rafmacalaba/data-use-mentions-extended huggingface dataset:
whether the consequences of austerity episodes depend on the type of fiscal consolidation and show that “expansionary austerity” can arise on increased business confidence and private investment. Corbi, Papaioannou and Surico (2019), studying Brazilian federal transfers show that fiscal spending has a positive multiplier that depends negatively on the income of recipient localities (i.e. poorer municipalities have a significantly larger multiplier).<sup>11</sup> We contribute to this literature by providing granular evidence that the reduction in the supply of bank credit towards local governments can increase credit supply to local private firms, with positive firm-level real effects. Such positive effects are also present at the state-level GDP and employment. However, and consistent with a significant reduction in social public spending, we also find increases in extreme poverty, which are stronger in states where public spending in social programs is relatively higher than on infrastructure.<sup>12</sup> Empirically, while there is evidence of a negative correlation between public debt and growth (see Reinhart and Rogoff (2010) among others), establishing causality is harder. Huang, Pagano and Panizza (2020) use firm-level data to show that local public debt in China crowded out the investment of private firms by tightening their funding, especially of more financially constrained borrowers.<sup>13</sup> Similarly, Hoffmann, Stewen and Stiefel (2021), using a German panel of firms, find that when spreads on local government debt are low, local public banks use their market power to charge higher rates to their customers, reducing investment; further, fiscal consolidation worsened this effect by putting pressure on the budgets of municipal governments which increasingly borrowed from local public banks. Different from these papers, we use an exogenous negative shock to local public debt, along with detailed loan-level data, to show a decrease in public spending and increase in taxes (fiscal consolidation) but with an overall positive effect on local bank lending to private firms, particularly to the more capital-intensive secondary
>***</sup>|0.42<sup>***</sup>|-0.08|
||(0.23)|(0.23)|(0.06)|(0.07)|(0.04)|(0.05)|(0.17)|(0.11)|(0.19)|(0.05)|(0.11)|
|100 cases(1)*lockdown(1)|-0.67<sup>***</sup>|-0.90<sup>***</sup>|-0.18<sup>***</sup>|-0.26<sup>**</sup>|-0.32<sup>**</sup>|-0.24<sup>***</sup>|-0.57<sup>***</sup>|-0.06|0.62<sup>*</sup>|0.65<sup>***</sup>|0.28<sup>***</sup>|
||(0.11)|(0.15)|(0.05)|(0.10)|(0.13)|(0.08)|(0.12)|(0.08)|(0.35)|(0.19)|(0.07)|
|No. observations|8208|8208|8208|8208|7872|7872|7776|7776|7968|7968||

Note: the data used covers January-April of 2018-2020. For every country where google trends data is available, the search index reflects searches in English and the most dominant local language.
clubs. Though the evidence is weaker for the latter two given the negative estimate of , the null of convergence is not rejected at 10 percent level of significance. γγ Figure 4(c) tracks the mean yields of the clubs over the years. The yield growth rate varies over a tight range initially but diverges in the later period. The growth in the leading club outpaces growth elsewhere, effectively creating a massive yield gap. The next two clubs do not see any marked difference in growth rates over the two periods. However, a higher growth rate for club 2 leads to a rise in the gap between the two clubs. The lagging club 4 fails to maintain even the modest growth of the first period and records a falling yield. Looking at the ACZs, it is no surprise that the northeastern zone has performed better given the evidence in Figure 2(c). The leading club exclusively consists of districts from the region with the remaining regional districts falling in club 2. However, club 2 does not entirely consist of northeastern districts. Of the 13 districts in club 2, seven and four districts lie in the southern and northwestern zones respectively. Like in the case of rice yields, the formation of non-leading clubs does not follow ACZ classification more generally. The other two regions have a balanced representation in clubs 3 and 4. The figure also shows the vast difference in the fortunes of the clubs. The mean log yield in the leading club increased by more than 75 percent during 2005–17. Yet, the mean log yields in clubs 3 and 4 in the most recent year are lower than that of the leading club in 2005.

The key takeaway is that Sub-period II is characterized by increased divergence in maize yields. Still, club convergence materialized at significantly difference levels. While the northeastern region pulled ahead, districts in other regions formed clusters. The worst performing cluster consisting of five districts lying in both the northwestern as well as the southern region experienced not much expansion during 2012-17.

# *
.21) sd which is negative but not statistically significantly different from zero.

In sum, these estimates present an intriguing perspective of the effects of teacher incentive programs. On one hand, there is supporting evidence that female teachers respond to both pecuniary and non-pecuniary incentives, and do so in ways that benefit all students, male and female. On the other hand, the estimates indicate that male teachers respond mainly to the pecuniary incentive, and do so in a way that benefits only female students. This behavior is compatible with them taking a strategic approach, since female students of male teachers tend to have markedly lower counterfactual test scores than male students.

