Ulyssea, Gabriel.** 2018. “Firms, Informality, and Development: Theory and Evidence from Brazil.” _American Economic Review_ , 108(8): 2015–2047.

- **Ulyssea, Gabriel.** 2020. “Informality: Causes and Consequences for Development.” _Annual Review of Economics_ , 12(1): 525–546.

- **Williams, Colin C.** 2013. “Evaluating Cross-National Variations in the Extent and Nature of Informal Employment in the European Union.
declining economic growth. To enhance productivity growth in recent years, China’s policy makers have focused on fostering innovation. By some measures, China’s innovation capacity has improved steadily in recent years, placing the country 14<sup>th</sup> on the Global Innovation Index (Cornell University et al., 2019).

At the same time, China remains, on average, quite distant from the global technology frontier and thus has substantial remaining potential for catch-up growth. The adoption of more advanced technology and management skills from high-income countries, as well as improving the efficiency of resource allocation, can provide a less costly and more certain source of growth over the medium term.

However, China’s access to foreign technologies has become more challenging in recent years. The country’s overseas investment expansion has raised concerns in high-income countries over reciprocity in investment conditions. In 2017, China introduced a negative list for FDI which restricts foreign investment in listed sectors and requires equal treatment of domestic and foreign firms in the rest of the economy. In 2018, foreign ownership restrictions in financial services and car, ship, and aircraft manufacturing were reduced and, in 2020, a new FDI law will take effect. These measures address some of the concerns over market access, intellectual property rights, and foreign investor protection, but their impact will depend on successful implementation.

Achieving productivity growth through reallocation will require deepening reforms to increase the role of the market, the private sector, and competition. China has recently introduced policies to improve the business climate. Besides the above measures to allow more foreign investment, in 2019 China climbed up to 46th in the World Bank Ease of Doing Business rankings, gaining more than 30 positions from the previous year (World Bank, 2019). The 20
Policy Research Working Paper 5983

# **Abstract**

Many more impact evaluations could be done, and at lower unit cost, if evaluators could avoid the need for baseline data using objective socio-economic surveys and rely instead on retrospective subjective questions on how outcomes have changed, asked post-intervention. But would the results be reliable? This paper tests a rapidappraisal, “shoestring,” method using subjective recall for welfare changes. The recall data were collected at the end of a full-scale evaluation of a large poor-area development program in China. Qualitative recalls of how living standards have changed are found to provide only weak and biased signals of the changes in consumption as measured from contemporaneous surveys. Importantly, the shoestring method was unable to correct for the selective placement of the program favoring poor villages. The results of this case study are not encouraging for future applications of the shoestring method, although similar tests are needed in other settings.

This paper is a product of the Director’s office, Development Research Group. 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://econ.worldbank.org. The author may be contacted at mravallion@worldbank.org.

_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 of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
as well as a decline in the use of facilities among the poor. Uniquely among these theories, Ronnen (1991) is the only one who suggests that even well performing facilities may see quality improvements in response to the regulation as they increase investments in order to maintain market power.

In order to establish the plausibility of each of these channels, we establish that (a) facilities invested in improvements that were (far) more costly than what was required under the regulation and were not optimizing decisions to meet compliance thresholds; (b) there was no difference in treatment outcomes between the inspection only (T1) and the inspection + information (T2) arms; and (c) quantile treatment effects by market density show that impacts were highest at the _top-end_ of the distribution of patient safety, where facilities were least affected by regulatory requirements, as well as in markets with greater competition from public facilities. Therefore, in addition to a direct regulatory channel, we conclude that the data are consistent with market power as a source of inefficiency; nevertheless, we caution that the experiment was not designed to test a specific mechanism and we consider several alternate explanations in our discussion.<sup>4</sup>

> 4Our results on the mechanism are speculative because most facilities could have been sanctioned under the regulation and, therefore, beliefs over how the regulation functions and what other facilities, in turn, believe will determine facility investments. While previous work uses rational expectations to model beliefs regarding inspections (Duflo et al., 2018), in the case of a new system like the one we evaluate, such an assumption is harder to sustain.

4
data layer is available since the 1980s to allow parcel-level analysis (Lark _et al._ 2017). For developing countries, the use of such imagery to provide reliable output or productivity indicators down to the plot level has recently been demonstrated (Lobell _et al._ 2020) with access to ground-based training data a key constraint (Burke _et al._ 2020). Easier access to imagery and cloud computing platform to store data and provide computing power has greatly increased extent, scope, and granularity with which satellite imagery is used to assess damages due to conflict since the review of studies on this topic by Witmer (2015).

