rafmacalaba/datause-encoder-data huggingface dataset:
services sector accounting for 39.5 percent of the labor force, while women employees in agriculture and industry account for 35.3 percent and 25.1 percent of the workforce, respectively.<sup>22</sup> As shown in Table 7, nearly 35 percent of women were employed in Sri Lanka in 2015, which means that only 35 out of every 100 women in the working age category participated in the labor market. Moreover, crop and animal production, hunting and related activities sector accounted only 45 percent share of total women’s employment. The service sector is currently the dominant source of demand for labor in Sri Lanka, and has been expanding in terms of its share of GDP. Among men, employment in the service sector has grown steadily and has become the predominant employment sector with 45 percent of all employed men. Agriculture and industry still account for roughly 25 and 30 percent of male employment, respectively. In 2010, nearly 50 percent of women workers were engaged in agriculture and related activities, however this share declined to 45 percent by 2014. In manufacturing, the textiles and garment sector has the highest share of women workers in Sri Lanka as compared to men.

**Table 7:  Women Labor Force Participation by Economic Activity in Sri Lanka**

|**S.No**|**Economic activity**|||**2010**||||**2014**||
|---|---|---|---|---|---|---|---|---|---|
|||**No.**<br>**of**<br>**Men**|**No. of**<br>**Wom**<br>**en**|**Share of**<br>**women in**<br>**sectoral**<br>**employm**<br>**ent (%)**|**Share of**<br>**the sector**<br>**in total**<br>**women**<br>**employm**<br>**ent (%)**|**No.**<br>**of**<br>**Men**|**No. of**<br>**Women**|**Share of**<br>**women in**<br>**sectoral**<br>**employm**<br>**ent (%)**|**Share of**<br>**the sector**<br>**in total**<br>**women**<br>**employm**<br>**ent (%)**|
|1|Crop and animal production,<br>hunting and related activities|1444|968|40.1|50.2|1374|910|39.8|45.0|
|2|Manufacture of wearing apparel|133|312|70.1|16.2|165|375|69.6|18.6|
|3|Retail trade, except of motor<br>vehicles and motorcycles|566|240|29.8|12.4|523|280|34.9|13.9|
|4|Manufacture of food products|126|110|46.6|5.7|155|122|43.9|6.0|
|5|Human health activities|42|67|61.5|3.5|53|73|57.9|3.6|
|6|Manufacture of textiles|29|39|57.4|2.0|31|59|64.8|2.9|
|7|Financial service activities,<br>except insurance and pension<br>funding|53|43|44.8|2.2|60|59|49.6|2.9|
|8|Food and beverage service<br>activities|49|31|38.8|1.6|83|53|39.0|2.6|
|9|Wholesale trade, except of motor<br>vehicles and motorcycles|83|26|23.9|1.3|138|31|18.5|1.5|
|10|Construction of buildings|363|9|2.4|0.5|449|11|2.4|0.5|
|11|Land transport and transport via<br>pipelines|394|7|1.7|0.4|468|11|2.3|0.5|
|12|Accommodation|54|12|18.2|0.6|54|10|15.6|0.5|

22 Sri Lanka female labor force participation could boost productivity, December 11, 2015 accessed online at <u>http://www.lankabusinessonline.com/sri-lanka-female-labor-force-participation-couldboost-productivity-kvintradze/.</u> 20
Next, we assess the traditional and AAA performance measures against contemporaneously matched administrative records from loan officer evaluations in Ghana and business coach assessments in Uganda.

Finally, we conduct a field experiment to test methodology versus medium, where a randomly selected half of the small firms in the sample were administered the AAA electronic tool, while the other half were administered an identical paper version of the AAA tool.

# **3.1. Data and Summary Statistics**

Table 1 presents characteristics of the small firms in our sample across the three contexts. In Ghana, our sample consists of more women entrepreneurs (71%) than in Uganda (42%) and Rwanda (45%). The mean age of the entrepreneurs is similar across all three settings, ranging from 31 to 36. The entrepreneurs in the Ugandan sample are relatively more educated than the other two samples, with 81% of entrepreneurs having a high school degree compared to 23% in Ghana and 33% in Rwanda.

Businesses have been operational on average between four to six years, and the majority of businesses are located in separate structures from the owner’s household. Businesses in Uganda are typically larger than those in Ghana and Rwanda, having an average of one permanent employee. Business performance is similar across the three settings. Fewer than 20% of businesses in each sample has outstanding loans. Average sales and profits range from 974 - 1,163 USD and 160 – 252 USD, respectively.

# **3.2. Comparisons in the Cross-Section and Panel Data**

As a first step, Figures 7 and 8 plot the kernel densities for sales and profit estimates across the three settings in our study, respectively. For sales, Figure 7 shows the density for AAA sales is consistently higher than self-reported sales for higher values, and confirms an upward adjustment in the sales estimates. Furthermore, the density plots confirm that the adjustment is in the entire distribution rather than just in the tails.

