rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> A high concentration of Informal Jobs in developing<br>4.8 countries<br>4.6 India<br>Indonesia<br>4.4<br>Kenya Philippines<br>4.2<br>Sri Lanka<br>Pakistan Thailand Argentina<br>4<br>Zimbabwe Mexico<br>Brazil<br>3.8 South Africa<br>Ethiopia<br>3.6 Egypt Chile<br>3.4 Turkey<br>3.2<br>3<br>5 6 7 8 9 10<br>ln(GDP per capita), 2008<br>Data source: OECD, 2009. World Development Indicators, 2010. ILO KILM.<br>Note: 52 countries with available data shown. Chart uses latest data on informal share of employment available<br>(1995-99 or 2000-07). GDP per capita is in 2005 constant PPP international $.<br>employment)<br>ln(informal employment share of non-agricultural<br><!-- End of picture text -->

**Figure 2: Informal sectors are exceptionally Persistent** (Share of Population in Informal Employment, Earliest and Latest Data Points Available)

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Data source: ILO KILM Table 8, "Employment in the informal economy". India: Annual Survey of Industries/National Sample Survey (Manufacturing only).

The relationship between the size of informal sector and development in Figure 1 is downward sloping. This is the stylized cross-section relaionship, established many times in the literature, which informs the mindset that predicts a decline in informality with development. But notice first of all that there is a 6
4-b Table 3: UMOA: COMMODITY COMPOSITION OF EXPORTS <!-- Start of picture text --> Burkina Cote<br>Country: Benin Faso d'Ivoire Mali Niger Senegal Togo<br>Product:<br>Cotton 21 35 68 5<br>Coffee 7 25 40<br>Cocoa 16 36 14<br>Uranium 5 97<br>Phosphates 16 45<br>Fruit and Veg 7 7<br>Groundnuts 44<br>Karite Almonds 36<br>Petroleum 35<br>Fish 28<br>Wood 15<br>Gold 14<br>Hides 7<br><!-- End of picture text --> Note: Table shows percentage of each country's exports to industrial countries of various commodities and products manufactured from those commodities Onlv commodities accountinp for more than 5% of total exports are included.

Table 4: UMOA: VARIABILITY OF EXPORT PRICES This panel shows the world price of each country's average basket of commodity exports

||<br>Benin|Burkina<br>Faso|Cote<br>d'Ivoi-r|e Mali|Senegal|Togo|Average|
|---|---|---|---|---|---|---|---|
|1980|<br>100|<br>100|<br>100|100|<br>100|100|100|
|1981|<br>97|<br>82|<br>81|86|<br>110|93|86|
|1982|<br>93|<br>78|<br>76|72|<br>69|84|76|
|1983|<br>92|<br>87|<br>86|82|<br>86|81|85|
|1984|<br>94|<br>96|<br>93|80|<br>100|87|93|
|1985|<br>87|<br>91|<br>89|69|<br>85|79|86|
|1986|<br>62|<br>100|<br>94|64|<br>65|85|86|
|19P7|<br>69|<br>84|<br>77|77|<br>.<br>65|71|75|
|1988|<br>58|<br>81|<br>70|71|<br>85|72|72|
|Variance|<br>227|<br>63|<br>88|104|<br>234|77|72|

This panel shLws the matrix of correlations

||Burkina<br>Faso|Cote<br>d'Ivoire Mali|Senegal|<br>Togo|
|---|---|---|---|---|
|Benin|0.067|0.415 0.667|0.625|0.711|
|Burkina Faso||0.927 0.206|0.130|0.474|
|Cote d'Ivoire||0.385|0.292|0.693|
|Mali|||0.679|0.681|
|Senegal||||0.608|
# **Table 4: Age and schooling differences between partners by birth cohort of the wife. Cambodia, Demographic and Health Survey, 2000.**

|**Birth Cohort**|**Age in**<br>**1979 (December)**|**Cohort size**|**Mean age difference**<br>**with partner**|**Percentage with**<br>**younger partner**|**Mean Schooling difference**<br>**with partner**|
|---|---|---|---|---|---|
|50-54|29-25|1119|3.93|0.164|2.212|
||||[0.212]|[0.0125]|[0.092]|
|55-59|24-20|1619|3.219|0.222|1.736|
||||[0.17]|[0.011]|[0.075]|
|60-64|19-15|2049|2.31|0.243|1.768|
||||[0.126]|[0.0100]|[0.063]|
|65-69|14-10|1994|2.137|0.234|1.516|
||||[0.1]|[0.0099]|[0.070]|
|70-74|9 to 5|1899|2.91|0.1525|1.49|
||||[0.1]|[0.0085]|[0.070]|
|75-79|4 to 0|1151|4.191|0.0911|1.75|
||||[0.127]|[0.0087]|[0.100]|

Standard deviation in brackets.

