rafmacalaba/datause-encoder-data huggingface dataset:
## - 36 -

# TABLE 19: PATTERII OF EXTERNAL TRADE FOR CAPITAL GOODS (as X of Dmomtic Output)

||1978|179|1980|/81|1984|185|19871|88|
|---|---|---|---|---|---|---|---|---|
|I|mport|EZDort|Imaort|Ezvort|Imvort|ExPort|ImDort|Export|
|Non-Electrical<br>Machin.|27.5|6.2|30.3|5.9|34.2|5.6|22.3|3.9|
|Machine-Tools|43.2|13.3|35.8|8.4|28.2|4.0|17.5|5.8|
|Electrical<br>Machinery|<br>9.4|6.3|4.5|2.1|6.9|<br>1.8|7.1|<br>1.2|
|General<br>Machinery|81.7|14.8|44.4|<br>0|25.6|<br>0|31.1|<br>5.5|
|Transport<br>Equipment|3.7|1.6|8.6|2.8|6.5|<br>1.0|12.8|<br>1.2|
|TOTAL Capital Goods|17.1|5.4|17.4|4.0|20.5|<br>3.6|14.9/|a 2.5|

/a Excluding Project Importp. If included, this ratio would increase to 24.92.

In conclusion, India increased its self-reliance in capital goods by increasing its protection, but it lost the advantages of specialization and most of its export potential.

71. Nominal and Effective Protection. Price comparisons (NPRs) for capital goods and machinery indicate that the tariff levels exceed what would be required to provide protection to the domestic industry.17/ Tables 20 and 21 show the nominal protection rates for a sample of 60 capital goods.

# TABLE 20s NOMINAL PROTECTION RATES (NPRs) FOR CAPITAL GOODS/a

|Product Group|XSample|Overall|1989|1987|
|---|---|---|---|---|
|Non-Electrical<br>Machinery|54|180|197|172|
|Machine-Tools|24|157|||
|Electrical<br>Industrial<br>Machinery|<br>14|135|||
|Electrical<br>Cables/Wires|3|120|||
|Small Telecommanications<br>Equip|5|121|||
|<br>Overall (1t60 NPRs)|100|163|||

# /a Plain averages.

17/ The samples are biased towards non-electrical machinery.
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challenge is of fundamental importance to India’s economic growth imperatives and its efforts to raise its level of human development. Within all of this, policy makers must balance important objectives like limiting pollution and environmental degradation with the expansion of energy resources to producers and consumers.<sup>12</sup> Economic growth often drives energy consumption in many developing countries, but there is variation. Within the South Asian region, annual data for Pakistan during the period 1960-2008 connects growth in output in commercial, manufacturing, and agricultural sectors to increased electricity consumption (Jamil and Ahmad, 2010; Asghar, 2008). Similar are the cases of Sri Lanka and Bangladesh, where growth has given way to larger electricity consumption and total energy consumption (Asghar, 2008). For India, however, Asghar (2008) does not find a causal connection between growth and energy consumption at an aggregate level. To the extent that this difference from India’s neighbors is confirmed, interpretations remain tricky (e.g., growth of consumption could be constrained by a weaker ability to expand supply).

In general, energy consumption has been critical for paving the way for economic growth and especially so for high-income countries (Chontanawat et al., 2006; Borozan, 2013). Recent evidence, however, suggests the existence of a unidirectional causality running from electricity consumption to economic growth in the case of developing countries like Bangladesh (Masuduzzaman, 2013), Brazil (Pao and Fu, 2013) and Cameroon (Fondja Wandji, 2013) . In cases where there have been no association or causality from energy consumption to economic growth, some observers of the industry attribute it to a lack of efficient planning and increased investment in electricity infrastructure development (see Akpan and Akpan, 2012, for the case of Nigeria), as highlighted above. In sharp contrast is the case of China, where Zhang and Yang (2013) find the existence of a negative bi-directional Granger causality running from aggregated energy consumption to real GDP. They propose that this negative relationship might be attributed to the growing economy production shifting towards less-energy-intensive sectors and excessive energy consumption in relatively unproductive sectors. For the specific case of India, the disaggregated causality analysis in Abbas and Choudhury (2013) suggests a bi-directional causality between the agricultural electricity consumption and the agricultural GDP in India.

