rafmacalaba/datause-encoder-data huggingface dataset:
Table 4. Regional debt during global recessions

||Govern|ment de|bt (perc|ent of|GDP)|Priv|ate debt|(percen|t of GD|P)|Tot|al debt|(percen|t of GD|P)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||t-1|t|t+3|t+5|t+10|t-1|t|t+3|t+5|t+10|t-1|t|t+3|t+5|t+10|
|East Asi|a and P|acific||||||||||||||
|1975|20.8|21.4|27.8|22.7|41.7|25.7|27.8|42.2|42.4|55.3|46.5|49.2|70.0|65.0|97.0|
|1982|24.8|32.4|41.7|59.6|41.9|44.5|46.2|55.3|63.9|76.6|69.3|78.7|97.0|123.5|118.5|
|1991|44.6|44.5|36.6|24.7|34.4|75.8|77.4|78.4|86.8|91.4|120.4|121.9|115.0|111.5|125.9|
|2009|29.2|35.2|34.6|39.4|54.0|98.3|122.2|137.9|161.5|183.8|127.5|157.4|172.5|201.0|237.8|
|2020|54.0|63.2||||183.8|200.3||||237.8|263.5||||
|Europe a<br>1975|nd Cent|ral Asia||||||||||||||
|1982||||||||||||||||
|1991||||44.7|48.5||||29.4|34.3||||74.1|82.8|
|2009|22.6|27.8|25.4|28.4|29.4|72.5|76.3|72.2|87.0|82.5|95.2|104.0|97.6|115.3|111.9|
|2020|29.4|38.2||||82.5|90.4||||111.9|128.5||||
|Latin Am|erica a|nd the C|aribbea|n||||||||||||
|1975|16.6|25.8|26.0|25.4|56.0|43.5|41.2|41.7|43.4|32.1|60.1|67.0|67.7|68.8|88.1|
|1982|40.0|45.3|56.0|88.9|56.0|42.7|38.6|32.1|34.2|50.1|82.7|83.9|88.1|123.2|106.2|
|1991|54.9|51.7|41.3|38.8|47.2|35.2|35.8|54.1|41.0|37.1|90.1|87.4|95.4|79.9|84.3|
|2009|45.8|48.5|46.6|49.4|68.0|43.6|45.7|50.6|55.6|63.3|89.4|94.2|97.2|105.0|131.3|
|2020|68.0|77.7||||63.3|69.3||||131.3|147.0||||
|Middle E|ast and|North A|frica|||||||||||||
|1975|16.4|22.4|37.4|39.4|41.6|11.2|15.0|19.6|18.0|25.6|27.6|37.4|57.0|57.4|67.2|
|1982|44.3|50.5|41.6|59.9|60.8|19.6|21.9|25.6|26.7|23.3|64.0|72.4|67.2|86.6|84.1|
|1991|45.8|50.2|67.1|57.1|49.7|24.4|23.3|25.9|25.3|36.0|70.2|73.5|92.9|82.4|85.8|
|2009|22.4|28.9|23.6|24.7|45.8|46.2|53.5|43.3|48.7|58.5|68.6|82.4|66.9|73.4|104.3|
|2020|45.8|52.8||||58.5|75.2||||104.3|127.9||||
|South As|ia|||||||||||||||
|1975|41.7|39.6|49.0|51.4|60.6|22.0|23.0|32.9|35.0|41.6|63.7|62.6|81.9|86.4|102.2|
|1982|50.1|56.5|60.6|68.3|72.0|36.0|38.6|41.6|43.0|41.5|86.1|95.1|102.2|111.3|113.6|
|1991|70.9|70.7|70.6|63.6|76.0|44.0|39.6|39.5|40.2|49.3|115.0|110.4|110.2|103.8|125.3|
|2009|68.5|67.8|64.9|64.0|71.3|83.8|84.4|89.9|85.9|76.9|152.3|152.2|154.7|149.8|148.2|
|2020|71.3|84.0||||76.9|82.7||||148.2|166.7||||
|Sub-Saha|ran Af|rica||||||||||||||
|1975|20.2|22.9|31.0|27.2|38.9|22.3|23.4|22.2|24.1|25.1|42.5|46.3|53.3|51.3|64.0|
|1982|22.6|25.4|38.9|53.9|61.9|18.8|19.2|25.1|26.8|30.0|41.4|44.6|64.0|80.7|91.9|
|1991|57.9|58.5|72.0|61.5|60.9|29.1|30.2|29.4|27.7|27.1|87.1|88.7|101.4|89.2|88.0|
|2009|25.0|28.9|30.6|33.6|53.3|31.4|33.7|30.1|28.7|29.6|56.4|62.6|60.7|62.3|82.9|
|2020|53.3|61.6||||29.6|29.5||||82.9|91.1||||

Source: Kose et al. (2021).

