rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table 5. The Robustness Check**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017. This table presents estimates from OLS regressions of investment on Titling Firm dummy variable. For columns (1)-(5), cross-sectional OLS regressions are implemented. The independent variable is the averaged investment rate (capital expenditure divided by PP&E) between 2013 and 2017 net of various benchmark levels. In Column (1), only firms headquartered in Shenzhen are included in the regression sample. In Column (2), only firms headquartered in Beijing, Shanghai, Shenzhen and Guangzhou, four metropolitans are included. In Column (3), city dummies are included. In Column (4), the propensity score matching method is implemented. Four characteristics, size, age, leverage and ownership are used to find the most similar firms in the universe of the control firms. In Column (5), the size of the takeover is controlled. The relative size of the takeover is calculated by the size of firms acquired by the listed firm normalized by the size of the listed firm. In Column (6), the panel data regression (regression 2) is implemented. For each firm, observations from 2008 to 2017 are included. The dependent variable for Column (6) is the annual investment normalized by PP&E. _Post_ is a dummy variable that is 1 if the year is after 2012 and 0 if not. In Column (7), the pre-trend test is implemented. For each year between 2008 and 2017, its interaction with the Titling Firm dummy is added in the panel data regression 2. All variables are winsorized at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification.
|**2011 and INDH**|0.060|˗0.063|˗0.146|0.015|0.142|˗0.213|
||(0.246)|(0.264)|(0.528)|(0.317)|(0.553)|(0.561)|
|**2013 and INDH**|˗0.074|˗0.036|0.800|0.371|˗0.192|˗0.115|
||(0.245)|(0.257)|(0.493)|(0.327)|(0.399)|(0.498)|
|**Constant**|||˗1.766***|˗0.282***|1.094***||
||||(0.095)|(0.092)|(0.084)||
|**N (Observations)**|1,839|1,835|955|1,097|887|948|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Notes:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level for the OLS models. Odds ratios are presented for the fixed effect logit (conditional logit) due to the conditional nature of estimates precluding standard marginal effects for this model.

In conclusion, the INDH did not have significant impacts on children’s health, nutrition, or preschooling. It should be noted that even though these areas were not the main targets of the INDH, we would expect some direct and indirect effects on ECD from the INDH activities.

# **_6 Discussion_**

# _6.1 Implications of the Study_

This study
# **1. Introduction**

Does the discovery of a new resource exacerbate the resource curse? It has been long debated whether natural resource endowments promote economic development (Van der Ploeg, 2011b). Previous research indicates mixed findings (Alexeev and Conrad, 2009; van der Ploeg, 2011a; Smith, 2015). Cross-country studies based on aggregate data suggest that resource abundance could have negative effects on economic growth (Sachs, 1995; Sachs and Warner, 2001).<sup>1</sup> The literature on the “natural resource curse” has identified several factors that influence the relationship between resource abundance and economic growth. These factors include conflicts (Angrist and Kugler, 2008), the presence of weak institutions, and the extent of democracy (Aragón, ChuhanPole, and Land, 2015).<sup>2</sup> The primary objective of this paper is to examine the impacts of natural resource discovery on welfare and employment at the sub-national level. We study Ghana’s offshore oil discovery in 2007, which is the largest and most significant oil discovery in the country’s history. Have the discovery of oil and subsequent oil production increased local employment and contributed to poverty reduction? Economic theory suggests that local economies benefit from natural resource discovery through increased local demand for labor not only in the mining sector but also in non-mining sectors due to spillover effects (Black, McKinnish, and Sanders, 2005; Marchand, 2011). In addition, households can benefit from windfall revenues if the government redistributes the revenues in the form of transfers or provision of public goods. Whether and how households are ultimately affected by a shock to natural resource availability is an empirical question, which we address in this paper.

We use three waves of household survey data covering the period 1998 to 2013 and information on oil fields to estimate the economic impacts of the oil discovery. We exploit the quasi-experimental nature of the discovery by comparing districts in regions associated with oil (treatment group) and
projects have either been cancelled or delayed (Paulais 2009).

# **Impact on Municipal Revenue**

The economic downturn has led to a decline in own revenues in cities around the globe. Municipal associations of European countries indicate that 61% of respondents have experienced a drop in own revenues (more exactly 61% for taxes and 42% for fees). In the USA, a survey of local governments shows an expected drop in revenues of 0.4% in 2009. Developing and transition countries—for example, Belarus, Burundi, Uganda, and Ukraine—expect decreases in their own revenues.