> 12 World Bank (2013) provides a detailed timeline of energy policy and major legislation for India. Gillingham et al. (2009) provide a comprehensive conceptual background on energy efficiency considerations in policy.

11
|**Table 10. The impact of petroleum subsidy reform and cash transfer on poverty and inequality**|
|---|

||Poverty<br>level, %|Change in<br>poverty, pp|Poverty<br>gap, %|Change in<br>poverty<br>gap, pp|Gini<br>coefficient|Change in<br>Gini<br>coefficient,<br>%|
|---|---|---|---|---|---|---|
|Pre-reform|13||2.4||33.66||
|Post-reform: no cash<br>transfers|14.6|1.6|2.9|0.4|33.94|0.8|
|Post-reform: cash transfer<br>perfectly targeted|12.4|-0.6|2.2|-0.2|33.08|-1.7|
|Source:Authors’calculation|s based onex|trapolatedHEIS|2010 data.||||

The impact of the petroleum products reform on government revenues Removing subsidies on petroleum products without compensation would generate an increase in government revenues by JD 389 million per year (table 11). More than 70 percent of the increased revenues come from higher LPG prices and 20 percent come from gasoline. Higher revenues from LPG are associated with the much higher increase in prices for LPG compared with that for gasoline (135 percent versus 14 percent). The removal of kerosene and diesel subsidies will generate only modest increase in revenues. As the subsidies were pro-rich in nature, with their removal, richer households would contribute proportionally more to the increased revenues: the poorest quintile accounts only for an estimated 11 percent of the increase in revenues, compared to 35 percent by the top quintile.

|**Table 11: The impact of petroleum subs**<br>|**idy eliminatio**<br>|**n on the governm**<br>|**ent revenue, m**<br>|**ln. JD**<br>|
|---|---|---|---|---|
|kerosene|LPG|Gasoline|diesel|Total|
|Quintile 1<br>2|37|3|0|42|
|Quintile 2<br>3|45|8|0|56|
|Quintile 3<br>4|51|13|0|68|
|Quintile 4<br>4|61|20|1|86|
|Quintile 5<br>5|85|36|12|137|
|Total<br>18|279|80|13|389|

Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Notes:</u> Population quintiles <u>based on consumption per capita before the reform.</u> The cost of the cash transfer program launched by the government was about JD320 million per year. This was in fact higher than the revenues generated to the government _from households_ from the actual reforms the government had carried out in November 2012. Though additional savings to the government were generated from consumers other than households, the cash transfer program appeared costly in the sense that it over-compensated a majority (almost 70 percent) of Jordanian households. (Araar et al 2013). The reform option we simulated in this section estimates the revenues/cost savings generated from households’ use of petroleum products (JD 389 mln.) to be higher than the cash transfer cost but still appears to be quite generous as it over-compensates almost half the population. To put matters in perspective, only 206 million of Jordanian dinars are needed to have the pre-reform poverty rate if transfers are universal. If transfers are perfectly targeted to the bottom quintile, only JD 41 million would be needed to bring poverty to its pre12
Croke et al. (2016) of the measured magnitude or assuming that true effects are considerably less than the measured effects (Ahuja et al. 2015).

While TMSDG estimate positive impacts of deworming treatment on weight of children known to be infected, they argue in contrast that there is “substantial evidence” that mass deworming does not improve weight or other child outcomes. Note that this is a considerably different -- and far more demanding -- statement than a claim that the null hypothesis of no effect cannot be rejected (for instance, due to a lack of statistical power), or that MDA has a positive impact in some settings but not others.

TMSDG has been controversial. Campbell et al. (2016) note substantial changes over time in the way the meta-analysis in the Cochrane Review on deworming is presented (see the evolution across Dickson et al. (2000), Dickson et al. (2007), Taylor-Robinson et al. (2012), and Taylor-Robinson et al. (2015)). Campbell et al. (2016) also note that although the text refers to a protocol (Taylor-Robinson et al., 2015, p.29), they were unable to find a publicly available pre-specified protocol for the updated Review, leading to confusion over the precise hypothesis being tested, uneasiness over how studies are grouped for analysis, and concern about which studies are included versus excluded. A number of authors (Campbell et al., 2016; De Silva et al., 2015; Montresor et al., 2015; Michael et al., 2000) have expressed concern over lack of consideration given to the effects of different STH species, treatments, and drug distribution strategies.

