rafmacalaba/data-use-mentions-extended huggingface dataset:
which includes town fixed effects. Column (3) shows that women continue to have significantly lower rates of participation in the labour force: thus, working age rural women were 50 percent less likely to participate in the labor force than men. As 71 percent of males were engaged in the labor force for the year under consideration, this suggests that LFP for women in 2015 was just above 20 percent, conditional on individual and district characteristics. While literacy has a modest positive impact on LFP (only after including town FE), the completion of secondary education (or better) continues to have a negative and significant impact on LFP.

When we restrict the sample to rural workers and gauge the predictors of non-farm employment [column (9), Table 11], we see that females continue to have a significantly lower likelihood of engaging in non-farm activities, conditional on participation in the labor force.<sup>14</sup> Thus, rural women continue to have lower rates of participation in the labor force and conditional on participation, significantly lower rates of participation in non-farm activities.

A major departure from the prior results with the NSS data is that marginalized rural workers (SC/ST individuals) have a higher likelihood of being engaged in non-farm activities. The coefficient is economically meaningful, suggesting that a rural SC/ST worker has a 10 percent higher likelihood of being engaged in non-farm activities, relative to non-SC/ST workers. We will discuss this finding in greater detail when considering the specific types of non-farm employment. Finally, while literacy does not predict rural non-farm employment, being secondary educated or better does. The coefficient on literacy is attenuated towards zero, while the coefficient on secondary educated indicates that the completion of secondary education increases the likelihood of being employed in non-farm activities by over 12 percentage points for rural workers.

Moving to the various employment categories in Table 12, we see that secondary education significantly reduces the likelihood of rural workers being employed in farm or informal – non-farm activities the two major
|4.03|7.98|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. Table reports export shares by sector over the period 1994-2004 for sectors are classified according to NACE Rev. 1 (2-digit) classification.

Table A.3: Average treatment effects using alternative measures of foreign ownership

|Dep. var.:|(1)<br>Employment (log)||(2)<br>Employment (log)|
|---|---|---|---|
|Post*German ownership (binary)|0.156***<br>(0.041)|Post*German shareholders (num)|0.088***<br>(0.020)|
|Post*French ownership (Binary)|0.155**<br>(0.066)|Post*French shareholders (num)|0.080**<br>(0.037)|
|Post*British ownership (binary)|0.099<br>(0.064)|Post*British shareholders (nun)|0.084<br>(0.055)|
|Year FE|Yes||Yes|
|Firm FE|Yes||Yes|
|Observations|271555||271555|
|Firms|61254||61254|

Notes: Authors’ calculations based on Orbis data. Each cell is obtained from a different specification where the number of employees is regressed on a time-varying indicator of foreign ownership indicated on the row. All regressions include firm and year fixed effects. Coefficients show the average treatment effects of foreign ownership. Results refer to firms that were observed both before and after accession. Data exclude the year 1999 due to anomalies in the data. Foreign ownership in column (1) is measured by a binary indicator taking value one when a firm had a foreign shareholder.
structural primary balance as provided in the IMF WEO dataset produces results similar to those obtained for advanced economies or countries with strong political institutions, which is consistent with the limited availability of this measure for countries outside these groups. All results are available upon request.

## **6.5 Design and intensity of fiscal rules**

We have extended our analysis to the potential role of the strength and intensity of fiscal rules. A growing body of research highlights the importance of rule design in promoting fiscal discipline (Guerguil, Mandon, and Tapsoba 2017; Caselli and Reynaud 2020; Gootjes, de Haan, and Jong-A-Pin 2021). Our results indicate that while strongly designed rules typically lead to a sustained improvement in the primary balance, weakly designed rules tent to generate only short-term effects.

However, when we account for the quality of political institutions, we find no significant medium-term differences between weakly and strongly designed fiscal rules. The key distinction lies in the speed of impact: strong rule design accelerates improvements in the primary balance, a desirable outcome. Further tests indicate that neither the number nor type of fiscal rules—whether sustainability-oriented rules (i.e., deficit and debt limits) or operational rules (i.e., expenditure and revenue constraints)—drive these results: Once we control for institutional quality, all findings consistently point in the same direction. Overall, these results emphasize the pivotal role of political institutions in embedding fiscal discipline into government budgets through fiscal rules. For brevity, these additional findings are available upon request.