Note: Year “t” denotes the year of global recessions: 1975, 1982, 1991, 2009, and 2020. Data are available for up to 153 EMDEs, consisting of 23, 24, 33, 18, 8, and 47 countries in East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, South Asia, and Sub-Saharan Africa, respectively.

35
The set of bound tests, on the other hand, provide a probabilistic upper bound for the debt dynamics under various assumptions on macroeconomic developments, borrowing costs and policy variables. The bound tests include real interest rate shock, real output shock, primary balance shock, exchange rate shock, a realization of contingent liabilities, and a combined shocks to all variables (see Box 1).<sup>6</sup> However, some caution is required when interpreting these stress tests, as they do not capture the relationships among the key debt determinants.

# _B Stochastic Simulations_

An alternative to stress tests is full scale Monte Carlo simulation (cf IMF (2003) and Celasun, Debrun and Ostry (2005)). Using estimated parameters of the joint distribution of all input variables, the distribution of these variables can be simulated jointly using Monte Carlo methods. In practice that means that for n input variables and a horizon of T years, n random numbers are simulated T times, transformed using the VAR estimation results in such a way that the resulting distribution conforms to the VAR estimates of the true distribution of the input variables (cf FSA Technical Manual, Bandieri e.a. (2006)). This is done repeatedly until the generated and empirical distribution are sufficiently close (by default 5000 runs are generated). And for each run, the model is applied to derive the full path of debt stocks and values of the required deficit reduction measure _rdr_ . In this way the full probability distribution of debt/output ratios and the _rdr_ measure at each future point in time is derived. The probability density of the outcomes of debt ratio and of the _rdr_ measure of necessary fiscal adjustment in each year can be plotted from the stochastic simulations, generating a so called “fan chart” for debt-to-GDP ratio and the _rdr_ variable.

A difficulty for many countries may be that estimating a VAR model requires long time series for key debt determinants; this maybe a problem for a number of developing countries either due to data availability or to structural breaks. To remedy these problems we also provide options to simplify the stochastic simulations. We also allow for the possibility to construct stochastic simulations using only the variances of the four random variables that are key debt determinants, ignoring any covariance that may exist between them. In this case, the tool uses as (over-writeable)

> 6 As pointed out in the IMF(2003), these tests come close to a 95-99 percent confidence interval around the projected evolution of public debt ratio, conditional on a debt crisis occurring.

14
market outcomes.<sup>1</sup> In fact, countries with greater gender gaps in unpaid care responsibilities tend to have lower FLFP.

The COVID-19 has the issue of childcare and its on FLFP pandemic brought impact to the forefront of policy discussions. Between 2019 and 2020, global female employment declined by 4.2 percent compared to a 3 percent decrease in male employment (ILO, 2021). Women were not only over-represented in the hardest hit sectors, but also bore the brunt of the lockdowns with additional childcare and housework responsibilities, worsening the gender gaps in labor market outcomes.

As the world emerges from the pandemic and policymakers think about tackling women’s increased domestic care burden, access to affordable childcare for families, especially working mothers, has been gaining momentum. An extensive literature has shown that access to affordable childcare services is a key component in improving maternal labor market outcomes. Olivetti and Petrongolo (2017) conduct a systematic review of both macro- and micro-level evidence from high-income countries on the impacts of family policies and finds that overall spending on early childcare is strongly associated with better labor market outcomes for women.<sup>2</sup> evidence on the of access Empirical benefits to childcare for maternal labor market outcomes in lower- and middle-income countries (LMICs) is also growing and aligns with the findings from developed countries. Halim et al. (2023) present a summary of 22 causal studies from LMICs that rigorously test if an

> 1Virginia et al. (2015) show that discriminatory gender norms related to the provision of care decrease FLFP by 12 percent. Conversely, addressing primary caregivers’ childcare needs could lead to a 10 pp increase in the LFP rate in countries such as Kenya, an 8 pp increase in Nigeria and South Africa, and a 5 pp increase in India.