# **3.3 Inspectors’ Classroom Observations**

We further investigate the ways through which teachers may have responded to incentives by using the data on teacher practices collected through the inspector classroom observations. The inspector assigned each teacher scores on two indicators of classroom quality and five indicators of teaching quality. We standardized each score to have a control group mean of zero and standard deviation of one, and then took the mean of these standardized scores to create an overall index of classroom and teaching quality, respectively. Table 4 reports the effects of the program on these indicators at midline. The specification is similar to that for 12
# Pl Planning, and Rn_sch

# Public Economics

Can tax policies be designed to encourage economic growth in developing countries? One view holds that by providing the govemment with a stable source of funding and reducing the current account deficit, tax revenues encourage long-term growth. In this view, the economic distortions aggravated by tax rates are slight in comparison to such institutional constraints as price controls, foreign exchange allocations, and trade quotas.

The other view is that high marginal tax rates constrain long-temi economic development by discouraging business expansion, investment, and foreign trade. The contention is that the benefits of a carefully designed, moderate tax structure exceed the costs of budget deficits or spending cuts.

This paper tests these v:.ews by measuring the effect of government spending and taxation on output growth. In theory, higher tax rates shift investment and employment to sectors with low - or even negative - tax rates, such as import-substitution or underground sectors. The lower returns to investment and labor in these sectors mean that the economy wiU generally record lower growth rates.

Data from 31 African countries show the medium- and long-term effects of fiscal policies on growth during 1965-73 and 1974-82. Govemment investments for the earlier period were sufficiently productive to justify the distortions imposed by the relatively high tax rates necessary to finance them. By 1974-82, however, the return on govenmment investments had fallen to almost zero, suggesting that the burden of personal and corporate taxes led to a contraction in growth. Although taxes on imports did not affect out-ut directly, such taxes reduce investment and thereby indirectly curtail grwth. On balance, sales and excise taxes had the most moderate effects on growth and investment.

In sum, a balanced increase in govenmment spending financed by sales and excise taxes, or by a shift from personal and corporate taxes to consumption taxes, can increase growth rates.

This paper is a product of the Public Economics Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please
that the number of days worked for the NREGS changes considerably with variation in rainfall levels (c.f. Table 3).<sup>38</sup> The greatest change is observed for

> 35The share of area under irrigation seems to increase output levels. In contrast, the dummies indicating whether or not a household applied fertilizer or high yielding variety (HYV) seeds are not statistically significant. This might seem somewhat surprising, but since expenditure on fertilizer and seeds is included in variable inputs, one should not attribute too much weight to this finding.

> 36These statistics are available at the state-level for major crops and for the years 1996 to 2006 from the Cost of Cultivation Statistics.

> 37Many of these commodities are traded internationally, such that risk-aversion of farmers alone can probably not explain the observed correlation between the riskiness of crops and their returns. Other reasons could be differences in the concentration of supply or demand between crops. Analyzing the reasons for the apparent relationship between risk and returns in crop portfolios is beyond the scope of this article.

> 38In the first two columns, the total number of days worked in the past 12 months is the dependent variable; in the last two columns it is the log of this variable.

21
survey data to catalog the project characteristics and relate them to success measures, focusing on a WEB—local economic activity. To this end, the paper used the differencein-differences methodology, conditioning on country context, project design and implementation, and complementary policies and institutions. The underlying multivariate meta-regressions thus linked project characteristics to local economic activity measured through geocoded data on nightlights intensity.

The analysis found that some initial conditions and project characteristics play a significant role in determining whether increased connectivity through a transport corridor will spur economic activity around the corridor. Large transport infrastructure investments in countries that have direct access to the sea are more likely to spur economic activity. The size of the country, its terrain, and the level of development appear to have no significant influence.

Some project design characteristics can better stimulate local economic activity. For instance, having a well thought out rationale or theory of change for the project appears to generate significantly more economic activity in the surrounding areas. This is likely because such projects place a larger focus on designing the project in a way that fits the specific needs of its users, which could vary across locals and population groups. The evidence on private sector engagement and involvement suggests that although consulting the private sector on the project design could generate higher WEBs, involving the private sector in the implementation of the project has so far dampened the creation of WEBs. This finding hints at the intricacies that need to be understood by international development organizations and governments for effective engagement of the private sector in corridor projects for greater socioeconomic benefits. On complementary policies and reforms, further research is needed to reach robust conclusions, which would require better proxies than the World Bank DPOs used in the paper.

