rafmacalaba/datause-encoder-data huggingface dataset:
"treated" after 2014. Forward-looking firms make entry, investment and hiring decisions, for example, with the goal of maximizing firm value, taking into account the entire flow of future after-tax profits, not only the present period. In that sense, we can consider this staggered adoption as treating all firms but with somewhat different intensities.

At the same time in 2014, the general corporate tax rate was lowered from 30% to 25%. Thus, half of the onshore-offshore rate differential was eliminated in 2014. This convergence process continued in 2019 when the tax rate for offshore firms was further increased to 13.5% and was finalized in 2021 when both the onshore and offshore tax rates were set to 15%. We do not include this further convergence period in the analysis for two reasons. First, it was arguably not as unexpected as the 2014 reform, which came on the heel of many years of deferral. Second, these additional reforms coincided with the Covid period, which would make the identification of impacts particularly challenging. It is also worth noting that onshore firms in a number of sectors faced a higher rate (35%) than the general rate throughout the period. These sectors, including for instance credit, insurance, oil refining, car dealership and hyper-markets, comprise a relatively small share of firms, which we exclude from the subsequent analysis.

According to data from Tunisia’s Ministry of Finance, the tax reform was associated with a reduction in corporate income tax revenues for the fiscal year 2014 (and collected in 2015). Overall tax revenues from non oil companies dropped by 28% to TD 1.6 billion dinars mainly driven by the rate reduction for onshores, which still represented the bulk of CIT collection.<sup>7</sup> Revenues from offshore represented around 4.4% of total CIT revenues, a share which eventually increased to 9.4% by 2018.

Importantly for our analysis, the aggregate data also suggest that the reform was not accompanied by other tax relief measures that limited the increased tax burden for offshores in the face of the rate increase. In fact the effective tax rate imposed on offshores (calculated as paid tax/taxable profit) hovered even slightly above the actual 10% rate in the period 2014-18 (11.8%-13.8%).<sup>8</sup> That is due to the fact that offshores can sell up

> 7This dominance of the onshore sector is due to two main reasons. First, during the period of analysis they are responsible for around three-quarters of total revenues. Second, as noted below, in 2014 only 20% of offshores were actually liable to pay taxes because of their age.

> 8This rate is computed by dividing the tax paid by offshore firms by their overall profits, both data

8
We could easily derive bootstrapped standard errors for the Gini index in order to account for sampling uncertainty (i.e. the fact that we have used a sample rather than the population). However, as Anand and Segal (2008) argue, these standard errors would not be appropriate, because they assume that there exists a single global household survey with a clearly defined sampling uncertainty. In contrast, we have combined a large number of national household surveys each of which has its own sampling uncertainty. As a result, plausible standard errors should probably be substantially bigger than the bootstrapped standard errors, making the observed changes insignificant.

As shown by Appendix Table 1, our estimates of the global Gini index are substantially greater than previous estimates in the literature.<sup>53</sup> The studies listed there differ fundamentally in their methodology, such as the use of national accounts aggregates, the type of PPP exchange rates and the interpolation for missing years. Most of the difference, however, is due to these studies’ using the “old” 1993-based PPP exchange rates which give substantially lower price levels for China, India, Indonesia, Bangladesh and several other Asian countries, and hence imply higher incomes in those relatively poor countries. The closest study to ours is Milanovic (2012) who also uses surveys only, and applies the 2005 PPP exchange rates. Our estimate of the global Gini coefficient is greater than Milanovic’s (2012), although the gap is falling over time, from 4.35 Gini points in 1988 to 1.76 Gini points in 2003. The direction of change between benchmark years is similar with the exception of the period between 1988 and 1993.

# **3.3** **<u>Alternative measures of inequality</u>**

We test for the robustness of these conclusions to different measures of inequality, such as the Generalised Entropy and Atkinson measures. The Gini index attaches a particular weight to inequality at different points along the income distribution. The Theil-L (or GE(0), or mean log deviation) index is particularly sensitive to differences in shares amongst low incomes, whereas the GE(2) index is sensitive to differentials at the top of the distribution (Cowell, 2009) and also sensitive to extreme values (Cowell and Flachaire, 2007). The Theil-T (or GE(1)) index is an intermediate case.

> 53 Appendix Table 1 draws on Milanovic (2002, 2005, 2012), Bourguignon (2012) and Anand and Segal (2008). Note that Anand and Segal (2008) erroneously refer to Milanovic (2005) as GE(0), when in fact it is GE(1). For Milanovic (2002, 2005), we use the full sample, because this is closest to our approach. The decomposition is only available for the sample of countries that is common across all the years.