> 2See Givord and Marbot (2015), Bettendorf et al. (2015), Geyer et al. (2015), Nollenberger and RodríguezPlanas (2015), and Herbst and Barnow (2008) for evidence from France, the Netherlands, Germany, Spain, and the United States, respectively.

2
flows allows us to recover additional export flows, thereby increasing the number of nonzero observations substantially.<sup>7</sup> We also use mirroring to recover services trade flows of two additional countries (Latvia and Lithuania) for which disaggregated output information exists in EUKLEMS but which do not report cross-border trade flows as part of OECD’s TiSP data set. Additional checks ensure that trade flows are consistent across different levels of the Even though the majority of OECD countries already accounts for a large share of global cross-border service trade<sup>8</sup> , we attempt to maximize coverage of global trade flows by augmenting the OECD TiSP data with information from the “United Nations International Trade in Services Database” as published by the United Nations Department of Economic and Social Affairs, Statistics Division. Since OECD’s TiSP constitutes our preferred data source, UN data serve to augment the data set only in instance when the corresponding OECD observation is missing.<sup>9</sup> An additional 120,000 observations can be gained by updating OECD data with UN data, which underscore the usefulness of drawing on both data sets.

Annual production data for services sectors are obtained from the “EU KLEMS Growth and Productivity Accounts: November 2009 Release” as updated in March 2011. The EU KLEMS Database provides for one of the most detailed sectoral breakdowns available. Coverage comprises mostly of OECD members which corresponds closely to the source for crossborder services trade. The raw data consist of “gross output at current basic prices” in millions of local currency units. We use data covering 2000-2007 as EU KLEMS series currently extend only up to 2007. As noted above, availability of services production data predetermines the dimensions of our sample to 28 countries, 12 sectors, and 8 years over the period 2000-2007, even though the gravity model estimations in section 4 use trade data for

> 7For within-OECD trade, the original export flow is always retained even if a matching mirror flow would be found to exist.

> 8In 2007, the 28 OECD members accounted for 74 percent of world exports and 69 percent of world imports.

> 9This implies that mirror OECD flows take precedence over original UN exports even if an exact match exists, and no mirroring is performed on UN data.

9
Table 1: Timeline of tariff increases

|Tariff wave|Date enacted|Products|2017 imp|orts|Tarif|f (%)|
|---|---|---|---|---|---|---|
|||(# HS-10)|(mil US$)|(%)|2017|2018|
|Panel A: Tariffs on U.S.<br>|imports enacted b<br>|y the United|States in 201|8|||
|Solar panels|7th Feb, 2018|8|5,782|0.2|0|30|
|Washing machines|7th Feb, 2018|8|2,105|0.1|1.3|32.2|
|Aluminum|Mar–Jun, 2018|67|17,685|0.7|2|12|
|Iron and steel|Mar–Jun, 2018|753|30,523|1.3|0|25|
|China 1|6th July, 2018|1,672|33,510|1.4|1.3|26.2|
|China 2|23rd Aug, 2018|433|14,101|0.6|2.7|27|
|China 3|24th Sep, 2018|9,102|199,264|8.3|3.3|12.9|
|Total||12,043|302,970|12.7|2.6|16.6|
|Panel B: Retaliatory tar<br>|iffs on U.S. export<br>|s enacted by t|rading partn<br>|ers in 2<br>|018<br>||
|China|Apr–Sep, 2018<br>|7,474|92,518|6|8.4|18.9|
|Mexico|5th Jun, 2018|232|6,746|0.4|9.6|28|
|Turkey|21st Jun, 2018|244|1,554|0.1|9.7|31.8|
|European Union|22nd Jun, 2018|303|8,244|0.5|3.9|29.2|
|Canada|1st July, 2018|325|17,818|1.2|2.1|20.2|
|Russia|6 Aug, 2018|163|268|0|5.2|36.8|
|Total||8,073|127,149|8.2|7.3|20.4|

_Notes_ : Reproduced based on Fajgelbaum et al. (2020). Panels display unweighted monthly 10-digit HS country average tariff rates. 2017 tariff rates computed as annual average; 2018 rates computed in December 2018. Total tariff rates represent trade-weighted average of row values. Import/export share denominator is total 2017 annual US$ value of all U.S. imports/exports. US government announced import tariffs on aluminum and steel on March 23 but granted exemptions for Mexico, Canada, and the EU which were later lifted on 1st June. Chinese retaliation dates are 6th April, 2nd July, 23rd August, and 24th September.