As the collection and public availability of spatial data on local development outcomes around the world progresses, more studies are needed to shed greater light on how the variation in project design and 18
There is some indication in the literature that urbanization alters women’s position inside and outside the household. More job opportunities, better access to education, lower fertility rates, and more favorable attitudes towards women’s paid work are some of the channels through which urbanization may positively impact women’s employment. Thus, we control for the percentage of the country’s population living in urban areas ( _Urbanization_ ). The data source for the variable is WDI, World Bank.

Differences in fixed costs associated with different technologies, production methods, degree of mechanization and automation imply that market size may play important role in the choice of production technology and therefore the demand for female relative to male workers. Thus, we control for market size proxied by the (log of) total population in the country taken from WDI, World Bank. Last, we control for macroeconomic stability using the rate of inflation based on the consumer price index as a proxy. The data source for the variable is WDI, World Bank.

# 3.5 _Robustness controls_

In the robustness section, we show that our main result of a positive and significant relationship between exports and the share of female workers continues to hold with additional controls. Some of these controls are not included in the baseline controls because they involve a noticeable decline in sample size (missing data). Hence, we use them for robustness purposes only.

We argued above that women owners and top managers are less likely to discriminate against women workers than men owners and top managers. Information on the gender of the top manager is available in the ES but missing for about 15 percent of our baseline sample. Thus, our first robustness control is a dummy variable equal to 1 if the top manager of the firm is a female and 0 otherwise.

18
27 THE INTENSIVE MARGIN IN TRADE <u>1</u> _N_ (2003) show that Θ<sup> ̄</sup> _≡ N_ � _n_ =1<sup>_θ_</sup> _i_<sup>(</sup><sup>_n_)</sup> is a consistent estimator of Θ _i_ , while the covariance matrix of Θ<sup> ̄</sup> _i_ is given by the variance of Θ<sup>(</sup> _i_<sup>_n_)</sup> , so we use this to construct confidence intervals for _θ_<sup> ̄</sup> _i_ . For each origin, we run 5 different chains that start at a different random starting value _θi_<sup>(</sup><sup>_i_). We then</sup> explore whether the different parameters in _θi_ converged to the same values across different chains and discuss the convergence of the chains in the Online Appendix F.

Loosely speaking, identification works as follows. First, data on export flows and the number of exporters across country pairs helps in identifying _dij_ and _μ_ ̄ _f,ij_ . Second, the variance of firm sales within each _ij_ pair helps in identifying the sum of the dispersion parameters for productivity and demand shocks, ̄ _σφ,i_ + _σα,i_ . Third, the extent of correlation of firm sales from a particular origin across different destinations helps in identifying _σφ,i_ separately from _σα,i_ : the more correlated firm sales are across destinations, the larger is _σφ,i_ relative to _σα,i_ . Fourth, the correlation between fixed costs and demand shocks can be inferred from the distribution of sales of small firms. Intuitively, if correlation is negative, then a firm with a bad demand shock would also likely draw a high fixed cost shock and thus will not export, hence, we would not see a lot of small firms in the data. Finally, to
insecurity, and natural hazards.<sup>12</sup> Table 1 shows the summary statistics.

> 10 To test the validity of our main messages, we decrease our probability threshold, which defines if a person is vulnerable or not to 25 percent.

> 11 Informality is defined as all workers who do not have health insurance or a signed working contract.

> 12 In order to measure these hazards we take advantage of questions asked on each three of these risks in the national household survey. For each of these three hazards, we calculate a risk index. The risk index is the sum of all subtypes of hazards falling into the respective category. More specifically, we create a dummy variable for each subtype of hazard. This dummy variable is equal to one as soon as a household experiences one form of, for example, crime, and zero otherwise. We then add up the number of, for example, crimes experienced. This then is our respective risk index. In case a household experiences five different forms of crimes, for example, the index has a value of five. In case the household experiences zero crimes, it has a value of zero, and so on. We opt for an index to measure risk exposure to also account for the serverity of hazards. For a detailed overview of the indices see Annex 5.

8
is, counties that experience more storms in our sample have on average more migrants moving in than moving out. This relationship is somewhat weak, but is statistically significant. It is also inconsistent with migration patterns where individuals systematically move away from locations that experience storms.

# **Moving to opportunity versus moving to adaptation**

Migration is often driven by economic opportunity, with individuals choosing to relocate to locations with better economic prospects. Even migration that occurs after weather shocks may be motivated by economic opportunity.<sup>34</sup> Using data on county level GDP as a proxy for economic opportunity, we observe that higher county GDP is strongly positively correlated with total net migration over our sample period (Figure 3a). Using the same data, we examine the extent to which there is a trade-off between moving to economic opportunity and moving to places that face lower storm risk.

