rafmacalaba/data-use-mentions-extended huggingface dataset:
transfer of funds among the budget categories and the lack of exact double-counting data. (Qiao, Fan, Rahemtulla, van Rijn & Li, 2022)

**_Sources_ :** Statistical Yearbook of China 2019, National Bureau of Statistics (2019), Finance Yearbook of China (2019), Ministry of Finance (2019), 2018 Annual Report of Housing Fund, Ministry of Housing and Urban-Rural Development, Ministry of Finance & The People's Bank of China, 2019.

Figure 1 shows that China lies around the middle of the distribution of upper-middle-income countries in terms of revenue collection. It also uses a mix that relies more on social security contributions rather than on personal income taxes or indirect taxes other than VAT. China’s social security contributions amount to 8.7 percent of its GDP, second only to Belarus (11.7- percent) among upper-middle income countries and is very close to the average of the high-income countries in the figure (8.8 percent). On the other hand, it collects just 1.5 percent of its GDP through personal income taxes, one of the lowest shares among upper-middle-income countries, far below highincome countries, and just slightly higher than average share of the low-income countries included in the figure (1.2 percent). China’s collection of VAT (8.6 percent of GDP) and all other indirect taxes (0.2 percent of GDP) places it around the median of the high-income country average of 8.3 percent of GDP. China’s own revenue mix also shows a notable shift between 2014 and 2018, with distinct increase in the share of revenues from social security contributions, VAT and a distinct reduction in the shares of other direct taxes, including personal and payroll taxes.

5
column (2) of Table 8 is positive, but not significant.<sup>30</sup> As sales may react with a lag to lower prices, column 3 regresses next period’ sales on PSI. The coefficient becomes positive and significant. Having expressed inventories in logs allows us to compare the elasticities. Consistent with the idea that lower markups have the objective to boost sales and empty out inventories, we find that the increase in sales is of similar magnitude as the increase in inventories.

To further corroborate the mechanism just described in Appendix Table A10, we provide two further tests. First, the coefficient of input PSI showed in column (1) shows that the application of this type of NTM on inputs does not have any impact on inventories of finished goods, which should be only partially impacted by restrictions on intermediate materials. Second, in column (2) of Appendix Table A10, we show that input PSI does not have any impact on employment, consistent with the idea that the increase in sales may not be related to a successful growth strategy of the plant.

# **6. Conclusions**

This paper builds a novel NTM dataset on all NTMs applied to the universe of goods produced in Indonesia from 2008 to 2015 and uses the data to explore the impact of NTMs in a large panel of manufacturing plants. The results suggest that on average NTMs do not harm plants’ performance, whether through the input or the output channel. To the extent that some of the benefits of NTMs in addressing market failures may not be fully captured by the plant-level outcomes, NTMs might even have a net positive impact on welfare. This is different to the case of import tariffs, which have a negative impact on productivity and - unlike NTMs - they also do not appear to have any positive impact on consumers.

This average neutral effect of NTMs masks a wide heterogeneity across individual NTMs. To tease out some of this heterogeneity, the paper focuses on two specific measures (APT and PSI
policy makers in LMICs face, investing in one well-maintained regulatory-grade monitor complemented with a dense network of low-cost monitors may strike the right balance. Barring any regulatory-grade monitors, calibration of PM2.5 measurements from low-cost monitors using satellite based estimates of PM2.5 can serve as an alternative. Given the better performance of satellite data at a lower temporal frequency, we also show that this alternative calibration should be applied at a daily level (rather than hourly), to achieve results that are better aligned with what would be expected from regulatory measurements.

We make three contributions to the literature. First, we contribute unique evidence comparing the three principal sources of air pollution data in a LMIC context. Fowlie _et al._ (2019) contrasts air pollution levels from satellite data with those derived from regulatory-grade monitors in the U.S. and concludes that differences imply potential misclassifications of E.P.A. attainment status. In Greece, Stavroulas _et al._ (2020) find a moderate to strong correlation between PM2.5 levels measured with lowcost monitors and with regulatory-grade monitors. However, these studies do not compare all three data sources and the results of these studies may not translate directly to contexts in LMICs because the accuracy and precision of the air pollution data can vary across low- and high-income contexts. The precision and accuracy of air pollution data from low-cost monitors is affected by environmental conditions (e.g. relative humidity) and the type of particulate matter, such as dust events (Jaffe _et al._ , 2023). The availability, accuracy, and precision of air pollution data from regulatory-grade monitors depends on a dense network of monitors and regular investments in maintaining and replacing monitors.

Second, we extend this literature beyond a comparison of the air pollution levels captured by the three 4
: “Wages and informality in developing countries,” _American Economic Review_ , 105, 1509–46.