Although own revenue decreases result from reduced economic activity and tax bases, other crisis impacts can affect revenues as well. Some countries also expect decreases from tax evasion and general difficulties with tax compliance (e.g., Australia, Ghana, New Zealand and Uganda). In some countries (e.g., Benin, Mali and Niger) the lack of overall liquidity due to a drop in (national and local) revenue has resulted in the treasury system of these countries giving priority to national expenditures and the diversion of local revenues (cash) to finance them (United Cities and Governments 2009). Revenue declines from shared taxes are another source of fiscal stress. European municipal associations, for example, indicate that 36% of respondents have experienced a drop in revenues from shared taxes. Significant reductions in revenue sharing from natural resources are reported by Bolivia (20 %) and Peru (30 %).

8
Policy Research Working Paper 8952

# **Abstract**

A common strategy to improve access to credit and mitigate the effects from large financial shocks is to channel public funds to firms via private banks at regulated interest rates. Using comprehensive loan-level data from Brazil, the authors study how government loan subsidies are transmitted (or not) to targeted firms when banks exert market power. They conjecture that banks with greater market power circumvent the interest rate ceiling by increasing the price of other products to firms that receive subsidized loans. The results of this analysis have important policy implications for the design and effectiveness of government interventions in credit markets.

This paper is a product of the Development Research 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 apedrazamorales@worldbank.org and cruizortega@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
# IV. **Conclusions**

This paper finds convincing evidence of the practical importance of the credit channel (more specifically, the bank lending channel) in the monetary transmission mechanism, particularly following the financial crisis. A number of major conclusions can be drawn from the empirical results presented in this paper.

First, as shown in the so-called "narrative" approach to identifying if the bank lending channel is operating (i.e., if the loan supply rather than loan demand effects are identified as being important), bank lending was found to play a significant independent role in amplifying the real effects of tightened monetary policy, which was implemented in response to the crisis (December 1997 episode). Bank lending channel reinforced the dampening effect of tightened monetary policy on real economic activity that would be expected had the policy been transmitted sorely through the interest rate channel. It seems most likely that the transmission mechanism from the financial distress in the banking industry due to the crisis to a decline in the real economic activity presumably have worked largely through a reduction in bank credit to small and medium-sized firms.

Second, when disaggregated bank balance sheet data were used to test for the existence of the loan supply effects over the 1990s, there was a much greater dampening effect on both lending volume and the securities holding of small bank group than those of the large bank group after a tightened monetary policy (i.e., a positive innovation in the call rate). These results would be largely consistent with the testable prediction of the bank lending channel that the sensitivity of both lending volume and securities holding to monetary tightening should be more pronounced for small banks typically facing higher costs in attracting non-deposit sources of external finance and thereby tighter liquidity constraints, just as for their non-financial counterparts (i.e., small and medium-sized firms). Notably, the lending volume for each bank group was found to drop more substantially in response to the crisis shock used as an alternative measure of the severe monetary contraction. This evidence
br>49%<br>4% 2% MNA MNA<br>33%<br>1% SAS SAS<br>39%<br>SSA SSA<br><!-- End of picture text --> Source: Global Monitoring Report 2015/16 and PovcalNet in August 2016. Note: All numbers for MNA region are projections.

Less appreciated perhaps is that, much as it was in 1973 when President McNamara announced the World Bank’s goal of reducing absolute poverty, 70 percent of the world’s extreme poor still live in rural areas. This is despite the fact that half the world is now urban, up from a third back in the 1970s. In 2020, half the people in the

> 13 The 2015 figures for global extreme poverty are statistical projections. The most recent estimate of the global extreme poverty rate is 12.7 percent for 2012.

27
This study further employs real per-capita expenditure data (measured in 2010 US dollars) from the Vietnam Household Living Standards Survey (VHLSS). This survey is conducted bi-annually and has several advantages: It follows the same communes over time and is not affected by boundary changes. It is generally believed to be nationally representative and has been used to compute official poverty statistics. However, there are certain limitations due to the survey design. While the household survey maintains a representative sample at the province level, it might not be so at the commune level (Hansen and Le, 2013) and might undercount migrant workers (Pincus and Sender, 2008). The household survey has a smaller geographical coverage than the enterprise data and comprises of about 3,000 communes. The expenditure data also suffers from small sample size as only three households are randomly drawn to be surveyed within each enumeration area. Nevertheless, the sampling issue should not be correlated with the remote sensing measure and is therefore unlikely to bias the results.

Figure in the appendix plots the distribution of the three variables that will serve as the main measures of economic activity together with commune size on a log scale. While commune size, the number of employees, and per-capita expenditure approximately follow a log-normal distribution, the number of enterprises is highly skewed and has the majority of its mass in the left tail.