Documenting conflict effects in urban areas requires very high resolution imagery as illustrated by Mueller _et al._ (2021) who develop a machine learning algorithm applied to Syria.<sup>2</sup> Effects of war on agricultural production can rely on lower resolution imagery as illustrated by studies such as Alix-Garcia _et al._ (2013) for Darfur and a number of studies covering <mark>areas of the former Soviet Union including Ukraine</mark> to assess

> 1 As exposure to violence often leads the poorest to revert to subsistence as in Colombia (Arias _et al._ 2019), levels of food consumption may not decline linearly with conflict exposure as shown in Afghanistan (D’Souza & Jolliffe 2013). This has led to use of dietary diversity as a more appropriate measure of conflict impact there (D'Souza & Jolliffe 2014), in Nigeria (George _et al._ 2020), and Cote d’Ivoire (Dabalen & Paul 2014).

> 2 Wouters _et al._ (2021) similarly use UAV imagery in Malawi to assess flood-related damage to houses.

4
Policy Research Working Paper 9488

# **Abstract**

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. This project set out to test whether an openly available dataset (Twitter) could be transformed into a resource for urban planning and development. The hypothesis is tested by creating road traffic crash location data, which are scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over age five and young adults. The research project scraped 874,588 traffic-related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. The project geolocated 32,991 crash reports in Twitter for 2012–20 and clustered them into 22,872 unique crashes to produce one of the first crash maps for Nairobi. A motorcycle delivery service was dispatched in real-time to verify a subset of crashes, showing 92 percent accuracy. Using a spatial clustering algorithm, portions of the road network (less than 1 percent) were identified where 50 percent of the geolocated crashes occurred. Even with limitations in the representativeness of the data, the results can provide urban planners useful information to target road safety improvements where resources are limited.

This paper is a product of the Development Impact Evaluation Group, Development Economics. 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 smilusheva@worldbank.org.

_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
is likely because of the adjustment we make to account for possible informality of consumption, which is likely to be more prevalent among households at lower parts of the income distribution. This would be consistent with other recent evidence that shows that VATs can be progressive when informality of consumption is accounted for. (Bachas et al 2023). Had we not accounted for the possible informality of purchases as we do here, the marginal contributions of VAT would be closer to -0.60, which suggests that the informality of purchases in rural areas (which we proxy for using measures from Thailand) shields poorer households from VAT to a certain extent.

# Discussion and Conclusion

In this paper we have applied the standard fiscal incidence analysis to data from the China Family Panel Study 2018 to study the effect of government taxes and spending on inequality in China. The analysis includes fiscal elements such as personal income tax, contributions to social insurance, value-added tax, consumption tax, cash transfers, contributory pensions, and spending on education and health and accounts for 63 percent of overall total revenues and 43 percent of total government spending. The findings suggest that China already achieves quite a bit of inequality reduction through its fiscal policies but there is ample room for the country to do more. Greater reliance on progressive fiscal instruments such as PIT would not only enhance redistribution but also generate fiscal resources China could use to address future drivers of inequality such as population aging and climate change. In addition, there is scope to make social assistance more effective in responding to economic transitions and demographic trends. Cash transfer programs could expand dynamic monitoring systems (currently being piloted in a few cities such as Chongqing and Hangzhou) to be able to quickly respond to vulnerable families that experience unexpected income falls or increases in critical spending (such as on health care). Rapid aging of the population also requires reconsidering reforms to increase the adequacy of the resident pension system while ensuring its sustainability.

Social spending and human capital investment already contribute significantly to reducing inequality, but the
borrowers in the region are financing public consumption (e.g. goods and services, compensation of employees). In other words, they might be looking for budget support. Finally, there are no considerable differences in the growth rate of investment between light and heavy borrowers — i.e. it fluctuates between 3.6 and 3.7 percent per year. However, total investment grew at 4.1 percent per year among heavy borrowers. The findings from Figures 20 and 21 show that heavy borrowers in the region are experiencing a debt-fueled inefficient investment.

**Figure 21. Growth of aggregate demand components across types of borrowers in SubSaharan Africa, 2012-17**

<!-- Start of picture text --> Private Consumption Growth Public Consumption Growth Investment Growth<br>6 4 5<br>5<br>4<br>3<br>4<br>3<br>3 2<br>2<br>2<br>1<br>1<br>1<br>0 0 0<br>Light Moderate Heavy Light Moderate Heavy Light Moderate Heavy<br>Percent per year, 2012-17 (group medians) Percent per year, 2012-17 (group medians) Percent per year, 2012-17 (group medians)<br><!-- End of picture text --> _Source: World Bank, World Development Indicators. Elaboration: AFRCE Staff estimates._ The rate of growth in the components of aggregate demand across types of borrowers in the region depicted in **Figure 21** conceals the great degree of heterogeneity across countries within each group. Additional insights are provided about the cross-country behavior of investment growth among heavy borrowers and that of public consumption growth among moderate borrowers.