- Monteiro, J. C. and J. J. Assunc ̧ ̃ao (2012): “Coming out of the shadows? Estimating the impact of bureaucracy simplification and tax cut on formality in Brazilian microenterprises,” _Journal of Development Economics_ , 99, 105–115.

- Moon, K., P. Bergemann, D. Brown, A. Chen, J. Chu, E. Eisen, G. Fischer, P. K. Loyalka, S. Rho, and J. Cohen (2021): “Manufacturing productivity with worker turnover,” _Available at SSRN 3248075_ .

- Moon, K., P. Loyalka, P. Bergemann, and J. Cohen (2022): “The hidden cost of worker turnover: Attributing product reliability to the turnover of factory workers,” _Management Science_ .

- Mueller, A. I. and J. Spinnewijn (2021): “Expectations data, labor market and job search,” _UT Austin and LSE Working Paper_ .

- Neumark, D. (2002): “Youth labor markets in the United States: Shopping around vs. staying put,” _Review of Economics and Statistics_ , 84, 462–482.

44
sub-national levels. The 2015 Intercensus sampled 5.8 million households, 56 percent of whom lived in urban areas. The Intercensus contains only household labor income and transfer income, and not total household income, necessitating the use of a small area estimation model to estimate municipal-level poverty rates.

CONEVAL creates these estimates using a unit-level model, with a large set of household and individual-level auxiliary variables, including demographic, labor, housing quality, and municipal characteristics at the individual, household, and municipal level. The municipal characteristics include average per capita income and other poverty estimates. The municipal estimates and detailed documentation on the methodology are available at the CONEVAL website.<sup>12</sup> The 32 Mexican states are divided into 6 groups<sup>13</sup> and a model is estimated for each, with R<sup>2</sup> values ranging from 0.52 to 0.57.

CONEVAL used the 2014 MCS-ENIGH survey in an empirical best household model to construct the small area municipal estimates. This implies that both the estimates that are evaluated below, based on geospatial data, and the CONEVAL benchmark data use the same 2014 survey. However, the R<sup>2</sup> of the unit-level model used to generate the CONEVAL estimates are roughly three times as high as the model used to generate the geospatial-based estimates, since the auxiliary data used by CONEVAL vary at the household level. Therefore, the CONEVAL estimates give far more weight to the predicted values and less weight to the survey, relative to the estimates derived from the satellite data. This reduces the mechanical correlation between the estimates and the benchmark due to the municipal random effects being conditioned on the same 2014 survey data. Furthermore, as a robustness check, we also compare the CONEVAL estimates with geospatial small-area estimates generated using the 2016 MCS-ENIGH survey, which eliminates all mechanical correlation due to the use of the same sample data.

# 3.3. CNN-Generated Auxiliary Geospatial Data

A key difference between
Table 6 shows the (rank) correlations of the various indicators to gauge the internal coherence of the set. The various indicators of gender differentials in health status are reasonably highly correlated: .77 between child mortality and the population sex ratio, and .36 and .40 between the health treatment disparity and child mortality and the population sex ratio, respectively. Between the enrollment ratio and the male-female literacy gap, the correlation is .57. The labor force participation of rural women is quite highly correlated with child mortality. The two indicators which are aggregates of various others, in particular, the "status of women" indicator which was created from various parts of the NFHS data, are (unsurprisingly) quite highly correlated with several of the other measures.

Table 6: Rank correlations among the various indicators of female _I_ male disparity

|||I|II|m|IV|v|VI|vii|VIIi|
|---|---|---|---|---|---|---|---|---|---|
|I|Child mortality'|1||||||||
|11|F**_I_**MPopulation Ratiob|.77|i|||||||
|m|F / M no treatment for ARI or fever'|.36|.40|1||||||
|IV|F_I_M school enrollment (ages 11-14)'|.22|.06|.31|1|||||
|V|M-Fdifference in rural literacyb|.31|.33|.23|.57|1||||
|VI|Rural female labor force participationb|.61|.23|.08|.20|.32|1|||
|VII|Status of women;|.75|.83|.28|.39|
on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994.

Central Government employment is estimated, on the basis of the information on IMF Report No. SM/95/306 of December 11, 1994 and relate to 1993 and the information on the other sectors of government, as the residual government employment. Education and Health employment are from IMF Report No. SM 93/200 and relate to 1992. Local employment comes from Local Governments in the CEE and CIS: an antholooy of descriptive papers. 1994, published by the Institute of Local Government and Public Services in Budapest, Hungary. Local data includes 15,967 civil servants employed by municipality mayors' offices and 9,446 civil servants employed by settlements' mayors' offices and relates to 1993. State-owned enterprise employment is taken from IMF Report No. 95/306 of December 11,1995 and relates to 1994. Military employment data include 51,300 conscripts, about 22,300 centrally controlled. The data do not include personnel of paramilitary units, i.e., the Border guards (12,000), the Security Police (4,000) and the Railway and Construction troops (18,000).