We find that places with greater economic opportunity, proxied by higher county level GDP 9
with 0, and re-estimate care readiness. We then repeat the same procedure, but instead of replacing the estimate for absenteeism at the facility level, we assume all facilities have the essential drugs for the condition(s) in question.

We present averages and 95% confidence intervals by first averaging all observations across facilities (or health workers) in each country (using country-specific facility weights to calculate a representative country average), and then averaging over the country means to calculate an overall average for the whole sample. (This is a suitable approach if we treat the country as the core unit of observation; i.e., we want to know how countries compare and how they perform as a group.) This procedure is equivalent to calculating a weighted average and its standard error across all observations in the sample, where the weight on observation in country is i = ∏ i , where i denotes the (facility) weight of observation in ii kk≠ii kk country jj and ωω FF is the sum of the facility weights in country × ff ff . ii kk rr rrkk jj FF = ∑ff kk

# **_Ethical considerations_**

Each of these surveys was carried out in collaboration with the Ministry of Health in the target country. Each interview respondent provided consent to participate in the survey and was made aware that they could desist from the survey at any point. The study involved no deception of participants. The current study involves secondary analysis of the Service Delivery Indicators data.

# **_Patient and public involvement_**

Neither patients nor the public were involved in the design of this study. However, each Service Delivery Indicators survey was discussed – both at design and results stage – at length with representatives of the country where the survey was carried out. This research paper presents the analysis of data across 10 countries.

# **_Role of the funding source_**

9
particularly in nations with inflexible budgetary structures (Ardanaz and Izquierdo, 2017). Public consumption, on the other hand, tends to be procyclical in the developing world, while findings in advanced countries are less conclusive.<sup>11</sup> In addition to these non-automatic spending categories, automatic stabilizers like unemployment insurance and targeted social programs have proven to be effective countercyclical tools. Their effectiveness in smoothing household income and consumption hinges on institutional design and budgetary size, with a more prominent role in advanced economies compared to developing nations, as previously mentioned.

Recent studies have also delved into the role of social security spending, yielding mixed results. Darby and Melitz (2008) discovered that retirement and pension transfers to households have a stabilizing effect in a sample of OECD countries. Wibbels (2006) revealed that social security spending, using a broader definition encompassing pension, has no direct link to the business cycle and is therefore acyclical, both in OECD and Latin American countries. Vegh et al (2021) found that social security spending demonstrates mild countercyclicality or acyclicity in advanced economies while being strongly procyclical in developing countries. In summary, the cyclical attributes of social security spending appear to vary depending on the country and the specific program under consideration.

# **_3.1.2 Breaking down spending cyclicality in Argentina_**

In Argentina, government consumption, social security spending, and public investment constitute the primary components of the overall budget. Based on official data from 2022, which aggregates the central government and the 24 sub-national administrations' public accounts, public primary spending amounted to an estimated 37 percent of GDP, with these three categories—government consumption, public investment, and social security—accounting for almost three-quarters of this expenditure ( **Figure 6** ). Public consumption represents the largest portion, constituting 13.5 percent of GDP (with the wage bill accounting for nearly 11 percent of GDP).<sup>12</sup> Social security spending reached 10 percent of GDP
However, she does not investigate the effect of the program on collaboration. Also, although the matching technique produces a comparison group of firms with similar observable characteristics, the results could potentially be driven by differences in unobservable characteristics. Lööf and Broström (2008) use a similar matching technique to estimate the impact of university collaboration on innovation in Sweden. They find that large manufacturing firms that do collaborate with universities have higher innovation sales (share of sales from new or improved products) and a greater number of patent applications, with again the same concerns about selection on unobservables.

This paper adds to this literature by studying the effect of the In-Tech program in Poland on science-industry collaboration, research and innovation, and product commercialization. The InTech program provides grants to consortia of research entities and firms for proposed research projects. Applications receive a score based on peer reviewer ratings and those with a score above a threshold are offered funding. Based on this funding rule, we use a regression discontinuity (RD) design to estimate the effects of receiving In-Tech funding for applicants to the 2012 and 2013 calls for proposals. We use data from In-Tech application forms to show that applicants above and below the cutoff have similar characteristics, suggesting that the RD approach is valid.

Follow-up information on projects and consortia outcomes comes from a 2016 survey of 400 applicants both above and below the funding cutoff that was specifically designed to measure the impact of In-Tech. The consortium leaders in our sample have a mean of 100 and median of 28 research employees and a mean of 140 and median of 41 technical and administrative staff members. Most projects are in a field of engineering, with the largest field being electrical, mechanical, or materials engineering.

Our findings show that receiving In-Tech funding increases the probability of a project being completed by almost 60 percentage points (from about 20% completed to close to 80% completed). The survey responses regarding collaboration reveal that most consortia had already collaborated before applying to the program (about 85 percent of applications). However