rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: Estimation results – net remittances**

|**VARIABLES**|**(22)**<br>**DVX LCX**<br>**Global**|**(23)**<br>**DVX**<br>**LCX SSA**|**(24)**<br>**FVA LCX**<br>**Global**|**(25)**<br>**FVA**<br>**LCX SSA**|**(26)**<br>**gvc_participation**<br>**LX Global**|**(27)**<br>**gvc_participation**<br>**LCX SSA**|
|---|---|---|---|---|---|---|
|log_distance|-0.817***|2.723***|-0.638***|3.409***|-0.113***|0.155|
||(0.169)|(0.645)|(0.192)|(0.660)|(0.0218)|(0.110)|
|log_GDP|0.926***|1.156***|0.743***|0.830***|-0.0422***|-0.0718***|
||(0.0365)|(0.107)|(0.0519)|(0.109)|(0.00797)|(0.0169)|
|LACEX_sk|2.318|19.12***|3.144**|22.09***|-0.0152|4.856**|
||(1.545)|(6.957)|(1.470)|(6.958)|(0.335)|(2.116)|
|LACEX_usk|-0.769|-4.491***|-0.380|-3.059**|0.0814|-0.512|
||(0.477)|(1.609)|(0.414)|(1.463)|(0.0972)|(0.429)|
|log_population|0.0554|-0.0543|0.0543|0.326***|0.0172**|0.0662***|
||(0.0431)|(0.124)|(0.0595)|(0.111)|(0.00816)|(0.0213)|
|HCI|4.212***|0.893|7.444***|9.201***|0.425***|0.549**|
||(0.513)|(1.712)|(0.669)|(1.848)|(0.0919)|(0.239)|
|net_remittances/GDP|0.436**|1.485*|1.854***|0.303|0.00830|-0.169|
||(0.172)|(0.808)|(0.280)|(0.944)|(0.0562)|(0.167)|
|FCS|0.00943|0.385**|-0.0314|0.430**|0.0257*|0.0103|
||(0.0403)|(0.150)|(0.0477)|(0.181)|(0.0153)|(0.0168)|
|non_oil_rich|0.481***|0.107|0.759***|0.563***|0.0996***|0.0969***|
||(0.169)|(0.173)|(0.199)|(0.190)|(0.0224)|(0.0302)|
|oil_rich|0.216|0.219|-0.547*|-0.450|0.0905**|0.101**|
||(0.242)|(0.353)|(0.318)|(0.379)|(0.0412)|(0.0509)|
|Constant|-9.541***|-43.60***|-9.167***|-52.72***|2.126***|-0.614|
||(1.622)|(5.881)|(1.901)|(6.218)|(0.220)|(0.973)|
|Observations|2,353|472|2,353|472|2,353|472|
|Number of panelid|875|195|875|195|875|195|
|Sector FE|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|
|||Robust<br>***|standard errors<br>p<0.01, ** p<0|in parentheses<br>.05, * p<0.1|||

For oil-rich countries, the effects are not statistically significant, which implies that oil-resource endowment does not necessarily help countries to engage more in manufacturing GVCs. This can partly be explained by the fact that oil-rich SSA countries are more engaged in mining/extractive GVCs than in manufacturing GVCs. In other words, natural resource endowments (both oil and non-oil resources) contribute marginally, at best, in comparison with human capital (skills and initial endowment) towards improving the engagement of SSA countries in backward and forward manufacturing GVCs. Lastly, average distance from the three major manufacturing hubs (Germany, the United States, and China) has a significant negative effect on both backward and 29
<!-- Start of picture text --> Figure 7a: Components of Adequate Environment<br>Components of Adequate Environment<br>93<br>84<br>81 81<br>79<br>75 76 75 76<br>65 64<br>56<br>53<br>47 46<br>39<br>33<br>29 28<br>20<br>18<br>15<br>11<br>3<br>BGD (HK, '11) BOL ('08) IDN (RKD, '10) NPL ('11) ZWE ('10)<br>BGD (IFPRI, '11) ETH ('11) KHM ('10) PER ('12)<br>Improved sanitation Improved community sanitation<br>Improved drinking water<br>Source: Author estimates.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text --> In contrast to the case of basic infrastructure, children appear to do better in having access to adequate preventive health services and facilities. In terms of vaccinations, there are two groups of countries. The first group consisting of Bangladesh, Bolivia, Cambodia, Peru and Zimbabwe, where more than two‐thirds of children have had received age appropriate vaccinations. The second group consisting of Ethiopia (15%), Indonesia (17%) and Nepal (30%) where less than one‐third have received them (Table 7b). More than half of the children in Bolivia, Cambodia, Nepal and Zimbabwe have had four or more prenatal visits, and less than a third did so in Bangladesh and Ethiopia. More than 50% of the children in the studies have received vitamin A supplementation, except for in Ethiopia (47%) and Peru (10%). In Ethiopia none of the components reach 50% prevalence.