18
# **1 Introduction**

A country engages in fiscal consolidation programs, increasing taxes or cut government expenditures, when the national debt level is deemed high enough to influence the ability of the country to borrow in the future or to prevent the government of the country to undertake necessary public spending programs. If the need for fiscal consolidation arises when the economy is at relative stagnation or decline, policymakers usually worry that fiscal consolidation efforts present an unnecessary drag on aggregate demand. It is therefore important to understand the output or consumption costs of fiscal austerity to weigh it against the benefit of reducing the national debt stock. The expansionary fiscal contraction (EFC) hypothesis states that fiscal austerity can increase, or at least not decrease output or consumption when a country is under heavy debt burdens because it sends positive signal about the country’s solvency situation and long-term economic wellbeing. If this hypothesis is true, policy implications for fiscal austerity programs are clear: the cost of austerity relative to its benefit will be low if debt burden is high enough, and more austerity programs should be undertaken in this case. The question remains whether there is sufficient support for the EFC hypothesis in the data.

This paper seek to bring new empirical evidence to bear on the subject of expansionary austerity by producing a new model-based definition of a high-debt regime, leveraging a newly constructed cross-country narrative dataset and using a new econometric methodology. An empirical answer on this subject will depend crucially on how one defines when the debt level is high enough for austerities to have anti-Keynesian effects and on how one defines austerities themselves. In this paper, I introduce a new definition of a high-debt regime by calibrating a model of expansionary austerity for each country in my sample, and I use this definition along with the conventional cutoff debt levels. I consider two alternative definitions of austerity: fiscal consolidations constructed by the reading of narratives, a direct measure, and structural shocks from identified VARs, an indirect measure. For the former, I use a new source of cross-country narrative dataset constructed by the Fiscal Affairs department of the International Monetary Fund. For the later, I employ a relatively new econometric approach, the proxy structural VAR approach, which combines the attributes two leading empirical methodology in this literature.

1
# **1. Introduction**

Economic theory suggests that carbon pricing is the most economically efficient policy instrument to mitigate climate change (Nordhaus, 1977; Poterba, 1991; Aldy et al., 2010). As of 2023, over 70 national or sub-national economies have introduced carbon pricing (World Bank, 2023). Hundreds of ex-ante studies have been carried out assessing the impacts of hypothetical carbon pricing schemes.<sup>1</sup> Yet a growing body of ex-post empirical evidence presents ambiguity, with some papers arguing that carbon pricing has produced only modest reductions in emissions (e.g. Green, 2021), others showing potentially substantial reductions in average emissions growth rates across jurisdictions (Best et al., 2020; Rafaty et al., 2020), and single-jurisdiction studies finding a mixture of large and small effects (e.g. Andersson, 2019; Pretis, 2022).

Emissions reductions from carbon pricing policies may have been modest - either on average or in specific jurisdictions - for a variety of reasons. It may be the case that prices levied on emissions have induced economically efficient, but small (and potentially costly) reductions in emissions. Alternatively, emissions prices may be inefficiently low due to economic concerns or political concessions, such as built-in exemptions that limit policy coverage (Rafaty et al., 2020). Moreover, carbon pricing policies may be complex, leading to delays in their implementation, or they may have uncertain futures due to political or economic reasons that lead firms to “wait-andsee” before changing input or output decisions. Finally, renewable energy policies also can take on a prominent role in promoting emissions reductions but may complicate the econometrics of estimating the effects of carbon prices. Many of these factors are relevant in the context we study.

This paper provides insights on the efficacy of real-world pricing instruments for greenhouse gas (GHG) reductions by focusing on the experience of Australia, which saw two major expansions (and subsequent adjustments to the expansions) of climate policy circa 2010. First, the ambition of Australia’s Renewable Energy Target (RET) was substantially increased in 2009.<sup>2</sup> The RET requires entities that acquire electricity in wholesale markets to also acquire green certificates in

> 1 Timilsina (2018) provides an introductory overview of different types of carbon pricing instruments, including carbon taxes, emissions trading system (ETS), and carbon offset mechanisms.

> 2 A useful short overview of the relevant legislation is provided by St. John (2014). In our description here, we focus on the large-scale component of the RET which provides well-defined incentives in the wholesale electricity market. There is also a small-scale component to the RET which is captured in our estimates but focuses on small-scale technologies.