# **2.1 Is TMSDG adequately powered?**

A simple calculation suggests that the TMSDG analysis is underpowered to rule out the possibility of effects that would make mass drug administration cost effective relative to 11
# **Do Politically Connected Firms Innovate, Contributing to Long-Term Economic Growth?**

## **David Francis**<sup>**a**</sup> **Sahar Hussain**<sup>**b**</sup> **Marc Schiffbauer**<sup>**c**</sup>

**JEL Classification codes** : O33, O47, D72, D24 **Keywords:** innovation, political connections, cronyism, productivity, firm-level data, Egypt.

> a David Francis. The World Bank. Email: dfrancis@ifc.org.

> b Sahar Husain. The World Bank. Email: shussain6@worldbank.org.

> c Marc Schiffbauer. The World Bank. Email: mschiffbauer@worldbank.org.
**Table 3: The Deterninants of Tax Effort. 1983-92**

||nInE|eInE|lInE|4InE|
|---|---|---|---|---|
|constant|-0.508<br>|-0.338|-0.296|-0.152|
||(-2.75)|(-3.00)|(-2.66)|(-2.10)|
|**in^(.1)**|**1.965**|0.815|0.687|1.465|
||_(5.50)_|(3.12)|(2.74)|(4.93)|
|Growth(-I)|1.378|0.867||1.43c|
||(1.75)|(1.82)||(1.67)|
|AInF(-1)<br>|||-0.002<br>(-1.24)||
|0lnM(-1)||||-0.264|
|||||(-1.34)|
|POL|0.006|0.005|0.005||
||_(1.55)_|(2.37)|(2.17)||
|DUMI|0.357<br>|0.251|0.237||
||(2.55)|(2,96)|(2.74)||
|DUM2||1.856|1.887||
|||(7.65)|(7.61)||
|adjR'<br>|0.518<br>|0.833|0.824|0.439|
|DW|2.67|2.31|2.47|2.40|

_w#aere iInE is the percentage clhange in total tax effort, infl(-l) lagged domestic inflation. Growth(-4) lagged GDP groAT/h, nF(-J) is changes net externalfinancing, f1nM(-I) is changes in MI, POL tihe number of months betwveen changes in Minister of Economy, DUMI a dummy variable for the period of the Convertibility, and DUM2 a dummy variablefor the third quarter of 1989 capturing the clange of Administration._ Table **4:** Tax Administration, Indicators of Collection Effort (1987-92)

||1987|1988|1989|1990|1?W|1992a/|
|---|---|---|---|---|---|---|
|Collection Orders|9,668|15,137|15,841|13,089|69,548|48,708|
|Control<br>|||||||
|Closures<br>|0|0|0|751|8,157|32,000|
|Effective Closures<br>|0|0|0|n.a.|5,466|17,184|
|Fines|n.a.|n.a.|n.a.|n.a.|40,933|nia.|
|Audits and Indictments<br>|||||||
|Indictments Initiated<br>|20,903|33.770|25,286|39,138|179,452|n.a.|
|Indictments Pending<br>|n.a.|n.a.|n.a.|n.a.|116,084|197,332|
|Internal and External Audits<br>|25,955|21,310|13.864|20,845|4i,313|n.a.|
|Preventive Audits|n.a.|40,475|38,483|119,969|228,821|n.a.|

**A/** Estimates based on first quarter of 1992 onLy. Source: **DGI** and WorLd Bank staff estimates.
fully in the labor force. Between 1970 and 1979, we observed a weaker negative impact of the labor participation rate, with an estimated coefficient of -4.66pp, suggesting that the negative impact of fertility on labor participation was still present but to a lesser degree than in the previous decade. Between 1980 and 1989, this impact further weakened, with an estimated coefficient of -2.52pp. That way, the impact of fertility on labor participation continued to diminish over time. In Colombia, the negative association between fertility and female labor force participation follows the same pattern found in high-income countries during the 1960s-1980s. This relationship has been explained by the quantity-quality trade-off and the time cost of raising children. It has historically resulted in a strong negative association between female labor force participation and fertility (De la Croix and Doepke, 2003, Galor and Weil, 1996).

