rafmacalaba/datause-encoder-data huggingface dataset:
were generally conducted in environments where such a contract was embedded in enduring family and patron-client relationships or where effort was easily monitored.

Even within households, imperfections in land and labor markets, together with the inability to commit, may prevent individuals from achieving an optimal allocation of productive factors. For plot level panel data from Burkina Faso, Udry (1995) finds that reallocation of factors from male to female controlled plots within the same household could increase output by 6% - less than half of the estimated output loss from imperfect allocation of productive factors at the village level (13%), but still significant. One interpretation is that, by "renting" out land to their husband, women would risk losing these rights. In the absence of other assets that could be transferred from the husband to the wife to provide assurance, they fail to do so, despite the productivity increases that this might entail.

All this implies that, although land rental markets can not completely eliminate structural impediments and bring about a fully efficient allocation of land in an economy, they can go a long way in bringing the operational distribution of holdings closer to the optimum. However, in quite a few countries, the extent of land rental markets has been greatly diminished by large landowners reluctance to engage tenants due to concern for potential challenges to their property rights. Furthermore, rental markets' potential to increase overall welfare was not well understood by governments. Consequently, the static productivity loss entailed in sharecropping tended to induce interventions that have limited the extent of rental transactions, thus causing a larger inefficiency in resource allocation. We turn now to discuss these and other policy issues related to land markets.

# **4 POLICY ISSUES**

This section draws the main policy conclusions from our earlier discussion, focusing on clarification and adjudication of property rights, ways to improve the functioning of land sales and rental markets, and redistributive land reform. These three steps form somewhat of a sequence, in the sense that it is difficult to improve the functioning land sales or rental markets without clarification of land use and ownership rights, or to conduct non-expropriative land reform in an environment where land markets are absent. This implies that government activity should be focused on eliminating distortions and taking measures to reduce market imperfections rather than attempting to compensate for imperfections and distortions in other markets.

26
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25. These studies likewise corroborate ANed and H4ossaln's (1988) Initial<sup>evidence</sup> on labor market llnkages. In Haryana and the Punjab, Increased<sup>demand for agricultural</sup> labor has resulted In the highost farm wages In India. By raising the opportunity<sup>cost</sup> of labor In nonfarm pursuits, this has led to a decline In very low-return<sup>household</sup> manufacturlng and a parallel rlse In higher-return modern small factories and services.

26. Other tito-serles evidence comes from the moderately prosperous<sup>agricultural</sup> region of North Arcot. Using a sknulatlon model for the region, Hazell and<sup>Ramasamy</sup> (1989) have esthiated domand multipliers emanating from agricultural growth<sup>over the</sup> 1970s. They estimate that as a result of productlon and consumption linkages<sup>every</sup> 100 Rs. Increase In agricultural Income Induced an additional 82 Rs. In<sup>Income In other</sup> sectors of the rural economy. Production llnkages accounted for about half of the increase and consumption linkages the other half.

27. To date, cross-section comparisons across districts and states have produced similar, although less robust, correlations between agriculture and nonfarm activity. In part, this arises because so many Important factors other than agriculture<sup>vary</sup> across areas, and they also Influence the level of nonfarm activIty. Raw material availability varies across regions; consequently leatherworking industries predominate In Rajastan, while wood processing Is largest In well-forested states like Bihar (Papola, 1985). Moreover, trad;tlon, caste, historical accident,<sup>9</sup> and Indla's elaborate<sup>system</sup> of subsidies and policy protectlon for small and village industries<sup>1 0complicate cross-</sup> section comparisons.

9 Popola (19 6), **for example, describes one district** in Utter **Pradesh specializing In th,** production **of** plastic **jewelry** which **it supplies** to such of **the rest** of Indin. **Despite an absence of** local **raw materials** or **market, the activity flourishes,** for no **obvious reason. He surmiseo** that tradition or historical accident **must *xplain this puzzle. 11 See Singla** *t **al.** (1988) **for evidence** on tho connection between government support **and level of nonfarm** activity.
24 second stage of the analysis Balassa employod simple rank coefficients and cross country regressions. His main finding was that outward orientation, represented by a high growth rate of exports, had positive effects on output growth. Although it was backed by individual country studies Balassa's synthetic piece had some limitations that, in fact, have been present in most of the subsequent literature. Some of these limitations refer to the arbitrary definition of export incentives, the lack of a role for real exchange rates in the explanation of export performance and the inability to deal convincingly with causality issues.