9
It seems little surprising that we find our RTI not only to be highly correlated with income per capita but also with Internet penetration rates (Figure 3). Richer economies have more resources to spend allowing them to invest in costly online solutions or other information material to make information more accessible to citizens. Online solutions also depend greatly on the degree of connectivity in an economy, which is strongly correlated with income per capita. Bridging the digital divide is costly and poorer countries are, hence, at a disadvantage when they want to engage more actively with citizens via the Internet. However, Figure 3 also illustrates that poorer countries make relatively greater efforts to invest in non-online solutions. In low-income countries, 50% of the fee schedules that can easily accessed cannot be found online but come in the form of brochures and similar material.

**Figure 3: Accessibility of regulatory information varies with income level and Internet penetration**

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9
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# TABLE OF CONTENTS

|1<br>INTRODUCTION ..................................<br>1|
|---|
|2<br>ANALYTICAL FRAMEWORK ..................................<br>4|
|3<br>CONTEXT ..................................<br>7|
|EVOLUTION OF LAND TENURE SYSTEMS ..................................<br>7|
|POLITICAL ECONOMY OF LAND REFORMS..................................<br>9|
|ANoTEONDATA..................................<br>12|
|SIZE DISTRIBUTION OF RURAL LAND HOLDINGS..................................<br>13|
|LANDLESSNESS AND ABSENTEE LAND OWNERSHIP<br>..................................<br>16|
|FARM SIZE AND PRODUCTIVITY<br>..................................<br>17|
|4<br>KEY POLICY ISSUES ..................................<br>19|
|LAND MARKETS ..................................<br>19|
|FRAGMENTATION OF HOLDINGS<br>..................................<br>22|
|GENDER AND LAND RIGHTS ..................................<br>25|
|ACCESS TO AND ENCROACHMENT<br>ON COMMONS ..................................<br>27|
|RECORDS OF RIGHTS IN LAND ..................................<br>30|
|5<br>SUMMARY AND POLICY OPTIONS ..................................<br>36<br>BIBLIOGRAPHY.39|
|LIST OF TABLES<br>TABLE 1: TYPOLOGY OF PROPERTY RIGHTS IN LAND<br>.6|
|TABLE2:TYPOLOGY OF STATES BY TENURE SYSTEM AND AGRICULTURAL<br>GROWTH RATE.<br>9|
|TABLE3:KEY PARAMETERS IN INDIA'S AGRARIAN STRUCTURE<br>.14|
|TABLE4:POTENTIAL STAKEHOLDERS<br>IN REFORM OF LAND REGISTRATION<br>.33|
|LIST OF FIGURES<br>FIGURE1:DISTRIBUTION OF HOUSEHOLD<br>OWNERSHIP HOLDINGS: ALL INDIA,1953-54TO1982.<br>14|
|FIGURE2:SHARE OF TOTAL OWNED AREA BY HOLDINGS SIZE: ALL INDIA,1953-54TO1982.15|
|FIGURE3:DISTRIBUTION OF HOUSEHOLD<br>OPERATIONAL<br>HOLDINGS: ALL INDIA,1953-54TO1982.<br>15|
|FIGURE4:SHARE OF TOTAL OPERATED AREA BY HOLDING SIZE: ALL INDIA,1953-54TO1982.16|

## LIST OF ANNEXES

|ANNEX 1: LAND REFORM LEGISLATION<br>IN INDIA, BY STATE.46|
|---|
|ANNEx2:STUDIES OF LAND SALE-PURCHASE<br>TRANSACTIONS<br>IN RURAL INDIA.48|
|ANNEx3:STUDIES OF LAND-LEASE MARKETS IN RURAL INDIA.49|

i
- 14 -

The concentration ratio of beef and veal in total meat production has been falling in general, except for several countries.<sup>22</sup> Second, two exogenous shocks have severely affected the global cattle industry since the late 1990s. One is the outbreak of the foot and mouth disease. Over the region a considerable amount of cattle had to be killed in affected areas. Another is the Bovine Spongiform Encephalophathy (BSE) disease crisis. Although the BSE disease is nothing new, many industrial countries have become more sensitive than before, resulting in a successive imposition of higher food standards on beef exporters.<sup>23</sup> Given the increasing public awareness of these diseases, the European Union, which is the major market for African exporters, required beef exporting countries to fence their cattle farms in 2000, and imposed a new food origin regulation in the next year. According to the new rule, beef products exported to the EU market must be traceable, particularly when imported from countries with a history of the BSE outbreak. The cost of complying with high export conditions is prohibitively high for small volume exporters, such as Swaziland and Botswana, though the latter is one of the few countries in Africa classified by the EU as being largely free from the BSE risk.

