rafmacalaba/data-use-mentions-extended huggingface dataset:
, sleeping under mosquito nets, and reporting any malaria cases in the last 5 years and 2 weeks, all interacted with Post. All regressions include state fixed-effects interacted with Post. Standard errors are clustered at the district level. Data come from the Indian Health Management Information System.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 Columns (1-3) examine overall incidence across both urban and rural health facilities. Although the coefficients are negative, they are not statistically significant. We observe that the campaign was effective in urban settings but not in rural ones. Column (4) shows that in treated subdistricts the number of monthly urban malaria cases per million people decreased by more than 6 cases in the months during and after the campaign, compared to the previous period. The coefficient is significant at the 90% confidence level and its magnitude and significance are robust to controlling for district-level controls (column 5) and subdistrict-level variables (column 6). Specifically, in the fully-saturated specification, the coefficient is significant at the 95% confidence level and suggests a decrease of 6.2 cases in the number of monthly urban cases per million people, which corresponds to about 30% of the overall monthly malaria incidence rate in the pre-campaign period. Finally, we do not find significant effects of the campaign on rural incidence. Columns (8-9) suggest a potential reduction, but the magnitudes are only a quarter of those observed in the urban sample, and the coefficients are not statistically significant.

> 35District-level controls include total population from the census as well as the shares of baseline survey respondents living in non-solid or kutcha dwellings, with university degree, sleeping under mosquito nets, and reporting any malaria cases in the last 5 years and 2 weeks, all interacted with Post.
on seven of those sub-periods, selected on the basis of a minimum duration (specifically, those comprising at least 249 daily observations, amounting to approximately one year). The relevant parameters characterizing these sub-periods are summarized in Table La. The first three sub-periods involve a peg to the **US** dollar with a band, while the last four involve a basket peg with a band.

Figure **1** displays the observed exchange rate in terms of the weighted basket numeraire, along with the announced bands. The figure shows that the trend of the peso has been to depreciate over time, with significant appreciations and depreciations on several occasions, and highlights the fluctuations of the exchange rate within the band, as well as the gradual widening of the latter. In some periods, like **1991-92,** the exchange rate is close to the lower band. In other periods, like **1994-95,** the exchange rate fluctuates farther inside the band. After suffering pressure on the peso, the authorities decided to narrow the band from 12% to **3.5%** in September **1998** to show their commitment to the value of the peso. The band was widened back to **8%** in December **1998.** Table **1.b** reports the results of the verifiability tests using the Chilean exchange rate data, based on first-difference **OLS** estimates of the basic equation. For each of the seven sub-periods under consideration, the table presents the cumulative rejection frequencies of the null hypotheses of Test **1** and Test 2 at increasing sample sizes **-** **_50,_ 100** and 200 observations. For example, a rejection frequency of **100** for **50** observations in Test 2 means that in **100** percent of the estimations with sample sizes smaller than **50** we
reference group, and (g) excluding rent expenditures (explicit as well as implicit) from the estimation. Estimates for the food poverty line coming out of these different specifications range from R$250 to R$280. Estimates for the (lower) total poverty line range from R$440 to R$500. This highlights that our preferred estimate constitutes a rather lower bound for the poverty line, with the choice of a relatively low minimum calorie requirement and assuming that the cost per calorie of FAFH is the same as food purchased for consumption at home.

In comparison with earlier work, mainly based on POF 2003, our poverty lines are generally similar in real values, although methodologies differ, and consumption patterns have likely changed over time. Converted to January 2018 prices and considering São Paulo (mostly metropolitan) lines in the case of regional lines, previous estimates range from R$485 to R$532 (Rocha, 2007; Silveira et al., 2007). Ferreira et al. (2003) used POF 1996 and estimated a lower poverty line of R$477 in January 2018 metropolitan São Paulo prices. Only World Bank (2007) estimated a considerably lower poverty line of R$272 in January 2018 metropolitan São Paulo prices.

Comparing our results with the World Bank poverty lines used for global poverty monitoring, by expressing them in 2011 purchasing power parities (PPP), we find that the total poverty line of R$455 is equivalent to approximately US$6.10 a day, and hence in the range of upper-middle income countries.<sup>17</sup> Moreover, it is very much in line with the societal poverty line one would obtain for Brazil using POF 2017/18 data.<sup>18</sup> The food poverty line of monthly R$258 is equivalent to a daily US$3.46 in 2011 PPP, above the international poverty line of US$1.90 per person per day.