The recent World Bank project directed by Choksi, Michaely and Papageorgiou is another ambitious multicountry study that has analyzed in great detail the liberalization experiences of 19 countries since World War II. Since this project has not been completed, it is not possible yet to discuss all its findings. However, some preliminary results have provided important information regarding the dynamics of trade liberalization. For example, Michaely (1988) reports that in only one of 36 liberalization episodes the trade reform resulted in an increase in the aggregate rate of unemployment. This indeed is a striking result that suggests that trade reforms do not have, even in the short run, a negative effect on output and employment. Also, this study confirms previous findings that indicate that consistently higher real exchange rates that support a liberalization process result in faster output growth. In addition to the generalized measurement problem, this study seems to have some limitations related to the lack of firm theoretical basis, as well as to uneven empirical investigations across countries.19 The World Development Reyort (1987) presents 19I say that it "seems" to have these limitations, because the final report has not yet been released.
# App. Table 1: Correlation between district traits and specialization/diversity values

||Spec|ialization in|dex|D|iversity ind|ex|
|---|---|---|---|---|---|---|
||Average|Initial|Change|Average|Initial|Change|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. District traits from 2001 Population Cens|us||||||
|Log population|0.023|-0.016|0.043|-0.118*|-0.111*|0.019|
|Log population density|0.148*|0.041|0.076|-0.207*|-0.131*|-0.041|
|Age profile (demographic dividend)|0.040|0.032|0.034|0.135*|0.035|0.075|
|Share of population in scheduled caste|0.028|0.071|-0.008|-0.011|0.009|-0.006|
|Share of population in scheduled tribe|-0.148*|-0.095|-0.041|0.106*|0.061|0.006|
|Female labor force participation rate|-0.207*|-0.100*|-0.051|0.197*|0.073|0.103*|
|Educated worker share (% pop graduate)|0.218*|0.079|0.093|-0.041|-0.126*|0.110*|
|Literacy rate|0.057|0.107*|-0.026|0.222*|0.070|0.135*|
|Infrastructure: electricity access|-0.110*|-0.115*|0.043|-0.119*|-0.009|-0.137*|
|Infrastructure: paved roads|0.085|0.089|-0.009|0.138*|-0.021|0.182*|
|Travel time to nearest of ten largest cities|-0.122*|-0.022|-0.069|-0.007|0.019|-0.018|
|Strength of household banking sector|0.091|0.050|0.077|0.068|0.024|0.067|
|Urbanization rate (% urban)|0.159*|0.075|0.042|-0.177*|-0.153*|0.000|
|Log average income per capita|0.124*|0.106*|0.044|0.217*|0.098*|0.100*|
|Manufacturing share of local employment|0.010|-0.013|0.059|-0.034|0.054|-0.099*|
|B. District traits for manufacturing sector fro|m ASI/NSS a|cross 1989-|2010||||
|Log growth in establishments|-0.157*|-0.231*|0.232*|-0.025|0.031|-0.043|
|Log growth in employment|0.073|-0.061|0.132*|-0.006|0.017|-0.036|
|Log growth in output|0.173*|0.156*|-0.084*|0.028|0.044|-0.042|
|Organized sector share of establishments|0.120*|0.211*|-0.224*|0.009|-0.082*|0.047*|
|Organized sector share of employment|0.161*|0.070|0.110*|0.146*|0.075|0.064|
|Organized sector share of output|0.392*|0.348*|-0.070|0.040|-0.067|0.120*|

Notes: Table documents univariate correlations between district traits and specialization and diversity index values. Average and initial index values are expressed on a ten-point scale representing deciles. Change values are expressed in unit standard deviations. District traits in Panel A are from the 2001 Population Census. District traits are expressed in log values or percentage point values as indicated. District traits in Panel B are calculated from the ASI/NSS data over the 1989-2010 period. An asterisk denotes a correlation is statistically significant at the 10% level.
America’s _NRD_ are not significant (column vi). These results confirm those obtained in columns (i) to (iii).

Column (vii) reports the effects of the _NRD_ from the three OECD regions simultaneously. The elasticity of TFP with respect to _NRD_ from Japan is .47, significant at the 5 percent level, while the elasticities of TFP with respect to _NRD_ from North America and from the EU are small and not significant. These results confirm those in columns (i) to (vi).