GDP is taken from World Tables 1995 and is an estimate for 1993. Data on Consolidated Central Government employment and salaries is taken from Government Finance Statistics, 1995 and is for 1993. Average Government wage estimate is taken from IMF Report 95/306 of December 11,1995 and relates to 1993.

Data on wages in manufacturing (monthly basis) are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1994.
where the majority of GPCs are located, making products and services available can also provide an antecedent of diplomatic good practice and trust characterized by a good sense of global responsibility, and therefore by more a stringent and well-established ethical code. The building of trust in governance is important in the development process of any nation. Open and unrestricted access to climate and weather information is strongly indicative of the confidence in the public service sector, and is a testament to the maturity of the political system in place and the integrity of the government.

Along with integrity, it also showcases that the government stands for the Universal Human Rights. According to Article 19 of the declaration, the rights to freedom of opinion and expression “includes the freedom to seek, receive and impart information and ideas through any media and regardless of frontiers” (UN 1948). Another passage from Article 27(1) further supports this claim: “Everyone has the right freely to participate in the cultural life of the community (...) and to share in scientific advancement and its benefits” (UN 1948). Thus, from these two quotes we can determine that one of the ultimate goals of any society striving for development is to focus on empowering every citizen through access to and use of forecasting information (GEO 2015).

This should further incentivize governments to encourage more funding allocation to improve the performance of GPCs to provide weather and climate forecasting data to LICs by defining it as a global public service. Further, the UN Secretary-General’s High Level Panel report on the post-2015 agenda called for “a data revolution for sustainable development, with a new international initiative to improve 20
To control for the size difference across firms, the number of employees ( _L_ ) is included with other firm-specific characteristics, _X_ . For transport connectivity _V_ , the same variables are used: _CITY_ , _BP_ and _MAI_ . Again, the equation is estimated by the dynamic panel data model.

Agglomeration economies can also be influenced by transport connectivity. To examine this, the following equation is estimated by the dynamic panel model: where _Njt_ is the number of firms registered in location or district _j_ at year _t_ . The dynamic panel data model not only eliminates any time-invariant location-specific characteristics _uj_ and but also addresses the potential endogeneity of transport connectivity _vk_ .

# **IV. Data**

On the firm data side, our sample data come from the official firm registry data in Kazakhstan, which comprises about 25,000 to 55,000 enterprises registered in the whole country after duplicated observations are excluded ( **Figure 21** ). It covers most formal enterprises, including both small and medium-sized and large firms, across all sectors, from agriculture to manufacturing and services. The data is available for the period of 2010 to 2018, constituting a very rich panel data, though unbalanced. In this paper, the period of 2012 to 2018 is only used because the available shipping data, which is used to measure transport connectivity, is less reliable in the first two year of the data monitoring program. Excluding observations with some missing data, about 41,000 observations are used in the cost analysis, i.e., to estimate Equation (2). Fewer observations are used for the inventory analysis (Equation (5)) and the agglomeration economies (Equation (6)), because of different specifications including more lagged dependent
a formal model structure as that presented in Section 4.

Appendix B provides further evidence on the relationship between firms’ participation in procurement and credit growth. We summarize the evidence here.

**Procurement and credit using quarterly frequency.** To provide tighter identification of the effect of procurement on credit, in Appendix B.2 we exploit higher frequency credit data and estimate the procurement effect on credit using quarterly data. Importantly, these specifications allow us to control for _firm_ × _year_ fixed effects, which absorb all firm characteristics, observable or unobservable, varying at the yearly frequency. First, we find that winning a procurement contract is associated with an annualized increase in credit of around 5.5 pct, which is similar in terms of magnitude to the effects on impact, i.e., _h_ = 0, estimated above in Section 3.1. And second, we also find that the effect is entirely driven by non-collateral credit.

**Winners vs. the rest.** In Appendix B.3, we use the sub-sample of procurement projects where we have information on all bidders as well as the final ranking (see Appendix A.2 for details on this

> 12Also, indirect inference motivated by our structural model, as seen in Section 5, provides evidence for procurement earnings relaxing firms’ credit constraints.

11
countries with which it has free trade agreements. Such a requirement appears to conflict with the World Trade Organization Agreement on Subsidies and Countervailing Measures, which prohibits any subsidies that are available contingent on the use of domestic inputs or “local content.” WTO rules also prohibit industrial subsidies contingent on export performance, which were used historically as an industrial policy tool—for example, in Korea.