# **2.2 Satellite Imagery**

This study uses remote sensing imagery from the DMSP-OLS nighttime light dataset and the Landsat program. This section introduces the Landsat program and describes the processing of the data.

7
way as people belonging to the majority or ruling minority. The indicator also covers certain fundamental political rights, such as the freedom of expression and freedom of religion. Participation of minorities in all three branches of government—legislative, executive, and judiciary—is a fundamental component of economic empowerment. If a group is unable to gain equal access to instituParliaments as institutions can do more to extend some procedural accommodation for the participation of minorities and indigenous groups in legislative process.

(Inter-Parliamentary Union and the United Nations Development Programme 2010) tions that determine the allocation of national resources (both monetary and nonmonetary), the allocation will likely be skewed in favor of the ruling group. Participation and representation of minorities in parliaments and local governments can affect government spending priorities toward more inclusive and equitable economic reforms. Similarly, representation of minorities in the judiciary can encourage respect for antidiscrimination laws and implementation of policies promoting the fair allocation of resources. As stated by the Inter-Parliamentary Union, “The parliamentary representation of minorities and indigenous peoples is essential for ensuring these groups’ effective participation in public affairs” (Protsyk 2010). Such disaggregated data in national parliaments are scarce, however, forcing the Leadership Council of the United Nations Sustainable Development Solutions Network (2015, 149) to call for action and urge states to collect such data as a necessary step toward the realization of the Sustainable Development Goals.

Of the six pilot economies analyzed, none has a formal legal quota system for the minimum participation of ethnic, religious, or sexual minorities in parliament. Vietnam, however, has a mechanism that allows for representation in Parliament of the 54 ethnic minority groups registered in the country. The Council of Ethnic Minorities is a body consisting of 40 deputies, and its main duties are to contribute to, investigate, and oversee legal documents (laws, acts, ordinances) and legal procedures regarding ethnic affairs. As a consequence, the Vietnamese parliament has passed various pieces of legislation that target the interests of ethnic minorities (Palmieri 2010, 11).
50 4.00<br><!-- End of picture text -->

**Sources: World Bank staff calculations partly based on World Bank (2014a) and OECD data.**

**Notes: Data are for 2010; higher scores represent stricter employment protection legislation.**

As mentioned above, employment protection legislation is another category of labor market regulations. It consists mainly of mandated risk-management regulations that lower the probability of

> 3 Hansen, Rand and Torm (2015) analyze the effects of the rapid alignment of minimum wages in the domestic and the FDI sector in 2012. They find that the alignment led to significantly higher average wages and a decrease in wage inequality. They do not investigate whether it had effects on employment.

- 10 -
|High school|-0.48|0.00|-0.43|0.00|
|Share classes in school with full-time day (7 or more hours)|2.51|0.00|3.77|0.00|
|Total duration of school day (hours)|-0.38|0.00|-0.67|0.00|
|Intercept|-5.80|0.00|-2.24|0.02|
|Number of observations||17874||13959|
|Pseudo-_R_ <sup>2</sup>||0.48||0.53|

_Sources:_ MEC/FNDE, _Prova Brasil,_ School Census, and INEP.

_Note:_ Propensity score was computed by estimating a Probit model (equation (4) in the paper) where the dependent variable is a dummy variable for schools that registered in _Mais Educação_ in 2008 and remained until 2011. Our control group is the schools that, by 2011, had not yet joined _Mais Educação._ Schools that joined the program in 2009, 2010, and 2011 were excluded from the sample to define the control group. Table reports the estimated coefficients (columns (1) and (3)) and _p_ values (columns (2) and (4)). Panel A reports these statistics for schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
to six provinces in the central area, including 26 cities. In 2008, eastern Inner Mongolia was further included, and finally in January 2009, the consumption-based value-added tax policy was implemented in all sectors and provinces of China.

# **3. Data, Variable Definitions and Summary Statistics**

## **3.1 Dataset**

The data for this analysis comes from a large dataset developed and maintained by the National Bureau of Statistics of China (NBS). The NBS dataset contains annual firmlevel unbalanced survey data of all “above scale” industrial firms with annual sales of more than 5 million RMB. On average, around 220,000 firms per year from 1998 to 2007 are included in the dataset, spanning 37 two-digit manufacturing industries and 31 provinces or province-equivalent municipal cities. They account for most of China’s industrial value added and have 22% of China’s urban employment in 2005.

The combined dataset contains detailed information about each firm’s identity, address, industry classification, incorporation year, ownership types, new products and total value of output, total fixed assets, fixed assets for production, sales revenue, profit, total workforce, export sales, total industrial sales, employee educations, income and value added tax payable, etc. These are the key variables based on which we estimate firm level total factor productivity and value added tax reform impacts.