The value of the
**|0.254***|0.119***||||
|Nigeria|0.291***|0.566***|0.042***|n/a|0.450***|0.200***|
|Rwanda|0.211***|0.395***|0.026||||
|Senegal|0.157***|0.110***|-0.025||||
|Sierra Leone|0.189***|0.253***|0.027||||
|Swaziland|0.174***|0.305***|0.115***||||
|Tanzania|0.295***|0.303***|-0.022|0.213***|0.111**|0.036|
|Togo|0.323***|0.319***|0.073**||||
|Uganda|0.364***|0.278***|0.175***||||
|Zambia|0.284***|0.255***|0.070***||||
|Zimbabwe|0.311***|0.147***|0.151***||||

Note: The table gives coefficients from a regression of standardized nutritional outcomes on the wealth index or standardized consumption per capita. Robust standard errors are used; * prob.<.10 ** prob.<.05 *** prob.<.01.

44
# <u>Data</u>

Croatia’s 2008 household budget survey contains information on household debt service obligations. In particular, there is a distinction between principal and interest payments. Also of interest is the information contained on income and household characteristics, such as socioeconomic status and educational level of household members, the composition of individual households, and information on sources of income for each household member. This detailed household information can be used to assess the income effect of unemployment that might affect one or more members in the household and to calculate household-specific subsistence consumption levels. The information is collected from a total of 3,108 households and, by using the sample weights, more than 1.4 million households in the country are represented in the survey. The HBS 2008 also has a section on household indebtedness. Specifically, households are asked, first, whether or not they have made payments on housing loans and other types of loans over the past 12 months and then, if they have, they are asked to provide the information on loan amounts, payment amounts and the number of installments.<sup>6</sup> The survey includes information on interest payments on mortgages and other loans. At the analytical stage in this paper, all the relevant calculations are adjusted by the corresponding sample weights.

While the use of household budget surveys has numerous advantages, its coverage on loan characteristics is largely absent. For example, there is no information on the currency of existing debts; nor is there any information on the type of interest rates (variable or fixed) in which loans have been extended. To circumvent these limitations, the methodology followed in this paper relies on central bank data on aggregate loan characteristics to define the distribution of household debt between (i) domestic and foreign currency loans and (ii) variable and fixed interest rate loans. The same data source is used to assess changes in interest rates and exchange rates. The weights are determined by aggregate central bank data and the shocks
|0.559|
|Labor Productivity|32|30|0.780|32|39|0.278|
|Financing Practices|48|50|0.730|53|52|0.855|
|Human Resources Practices|42|40|0.738|44|43|0.784|
|Logistics Practices|43|43|0.989|49|43|0.175|
|Marketing Practices|43|44|0.934|46|46|0.948|
|Production Practices|46|54|0.229|47|44|0.371|
|Level 2 Supplier|0.93|1.00|0.496|0.92|0.94|0.758|
|Metal Products|0.50|0.57|0.731|0.47|0.65|0.242|
|Plastic Products|0.15|0.29|0.390|0.19|0.24|0.738|
|Firm Age (Years)|23.3|21.8|0.829|20.9|24.6|0.375|
|Anexo K score|44.4|46.5|0.679|47.8|45.7|0.487|
|USD Sales in 2013|3158858|7547448|0.189|2767765|2469362|0.799|
|Export at all in 2013|0.43|0.29|0.465|0.47|0.41|0.687|
|Sample Size|46|7||36|17||

Table A4.2 compares the characteristics of those firms for which we have December 2017 sales and employment data to the attritors, and then shows the sample of non-attritors is reasonably well balanced on baseline characteristics.

54
in oil and energy industries in characteristics that are thought to be related to the potential for children’s education. In fact, the pre-treatment trends of outcome variables (family income and education outcomes) are not parallel for these two groups. Also, there is no single unaffected industry that provides a comparison for the affected industries. Hence, I use the SCM to find a combination of industries not (or less) affected by the sanctions as a synthetic control group and estimate the counterfactual for treated group. I consider all other industries but financial, real estate, and administrative and support service industries in the donor pool (11 industries),<sup>35</sup> though I check the sensitivity of results using different selected donor industries in section 6 (Table S4).