**Figure7: Effect of the fertility rate on the female labor force participation rate**

<!-- Start of picture text --> 10.0<br>5.0<br>0.0<br>-5.0<br>-10.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Fertility rate<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

During the first few decades studied, married women and women with children faced significant obstacles to joining the labor market. As a result, any reduction in fertility rates during this period was associated with a larger increase in labor force participation rates for women. From 1960 to 1985, Colombia underwent one of the most rapid declines in fertility worldwide, with the number of children per woman decreasing from 7 to 3 (Jaramillo-Echeverri, 2023; Iregui-Bohórquez, et al., 2021). However, since the 1990s, there has been a marked increase in the labor force participation rates of married women and women with children, reaching between 40% and 50% for this population segment. Therefore, from 1990 to 1999, the coefficient of 1.04pp suggests a positive effect of fertility on labor force participation, within the range of fertility rates observed during this period, as fertility rates were relatively low (with an average of approximately three children per woman).

13
- 38 -

Prasnikar, J., Svejnar, J., M1ialjek, D., and V. Prcanikar, A Test of Enterprise Behavior under Labor-Hanagemunt University of Pitteburgh, Dept. of EconOmic6, 1990, processed Prasnikar, J3. and J. Svejnar, Workers' Participation in Management. Yugoslav Lessons for Transfoming Socialist Economies, University of Pittsburgh, Dopt. of Economics, 19909 processed Spinnevyn, F., and J. Svejnar, Optimal Membership, Employment, and Income Distribution in Unionized and Labor-Managed Firms, Journal of_Labor Econoamic. 1990, 8,39 pp317.340 Spinnevyn, F., and J. Svejnar, On the Dynmics of a Participatory Firm: A Model of Employer-Worker Bargaining, in R. Quandt9(ed), Mfieromodels for Planning #ad Makeats. 1990, Westview Press, Boulder Vek, **J,** 9The enal Theo of Labor-anaed arklet Economige, Ithaca, Cornell University Presse, 1970 Vickersg, J. Signalling in a model of monetary policy vith incomplete information, Oxford Ecosnoi Palers. 38, 1986, pp443-455 Ward, B., The Firm in Illyrias Market Syndicalism, Aerlican Economic Review, 48, 1958, pp<sup>5 6</sup> 6-<sup>5 8 9</sup> World Bank, Hungary: Reform of the Social Policy and Distribution, Country Operations Division, EMNEA, February 8, 1991, processed
B.5), among others. We also verify that cases of reporting rule violations documented by the IMF Board align with the debt data revisions in our dataset (Section B.6).

**Limitations:** We emphasize two distinct limitations to our measure. First, loans that are initially missing from the IDS may have been reported in other debt databases. Such instances would still constitute a violation of World Bank reporting requirements but would imply less secrecy. Similarly, revelations within the IDS may follow revelations through other sources with a lag (see, e.g., the Mozambique case study in Section 2, where the hidden loans were added to the IDS after being revealed by an international audit). This issue warrants caution in regard to treating our debt data revisions as pure “news shocks” (Arezki et al., 2017).

Finally, our measure of hidden debt is a lower bound for the true level of unreported or hidden debt. Our revision-based measure of hidden debt captures only instances in which initially unreported debt is revealed at a later point in time. By definition, our measure does not capture the possibly large amount of unreported debt that remains unreported and that is never incorporated into debt statistics.

# **4 Main empirical findings**

In this section, we present the key statistical properties of debt data revisions and discuss the magnitude, characteristics and timing of hidden debt and its revelation. Our analysis reveals three key stylized facts about hidden debt.

- **Stylized fact 1: The size and characteristics of hidden debt** Hidden debt is large and common. Debt data revisions are systematically upward biased with a statistically significant mean revision of approximately 1% of GDP. Under-reporting of debt has occurred persistently across all decades, debtor regions and income groups, particularly in countries with weak institutions. On the creditor side, under-reporting is most severe for bilateral and non-bond commercial lending.

- **Stylized fact 2: The timing of hidden debt revelations** Hidden debt tends to be accumulated during boom years and is revealed during bad years, often in the context of IMF programs and external sovereign defaults.

- **Stylized fact 3: Hidden debt and sovereign defaults** During default episodes, hidden debt is associated with larger creditor losses (haircuts) and longer default spells.

The following subsections discuss in turn each of these findings.