The BSE impact appears to have been tremendous in the EU beef market. Consumers became more sensitive to the country of origin. Loureiro and Umberger (2005) estimate the mean willingness to pay for a mandatory origin labeling program at about 20 U.S. cents per pound. The average import prices from non-EU member countries jumped from 2.5 euro to 4 euro after the SEAC report warning the BSE disease (Figure 5). This means either that the food safety measures increased production costs or that a supply shortage due to suspended imports from disqualified countries raised the market price. As per Mazzocchi _et al_ . (2004),

> 22 Botswana reduced the beef share from 85 percent in 1980 to 65 percent in 2004 and instead increased chicken production (source: _FAOSTAT_ ). There are only seven African countries that experienced an increase in the share of beef in total meat production during the same period: Benin, Chad, Djibouti, Guinea Bissau, Mali, Namibia and Rwanda.

> 23 In March 1996, the U.K. Spongiform Encephalopathy Advisory Committee (SEAC) reported that human beings might be able to be infected with the variant Creutz-feldt-Jakob disease through the BSE-infected bovine offal.
||**Rank**|**No. of do**<br>**expo**|**cs for**<br>**rt**||**Tim**<br>**export**|**e for**<br>**(days)**||**Ta**<br>**Cost to**<br>**(US$**<br>**conta**|**ble 2. T**<br>**export**<br>**per**<br>**iner)**|**rading A**|**cross B**<br>**Documen**<br>**impo**<br>**(numb**|**order**<br>**ts for**<br>**rt**<br>**er)**|**s**|**Time for**<br>**(da**|**import**<br>**ys)**||**Cost to**<br>**(US**<br>**cont**|**import**<br>**$ per**<br>**ainer)**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2008**|**2006**|**2008**|<br>**change**|**2006**|**2008**|**change**|**2006**|**2008**|**change**|**2006**|**2008**|**change**|**2006**|**2008**|**change**|**2006**|**2008**|**change**|
|Algeria|117|9|8|<br>1|15|17|-2|1,606|1,198|408|9|9|0|22|23|-1|1,886|1,378|508|
|Bahrain|20||5|<br>-5||14|-14||805|-805||6|-6||15|-15||845|-845|
|Egypt|21|8|6|<br>2|27|15|12|1,014|714|300|8|6|2|29|18|11|1,049|729|320|
|Iraq|178|10|10|<br>0|102|102|0|3,400|3,400|0|10|10|0|101|101|0|3,400|3,400|0|
|Jordan|64|7|7|<br>0|28|19|9|720|680|40|12|7|5|28|22|6|955|1,065|-110|
|Kuwait|99|10|8|<br>2|20|20|0|935|935|0|11|10|1|20|20|0|935|935|0|
|Lebanon|84|6|5|<br>1|22|27|-5|969|1,027|-58|11|7|4|34|38|-4|752|810|-58|
|Morocco|68|6|8|<br>-2|18|14|4|700|600|100|11|11|0|30|19|11|1,500|800|700|
|Oman|109|10|10|<br>0|22|22|0|665|665|0|10|10|0|26|26|0|824|824|0|
|Qatar|36||5|<br>-5||21|-21||735|-735||7|-7||20|-20||657|-657|
|Saudi<br>Arabia|33|5|5|<br>0|13|19|-6|654|1,008|-354|9|5|4|34|20|14|604|758|-154|
|Syria|127|12|8|<br>4|49|19|30|1,300|1,300|0|18|9|9|63|23|40|1,822|1,760|62|
|Tunisia|28|5|5|<br>0|18|17|1|770|540|230|7|7|0|29|22|7|600|810|-210|
|UAE|11|7|5|<br>2|13|10|3|462|462|0|8|7|1|13|10|3|462|462|0|
|Yemen|126|6|6|<br>0|33|31|2|1,129|1,129|0|9|9|0|28|28|0|1,475|1,475|0|

Source: World Bank, _Doing Business, 2008._ 7