29
# **2.3 Caveats and limitations**

The changes described in Figure 1 reshape national income distribution under a set of strong assumptions. In particular, income inequality within population subgroups formed by age, skills, and sector of employment is assumed to be time invariant. Moreover, data limitations affect estimates of the initial levels of inequality. Consumption expenditure is a more reliable welfare measure than income, and its distribution is normally more equal than the distribution of income, consumption data are not available for all countries’ surveys. To get a global picture, the GIDD dataset includes also countries for which only income data were available. Finally, measurement errors implicit in purchasing power parity exchange rates, which have been used to convert local currency units, also affect comparability across countries.

The results of the CGE model are also affected by some caveats and growth rates for any specific country or region estimated with LINKAGE are subject to a large margin of error. These growth rates depend on _exogenous_ assumptions, the most important of which include: (i) accumulation of factors (employment growth, depreciation of capital) and (ii) productivity changes – which, as mentioned, are partially judgmental. Among the _endogenous_ mechanisms the most relevant for growth are: (iii) rates of investment (i.e. accumulation of capital) which depend on the availability of savings which, in turn, are a function of demographic factors (dependency rates) and endogenous relative prices of capital goods. There is no consensus on the exogenous values governing (i) and (ii) and on the correct parameterization of the savings function. It is clear then that the level of uncertainty on the resulting growth rates is quite large. However, the main advantage of a model-based analysis is not of providing exact forecasts, but on having a framework which is consistent with economic theory. This framework can then be used to test and explain the _ceteris paribus_ effects of changes in growth rates and factor rewards on global inequality and thus address the key questions of this paper.

Besides, and this is an important point, given our objective of addressing the changes in the _distribution_ of incomes, a structural model is required. Distribution is affected, among other things, by changes in relative factor prices, shifts in sectoral employment, and changes in relative prices of consumption goods. A model that provides only aggregate growth cannot be used for incidence analyses. Thus, even if the robustness of macro-econometric models (normally used for predicting growth rates) can be assessed more easily than that of a CGE model, a macro aggregate model cannot be used to assess distributional shifts.

In summary, the macro (LINKAGE) – micro (GIDD) modeling framework used here is easy to criticize. However, if one accepts the usefulness of “predicting” – obviously subject to great uncertainty – the plausible worldwide distributional shifts in the next two decades, then it is not easy to propose a clearly superior alternative.

9
not rigorously followed by all researchers. These considerations imply that one must, especially when comparing data from different<sup>sources, be</sup> aware that there may be rather wide divergences<sup>between the data presented</sup> and the concepts they purport to measure.<sup>It is our impression that data</sup> problems are more serious in flow of funds data<sup>than in most other</sup> macroeconomic data sets, but we believe that it<sup>is worthwhile to examine</sup> the existing data.

One of the most common conceptual problems<sup>relates to capital value</sup> changes. Many elements in the flow of funds<sup>are derived from comparison of</sup> successive balance sheets of financial institutions.<sup>To the extent that</sup> the differences between these balance sheets reflect<sup>not only flows in and</sup> out of the institution, but also capital value changes, the year-to-year changes should not be entered as net flows without<sup>adjustment for the</sup> capital value changes. In principle, the valuation changes<sup>should be shown</sup> separately in a reconciliation account. However, it is not clear that such corrections have been made systematically in the data sources which we have used.

The second major problem - actually a variant of the<sup>first-relates to the</sup> problem of inflation. Even where inflation<sup>is fully anticipated,</sup> contracts denominated in mcney terms involve a transfer of real<sup>resources betweer.</sup> the parties over time that is concealed by money<sup>accounting. Some authors have</sup> recommended that analysis of sectoral savings and<sup>financial surpluses</sup> should be carried out in inflation adjusted terms.<sup>When this is done, the</sup> 8 patterns can change dramatically. For example, the savings of the 8 Cf. Kennedy, N.O., "Inflation-adjusted sectoral saving and financial balances", Bank _o England Ouartrlv Bulletin, May 1988.