26
Gates, Scott, 2002. “The Microfoundations of Rebellion.” Journal of Conflict Resolution 46(1):111130.

Gleditsch, Kristian Skrede & Kyle Beardsley, 2004. "Nosy Neighbors: Third-Party Actors in Central American Conflicts." _Journal of Conflict Resolution_ 46:379-402.

Gleditsch, Kristian Skrede, 2002. _All International Politics is Local: The Diffusion of Conflict, Integration, and Democratization._ Ann Arbor, MI: University of Michigan Press.

Gleditsch, Kristian Skrede & Michael D. Ward, 2001. "Measuring Space: A Minimum Distance Database and Applications to International Studies _." Journal of Peace Research_ 38:739-58.

Gleditsch, Kristian S. & Michael D. Ward, 2002. "Peace and War in Time and Space: The Role of Democratization _." International Studies Quarterly_ 44:1-29.

Hegre, Håvard, Tanja Ellingsen, Scott Gates & Nils Petter Gleditsch, 2001. Toward A Democratic Civil Peace? Democracy, Political Change, and Civil War 1816-1992.” _American Political Science Review_ 95(1):16-33.

Hegre, Håvard & Clionadh Raleigh, 2005. 'Population Size, Concentration, and Civil War: a <u>Geographically Disaggregated Analysis', presented at the Summer Meeting of the</u> _Polarization and Conflict Project_ , Konstanz, 2–5 June.

Huntington, Samuel, 1996. _The Clash of Civilizations and the Remaking of the World Order_ . New York, Touchstone.

Lake, David and David Rothchild, 1998. _The International Spread of Ethnic Conflict_ . Princeton, New Jersey, Princeton University Press.

Murdoch, J. and T. Sandler, 2002. “Economic Growth, Civil Wars, and Spatial Spillovers.” _Journal of Conflict Resolution_ 46(1): 91-110.

Mansfield, E.D. and J. Snyder, 2002. “Democratic Transitions, Institutional Strength, and War.” _International Organiza_ tion, Spring: 297-337.

Nkurunziza, Janvier and Floribert Ngaruko, 2002. "Explaining Growth in Burundi: 1960-2000.” _Centre for the Study of African Economies Working Paper Series. Working Paper 162._ O'Loughlin, J., M. D. Ward, et al., 1998. “The Diffusion of Democracy, 1946-1994.” _Annals, Association of American Geographers_ 88(4): 545-574.

Sachs, Jeffrey, 2005 _. The Poverty Trap_ . Penguin, New York.

30
|Exogenous<br>variables**|Upper‐case Latin letters with a bar*|_f_<br>_QFS_|
|---|---|---|
|Parameters**|Lower‐case Latin letters* or lower‐case Greek<br>letters(with or without superscripts)|,_c a_<br>_ica_ ;<br>_q_<br>_c_<br>|

*The names of Latin letter variables and parameters that refer to prices, quantities, and factor wages (rents) start with _P_ , _Q_ , and _WF_ , respectively.

**The distinction between exogenous variables and parameters is that the latter always have exogenous values whereas the former under alternative assumptions may be endogenous.

**Table A.1.2. Sets**

|**Name**|**Description**|**Elements**|
|---|---|---|
|_a_<br>_A_<br>|activities (production sectors or<br>industries)|act‐prv, act‐<br>gov|
|_c_<br>_C_<br>|commodities (i.e., goods and services)|com‐prv,<br>com‐gov|
|<br><br>_c CD_<br>_C_<br><br>|commodities with domestic sales of<br>domestic output|com‐prv,<br>com‐gov|
|<br><br>_c CE_<br>_C_<br><br>|exported commodities|com‐prv|
|<br><br>_c CM_<br>_C_<br><br>|imported commodities|com‐prv|
|<br><br>_c CT_<br>_C_<br><br>|transactions commodities (services paid<br>under distribution margins)||
|<br><br>_d_<br>_D_<br>_INSD_<br>_FCAP_<br>_A_<br>_TAC_<br><br><br><br>|domestic demanders (or demand types):<br>institutions (for consumption),<br>investment by capital type, activities,<br>transactions(distribution margins)|hhd, gov, f‐<br>cap, act‐<br>prv, act‐gov|
|_f F_<br>|factors|f‐lab, f‐cap,<br>f‐capg|
|<br><br>_f_<br>_FVA_<br>_F_<br><br>|factors that earn value added (in SAM)|f‐lab, f‐cap|
|<br><br>_f_<br>_FCAP_<br>_F_<br><br>|capital factors|f‐cap|
|<br><br>,<br>_f_<br>_FCAPG_<br>_FCAP_<br>_FVA_<br><br><br>|gov’t capital factors (do not earn value‐<br>added)|f‐capg|
|<br><br>,<br>_f_<br>_FCAPNG_<br>_FCAP_<br>_FVA_<br><br><br><br>|<sup>non‐gov’t capital factors (earn value‐</sup><br>added)|f‐cap|
|<br><br>_f_<br>_FLAB_<br>_FVA_<br><br>|labor factors (earn value‐added)|f‐lab|
|<br><br>,<br>,<br>_f_<br>_FOTH_<br>_FVA_<br>_FLAB_<br>_FCAP_<br><br><br><br>|other factors (earn value‐added; not<br>capital or labor)||
|<br><br>_f_<br>_FUEND_<br>_FVA_<br><br>|factors with endogenous unemployment<br>rate|f‐lab|
|_i_<br>_IN S_<br>|institutions|hhd, gov,<br>row|