39
Fiscal rules can be of four types – expenditure rule, revenue rule, budget balance rule, and debt rule. It is also very common for countries to have several of these rules in place simultaneously. If a country has adopted any of these rules we classify it as fiscal rule adopter. We compare the sources of GDP volatility for the two groups of countries in Table **5** .

|||**Table**|**5. Varianc**|**e decomp**|**osition of**|**GDP volatility**|**: By the pr**|**esence of**|**fiscal rule**|**(s)**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Countr**|**ies with fis**|**cal rule(s)**||**Countr**|**ies with no**|**fiscal rule**|**(s)**|**mean diff**|**median diff**|
||N|mean|median|p25|p75|N<br>mean|median|p25|p75|||
|**rrate_us**|40|0.116|0.081|0.042|0.149|8<br>0.102|0.123|0.043|0.134|-0.014|0.042|
|**(log) gdp_us**|40|0.119|0.077|0.016|0.182|8<br>0.074|0.032|0.029|0.066|-0.045|-0.045|
|**(log) govexp**|40|0.093|0.075|0.026|0.143|8<br>0.145|0.088|0.020|0.221|0.052|0.013|
|**tby**|40|0.065|0.044|0.014|0.097|8<br>0.070|0.046|0.012|0.114|0.006|0.002|
|**rrate**|40|0.035|0.025|0.010|0.046|8<br>0.022|0.017|0.011|0.028|-0.013|-0.008|
|**(log) gdp**|40|0.514|0.553|0.447|0.626|8<br>0.538|0.607|0.336|0.662|0.024|0.054|
|**(log) reer**|40|0.059|0.025|0.009|0.063|8<br>0.049|0.030|0.009|0.084|-0.009|0.005|

Note: Authors’ calculations. * p<0.10, ** p<0.05, *** p<0.01 Two interesting results stand out from the table. First, shocks to world demand play a more important role in countries with fiscal rules relative to non-adopters. This could be a consequence of the fact that fiscal rule adopters tend to be larger countries (see Hnatkovska and Koehler-Geib, 2016) and, as we showed above, larger countries tend to be more susceptible to world demand shocks. Second, countries with no fiscal rules have a larger fraction of their GDP volatility driven by shocks to government expenditures. Compare the contribution of these shocks equal to 15% in non-adopters with 10% in adopters, on average. The contribution of all other shocks to GDP volatility tends to be symmetric in fiscal rules adopters and non-adopters.

Overall, our results suggest that there exist some interesting differences in the sources of volatility between fiscal rules adopters and non-adopters. However, the small number of countries with no fiscal rules in our sample precludes us from finding statistically significant differences between the two groups.

Next, we investigate whether there are significant differences in the responses of GDP to various shocks in fiscal rule adopters and non-adopters. For this purpose, we estimate a panel VAR separately for the two groups of countries and plot the estimated impulse responses of GDP. Figure **7** summarizes the results for countries with fiscal rules, while Figure **8** does the same for countries with no fiscal rules.

<u>Figure 7. Impulse Responses of GDP to Various Shocks: Countries with fiscal rules</u> 22
The phone survey data seem to pick up some of the changes faster and more immediately than official employment statistics. In particular, the ILO definition of employment classifies those who are not working but expect to go back to work, for example because of vacation, as employed. Under normal circumstances this is a small share of workers, but the phone survey data from LAC mentioned above suggests a large share of workers fell into this category immediately following the onset of the pandemic. This suggests that the measure of stopping work in the phone surveys may better capture the full extent of labor market disruption than the official ILO concept of employment in the immediate aftermath of the crisis, despite legitimate concerns about the representativeness of the phone survey samples. This provides additional evidence that the HFPS phone survey is useful in measuring initial impacts and captures dimensions missed by other sources of data.