13
COVID-19. Job losses remained stark as well in _Wholesale and retail trade_ , which lost jobs for approximately35.6 percent of workers, compared to 10.1 percent in the construction sector.

5.5 Projected Job Losses by Demographic Group Next, we examine the rate of projected PFPS job loss by the demographic characteristics of pre-COVID job holders. Because our method can only reflect how shocks at the firm-, sector-, and production/nonproduction worker levels play out in terms of jobs lost (or at risk), our demographic projections would not capture any effects of differential individual or firm behavior that may result in divergent outcomes by gender, age, or nationality. As such, a comparison of our projections with actual outcomes provides a sense of the degree to which pre-pandemic employment patterns alone would produce demographic differences and how much of those differences reflect behavioral factors.

# **5.5.1 Jordan**

**Gender** : In both waves of the CFUWBES, our model projects a higher percentage loss of PFPS jobs for men than for women (see Table 5-14). For wave 2, the rate is 26 percent for men versus 19 percent for women. The survey-measured projection yields a more equal gender distribution of job losses than the model-based projection. However, the modeled prediction more closely aligns with the CMMHH data, which shows a 17.3 percent rate of job loss for females and 24.8 percent for men. Female PFPS job losses were lower because in

|_Table 5-14 Jordan: Co_<br>_due to firm closures),_|_mparison of PFPS J_<br>_by Demographic Gro_<br>CFUWBES1<br>|_ob Losses, Modeled, an_<br>_up_<br>CFUWBES2|_d Survey-Measured (_<br>(Cumulative)|_including jobs lost_<br>|
|---|---|---|---|---|
||Modeled|Model-predicted|Survey-measured|CMMHH|
|By gender|||||
|Female|13.1|19.0|21.3|17.3|
|Male|16.2|26.0|26.1|24.8|
|By age group†|||||
|below 25|15.5/15.3|25.6/26.9|24.6/24.9|23.1|
|25-45|16.5/16.3|25.2/24.9|26.3/25.6|20.6|
|above 45|11.1/14.3|21.3/24.3|23.9/25.9|38.3|
|By nationality|||||
|Jordanian|14.7|23.8|24.2|21.2|
|Syrian|14.1|21.4|25.1|6.8*|
|Palestinian|13.1|28.2|20.7|40.5|
|Other|22.6|32.2|33.5|-|
|Total<br>Notes: The cumulative jobs lo<br>the modeled incremental jobs<br>*May be unreliable due to sm<br>†Age group results are shown<br>between 2016 and 2020.|15.8<br>st by the time of CFUWB<br>lost from R1 to R2.<br>all sample size.<br>assuming the same wor|25.2<br>ES2 is predicted by aggreg<br>kers aged / assuming the ag|25.5<br>ating modeled jobs lost i<br>e structure of employm|23.3<br>n CFUWBES 1 and<br>ent is replicated|

Jordan female PFPS workers tend to be highly selected among the female working population; pre-COVID, 36
is considered a burden, and lowers the value of a property, in principle to the extent of the present value of the discounted stream of anticipated tax payments. If the value of the tax payment is positive, it suggests that households value the local services provided by the municipal government, and possibly that the household understands that the provision of local services is somehow connected to the payment of local taxes.

The results in Table 2 provide _prima facie_ support for the benefit view of local taxes. A 40 percent increase in the tax rate will on average increase the rental value of the property by 11 Rupees, or about 1.3 percent. The median house price in Pune is Rs.200,000, and the median rent is Rs.1000, implying a roughly 5 or 6 percent annual return on investment,<sup>12</sup> which is considerably lower than current (July 2005) Indian mortgage interest rates of around 10 percent. The mean tax rate is 0.7 percent of the purchase price. At a discount rate of 5 percent, the net present value of tax payments is about 1.2 percent of the purchase price, implying an elasticity of house prices with respect to taxes of about 1.5. At a ten percent discount rate, the elasticity of house prices to tax rates is about 0.8. These are rather high tax capitalization rates, relative to those found in the US, of around 0.6. Even so, these estimates support the view that on average, households value the benefits they receive in return for payment of local taxes.

These averages mask significant variation across households. In the third stage, we regress the individual willingness-to-pay measures derived from the GAM results on a parsimonious set of household characteristics. Selected results from these regressions are presented in Tables 3.1 to 3.3.