2
Table 7: Impact on Savings and Loans

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Has Savings in<br>Bank Account|Has Saved in<br>last 6 Months|<br>Will Save in<br>next 2 Months|<br>Saving in last 6<br>Months:<br>Level (USD)|Saving in last 6<br>Months:<br>Log (Amount)|Remittances<br>used to Save:<br>Level (USD)|Remittances<br>used to Save:<br>Log (Amount)|Savings<br>Outcomes:<br>Aggregated|Has Taken Loan<br>in last 6<br>Months|
|Migrant-Only Training (A)|0.054|-0.005|0.006|13.757|0.436|33.272|0.716|0.048|-0.061|
||(0.056)|(0.045)|(0.045)|(15.147)|(1.056)|(31.946)|(0.581)|(0.074)|(0.044)|
|Family-Only Training (B)|0.038|-0.021|0.026|11.141|1.286|36.415|0.438|0.037|0.043|
||(0.055)|(0.046)|(0.045)|(15.022)|(1.116)|(36.226)|(0.551)|(0.074)|(0.047)|
|Migrant and Family Training (C)|0.102*|0.101**|0.115**|15.983|2.253**|74.324**|1.496**|0.186**|-0.089**|
||(0.056)|(0.050)|(0.050)|(14.512)|(1.016)|(37.006)|(0.641)|(0.075)|(0.045)|
|R-squared|0.015|0.019|0.034|0.006|0.014|0.023|0.009|0.013|0.015|
|Sample Size|1133|1133|1133|1133|1133|1133|1133|1133|1133|
|Number of Clusters|394|394|394|394|394|394|394|394|394|
|Mean of Dependent Variable in Control Group|0.397|0.324|0.369|59.303|6.566|75.246|1.894|-0.015|0.331|
|Test: A-B = 0 (p-value)|0.775|0.723|0.641|0.856|0.427|0.939|0.644|0.891|0.025|
|Test: A-C = 0 (p-value)|0.410|0.029|0.025|0.873|0.062|0.328|0.254|0.071|0.515|
|Test: B-C = 0 (p-value)|0.259|0.013|0.068|0.714|0.347|0.403|0.109|0.052|0.005|
|Test: A = B = C = 0(p-value)|0.337|0.065|0.085|0.721|0.113|0.214|0.130|0.078|0.021|

This table presents pooled regression analysis across three follow-up survey rounds for measures of savings and loans. Column (8) is a z-score of Columns 1,2,3,4, and 6. Separate regression analysis by follow-up survey round is presented in Appendix Table A6. All regressions include survey round dummies. Standard errors in parentheses, clustered at the individual level. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
closer to final demand, while the other countries move upstream. China’s move downstream can also be seen in columns 8 (M share), which shows a reduction in the intermediate share of China’s exports.

# **5.4.3 Reduced transaction costs in China**

The welfare effects of a 10% reduction in transaction costs in China are similar to those that were calculated for the Chinese productivity shock. China’s welfare rises by almost 10%. The welfare gains of the countries outside of China are, however, somewhat larger than in the case of the Chinese productivity shock. In contrast with the previous counterfactual, the shock to internal transaction costs moves China’s production upstream, as indicated by the movements in _D_<sup>_∗_</sup> , _D_ and _N_<sup>_∗_</sup> . Similarly, the increase in _N_ reflects greater fragmentation of production within China. Other countries also move upstream because falling Chinese transaction costs lead to longer chains. In broad terms, a move upstream by China is consistent with the evidence presented in Kee and Tang (2013), who find that Chinese exporters have been shifting their purchases of inputs from foreign to domestic sources.

Table 6: Counterfactual 3): 10% decrease in Chinese transaction costs _γi_

|(10 x change)|Welfare|DX|D|D*|N|N*|M share|VAX|
|---|---|---|---|---|---|---|---|---|
|**CHN**|0.953|1.609|3.983|0.033|3.490|-0.027|0.119|0.055|
|USA|0.026|0.341|0.098|0.025|0.143|0.016|0.030|0.008|
|SGP|0.050|0.187|0.166|0.016|0.116|-0.003|0.013|0.002|
|JPN|0.027|0.256|0.096|0.022|0.164|0.015|0.025|0.005|
|TWN|0.042|0.195|0.159|0.019|0.153|0.003|0.016|0.002|
|KOR|0.038|0.210|0.161|0.020|0.163|0.007|0.015|0.002|
|MYS|0.047|0.192|0.179|0.017|0.125|-0.001|0.013|0.002|
|THA|0.045|0.182|0.169|0.019|0.133|0.002|0.013|0.001|
|IDN|0.048|0.372|0.363|0.033|0.107|0.001|0.017|0.003|
|PHL|0.050|0.186|0.194|0.011|0.099|0.001|0.003|-0.001|