# **7. Conclusions**

An increasing number of countries have adopted fiscal rules to ensure fiscal sustainability and constrain suboptimal macroeconomic stabilization policies. This trend stems from two factors: rising government debt levels requiring more disciplined fiscal governance frameworks, and the fact that as more countries adopt these rules, they are also becoming the _de facto benchmark_ for fiscal policy.

Our paper fills a gap in the literature by exploring how the effects of fiscal rules develop over time and
|
|Jammu & Kashmir|n.a.|0.097|0.334|0.709|7.0|
|Jharkhand|43.50|0.192|0.517|0.628|34.3|
|Karnataka|31.34|0.080|0.228|0.648|36.8|
|Kerala|11.74|0.112|0.232|0.517|88.2|
|Madhya Pradesh|45.85|0.406|0.646|0.371|44.3|
|Maharashtra|33.90|0.044|0.277|0.840|39.8|
|Orissa|49.93|0.220|0.507|0.567|36.3|
|Punjab|19.44|0.052|0.312|0.833|26.0|
|Rajasthan|31.2|0.618|0.732|0.155|66.9|
|Tamil Nadu|22.81|0.335|0.414|0.190|82.9|
|Uttar Pradesh|40.75|0.162|0.350|0.536|21.7|
|Uttarakhand|n.a.|0.292|0.406|0.280|40.1|
|West Bengal|35.05|0.432|0.658|0.344|33.4|
|All India|36.43|0.249|0.447|0.444|48.1|

_Notes and sources_ : Poverty rates are based on Tendulkar poverty lines updated from 2004/05 to 2009/10 using statespecific consumer price indices for agricultural laborers (CPIAL) and per capita consumption expenditures in Schedule 1.0. Poverty rates for J&K and Uttarakhand not
**Figure 2.10. Employment increased driven by urban areas.** Employment rate (Percent of LFP), 2009– 2019

<!-- Start of picture text --> 100<br>2009 2014 2019<br>80<br>60<br>40<br>20<br>0<br>Source: Authors’ elaboration using data<br>the CSES household survey.<br>Employment rate<br>Urban Male Rural Male Female Urban Female rural Urban Rural Male Female Cambodia<br><!-- End of picture text -->

**Figure 2.11. The unemployment rate halved since 2009.**

Unemployment rate (Percent of LFP), 2009–2029 <!-- Start of picture text -->
1.4<br>2009 2014 2019<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Source:  Authors’ elaboration using data<br>the CSES household survey.<br>Unemployment rate<br>Urban Male Rural Male Female Urban Female rural Urban Rural Male Female Cambodia<br><!-- End of picture text -->

Structural transformation and economic growth have translated into wage gains. Over the past decade, real wages for employees in Cambodia nearly tripled (Figure 2.13). These wage increases have been greater for women than for men, contributing to a reduction in the gender pay gap. Over the last ten years, the wage gender gap decreased by 10 percentage points from 27 percent in 2009 to 18 percent in 2019 (Figure 2.12).

Between 2009 and 2014, the gender wage gap narrowed by 6 percentage points, primarily driven by substantial wage growth in sectors where women had relatively higher employment rates, such as mining and apparel. In the
Focusing now on firms in establishments, the proportion of firms that are formal according to the basic definition is comparable across the two ELMPS waves (49%-55%), somewhat lower in the EsC (39%), and much lower in the EcC (14%), likely because of the way that some EcC survey instruments excluded the question about registration. Moving to the extended definition more than doubles the share of firms that are formal in the EcC (34%), does not change the estimate of 39% for EsC, and raises the estimate of formality by a few percentage points (to 54-61%) in the two ELMPS waves. Thus, the extended estimates are broadly consistent with each other and suggest that somewhere from over a third to half of all private non-agricultural firms of fewer than 25 workers operating in establishments are formal, depending on the data source. Hereafter, we use the extended definition of formality as our primary measure and focus on firms that operate inside fixed establishments in our analysis. We note any exceptions to this approach.