Table 3.1 presents the results for dwelling unit attributes. In all cases, the value of services varies across wealth: wealthier households will pay more for services than 21
> <sup>26</sup>

However, only the significance of the checks and balances variable reflects the usual logic in the literature: that political institutions influence the credibility of government policies by making them difficult to reverse. The significance of years of competitive elections and newspaper circulation indicate a second channel is also at work: that the security of property rights is also a public good, the provision of which is influenced by the conditions of political competition.

**Table 3:  Does the effect of politics on financial sector development go through the security of property rights?**

|Lending to the private<br>sector by banks and<br>non-bank financial<br>institutions|Growth<br>75 – 00|Growth<br>75 – 00|Level 2000|Level 2000|
|---|---|---|---|---|
|Security of Property<br>Rights(1984)|.0009<br>(_.06_)||.12<br>(_.001_)||
|The component of prop<br>predicted by:|erty rights||||
|Competitive Elections<br>(1975)||.001<br>(_.29_)||.009<br>(_0.64_)|
|Checks and Balances||.002||.018|
|(1975)||(_.01_)||(_.07_)|
|Continuous Years of||.002||.018|
|Competitive Elections<br>(1975)||(_.004_)||(_.08_)|
|Newspaper Circulation||.002||.036|
|(average 1975-2000)||(_0.01_)||(_0.0_)|

N.B. Columns 1 and 3 use the specifications of columns 1 and 2 in Table 1a, substituting the property rights measure for the political variables in Table 1a. Columns 2 and 4 report the results from estimating equation (4) in the main text, using the predicted value of the security of property rights from equation (3) in the main text. The specification of the system of equations 3 and 4 is based on columns 1 and 2 in Table 1a, respectively. The table presents the coefficient estimates for the respective political or property rights variables from 15 different regressions. Correspondingly, no constants or conditioning variables are reported. The _p_ -values are reported in parentheses, based on robust standard errors.
# **TABLE** OF CONTENTS **(Cont'd)**

|IV.<br>A BRI|EF LOOK AT QUALITY .......................<br>29|
|---|---|
|A.|Quality of PHN Projects<br>....................<br>......................<br>29|
||1.<br>Strategy: Addressing Both Short- and Long-Term Needs ....<br>............<br>29|
||2.<br>Flexibility/Opportunities for Learning and Fine-tuning .................<br>30|
||3.<br>Project Management ......................................<br>30|
||4.<br>Monitoring and Evaluation ..................................<br>31|
|B.|Quality of PHN Services<br>...................<br>......................<br>32|
||1.<br>India AIDS Project .........<br>..............................<br>33|
||2.<br>Sector Report on Quality of Health Care in Cote d'Ivoire ....<br>............<br>33|
|V.<br>PROS|PECTUS .........<br>35|
|A.|Future Work Portfolio<br>. ..........<br>.<br>.<br>............<br>35|
|L.|Conclusions and Recommendations.<br>..........<br>..............<br>35|
||1.<br>Poverty..<br>...............<br>35|
||2.<br>Quality of Projects..<br>...............<br>36|
||3.<br>Quality of PHN Services..<br>...............<br>36|
||4.<br>Overview..<br>...............<br>37|
|TEXT TABLE<br>|S:<br> <br>|
|1.|Non-PHN Projects with PHN Components ...........<br>...<br>. ...<br>.. .. . .. .. .. .<br>. 3|
|2.|Composition of PHN Projects Approved in FY91 and FY92 .......<br>. . . . . . . . . . . . .<br>. 4|
|3.|Trends in Lending to the PHN Sector by Region, FY90-92 .......<br>. . . . . . . . . . . . . .<br>. 4|
|4.|Sectoral Composition and Funding Characteristics of PHN<br> <br>|
||Portfolio, FY92 ..............................<br>.......<br>......<br>....<br>. 5|
|5.|Cofinancing from Official Sources in PHN Projects, FY91 and FY92 .....<br>. . . . . . . . .<br>. 7|
|6.|Classification of FY92 PHN Projects by Poverty Objectives and Content ..... . . . . . .<br>.<br>19|
|TEXT FIGUR<br>|ES:<br>|
|1.|Lending for the Population, Health and Nutrition Sector,|
||FY 80-92..<br>..........................................<br>3|
|2.|Regional Distribution of PHN Lending, FY92 ...........<br>.. .. .. .. .. .. .. . .. .<br>. 6|
|3.|Breakdown of Total Project Costs by Expenditure Category .......<br>. . . . . . . . . . . . . .<br>. 7|
|ANNEXES:||
|1.<br>Statist|ical Annex<br>.<br>.<br>..<br>......<br>39|
|Tables|:|
|1.|FY92 Population, Health and Nutrition Portfolio .........<br>.. . . .. . . .. . . .. . . .<br>. 41|
|2.|Primary Health Care Component of PHN Lending, FY92 .......<br>. . . . . . . . . . . . .<br>. 42|
|3.|Population, Health and Nutrition Lending Volume, FY80-92 .......<br>. . . . . . . . . . . .<br>. 43|
|4.|FY92-Approved PHN Projects: Funds Components ........<br>. .. . . . . . . .. . . . . .<br>. 44|
|5.|IBRD/IDA Financing of PHN Projects, FY1980-92 ........<br>. .. . . . . . . .. . . . . .<br>. 45|
|6.|Population, Health and Nutrition Sector Reports Completed by<br>|
||Region, FY83-92 .46|
|7.|Population, Health and Nutrition Sector Reports|
||Completed in FY92 .47|
years), and the quarterly error standard deviation is set to 9% (which implies an annual standard deviation of 22%).