The same results obtain with one-tailed tests. The elasticities of _NRD_ from North America and from the EU continue to be non-significant, while the elasticity of _NRD_ from Japan is significant at the 2.5% level.

The results obtained are extremely robust since they provide identical results under three alternative specifications – i.e., regressions with one country, two countries, and with all three countries – as well as with two statistical tests of significance (onetailed and two-tailed tests).

# 4.2. Mexico

Similarly, the results in Table 3 give a consistent message across all three specifications and the two statistical tests. The elasticity of TFP with respect to the _NRD_ from North America varies between .55 and .61, significant at the 10 percent level (with one of them at the 13% level), while the elasticities with respect to _NRD_ from Japan and the EU are not significant.

> 8 Imports from Japan are 17% higher than those from the US, while their R&D content --weighted by trade shares --is 43% higher, implying a 22% higher R&D content.

8
Conversely, private investment in transportation and communications has maintained its level after its sharp increase in the mid-1980s and has been on the rise again in recent years. Private participation in the electricity sector is still quite limited.

Figure 5 depicts the time path of more recent investment for 2003-2007 by destination of five infrastructure sectors and by sector of origin. In particular, investment data in the three core infrastructure sectors (transportation, communications, and electricity) are presented separately, allowing a more precise assessment of their recent trends.<sup>25</sup> As shown in Panels (a) through (e) of Figure 5, the shares of investments in both water and Suez Canal are quite small compared to three core sectors. Investment in the water sector increased drastically in 2007, while that in Suez Canal has fallen since 2004. In additions, the figure reveals that public sector continues to play a major role in investment in electricity, water, and Suez Canal.<sup>26</sup> Repeating the findings in Figure 4, public investment in transportation, communications, and electricity has been falling, while a rise in private investment has become more obvious in transportation and communications in recent years. In particular, the private sector has become a dominant player in investment in the communications sector since 2005.

# **B. Cross-Country Comparison of Infrastructure Investment**

In this section, we compare the level of infrastructure investment in Egypt to that of other developing countries. Panels (a)–(c) of Figure 6 plot infrastructure investment (total, public, and private) as ratio to GDP for 2000-05 against GDP per capita in 2000 across countries.<sup>27</sup> Total and public infrastructure investment fall as the income level of the country increases, while the opposite happens for private investment.

> 25 In Appendix 4, we present disaggregated investment data for transportation (including investment in Suez Canal) and communications as ratio to GDP for 1983-2007 (see Figure 4-A).  These data has been made available only recently.

> 26 There is no private investment for water and Suez Canal. As for electricity, no private investment has been carried out after 2004.

> 27 Infrastructure investment data are obtained from Calderon, Odawara, and Serven (2008). Total investment consists of investment in three major infrastructure sectors: transport, telecommunications, and electricity.

33
NEPRA’s decisions must be made publicly available, whether they are binding or not. The regulator’s decisions are advisory regarding tariffs and binding on the other regulatory matters under its mandates. Where NEPRA plays a purely advisory role, its recommendations are required to be made publicly available, as well as the responses of the government body receiving the recommendations. Moreover, if the government body receiving the recommendations rejects or modifies them, it is required to provide a public explanation for doing so.

**Table 16: Formal regulatory governance in Pakistan and comparators, 2015**<sup>**46**</sup>

||**India, AP I **|**ndia, Odisha **|**India, Raj **|**Pakistan**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Regulatory Governance**|**68%**|**68%**|**68%**|**72%**|**59%**|
|**Accountability**|**91%**|**91%**|**91%**|**79%**|**83%**|
|_Regulatory Oversight_|_100%_|_100%_|_100%_|_67%_|81%|
|_Legal Appeals_|_100%_|_100%_|_100%_|_100%_|100%|
|_Transparency_|_73%_|_73%_|_73%_|_70%_|67%|
|**Autonomy**|**75%**|**75%**|**75%**|**92%**|**71%**|
|_Decision-Making Autonomy_|_100%_|_100%_|_100%_|_92%_|79%|
|_Budgetary Autonomy_|_50%_|_50%_|_50%_|_100%_|80%|
|_Leadership Autonomy_|_50%_|_50%_|_50%_|_75%_|66%|
|_Managerial Autonomy_|_100%_|_100%_|_100%_|_100%_|59%|

Source: Data collected for the Rethinking Power Sector Reform Project.