Although Figure 1 shows the rate of formality for firms, since we have the number of employees in each firm, we can also calculate the number of employees in each data source within the universe of nonagricultural firms with 1-24 workers in establishments. Likewise, among these workers, we can calculate the number within formal firms versus informal firms. In the ELMPS 2012, using the extended definition, there are 3.5 million workers overall and 2.4 million within formal firms – although not necessarily formal employment. This compares to 5.8 million workers in the contemporaneous Economic Census, of whom 2.6 million were within formal firms. In the ELMPS 2018, there were 3.0 million workers, of whom 1.9 million would be within formal firms. The Establishment Census generates a much larger number of workers, 9.2 million, of whom 4.5 million were within formal firms.

# **4.2
<br>scale energy retrofit program for the existing<br>residential building stock is highly cost-effective<br>and provide a reduction of 957 GWh in annual<br>electricity consumption.|
|Jacobsen et al.<br>(2012)|A cross-sectional probit model is<br>used based on a green electricity<br>program for households in Memphis,<br>Tennessee from 2003 to 2008.|Participating households that enroll at the minimum<br>level (i.e., 150 kWh/month) of green electricity<br>program, increase their electricity consumption by<br>2.5%.|
|Kneifel (2010)|A life cycle cost analysis is used for<br>a total of 576 energy simulations are<br>run for 12 prototypical buildings in<br>16 cities, with 3 building designs for<br>each building-location combination.|EE technologies, such as installation of smaller and<br>cheaper HVAC equipment, can be used to decrease<br>energy use in new commercial buildings by 20-30%<br>on average and up to over 40% for some building<br>types and locations.|

# **4.5 Agriculture sector**

Unlike in other sectors, studies examining the impacts of the adoption of clean energy technologies are limited in the agriculture sector. We are highlighting here a few studies available. Efficiency for Access Coalition (2019) reports that an application of solar water pumps for irrigation in remote areas increases farmers' yields by as much as three-fold compared to relying solely on rainfall. Based on an interview of 375 solar water pump users in three African countries (Kenya, Tanzania, and Uganda), the report also finds that majority of the farmers (75%) saying their productivity, such as working less time, increased yields, lower farm operating costs, and improved income, have increased since using their solar water pump, have increased. In Kenya
regressions is high (Sala-I-Martin et al. 2004; Durlauf et al. 2008) and there are numerous potential determinants of growth that could be included. Without considering model uncertainty, researchers examining the finance-growth nexus risk omitted variable bias and inconsistently estimated parameters.

Second, the previous literature examining the finance-growth nexus largely employs measures of financial depth (for both the banking sector and stock markets) but rarely examines measures of the efficiency of financial intermediaries or financial stability. For this reason, we use newly developed financial indicators from the World Bank’s GFDD. These indicators capture not only depth but also efficiency and stability. It is vital to revisit the finance and growth literature because recent studies report that excessive financial development harms growth (Cecchetti and Kharroubi 2012).

Using the updated well-known cross-country growth data set by Fernandez et al. (2001), we find that traditional indicators of financial depth are not robustly related to long-term economic growth. The measures of financial depth and financial stability exhibit posterior inclusion probabilities well below 50%. However, our results suggest that bank efficiency, as proxied by the net interest margin, is crucial for long-term growth. The corresponding posterior inclusion probability is on average around 90%. This result is in line with theory, which indicates that the financial sector is essential in channeling resources from savers to borrowers Pagano (1993). These results are robust to different parameter and model priors in Bayesian model averaging. The results are also robust once we address the endogeneity of financial indicators. In addition, we do not find non– linearities and various interaction effects (such as the effect of the interaction of credit and financial stability) important for finance–growth nexus.

Overall, we find that the measurement of financial development is crucial in determining the estimated effect of finance on growth. Based on our global sample, 45
value added per employee of informal firms is 14 percent that of formal firms. Therefore, expanding the share of workforce in formal employment is a critical policy objective for developing countries in order to increase productivity.

> 6 The lower the score, the least flexible regulation, with 0 being the lowest score possible. The Ease of Hiring Index estimates the availability and maximum length of a fixed-term contract for a task related to the permanent activities of a firm, the probationary period, and the ratio of the minimum wage to value added per worker. The Ease of Redundancy Index estimates characteristics of regulation governing notification and approval requirements, retraining obligations, and priority rules for dismissal and reemployment.