‐31‐
Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data<sup>_∗_</sup>

**Javier E. Baez**<sup>_†_</sup> **Varun Kshirsagar**<sup>_‡_</sup> **Emmanuel Skoufias**<sup>§</sup>

# **JEL CLASSIFICATION** : I32, Q54, N57

**KEY WORDS** : Poverty, Child Welfare, Climate Change, Poverty Targeting, Social Protection, Child Malnourishment, Stunting

> _∗_ This paper was funded by the World Bank’s Poverty and Equity Global Practice under its global knowledge program "Welfare Implications of Climate Change, Fragility and Conflict Risks". We would like to thank John Baffes, Molly Brown, Frank Davenport, Gary Eilerts, Chris Funk, Madhur Gautam, Sharada Ramanathan, and especially David M. Johnson for invaluable conversations. In addition, we benefited from comments from participants at presentations at the World Bank and the 2019 American Agricultural Economics Association Meetings.

> _†_ Senior Economist at the World Bank. Email: jbaez@worldbank.org

> _‡_ Independent. Email: varun.kshirsagar@gmail.com

> §Lead Economist at the World Bank. Email: eskoufias@worldbank.org
criterion, by using iterated feasible generalized least squares (IFGLS).<sup>8</sup> . The model is estimated by imposing cross-equation restrictions such as symmetry, adding up and homogeneity constraints. Given that the share equations sum up to one, we dropped one equation to avoid singularity of the residual covariance matrix. Parameters of dropped equations are recovered through the homogeneity and symmetry constraints (Moschini and Rizzi, 2007; Poi, 2008).

The model is estimated assuming that the products with predetermined quantities, _Z_ , are the four ration products: brown wheat flour, rice, sugar and vegetable oil, representing 98% of ration expenditure. These are the quantity-rationed items, i.e., items for which only a certain quota can be purchased at the associated prices. For the X group of foods with predetermined price, we choose to include the free market counterparts of these rations. These are aggregated into four groups; wheat, sweets, rice and oil.<sup>9</sup> The model therefore includes 8 items representing, on average, 35% of food expenditure.

Total expenditure on the eight food items is used as a proxy of income, because measurement errors may be important with the latter, and because we concentrate on consumption of these eight items only [Löfgren and El-Said (2001)]. Prices of all commodities are computed as unit values by dividing the product expenditure on its consumed quantity. All prices are included in their logarithmic form. For the aggregate free market food groups, the share-weighted Stone formula is used to compute composite price indices: where ��� is the price index for the _I_<sup>_t_h</sup> food group ( _I_ =Free market wheat, Free market rice, Free market sweets and Free market oil), �� is the share of each product included in group _I_ from the total expenditure of _I_ , and _Pi_ is its price in logarithmic form.

Table 2 shows the average share of each ration product in the expenditure of the four ration items included in the model. Brown wheat flour has the highest expenditure share, in both urban and rural areas for all income quintiles, followed by vegetable oil, sugar and rice. The ration expenditure share is lower in urban areas than rural areas, which is natural given the lower welfare levels and consequently higher rates of poverty in rural Iraq. And as expected, the rationed products are mainly consumed by the two lowest expenditure quintiles.

Given that consumption is very heterogeneous across commodity groups, we followed Ramadan and Thomas (2011) to solve the issue of frequent zero expenditures for some food items in the following way. We estimate

> 8 The model is estimated using the nlsur command in STATA 13.

> 9 For more details about the items included in each group, see Appendix I.

10
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# The effect of weather-induced internal migration on local

labor markets. Evidence from Uganda.<sup>_∗_</sup> Eric Strobl<sup>_†_</sup> Marie-Anne Valfort<sup>_‡_</sup> September 9, 2013 _Keywords_ : weather shocks, internal migration, labor market, Sub-Saharan Africa.