Finally, we compare the WEO macroeconomic growth projections with the ILO’s reported information on employment changes in Figure 12. The ILO measure and the WEO measure of change for this comparison include 26 countries, 12 overlapping with the HFPS phone survey and 14 that are not, which are all developing countries. Here, again it seems that the WEO GDP projections show a different pattern than the ILO employment change. For example, Georgia and Chile have similar changes in GDP growth projections despite the latter having much greater employment loss in the ILO data. This is a further indication that WEO GDP projections are not necessarily picking up employment outcomes on the ground. The HFPS phone survey data in particular appear to be reflecting short-term employment changes that might take more time to appear in the official firm and labor force surveys, national statistics and therefore in macroeconomics and employment projections.

# **4.5 Robustness: Alternative Weighting**

In our main estimates, we use the HFPS survey weights. Figure 13 shows scatterplots for six key measures (share stopped working, share with partial or no payments, share changed job during the pandemic, shared with reduced consumption, share with hungry adults, and share 24
<!-- Start of picture text --> 120-<br>58 —- Cluster<br>a) 60- a gf | ;<br>3 ———— 2<br>Zz<br>30-<br>o-<br>1: 1998to 2002 2: 2003't0 2019<br><!-- End of picture text -->

<!-- Start of picture text --> 120- |<br>3 ==]<br>3 - SS _- Cluster1<br>* : —— a —S<br>o-<br>AL 1998 t0 2002 22003 to2019<br><!-- End of picture text -->

<!-- Start of picture text --> = ST Cluster<br>8 oo. U | a | ‘<br>33 <a —— 2<br>Zz<br>30-<br>o-<br>1: 1998to 2002 2: 2003'to 2009 3: 2010'to 2019<br><!-- End of picture text -->

<!-- Start of picture text -->
|<br>3 — —<br>; 60- as Cluster1<br>204<br>— — j=—s—<br>o-<br>1 1998 t02002 2:2003 'to2009, 3:2010'to 2019<br><!-- End of picture text -->
Policy Research Working Paper 9900

# **Abstract**

Across Sub-Saharan African countries with customary tenure systems and low levels of documented land ownership, there are limited nationally representative insights on men and women landowners’ rights over land. Variations in institutions and norms governing land ownership further complicate cross-country comparisons. Using machine learning techniques and nationally representative, intrahousehold survey data elicited in private from men and women on their ownership of assets, this paper creates unique profiles of landowners in Ethiopia, Malawi, and Tanzania, anchored in a range of constructs related to self-reported rights and control over land parcels. The analysis reveals a high degree of cross-country consistency in the new insights. Landowners, particularly women, often do not have full rights and decision-making power over land. Multiple correspondence analysis demonstrates that transfer rights (rights to bequeath, sell, rent out, and use as collateral) contribute the most to the variation in the composition of the constructs related to rights and control over land. Hierarchical clustering shows that landowners can effectively be clustered into three categories: (1) owners with mostly exclusive transfer rights, (2) owners with mostly joint transfer rights, and (3) owners with no/limited transfer rights. Owners with transfer rights tend to have all other rights and measures of control. Women are overrepresented in the cluster of landowners with no/limited transfer rights, and in moving from the cluster with mostly joint transfer rights to the one with mostly exclusive transfer rights, the increase in the share of individuals not needing permission to exercise any right is considerably greater among women than men.

This paper is a product of the Development Data 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://www.worldbank.org/prwp. The authors may be contacted at ardinah@umich.edu or tkilic@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
66

|**Table 23 Investments of Pension **|**Funds with differe**|**nt degrees of **|**Risk Coverage**|
|---|---|---|---|
|in CHF (2002)|Total Inv.<br>(in billions)|# Members|Inv./Member|
|autonomous|298,6|1,143,142|261,199|
|autonomous (with reinsurance)|63,3|450,479|140,606|
|semi-autonomous|39,8|504,291|78,861|
|non-autonomous|10,9|1,205,320|9,031|

Source: Bundesamt für Statistik (2004b), p.32, T8 Finally, Figure 25 depicts the relationship between the amount invested in different assets for pension funds under private law and under public law. This figure reveals that there is no apparent difference between the investment strategy of pension funds managed under private and those managed under public law.