# **5.4.4 Reduced trade costs between China and the US**

As noted above and in Yi (2010), international fragmentation raises the elasticity of trade to trade costs. In order to explore the quantitative magnitude of this effect, we shock trade costs for a single country pair (US and China) and measure trade responses. We compare results in our model to those in a version of the same model, except that no trade in intermediates is allowed in the comparison model. Both models are calibrated in the same manner. We conduct one set of exercises using our baseline calibration, and one that does the same for a lower level 49
Policy ReseaRch WoRking PaPeR 4476

# **Abstract**

The authors use firm-level, cross-county data from Investment Climate surveys in 49 developing countries to investigate an important channel through which informality can affect productivity: access to credit and external finance. Informality is measured as selfreported lack of tax compliance in a sample of registered firms that also answered questions on a large set of other characteristics. The authors find that more tax compliance is significantly associated with more access to credit both in OLS and in country fixed effects estimates. In particular, the link between credit and formality is stronger in high-formality countries. This suggests that firms’ balance sheets are relatively more informative for financial institutions in environments where signal extraction is a less noisy process. The authors’ results are robust to the inclusion of a wide array of correlates and to two-stage estimation.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to examine the causes and consequences of informality. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at rgatti@worldbank.org and mhonorati@ 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
17 regressions therefore include a second political control, _Ruling coalition size_ . In addition, we show that our results are robust to controlling for two additional political variables. Because many new democracies choose presidential systems and are also known to exhibit larger political budget cycles, we show that parties continue to have a robust effect after controlling for _system_ , from the DPI, capturing whether a country is presidential, semi-parliamentary, or parliamentary. In addition, previous research has associated political budget cycles with political polarization (Alt and Lassen 2006a,b; Hanusch and Magleby, forthcoming). Controlling for a measure of polarization from the DPI has no effect on our results.

Finally, we replicate our results using all of Brender and Drazen’s (2005) original control variables, including the share of international trade as a percentage of GDP, and the fractions of the population aged 15-64 and above 65. Where our sample does not overlap with theirs, we obtain these variables from the WDI.

# **Expenditure timing**

Two important timing issues are relevant for empirical tests of political budget cycles. The first is matching the timing of expenditures, which are reported by fiscal year, with the timing of non-fiscal variables, which are reported by calendar year. The second is how to take into account the timing of elections within a year: when elections are held late in the fiscal year, electoral expenditures occur mostly in the same fiscal year; when elections are held early in the fiscal year, the electoral expenditures may occur mostly in the previous fiscal year.

We follow Brender and Drazen in addressing these issues, but test robustness to an alternative timing rule. They assign fiscal measures to the calendar year that overlaps the most with the fiscal year. For example, in the US the fiscal year 2011 runs from 1 October 2010 to 31 September 2011. Nine months of the fiscal year thus fall in the calendar year 2011. Accordingly, the fiscal data reported for fiscal year 2011 are matched with calendar year data from 2011. In the UK, the fiscal year 2010/2011
- 17 -

that correct for non-random selection into the PROBECAj program. The rows in this table corresnond to results for models estimated without corrections for the oversampling of trainees and models which include choice-based weights. The reported coefficients in Panel A and Panel C may be interpreted as the average percentage change in monthly earnings and hourly wage attributable to participation in the PROBECAT program; coefficients in Panel B are the mean changes in weekly hours worked attributable to participation in training.

First, consider the monthly earnings of the male sample. Recall that male trainees reported monthly earnings that, on average, were 7 percent higher than those of the control group. However, the unweighted model specification (i) without selectivity correction indicates that the monthly earnings of male trainees are 12 percent lower than that of the controls, suggesting that the two groups have very different attributes. In model (2), which corrects for selectivity bias (and these group differences), this earnings differential is reversed. The unweighted estimates now show that the monthly earnings of trainees are 17.7 percent higher, a difference that is statistically significant; however, the weighted results only show a training effect that is marginally significant.

The results also suggest that training is associated with increased labor supply for the male sample. In the raw data, trainees reported working about two hours more a week as compared to the controls. The hours regression without selectivity correction (model 1) reveals no significant differences in hours worked between the two groups. However, model (2) shows that trainees supply., on average, between 7.8 (unweighted) and 8.4 (weighted) more hours per week th-an the controls. In rhe hourly wage results, selectivity correction reduces or even reverses the negative effect of PROBECAT on hourly wage. The weighted results with selectivity correction (model 2) revealed no differences in hourly wage between the two groups. Together, these results suggest that, on average, training raises monthly earnings of male trainees through greater supply of hours worked per week, not through higher hourly wages.

A similar pattern of training effects is found for the women. In the aggregate data, women trainees received lower monthly earnings and hourly wages, but worked more hours per week, than the control group. In model (1), these program effects on earnings, hourly wage, and hours worked are generally statistically significant. In model (2), however, many of these differences disappear. The only statistically significant effect of PROBECAT is in the unweighted results for hours of work; here, the results suggest that women trainees work approximately six extra hours of work per week as compared to the control group.