_Sector Boards_ : Transport (TRAN); Environment (ENV).

_JEL_ : E24, J21, J61, Q54, R23.

> _∗_ The research for this article was financed by the Agence Nationale de Recherche [Grant ANR-JCJC-012701]. The authors thank two anonymous referees, Gani Aldashev, Jean-Marie Baland, Milo Bianchi, Pierre Cahuc, Guilhem Cassan, Marie Gachassin, Catherine Guirkinger, Mathilde Maurel, Katrin Millock, JeanPhilippe Platteau, Maelys de la Rupelle, Fran ̧cois Rycx, Ilan Tojerow, Philip Verwimp, and participants at the DIAL Development Conference “Shocks in developing countries” (Paris, 2011), at the Research Seminar in Applied Economics and Management (Universit ́e Libre de Bruxelles), at the Applied Economics Seminar (University of Rennes 1), and at the Economics Seminar (University of Namur) for their generous guidance and helpful comments.

> _†_ Ecole Polytechnique Paris and IPAG Research Lab. Department of Economics. Route de Saclay. 91128 Palaiseau Cedex. E-mail: eric.strobl@polytechnique.edu.

> _‡_ Paris School of Economics - Paris 1 Panth ́eon Sorbonne University. 106-112, Boulevard de l’Hˆopital. 75013 Paris. France. E-mail: marie-anne.valfort@univ-paris1.fr. Corresponding author.

1
# **4.1 Impact of the Asian crisis on the financial structure of utilities**

Table 8 presents the results for the balanced sample with regard to the average leverage levels in the three utility sectors for the 5-year period following the Asian crisis.

**Table 8: Average leverage by sector, balanced data (%)**

||**Leverag**|**e 1 – Market**<br>|**based**|**Leverage**|**2 – Accountin**<br>|**g based**|
|---|---|---|---|---|---|---|
|**Year**|**Electricity**|**Gas**|**Water**|**Electricity**|**Gas**|**Water**|
|1997|22.2|13.4|7.5|19.2|25.0|41.9|
|1998|41.3|20.8|24.4|24.6|28.5|36.4|
|1999|35.6|26.5|21.6|23.2|32.5|40.0|
|2000|42.7|31.6|19.2|25.9|27.3|27.6|
|2001|39.6|30.2|26.0|28.4|32.9|17.8|
|2002|42.9|27.4|48.6|34.5|29.1|8.7|

Source: Datastream and authors’ calculations.

According to the market valuation of assets, there appears to be a clear upward trend in leverage ratios in all sectors. As expected, the acceleration started right after the 1997 crisis as companies saw the real market value of their equity drop. Moreover, for many of the companies, the debt level did not change, or actually increased, in local currency terms, because, for many of the operators, the debt is often contracted in foreign currency. After a 2- year slowdown in the acceleration, the debt/equity ratio has again started increasing following the stock market bust in 2000. The highest leverages are still observed in the electricity sector, and the lowest for the water sector, suggesting that lenders perceive risk to be lower in the energy sector than in the water sector in developing countries. For the electricity sector the results from the balanced sample closely resemble those based on all identified data reported earlier. The average leverage(1) has increased significantly from just above 20% in 1997 to above 40 % in 2002.

The book valuation tells a slightly different story. For electricity and gas, leverage(2) does not exhibit an increase similar to that revealed by the market valuation leverage. It stays virtually constant between 20% and 30% after the 1997 Asian crisis, with only a slight upward trend. For water companies, however, there is a marked difference between the two leverage ratios. Leverage(1) starts from an average level of below 10% and gradually increases over the period to almost 50% in 2002. In contrast, the average level of leverage(2) for water companies was close to 50% in the beginning of the period, and has declined substantially over the period to about 10% in 2002. For this sector, however, it is important to remember that the companies’ local equity valuation may have been driven up by a series of strategic changes in the size and management of the global parent companies with which they were associated. The sector is now controlled by four major players that have acquired many of their smaller competitors during this period—i.e. many of the smaller Spanish water companies that were key players in Latin America in the earlier years of privatization have been acquired by the two largest French companies. Also, the equity appreciation of these 12
Table 7: World Bank (1987) Measure of Trade Orientation and Relative Price Dispersion Within Income Categories

|es<br>-|_|iRelati<br>Price|ve<br>Dispersion|l|
|---|---|---|---|
|World Bank Measure|| Low<br>||Medium|| High|
|<br>Outward Oriented|<br>|<br>Korea'||
|Moderately<br>Outward Oriented||Brazil<br>ChileA<br>Uruguay|Israel|
|Moderately|Columbian|Guatemala|Honduras*|
|<br>Inward Oriented|Costa Rica<br>El Salvador<br>Pakistan|Philippians|Indonesia<br>Sri LankaA|
|Inward Oriented|Argentina'<br>Doniinican|Peru,"|Bolivia|

* iniicates high average price level

A indicates high relative equipment prices might approximate trade intervention more closely.