**Figure 25  Relative Investments of Pension Funds under private and under public Law**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Liquid Investment Bonds Morgages Shares Real estate Others<br>assets, in empolyer<br>short-term<br>investments<br>under private law under public law<br><!-- End of picture text --> Source: Bundesamt für Statistik (2004b), p.53, Tab. A.05

# **5.4 Financial Situation of Pension Funds**

Extensive discussions about the financial situation of pension funds have been recurrent in recent years. The reason is threefold: Firstly, the decline in financial market returns reduced the value of investments in securities. Secondly, not all pension funds had enough reserves to balance out those fluctuations in asset values. Thirdly, the accumulated increase in life expectancy could not be fully compensated anymore by market returns exceeding the minimum interest rate specified in the law.

In 2002, nearly 20 percent of all pension funds (excluding Collective-Institutions) were underfunded. In 2003, this figure decreased to 12 percent and in 2004 even further to 10 percent. A pension fund is deemed as underfunded if its required old–age capital is more than the available capital plus reserves. This calculation is based on the report submitted by each individual fund. By law, the technical rate is fixed between 3.5 percent and 4.5 percent (Art. 8 FZV). The specific rate, however, is incumbent on the technical expert. The expert
**<u>Figure H1: Unconditional quantile regression of height-for-age z-scores on food security measures, limited controls (Tanzania)</u>**

<!-- Start of picture text --> Per Capita Caloric Availability 1-SSR<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>1-SSEXR DDS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>RIF-OLS 95% Confidence Interval OLS<br>RIF-OLS<br>.4 .4<br>.2 .2<br>0<br>0<br>-.2<br>-.2<br>-.4<br>-.4<br>.4<br>.4<br>.2<br>.2<br>0<br>-.2 0<br>-.4<br>-.2<br><!-- End of picture text --> 100
# **SUMMARY**

4.27 In conclusion, it is clear that current social policies for the infrastructure sectors have a mildly regressive distributional incidence, with concentration coefficients clustered in the interval from –0.10 to +0.10. The most regressive subsidies are those destined to finance metro rail and bus services in the Metropolitan Area, while the most progressive appear to be the social tariffs for water and electricity practiced by some of the provinces achieving concentration coefficients below –0.10<sup>9</sup> . The errors of inclusion of these subsidies are uniformly high, ranging from 40% to 70%. Furthermore, the subsidies appear to exclude between 60% and 90% of the total poor nationwide.

**Table 6: Summary of targeting performance for existing social** **<u>policies</u>**

||**Error of**<br>**inclusion**|**Error of exc**<br> <br>Target<br>population|**lusion**<br>Nationwide|**Concentration**<br>**coefficient**|
|---|---|---|---|---|
|Water Metropolitan Area (TBB)|57%|14%||+0.07|
|Water Metropolitan Area (Universal<br>Access Charge)|40%|0%|78%|-0.03|
|Water Provinces (Social Tariffs)|44%|73%|76%|-0.15|
|Electricity (Social Tariffs)|39%|94%|93%|-0.37|
|Gas (Patagonian subsidy)|70%|64%|98%|+0.08|
|Energy (Pensioners subsidy)|52%|27%|92%|-0.13|
|Public transport (Metropolitan Area)|59%|0%|64%|+0.16|
|Rail|57%|0%|91%|+0.09|
|Bus|59%|0%|70%|+0.16|
|Metro rail|73%|0%|97%|+0.36|

_Source:_ OPSM, May 2002 4.28 Finally, it is important to put these results in broader international perspective, by observing that it is not unusual for utility social policies to present high errors of inclusion and exclusion. For example, a recent study of water sector subsidies in Chile and Colombia found errors of inclusion in the 60-80% range, and errors of exclusion of around 80% in Chile and 30% in Colombia<sup>10</sup> . Furthermore, a number of studies on

> 9 Given that the Gini coefficient for per capita income amounts to 0.44, it is evident that the distribution of subsidiesalthough regressive in absolute termsis much less regressive than the distribution of income in the country.

> 10 Gomez-Lobo, A., and Contreras, D., ‘Subsidy Policy for the Utility Industries: A Comparison of the Chilean and Colombian Water Subsidy Schemes’, University of Chile, August 2000.

29
To shed light on whether GDP manipulation is widespread among Chinese cities, we first look into the distribution of a city’s over-performance, defined here as the gap between the reported growth and the target growth. We then conduct the manipulation test proposed by Cattaneo, Jansson, and Ma (2018) to test for a discontinuity in the distribution of the over-performance around 0, the threshold for meeting the target. As shown in Figure 2, the gap between the density estimate on the two sides of zero is large and statistically significant ( _p_ =0.001). At the discontinuity point, the density is significantly higher on the positive side than on the negative side.