The original dataset includes 2,226,104 firm-year observations. Since the paper focuses on manufacturing firms, we eliminate non-manufacturing observations. To further clean the sample, we delete observations whose information on variables such as firm identifiers, county code, sector id, year established are missing, or observations with 7
performance of commercial areas, it is necessary to have specific revenue indicators (Doganis, 1992).

**Table 5.2. Economic and productivity indicators**

|Overall cost performance indicators|*Total cost per WLU (after depreciation and interest)<br>*Operating costs per WLU (excluding depreciation and<br>interest)<br>*Capital cost per WLU<br>*Labor cost per WLU<br>*Labor costs as percentage of total costs<br>*Capital costs as percentage of total costs<br>*Aeronautical costs per WLU<br>*Capital costs to value added ratio<br>*Labor costs per employee|
|---|---|
|Labor productivity indicators|*WLU per employee<br>*Total revenue per employee<br>oValue added per employee<br>*Value added per unit of staff plus capital costs<br>oValue added per unit of staff costs|
|Productivity of capital employed|*Value added per unit of capital costs<br>*WLU per £1,000 net asset value<br>oTotal revenue per £1,000 net asset value|
|Revenue generation performance|oTotal revenue per WLU<br>*Adjusted revenue per WLU<br>eAeronautical (or non-aeronautical) revenue as a percentage of<br>total revenue<br>*Aeronautical revenue per WLU<br>*Non-aeronautical revenue per WLU|
|Performance of commercial activities|oConcession plus rental income per passenger<br>*Concession revenue per passenger<br>oRent or lease income per passenger<br>*Concession revenue per m2<br>*Rent or lease income per m2<br>*Airport concession revenue as percentage of concessionaires'<br>turnover|
|Profitability measures|*Surplus or deficit per WLU<br>*Revenue to expenditure ratio|
|Source: Doganis (1992)||

_50_
therefore predicted more accurately than those further away. The plot reveals no systematic tendency for over or under prediction at any level of observed prevalence. The validation statistics (Table 1) further indicate a relatively well performing model, with reasonable correspondence between predicted and observed values (correlation 63%), precision (mean absolute error 11%) and accuracy (mean square error 2%). The latter two metrics are expressed in the same units as the predictions – i.e. prevalence on a scale of 0‐100%.

# _Pixel‐level predictions of stunting prevalence_

Figure 2 shows the predicted map of stunting prevalence in children under five years old, constructed using the mean of the predicted posterior distribution within each 1×1km grid cell. Many areas of the country are within the yellow part of the color scale, which is similar to the national mean stunting prevalence reported in the DHS survey of 34% [DHS, 2016]. Areas of notably lower stunting prevalence (shown in the greener shades) include the predominately urban areas around Dar es Salaam, Arusha and Dodoma, as well as a broader region to the south of Lake Victoria centered around the city of Tabora. Areas of consistently high stunting rates (red shades) tended to be more common in the more rural parts of the country, especially throughout the western and southwestern border areas.

Figure 3 shows how the uncertainty of the stunting prevalence predictions varies geographically. The uncertainty metric used is the width of the posterior 95% credible interval for each grid cell. Cells where prevalence could be predicted with high certainty had narrow posterior distributions around the point estimate, and thus the range of possible prevalence values between the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles of the distribution was small, and vice‐versa. Areas predicted with relatively low prevalence (green in Figure 2) tended to also have smaller uncertainty, represented by the blue shades in Figure 3,
an intention to treat analysis and are not adjusted for any baseline covariates. The dependent variable in Column 1 is a dummy variable that takes a value 1 if the respondent reported giving agricultural inputs to a nonbeneficiary friend. The dependent variables in Column 2 and 3 are dummy variables that take value of 1 if the respondent reports using improved seeds and inorganic fertilizers in 2009/10, respectively, using household survey data collected at baseline. In Columns 4 and 5, the dependent variables are dummies that takes value of 1 if respondent reports holding the position of or being related to and having regular social interaction with DALDO, WEO, VEO, VVC member, or Village or Hamlet Chair. The dependent variable in Column 6 is a dummy variable that takes value of 1 if respondent reports at follow-up household survey having paid to become a voucher beneficiary in 2010/11. Data for the Columns 1 and 6 were collected using household survey at follow-up while data in Columns 2-6 come from household survey at baseline. P-values are computed using the t-asymptotic wild cluster bootstrap procedure described in Cameron et al. (2008) and reported below the coefficients. Clustering was done at the village level. ***,**,* denote statistical significance at the 1, 5 and 10 percent level, respectively. See Appendix Table 1 for definition of variables.