# _3.4.2.2 Autonomy_

NEPRA’s performance on regulatory autonomy at 92 percent is relatively high compared to Indian and wider international benchmarks (Table 16). This can be largely attributable to a greater degree of budgetary and leadership autonomy. NEPRA has the power to determine its own structure and allocation of budget while the Indian regulators do not. Also, NEPRA’s budget is entirely funded from levies and taxes while in India the regulator’s budget comes directly from the government budget.

NEPRA grants licenses for electricity generation, transmission, and distribution; determines tariffs, rates, charges, and other terms and conditions for supply of electric power services by the generation, transmission and distribution companies and recommends them to the federal government for notification. According to the NEPRA Act, it shall prescribe and enforce performance standards for generation, transmission and distribution companies; and prescribe procedures and standards for their investment programs.

For the regulator’s **autonomy** , it is reported that there are no formal provisions under which a ministry or other government body can overturn NEPRA’s decisions. Nevertheless, it should be noticed that the main substantial decisions – i.e. on tariffs, rates and charges — are subject to

> 46 For the detailed regulatory governance index, see the annex.

50
Policy Research Working Paper 7863

# **Abstract**

Controlling for bond and issuer characteristics, bond spreads are expected to be equal across different legal jurisdictions, and differences are expected to disappear through arbitrage. However, an analysis of 435 U.S. dollar–denominated bonds issued by 53 emerging market sovereigns during 1990–2015 reveals that after the financial crisis of 2008, the launch spread of sovereign bonds issued under U.K. law has been higher than those issued under U.S. law, by 130 basis points for BB+ bonds and 175 basis points for B- bonds. This effect was not significant for investment grade bonds. On average, bonds issued under U.K.

law had weaker ratings and shorter tenors post-crisis. The post-crisis impact of governing law on sovereign bond spreads is not explained by collective action clauses, or first-time bond issuances. Instead, the difference seems to be related to the perception that U.S. law offers stronger investor protection, and that the investor base for bonds issued under U.S. law is larger than that for bonds issued under U.K. law. The difference in spreads persists in the secondary market even after 180 days, perhaps because of the lack of liquidity, as investors tend to buy and hold these more attractive bonds on a longer term basis.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dratha@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
industry structures might be subject to similar general trends. The equivalence is achieved by including exposure-weighted averages as controls in model (3).<sup>32</sup> Nevertheless, in our setting, similar industry structures which are affected in similar ways, are not necessarily worrisome as mentioned in Section 4.<sup>33</sup> Column (1) in Table A.6 shows the results at the municipality level and using robust standard errors at the municipality level. Column (2) redoes the analysis at the industry level and uses robust standard errors at the exposure level. While our first stage F-Statistic stays significant, the significant level of our coefficient decreases; however, is still significant at the 10 % level. We acknowledge the fact that our coefficients differ slightly, however, we suspect that the difference arises due to the use of fixed effects in the panel data setting. Overall, we conclude that our instrument is still relevant at the industry level; however, the second-stage results have a lower significance level.

# **5.3 Heterogeneities**

We next conduct heterogeneity analyses to examine how differences in municipal and individual characteristics shape families’ exit dynamics in response to “rising tides”.

**Positive versus negative shifters** First, we are interested in the question of whether the positive or negative shifters drive our results. Panel A of Table 6 shows the results of splitting the data in those families that live in a municipality that experienced on average positive employment growth and those that experienced on average negative employment growth. Results are driven by positive shifters, causing an increase in the probability of leaving BF. While those results display an interesting heterogeneity, they also increase the credibility of our main question – if employment growth (instead of decrease) causes an increase in the likelihood of leaving the program.

**Informality** Second, we aim to disentangle the effect for areas with historically high informality rates and areas with historically low informality rates. We classify municipalities by using the census data from 2010 into municipalities that have informality rates above and below

> 32The STATA command _ssaggregate_ by Borusyak _et al._ (2022) is used. The command converts the shift-share IV dataset – in our case, at the municipality level – into a dataset of weighted shock-level aggregates. In other words, the outcome and treatment variables are regressed on exposure weights and a set of control variables to get to the industry-level aggregates.

> 33As outlined above we are not trying to isolate a specific factor that affects employment but rather look at general employment trends. This is confirmed by Adao _et al._ (2019), who find that in their application, the first stage differs less when looking at the application of Bartik (1991) since “the Bartik IV absorbs the bulk of the shift-share covariates that affect the change in the employment rate and wages across commuting zones.”

21