A similar test using the cumulative _quarterly_ GDP growth data is also revealing. Since the evaluation of bureaucrat performance is based on annual rather than quarterly data, if local governments indeed manipulate GDP data, the need for manipulation emerges only as the year-end approaches. Thus, the discontinuity in over-performance should emerge mainly in the latter half of the year (Lyu et al. 2018). To test this, we use the provincial-level data here since the city-level quarterly GDP data are not widely available. As shown in Appendix Figure A.1, the break in density of overperformance in growth is significant only in the third and fourth quarters, and the magnitude steadily increases over the course of the year. This result is again suggestive of performance manipulation among the Chinese cities.

# **3 Data and Variables**

## **3.1 Nighttime lights data**

The nighttime lights data were collected by the U.S. Air Force Defense Meteorological Satellite Program (DMSP) using the Operational Linescan System (OLS) sensors. They were processed and distributed by the scientists at the National Oceanic and Atmospheric Administration’s (NOAA) National Geophysical Data Center (NGDC), available from 1992 to 2013. The DMSP satellites observe every location on the planet each night at the local time from 8:30 pm to 10 pm. In processing, some restrictions are imposed to remove observations affected by sunlight, glare, moonlight, aurora as well as cloud cover, and readings that likely reflect ephemeral events (such as fire) are set to zero, leaving mostly man-made lights from human settlements.<sup>20</sup> In each satellite-year data set, light intensity is recorded as a Digital Number (DN) ranging from 0 (no lights) to 63 (top-coded), for every 30 arc-second output pixels (about 0.86 square kilometers at

> https://www.scmp.com/comment/insight-opinion/article/2130132/now-china-must-come-clean-about-its-nationaleconomic-data.

> 20 See Elvidge et al. (2009), Chen and Nordhaus (2011) and Henderson, Storeygard, and Weil (2012) for details.

10
# introduction

The purposo of this paper Is to analyze **the** mcroeconomics of uniform trade **taxes** uwder capital nobility **and** currency convertlbllity. Since uniform tariff-cum-subsidy (UTCS) policits **are** often proposed **as** alternatives to devaluation of the exchange rate, we emphasize the comparison between these alternatives, as well as the intermediate came of a dual exchange rate system. We do the analysis in a two-period, representative consumr framwork, in order to bring out the iportance of the time path of the alternative policies. The nodl is idealixed In most respects, but should morv as a usoful benchmark for further analysis in more realistic settings.1 The paper is organized as follows. In Section 1, we set up a model based an Adas and Groenwood (1985), with exogenous output, lump-sum taxes, no gsvernomnt sponding, and perfect capital mobility. The model is aimed at 1 This paper was motivated by the UTCS scheme recently instituted in Cote d'Ivoire. Cote dlvoire is a member of the West African Monetary Union, a group of countries whose coion currency (the CFA franc) is freely convertible into French francs by agreemnt with the French Treasury, which guarantees convortibility by extonding overdraft privileges to the Union's Central Bank (see Krum (1985)). Increases in domestic inflation starting in the mid 1970s, together with the recent nominal appreciation of the French franc and significant nominal depreciations in neigboring Ghana and Nigeria, have produced roal appreciations in Cote dl'voire and a number of other CFA countries. This has led a number of authors (e.g., Krum (1987), * varajan and da Halo (1987)) to suggest that an optimal macroeconomic policy package would include a devaluation of the nominal exchange rate, if it were not for Cots d'Ivoire's responsibilities to the CPA Zone. The argument, and the rationale for the UTCS scheme that is currently in place in lieu of a parity change, is that sluggishness of domestic price adjustments is making adjustment to terms of trade shocks and lnternational borrowing shocks excossively contractionary. Initial experience with the UTCS has not boon encouraging, however; there is anecdotal evidence of widespread overinvoicing of Liports, and there **has** Leen late payment by the government of the export subsidy. The purpose of this paper is to provide a theoretical framework in which these **and** other macreoconosic aspects of UTCS schemes can be analyzed. **We** assume perfect capital mobility because the CFA countries have mintial capital controls regarding transfers with France, and therefore "import" the relatively free French regime regardlng capital account transactions.

1