**Figure 22** reports the annual average growth rate of gross capital formation across 18 heavy borrowers in Sub-Saharan Africa. Three countries in this group experienced a collapse in investment growth (Equatorial Guinea, Chad and Sierra Leone). The
, as described in Rao and Molina (2015), is obtained as follows:

1. Using the survey data, fit model (1) via any method providing consistent estimators. This yields the vector of parameter estimates:

Usual fitting methods under this approach are maximum likelihood (ML) or restricted maximum likelihood (REML), both based on the normal likelihood, and H3 method, which does not require to specify a distribution. In the implemented sae R package, REML is used.

2. Use the parameter estimates obtained in step 1 as true values to simulate a vector of welfare in the census. First, the welfare from each out-of-sample household is generated as follows, ln( _ych,r_<sup>_∗_) =</sup><sup>_xch,rβ_ˆ0+</sup><sup>_η_</sup> _c_<sup>_∗_+</sup><sup>_e∗_</sup> _ch,r_<sup>_,_</sup> where here we add the subscript _r_ to indicate that the household is not sampled. Here, for an area _c_ that is included in the sample, the area effect _ηc_<sup>_∗_isgeneratedas</sup> where _η_ ˆ _c_ 0 and _γ_ ˆ _c_ are respectively given by Here, _y_ ̄ _c,s_ and _x_ ̄ _c,s_ are respectively the sample means of the welfare variable and the correlates in area _c_ . If area _c_ is not sampled, the area effect is generated as _ηc_<sup>_∗∼N_</sup> �0 _,_ ˆ _ση_<sup>2</sup> 0�. Finally, the household error _e_<sup>_∗_</sup> _ch,r_<sup>isgeneratedas:</sup> For an area _c_ that is sampled in the survey, the generated non-sample vector _yc,r_<sup>_∗_is then augmented<
.0540522|0.0540522|

Source: Authors‘ calculation from the Investment Climate Survey Databank, World Bank Having found that there exists wide variation in technical efficiency among the sample firms it is important and useful to understand the impact of the overdraft facility policy.

# 5.2 Propensity Score Matching Estimation.

To better construct the match, table 6 below shows tests for mean differences between supported and non-supported groups for the impact and other key variables. Identification of the matching technique to create the propensity score requires that the impact variables are independent of the treatment conditional on the propensity score. Few statistically significant differences between participants and non-participants were observed, with the exception of the large and younger firms being more prevalent among those receiving overdraft facilities.

18
value 1 in period _t_ after the event and 0 otherwise. Since the time units differ between the CP and PoI data, we set _t_ as 1 month for the household consumption regression and four months (equivalent to one wave) for the individual-level time usage regression. For example, in the household-level data (CP data), _Time_ = 0 corresponds to 1 month before the event; in the individual-level data (PoI data), _Time_ = 0 corresponds to 1 wave (equivalent to four months) before the event. The omitted base period (or _Time_ = 0) is the wave (or month) of the survey before the start of the program. We then pool the preand post-treatment periods together to estimate In both equations (6) and (7), _αi_ are individual (or household) fixed effects that control for all time-invariant characteristics of an individual (or a household). _αt_ represents the time-fixed effects, encompassing month and year-fixed effects in the regression that examines changes in household consumption. In the analysis of PoI data, _αt_ comprises wave and year-fixed effects. Appendix Table D2 provides a detailed definition for _Timet_ and _Postt_ . To account for the impact of the pandemic on people’s behavior, we also control for average daily confirmed COVID-19 cases as a share of the population. Because the treatment is at the state level, we cluster standard errors at the state level for statistical inference. Our parameters of interest are _βt_ and _β_ .

# **5 Treatment Effect on Expenditure**

We use both the CMIE CPHS data and the Delhi survey to isolate the effect of treatment on transportation expenditure.

## **5.1 Evidence from The CPHS Sample**

To examine the of the _Pink_ on _household_ we impact _Slip Program_ monthly expenses, plot the estimated coefficient _β_<sup>ˆ</sup> _t_ from the event-study specification (6) in Figure C1 and detect no pre-trends. However, after implementation, we see a substantial decrease in household expenditure on transportation in