# _C. The Optimal Marginal Propensity to Spend Commodity Revenues_

In this subsection, we show how procyclical the optimal simple fiscal rule should be based on consumption smoothing and economic stabilization, and how it depends on the ER regime and the persistence of commodity price shocks. We also compare the optimal fiscal rule to common real world fiscal rules: BBR and SSRs.

Fiscal policy has one instrument––transfers––but two goals: consumption smoothing and economic stabilization. We first discuss the goals of fiscal policy and then show that, under some circumstances, these goals are conflicting, and the government faces a trade-off between being procyclical to smooth the consumption of the hand-to-mouth households or being countercyclical to stabilize the economy.

**Consumption smoothing.** The PIH says that as risk-averse households prefer a steady stream of consumption to a volatile one, they should consume based on the permanent annuity value of income rather than current income. A corollary of the PIH is that households should (mostly) save transitory income shocks and (mostly) spend persistent income shocks. In our model, Ricardian households can smooth consumption by borrowing/saving, but hand-to-mouth households cannot, creating a role for fiscal policy to use transfers to smooth consumption on their behalf. Consequently, the persistence of commodity price shocks determines the fiscal response in resource-rich countries: when the shock persistence is low, most of the windfall from commodity price shocks must be saved and fiscal policy should be acyclical (only spending the interest rate). But when the persistence is high, most of the windfall must be spent and fiscal policy should be procyclical.

**Economic stabilization.** Sticky prices in the NK model mean that the terms of trade between home and foreign goods do not adjust quickly to shocks, which can result in output and employment that depart from those in the (efficient) flexible price allocation. For example, a fall in commodity prices would lead to a fall in the prices of home goods if prices were flexible, shifting demand toward home goods that would stabilize domestic employment. But when prices are sticky (and the ER

15
# <u>1. Global inequality in the early XIXth  century</u>

_The data_ . Let us start with the two building blocks. For GDP per capita, I too use Maddison’s data for all countries for which they are available. I do this, to some extent, _faute de mieux_ because Maddison’s data may be in the need of serious revisions on account of new, and for China, India, Indonesia, etc., dramatically different, estimates of the domestic price levels by the 2005 International Comparison Project. The upward revisions of domestic price levels led to the corresponding reduction of these countries’ current PPP (purchasing power parity) "real" incomes. This level change then “percolates” (carries over) to the historical income levels which are calculated by applying past domestic real growth rates to the 2005 level.<sup>6</sup> However, since these revisions have not yet been done by Maddison or anyone else (and they would need to be massive), I use Maddison’s 2004 GDP per capita data expressed in Geary-Khamis 1990 international dollars.

The second building block, the income distribution data, comes from the social tables for 13 countries that have been calculated by different authors and put together within a single framework by Milanovic, Lindert and Williamson, MLW, (2009). The social tables are our unique source of information about income distribution in preindustrial societies where neither household surveys nor tax censuses were conducted. Following upon the first one created by Gregory King for England and Wales in 1688, social tables list salient social classes and give estimates of their average income (by class) and number of people or households who belong to it. Some are more detailed than others, and cover the entire range of the of income pyramid, from the top (e.g. high nobility) to the bottom (vagrants and beggars). Others are, of course, more parsimonious or simple and may include just a few classes (say, nobility, merchants and urban population, and farmers). MLW (2009) studied preindustrial inequality using thirty such social tables, some of which were created by the contemporary authors and others by economic historians. (Detailed explanation of all social tables is available in an annex to MLW paper.)

> 6 See Milanovic (2009).

4