# **6.1** administrative measures and relative price dispersion

If average tariff measures and NTB coverage ratios characterized the effects of intervention, we might expect to see them correlated with relative price dispersion in the traded sector. They're not<sup>12</sup> . The rank correlation between NTB coverage ratios and price dispersion is low (13% for middle income countries and 4% for low income countries; see Table 5), but at least it is positive. The same is not true for tariffs; high average tariffs are seen in countries having relatively low levels of price dispersion. This suggests that average tariff levels are a poor indicator of the effects of intervention. As Helleiner points out "moving towards neutrality (by reducing tariff)...is not evidently "liberal" if it is accompanied by increasing overall dispersion of incentives." (p.884)

> 12 . Itl oilgih t e Iiieasire, a rt roIIect (-d bi I N( T 'LD for **t** lf !-ar 1X1< o **oI COiijteietPn inI** t ra(le polic regime over timiee **ix** expected for a majorit.% of **comIIit** rif

27
27 compromises the performance of meaningful backtesting. It will then be natural for banks to respond by reducing their effective capital costs. This can be done through the increase in their multiperiod risk relative to their daily VARs - for instance by increasing the use of option securities with nonlinear payoff - thereby gaming the regulations once again. Alternatively, they can simply choose to forego the development of their own internal models in favor of the BBA".

Fourthly, the VAR concept itself focuses solely on the probability of losses greater than a specified amount but totally ignores how large those losses are expected to be when they occur<sup>49</sup> . Although the fixed VAR multiplier can be thought of as providing an additional layer of prudence, designed to account for the extent of maximum losses, as well as for possible market illiquidity and for leptokurtotic distributions of financial returns, the multiplier only addresses the average-loss distribution. This might not be so bad in the sense that the regulators now clearly delineate a situation of extreme financial stress, beyond which they can be expected to intervene. However, the system is open to gaming since banks can invest in projects that trade slightly higher expected returns for larger, though no more likely, potential losses.

Fifth, adjustments for conservatism are reflected in many of the BIMA's quantitative constraints, even though not all VAR measurement shortcomings err on the non-conservative side. This does not lead to a transparent risk measurement. Banks<sup>5 0</sup> have argued that, if regulators want to add conservatism, they should it with other means for the 1987 stock market crash, the 1990 Gulf War, the 1992 ERM crisis, and the 1994 bond market decline.

4B This concern is already present in the _Financial Times_ survey of derivatives (16 November 1995): "The reality for most UK banks is that the more traditional 'building block' approach... will ultimately require a smaller capital charge and therefore prove a cheaper alternative." (article by Richard Irving on shockabsorbing models). It is also confirmed in work done by members of the London investment Banking Association (LIBA) and of the ISDA.

49 Boudoukh, Richardson and Whitelaw, "Expect the Worst" in _Risk_ magazine, Vol. 8, No. 9 (September 1995), suggest a measure related to stress testing, designed to measure maximum losses over a holding period, called 'worst-case scenario' analysis.

50 See _J. P. Morgan_ (2 August 1995).
# Does Climbing the Jobs Ladder Promote Poverty Reduction?<sup>1</sup>

Yunji Choi<sup>*</sup> Mario Maddalena Joshua D. David Newhouse<sup>+</sup> Gronert<sup>†</sup> Honorati<sup>†</sup> Merfeld<sup>‡</sup>

## **Keywords:** jobs, growth, employment, poverty, education, skills, informality. **JEL codes:** I31, J21, J46, L16, O10, O50.

> 1 The paper is a background paper to _A Jobs Lens to Development, Navigating Old and New Employment Challenges_ analytical work program developed by the Jobs Group at the Social Protection and Jobs Global Practice. The program was funded by the Jobs Multi-Donor Trust Fund (MDTF) and managed by the Jobs Group. This work does not necessarily reflect the views of WBG and the MDTF donors. The authors would like to thank Wendy Cunningham and Nistha Sinha for helpful peer review comments; Dino Merotto, Federica Saliola, Markus Poschke and Andreas Eberhard for useful suggestions and comments, and Luis Diego Rojas Alvarado for the initial data analysis.

- Hong Kong University of Science and Technology

- World Bank

- World Bank (mhonora�@worldbank.org)

- KDI School and IZA

- + World Bank and IZA