An underlying principle behind international trade agreements is to avoid subsidy races, in which dumping or domestic content rules spread from country to country. This fear has some foundation. Using data from the Global Trade Alert and other sources, Evenett et al. (2024) show that implemented industrial policy “measures are correlated with the past use of measures 18
# **VI Estimating the Pass-Through Factor to Generate Estimates for 2015**

The previous sections describe in detail the methodology used to generate urban and rural estimates of extreme poverty for 2014-15, as well as a battery of validation and robustness checks designed to build confidence in the method and results. This section considers the final portion of the exercise, which is the process of determining the appropriate pass-through factors to apply to Household Final Consumption Growth that replicate these poverty rates. This portion has two main steps. The first is to calculate a growth rate, that when applied uniformly to the 2011-12 welfare distribution, generates the predicted 2014-15 poverty rate of 10.0% for urban areas and 16.8% for rural areas. The appropriate growth rates are 9.6% in urban areas and 12.6% in rural areas. The second step is to calculate these growth rates as a fraction of HFCE growth during that period, which was 17.2%. Dividing 9.6 by 17.2 gives an estimated pass-through rate of 55.9% for urban areas. Similarly, dividing 12.6 by 17.2 gives the 73.3% pass-through rate for rural areas. These pass-through rates were then applied to the 2011-12 distribution in order to project out to 2015. The final $1.90 poverty rate for 2015 is 9.5% in urban areas, 15.3% in rural areas, and 13.4% nationally.<sup>34</sup>

# **VII Conclusion**

This analysis was motivated by concerns that the standard method of applying growth in HFCE to the most recent survey measurement of per capita consumption would overstate poverty reduction in India, and therefore the world. These concerns were heightened by the age of the most recent available poverty data from India, which was collected three and a half years before the 2015 target estimate. This paper describes an alternative method for estimating poverty, which utilized a more recent nationally representative survey from 2014-15
Tracking systems for trucks and freight trains exist only at the country or regional level. Lacking a global repository, these modes (road and rail) are not yet included in the new KPIs. However, corridor performance information is available from container tracking data to/from inland destinations.

The rest of the paper is organized as follows. Section 2 summarizes the data sources and presents the indicators. Section 3 presents the production process of the indicators. Appendices describe the datasets, their cleaning methodology and descriptive statistics.

# **2. Data sources**

Universal Postal Union (Postal data) The volume of e-commerce has surged in the past decade. According to McKinsey projections,<sup>8</sup> by 2030, cross-border e-commerce in goods will grow anywhere between $1 trillion and $2 trillion of merchandize value from its current $300 billion, resulting in significant changes in supply chains. The volume of e-commerce activities was equivalent to 30% of global GDP in 2019,<sup>9</sup> so its role in economic development cannot be overlooked.

Information on postal services, including data on postal infrastructure, processing volumes, and speed allows to assess the performance of logistics, customs, and delivery services at the country level. In many countries, postal networks constitute an essential infrastructure and play a key role in guaranteeing access to universal communication services and global value chains. This is particularly valuable for the youth, women, and small businesses in vulnerable, marginalized, and rural areas. According to research conducted by UPU in collaboration with UN Global Pulse, various characteristics of the international postal network are closely related to countries’ economic and social advancement, playing an important role in their trade development.<sup>10</sup> The extent to which international e-commerce is interconnected with postal services was pointed out by a McKinsey report<sup>11</sup> published in March 2022, specifying that the UPU is responsible for handling about two-thirds of all letter-parcel (up
ha, mean self-reported area is 0.33 ha larger than what was obtained using remote sensing. Despite a strong incentive to over-report area cultivated, this suggests mis-targeting was rather limited, especially if compared to widespread reports about irregularities in earlier farm support programs in Ukraine.

# **4.2 Methodology and results**

To estimate the impact of subsidy receipt on area cultivated, we use the fact that applicants were informed about the status of their application in November 2022 and the money was transferred in December 2022 or January 2023. As this was too late to affect decisions about the area to be planted to winter crops in the 2023 agricultural season, PSG receipt would only affect area decisions for summer crops, allowing us to use a difference-in-differences design with 2023 as the outcome variable. Formally, indexing farms by _i_ and time by _t_ , we use a farm fixed effects regression of the form i + P ∗P P i i ii ii tt where _Yitit_ is the area cultivated with summer crops in YY = αα PP _t_ , + XX γγ+ λλ + εε i + P ∗P P i i (1) where _Yitit t_ is a farm fixed effect; _PSGi_ is an indicator variable that equals one if farm _i_ participated in the PSG program and zero otherwise; ii _Post_ indicates timing after the αα

> 23 As historical registry records are not accessible, we are able to observe when farmers acquired rights to parcels they currently cultivate and can make adjustments accordingly (implicitly assuming that the last recorded right is the current one and that registration created a new right rather than extending or renewing an existing one), we cannot adjust for rights that were transferred out.

13