Weights are determined to maximize the similarity between the synthetic control and the treated households in terms of matching variables. For the main analysis, following Botosaru and Ferman (2019),<sup>36</sup> I consider only pretreatment family income as the matching variable. As a robustness check, I include observed covariates (parent’s education, employment status, age, etc.) and find similar results (Table S4). In particular, I find that algorithms that minimize the distance between the treated units and the synthetic control put small and ignorable variable weights for covariates if more pre-treatment outcomes are included. I also use a modified SC estimator by Ferman and Pinto (2021) and demean the data using information from the pre-treatment period because the values of pretreatment family income for the affected industries do not fall inside the convex hull of the corresponding values for the donor pool and can yield an imperfect fit. Thus, I construct the synthetic control using the demeaned data. Since there is more than one treated unit, based on Abadie et al. (2010), I aggregate the treated units into a single unit (pooled SCM). Pooled SCM can yield poor unit-specific fits. Thus, I check
a positive average treatment effect of increased education on pro-environmental behavior. Using two waves of Eurobarometer surveys, he finds a positive local average treatment effect for 7 of 8 pro-environmental behaviors. An analysis of related questions on the survey supports the notion that education causes individuals to be more concerned with social welfare and to accordingly behave in a more environmentally friendly manner (Meyer 2015). However, the study is restricted to 14 European countries where</mark> <mark>10</mark>
Policy Research Working Paper 9359

# **Abstract**

To promote the development and diffusion of electric vehicles, central and local governments in many countries have adopted various incentive programs. This study examines the policy and market drivers behind the rapid development of the electric vehicle market in China, by far the largest one in the world. The analysis is based on the most comprehensive data on electric vehicle sales, local and central government incentive programs, and charging stations in 150 cities from 2015 to 2018. The study addresses the potential endogeneity of key variables, such as local policies and charging infrastructure, using the border regression design and instrumental variable method. The analysis shows that central and local subsidies accounted for over half of the electric vehicles sold during the data period. Investment in charging infrastructure is much more cost-effective than consumer purchase subsidies. In addition, the policy that merely provided electric vehicles a distinctively license plate was strikingly effective. These findings demonstrate the varying efficacy across policy instruments and highlight the critical role of government in promoting fuel-saving technologies.

This paper is a product of the Office of the Chief Economist, Infrastructure Vice Presidency. 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 fzhang1@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
readily into net exports when they complete the import substitution phase for a good, smoothing the process of structural change that accompanies 47 development of the path of investment.

Balassa's paper is the most exhaustive. He shoots down such dead ducks as dependency theory, the once popular "delinking" proposals according to which Southern countries should cut themselves off from Northern ones, and the concept of the New International Economic Order (NIEO) enforc,ed through a global primary commodities cartel. He reviews the post-war history of development, trying to understand why outward orientation is so beneficial to growth. An outward-oriented strategy is defined as one where in the spirit of the Lerner's symmetry theorem, import protection is compensated by export subsidies.

Balassa is concerned that economic theory does not explain why outward orientation is so beneficial, and searches for "dynamic gains from trade" that might clarify the mystery. Entirely properly, he complernents cross-country regressions by in-depth country studies based on a personal data base that he kept up to date for ten years after the first oil shock. The reported results show that outward-oriented countries have adjusted better to external shocks, and in particular have pursued better macro strategies than those resorted to by users of import substitution strategies. They have also achieved more growth per unit of investment and higher rates of saving than import substituting countries. Estimates of this sort have been replicated by too many excellent authors to be dismissed by a curt remark on the fragility of cross-country estimates. A broad spectrum of policies appears to be associated with the selection of trade strategies. Why this is so is unclear.

> **47** Balassa makes a similar point in chapter 31.

28
been above 10 percent since 2014. The RBI’s Asset Quality Review in late 2015 resulted in a large migration of restructured loans into NPAs and new NPA recognition.<sup>42</sup>

**Figure 21: Trend in banking credit from Scheduled Commercial Banks**

<!-- Start of picture text --> Credit Growth Credit to GDP ratio<br>35 60<br>30 50<br>25<br>40<br>20<br>30<br>15<br>20<br>10<br>5 10<br>0 0<br>1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Note: Data is from the Reserve Bank of India. Years refer to respective fiscal years.

There is a predominance of banks in the Indian financial system, and that of public sector banks (PSB) in the Indian banking sector. Banks account for 60 percent of financial system assets, while 70 percent of banking assets are held by the PSBs. Interestingly, the share of publicly owned banks has remained largely unchanged, even as the ownership has decisively become more mixed in other hitherto majority government-owned sectors of the economy, such as aviation and telecom.