## **4.1 Hidden debt is large and common**

Figure 5 shows the distribution of hidden debt, as measured by data revisions to external public and publicly guaranteed debt across all vintages, years, and countries in our database.

13
Table N7: SNAP participation and effect of SNAP-benefit changes on prices by product department, grocery stores

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Product department|Health & Beauty Care|Food|Non-food Grocery|Alcohol|General Merchandise|
|VARIABLES|||Log price index|||
|_Baseline_||||||
|Log benefts p.p.|0.0747**|0.0741***|0.163***|0.0910|-0.0116|
||(0.0357)|(0.0267)|(0.0451)|(0.0544)|(0.0286)|
|_Interaction_||||||
|Log benefts p.p.|0.0660**|0.0667**|0.148***|0.0893|-0.0169|
||(0.0326)|(0.0277)|(0.0453)|(0.0537)|(0.0279)|
|x Participation rate|0.140***|0.117***|0.247***|0.0260|0.0857**|
||(0.0481)|(0.0397)|(0.0688)|(0.0728)|(0.0372)|
|Observations|381612|382140|381900|364044|380988|
|Number of units|7951|7962|7957|7585|7938|
|Number of clusters|48|48|48|48|48|
|Revenue share|0.0512|0.768|0.0949|0.0653|0.0206|

Notes: Robust standard errors are in parentheses, clustered at the state level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Controls refer to the full set of controls including log housing prices, log unemployment rate, log employment to population ratio, log population, log average wage, poverty rates at both the state and county levels, as well as log SNAP participants at the state level. Store and period fixed effects are also included. Log benefits p.p. refers to log benefits per population. Participation refers to the ratio of SNAP participants to population in each county fixed to the pre-period of 2006.

77
<!-- Start of picture text --> Regression-based<br>GDP-Employment Elasticities<br>35<br>30<br>25<br>20<br>15<br>10<br>;5 |<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text -->

<!-- Start of picture text --> Regression-based<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>5<br>:Ue L |Beas<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text -->

<!-- Start of picture text --> Regression-based<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>5<br>:© LJTT<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text --> <!-- Start of picture text -->
1.00<br>m LICs m LMICs m UMICs<br>0.80<br>0.60<br>0.40<br>0.00 [J=a a<br>aewe\ <0"ar 59,yo" i.we\ 0"en? so,0% anwe\ cO'en? SO.0%<br>po’ of ges pros oe ges” pros ve ged<br>Manufacturing Services Agriculture<br><!-- End of picture text -->
The transformation of any economy requires significant labor reallocations. Experience clearly shows that growth in labor productivity, within and between sectors, typically requires the elimination of some jobs and the creation of new ones. The Korean plywood industry is a case in point. Employment in Korea's manufacturing sector grew from 771,000 jobs in 1965 to almost 5 million in 1990. During this period, the manufacturing sector's share of total employment increased from 10 percent to 30 percent. However, this economic success was unevenly distributed across industries and often resulted in the destruction of jobs. Real wages grew rapidly during this period, inducing a productivity-enhancing transformation in plywood industry. Employment in the plywood industry expanded from 23,000 jobs to 32,000 jobs between 1970 and 1979 but then fell after 1980 ( see Figure 3).

**Figure 3: The rise and** fall **of the Korean plvwood industrv**

Source: UNIDO data, Rcpublic of Korea Surnex of Mining and Manufacturing, various vears.

<!-- Start of picture text --> 'Fhe rise and fall of one industry Real wages in manufacturing<br>Enmploymenit in plvwood (Korea) and in the plywood industry<br>3 S00, A .);<br>30(00<br>12(00-<br>200 0i':'<br>I iWA~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I(I<br><!-- End of picture text --> The Korean example is dramatic, yet it exemplifies the impact of competitive pressures on specific industries in all countries. Productivity-enhancing transformation is in part based on the capacity of entrepreneurs and resource owners --including workers-to leave low-productivity activities and move to higher-productivity ones. Yet, in a number of countries, including Indonesia, government policy protects workers from loosing their jobs, or makes it difficult for employers to dismiss workers. These labor laws slow down job destruction , but at the same time they discourage risk taking, and therefore slow down the job creation process in the formal sector.

Recent empirical work in developed and developing countries demonstrates the importance of flows in and out ofjobs in the labor market. Hamermesh (1993) examines 19