> 7 Based on the Doing Business database, the authors create an index of labor market regulations for 2014. 8 The World Economic Forum’s Global Competitiveness Index aims to measure the drivers of total factor productivity (TFP). These drivers are organized into 12 pillars: Institutions; Infrastructure; ICT adoption; Macroeconomic stability; Health; Skills; Product market; Labor market; Financial system; Market size; Business dynamism; and Innovation capability. The labor market pillar aims to assess the flexibility, incentives and merits of the labor market.

6
**Figure 4. Share of Sugar Export Quantities sold to EU and U.S., 1998-2002**

<!-- Start of picture text --> EU US<br>100<br>80<br>60<br>40<br>20<br>0<br>Barbados BelizeDominican Rep. Guyana JamaicaSt. Kitts & NevisTrinidad & Tobago<br>Percent<br><!-- End of picture text --> Source: Comtrade data.

# **_Risk of Preference Erosion_**

The EU and U.S. sugar programs are under substantial pressure for reform and these reforms will likely affect the quota holding countries of the Caribbean. These reforms are expected to reduce prices for exports, but not the quantities under quota because of international agreements. The EU-ACP quotas are bound by the Lomé Convention and its successors and are not expected to be reduced beyond the levels shown in Table 2 (except when individual countries do not meet their quotas which can lead to reallocation of quotas to other ACP countries). However, the EU’s Special Preference Sugar imports can be reduced and that program would be phased out under a reform proposal by the European Commission. The U.S.’s import quotas shown in Table 4 are bound by agreements made by the U.S. in the WTO Uruguay Round Agreement on Agriculture and are not expected to be reduced. The prices received for these quotas are not bound by agreement and could be changed.

The pressure for reform of the EU sugar program intensified when the WTO released its ruling in August 2004 and upheld the ruling on appeal in April 2005 in the dispute case brought by Australia, Brazil and Thailand in 2003. The ruling concluded that the EU’s sugar program subsidized exports in excess of the levels agreed in the Uruguay Round Agreement on Agriculture. The European Commission released a proposal for reform of the EU’s sugar regime in July 2004 which was not acted upon and a subsequent
- Djankov, Simeon, Rafael La Porta, Florencio López-de-Silanes, and Andrei Shleifer. 2002. “The Regulation of Entry.” _Quarterly Journal of Economics_ 117 (1): 1–37.

- Dollar, David, Mary Hallward-Driemeier, and Taye Mengistae. 2005. “Investment Climate and Firm Performance in Developing Countries.” _Economic Development and Cultural Change_ 54 (1): 1–31.

- Dunne, Timothy, Mark J. Roberts, and Larry Samuelson. 1989. “The Growth and Failure of U.S. Manufacturing Plants.” _Quarterly Journal of Economics_ 104 (4): 671–98.

- Evans, David S. 1987a. “The Relationship between Firm Growth, Size and Age: Estimates for 100 Manufacturing Industries.” _Journal of Industrial Economics_ 35: 567–81.

- ———. 1987b. “Tests of Alternative Theories of Firm Growth.” _Journal of Political Economy_ 95: 657– 74.

- Fisman, Raymond, and Jakob Svensson. 2007. “Are Corruption and Taxation Really Harmful to Growth? Firm-Level Evidence.” _Journal of Development Economics_ 83 (1): 63–75.

- Freund, Caroline, and Nadia Rocha. 2010. “What Constrains Africa’s Exports?” Policy Research Working Paper 5184, World Bank, Washington, DC.

- Galindo, Arturo José, and Alejandro Micco. 2007. “Creditor Protection and Credit Response to Shocks.” _World Bank Economic Review_ 21 (3): 413–38.

- Gelb, Alan, Vijaya Ramachandran, Manju Kedia Shah, and Ginger Turner. 2007. “What Matters to African Firms? The Relevance of Perceptions Data.” Policy Research Working Paper 4446, World Bank, Washington, DC.

- Hall, Bronwyn H. 1987. “The Relationship between Firm Size and Firm Growth in the U.S. Manufacturing Sector.” _Journal of Industrial Economics_ 35: 583–606.