# **Figure 22: Ownership structure of the Banking, Aviation, and Telecom Industries**

22 A: Share of private sector continues to increase in the aviation and telecom industry

> 42 See World Bank’s FSAP, 2017.

38
we do not have exporter-level data on that chapter for important oil exporting countries such as Burkina Faso, Cameroon, Iran, Kuwait, or Yemen.<sup>23</sup>

**f) Value:** The unit of measurement of export values in the cross-country raw dataset is the US Dollar (USD). The export values in the raw data were already in USD for many countries. For countries for which export values were provided in local currency, they were converted to USD using an annual official exchange rate series.<sup>24</sup> The exchange rates used differ slightly from those used in the United Nations COMTRADE database that are weighted average annual exchange rates based on monthly exchange rates and monthly trade volumes (as weights).<sup>25</sup> The resulting difference in the exchange rate series creates a small discrepancy (less than 0.5 percent in all cases) between our figures and COMTRADE’s figures for the countries that provided their export values in local currency.

Export values in our dataset are Free on Board (FOB) figures, except for El Salvador and Senegal, whose export values represent Cost, Insurance and Freight (CIF) figures rather than just the pure value (FOB) of the good. This difference should be taken into account for cross-country comparisons of measures related to the size of exporters, exports per product or destination but is not expected to affect other measures related to concentration, diversification and firm, product and market dynamics.

**g) Period:** The information in the cross-country raw dataset has a yearly periodicity.

> 23 Possible reasons for this lack of data are confidentiality reasons or the fact that goods exported through pipeline  are not recorded at customs but instead are recorded by other government/private institutions in the countries.

> 24 The specific series used for this conversion is PA.NUS.FCRF taken from the World Development Indicators (whose original source is the IMF’s International Financial Statistics)
The program was first introduced in some communities in early 1998, but as noted above, local capacity constraints likely led to differences in the timing of program roll out. Program coverage, as indicated in Table 1, was low at the beginning of the period (early 1998) because the crisis had not been anticipated in the 1997-1998 fiscal budget.<sup>11</sup> It was only during the following fiscal year (1998-1999) that coverage of the program expanded above 70 percent of communities. Program coverage then expanded to over 89 percent of communities in 1999-2000 before declining to 80 percent during the first half of fiscal year 2000-2001.<sup>12</sup> During the 1998-2000 period most of the sampled communities (95.4 percent) were exposed to the program for some period of time.

# **4. Evaluation of the PMT Using the Indonesian Family Life Survey**

The data used in this study come from the 1997 and 2000 rounds of the Indonesia Family Life Survey (IFLS), which covers periods before and after the 1997-1998 Asian Financial Crisis and implementation of the child supplementary feeding program. The IFLS is a panel survey that enumerates information on various aspects of individual, household and family livelihoods. In addition, information on characteristics of communities is also collected, including features of the socio-economic environment, physical infrastructure, health and education facilities, and programs administered through these facilities.<sup>13</sup> In this study we link community-level data on implementation

> 11 The national budget plan (fiscal year) in Indonesia prior to 2001 ran from April 1 to March 31.  After 2001, the Government of Indonesia changed the fiscal year to correspond to the calendar year.

> 12 Pritchett et al (2002) note that the initial fiscal year 1998-1999 did not provide for broad emergency programs to cope with negative impacts of the crisis. In July 1998 the current budget was revised to include an explicit Social Safety Net Program.

> 13One author of this paper was
2012 2013 2014 2015 2016<br><!-- End of picture text --> _Source:_ Author estimates based on NBS data.

# **Results**

# **_Investment flows_**

The estimates of sectoral investment flows quantify the marked re-orientation of the structure of capital formation in China in the past decade. There has been a shift away from investment in the business sector towards investment in housing and infrastructure (Figure 6, Panel A). A number of factors have driven this change. In the business sector, the rate of investment depends crucially on the expected growth of the economy. A fall in the expected growth rate of the economy will result in business sector investment dropping until it reaches a level that brings the growth of the capital stock to be once again in line with the lower expected growth of the economy. Indeed, in the period 2008 to 2016, economic growth slackened, from around 9.6% to about 6.7%. With growth slackening, enterprises lowered their expected need for capital and this resulted in a sharp drop in business sector investment from an estimated 20% of GDP in 2008 to 16% in 2016 (Figure 6, Panel B).

15