- Hausmann, Ricardo, Dani Rodrik, and Andrés Velasco. 2005
Finally, we provide some tentative evidence of a positive effect from quality of governance on the quality of infrastructure. In general, these results cast doubts on the real contribution of the government sector to GDP and on the last as a measure of value added. Section 2 presents some stylized facts concerning public investment and institutional quality. Section 3 summarizes the cross-country empirical literature. Section 4 describes our panel dataset, the estimation strategy and the empirical results. Section 5 assesses the policy implications of the findings and provides a brief conclusion.

# **2. Stylized Facts**

Table 1 reports the average levels of public investment over both GDP and total investment for each quartile of the distribution for both a quality of governance index and a measure of government’s checks and balances.<sup>3</sup> Public investment levels are somewhat stable for the first three quartiles of quality of governance, averaging 6.5 percent for public investment as a share of GDP and 33.9 percent for the ratio of public investment to total investment. The fourth quartile displays much lower values for both ratios, 4.3 percent and 18.7 percent respectively, suggesting that countries with the highest quality of governance scores spend less in public investment relative to the other countries.

For checks and balances, the pattern is similar, as the level of public investment over GDP (total investment) steadily decreases from an average of 8.2 (38.1) percent of GDP in the first quartile to 5.3 (26.6) percent of GDP (total investment) in the fourth quartile. The third quartile reports slightly lower values than the fourth one, but this may be also due to the dramatic reduction in the number of observations from the second to the last quartile.

The negative correlation between institutional quality and public investment levels can also be observed in the scatter plots in Figure 1, which also show the probability density functions for both the institutional quality variables. While quality of governance exhibits a
113|0.107|0.107|||||

This table reports probit estimations of the probability of a firm with previous loans defaulting on a loan on a set of firm-level characteristics (Panel A) and the resulting predicted default probabilities for firms with and without previous loans (Panel B) for the default model sample. The dependent variable is a dummy equal to one if the firm defaults on a loan during 2019 (has a payment past due over 90 days) and zero otherwise. All explanatory variables are calculated as of December 2018. Given that the data on firms’ net worth are not available for all firms, all specifications include an unreported dummy variable equal to one if the data for the firm’s net worth are missing and zero otherwise. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Conversely, firms without previous loans are those with no credit records in the banking system throughout the same period. Columns 1–4 include real characteristics, and Columns 5–8 add financial characteristics. Columns 1–8 include different sets of fixed effects (FE). The table uses the 2018–2019 sample of formal firms with previous loans. Standard errors, shown in parentheses, are clustered at the industry and municipality level.

43
same decade to all countries in the same geographical region as the actual borrower. I then construct my instrument by aggregating these predicted loan-level disbursements on previously-approved loans to the country-year level. By construction, aggregate predicted disbursements reflect only the combination of country-specific loan approval decisions from previous years with typical disbursement profiles, based on averages taken across many loans to many countries. My identifying assumption is that these loan approval decisions do not anticipate future shocks to growth, and under this assumption, changes in aggregate predicted disbursements will be uncorrelated with the error term in Equation (1). I can therefore use changes in predicted disbursements as an instrument for changes in total government spending when estimating the government spending multiplier based on Equation (1).

# **3.  Data**

I work with loan-level data drawn from the Debtor Reporting System (DRS) database maintained by the World Bank. The DRS database contains information on loan commitments, terms, disbursements, and repayments, for all external loans contracted or guaranteed by the government in the borrowing country, beginning in 1970. The DRS data are, in principle, comprehensive in their coverage of all individual external public and publicly-guaranteed debt obligations, from all creditors, and for all countries that borrow from the World Bank. This is because annual reporting to DRS is mandatory for World Bank clients: a country must be in good standing with respect to these reporting requirements in order for new projects for that country to be considered by the Board of Directors of the World Bank.<sup>6</sup> Countries are required to report basic information on the amount, terms and purpose of new commitments, drawings and repayments on existing loans, and details of loan restructurings when applicable.<sup>7</sup> Loan-level transactions reported in DRS are confidential. However, the aggregation of this loan-level data to the country-year level provides the basis for country-level debt data published by the World Bank in its annual Global Development Finance publication.<sup>8</sup>