rafmacalaba/datause-encoder-data huggingface dataset:
## **Figure 6 Services trade restrictiveness index, 2018**

(simple average of STRI across 22 service sectors) <!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Latvia Netherlands Czech Rep. Germany Ireland UK Australia Lithuania Denmark Japan Portugal Chile Spain New Zealand Canada Sweden Finland Slovak Rep. Luxembourg France Estonia US Slovenia Costa Rica Austria Greece Norway Hungary Belgium Colombia Poland Italy South Africa Korea Switzerland Malaysia Brazil Israel Turkey Mexico Iceland China Russia Indonesia India<br><!-- End of picture text --> Source: OECD; authors’ calculations. Note: A higher STRI indicates a more restrictive regime.

# **2. Factors behind the recent slowdown in productivity growth**

The post-global financial crisis slowdown in labor productivity growth in China was driven by a significant deceleration in TFP growth from 2.8 percent per year in the decade before the global financial crisis to just 0.7 percent afterwards. The growth contribution of physical capital accumulation increased over that period.

## **Figure 7 The recent widespread decline in TFP growth**

(annual average TFP growth in 2010–17 (y-axis) versus 2000–07, percent) <!-- Start of picture text --> CHN<br>-10 -5 0 5 10<br>2000-2007 annual average TFP growth-5<br>10<br>5<br>0<br>-5<br>2010-2017 annual average TFP growth<br>-10<br><!-- End of picture text --> A slowdown in TFP growth has been observed in many advanced and developing countries since the 2008 global crisis (Ball, CHN 2014; Foda, 2016; Adler et al., 2017; Ollivaud and Turner, 2014). In the PWT 9.1 data, world TFP growth (excluding China) decreased from 1.2 percent in 2000–07 to 0.4 percent in 2010-17 (Figure 7). For advanced countries, the deceleration in TFP growth predates the crisis (Foda, 2016), -10 2000-2007 annual average TFP growth-5 0 5 10 pointing to a declining effect of the information and communication technology Source: PWT 9.1; authors’ calculations. boom, an aging workforce, slower human Note: An observation below (above) the 45-degree line capital accumulation, and slowing global indicates that a country’s TFP growth was lower (higher) trade integration, including the maturing of post-GFC compared with the pre-GFC period. OECD countries in orange. China’s integration into world trade (Adler et al., 2017). Albeit less relevant in the case of China, other factors further reduced global TFP growth: overheating and resource misallocation before the crisis (Ollivaud and Turner, 2014; 7
Figure 3.4 Student’s own expectation of attending tertiary education (marginal change in probability) <!-- Start of picture text --> Mother's education post-secondary<br>Mother's education sr. sec<br>Mom's education jr sec<br>Family economic condition-well off (student response)<br>Family economic condition-middle (student response)<br>Student performance ranking - high (self assessment)<br>student performance ranking - midde (self assessment)<br>Student standardized test score<br>School is in rural area<br>school is in city<br>-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35<br><!-- End of picture text --> _Source_ : Authors’ calculation based on the CEPS.

# Financing trends

Financial resources supporting senior secondary schools have been increasing rapidly over the past decade, with an even faster rate for the academic track. Figure 3.5 shows that from 2008 to 2015, total financing to senior secondary education increased rapidly. In 2015, the total amount reached nearly CNY 530 billion (about US$80 billion). The annual growth rate was 15 percent on average. Noticeably, even though the explicit policy of promoting vocational education was in place during this period, the total financing for regular senior secondary schools was growing faster.

Figure 3.5 Total financing is increasing (in CNY) <!-- Start of picture text --> 400,000,000<br>300,000,000<br>200,000,000<br>100,000,000<br>0<br>2008 2012 2015<br>Total vocational Total regular<br>Linear (Total vocational) Linear (Total regular)<br><!-- End of picture text --> _Source:_ National Bureau of Statistics of China 2008, 2012, and 2015a.

A breakdown of the financial resources also shows consistent trends with household demand. Public financing is the most important source for senior secondary schools in China (Figure 3.6). A majority of schools are under the oversight of the local government—over 80–90 percent of the schools are under the purview and financing of the county-level government. While the overall level of resources that the sector receives is increasing, the composition of the sources of financing 27
# **1  Introduction**

The discovery of oil and gas reserves in Ghana, Kenya, Tanzania, Mozambique and other low-income African countries has revived interest in the management of resource windfalls. On one hand, the experience of Africa’s traditional resource exporters has been sobering. Despite hundreds of billions of dollars in oil revenues, countries such as Nigeria, Gabon and Angola continue to suffer from high levels of poverty, low human development and a capital stock that is scarcely adequate for the post-oil era. On the other hand, countries such as Norway and Chile, which used strict fiscal rules to ensure that resource windfalls are saved and not subject to the irresistible temptation to spend, have achieved great progress. Yet, there is an argument that poor countries, being capital-scarce, should be investing their resource windfalls domestically rather than saving them abroad.

In this paper, we attempt to resolve these issues by simulating the impact of resource windfalls and policy responses in a model that captures both the implications of current decisions on future growth and welfare, and the uncertainty that is intrinsic to resource prices. We ask: What rules-of-thumb does dynamic stochastic general equilibrium (DSGE) analysis provide about budgetary spending of uncertain and fluctuating resource windfalls, especially in a low-income and newly resource-rich developing country like Niger? In particular, we analyze in the presence of export price uncertainty the economic implications of budget rules associated with public revenue from natural resources in a developing country. We ask how budget rules should be set in countries that are facing spending pressures for social needs in the context of highly volatile mineral revenue. Several low-income developing countries (like Uganda and Niger) are recently emerging as mineral exporters. With low capacity to hedge or stabilize revenue, budget benchmark spending rules are increasingly being used as simple ways to manage and allocate mineral revenue over time. The experiences of Norway for oil and Chile for copper illustrate the outcomes of well-defined fiscal rules. In that setting, Nigeria adopted in 2004 a fiscal rule for oil revenue, whereby any revenues in excess of the benchmark level for budgetary use are transferred into the "Excess Crude Account (ECA)" for future use. The Democratic Republic of Timor-Leste’s natural resource fiscal rule, the Petroleum Fund Law, defines the estimated sustainable income (ESI) as 3 percent of the country’s petroleum wealth based on an assumed reference price.

Two features are potentially appealing in their applications to developing countries - a well-defined and conservative budget price or spending rule, which is monitored publicly or by stakeholders, may substitute for weak institutions; and transparent mechanisms may make it easier to plan long term, achieving the necessary savings for future generations and guarding against unsustainable consumption in the present.

2
# **1. Introduction**

The world of official (bilateral and multilateral) lending remains largely unexplored in the academic literature, despite the scale and relevance of this type of cross-border flows. Most studies focus on a single government or institution, such as US lending as part of the Marshall Plan or IMF bailouts during the Asian Crisis. Compared to the vast literature on private cross-border flows, research on official international financial flows has been limited by a lack of systematic data. Existing datasets on official debt and inter-governmental lending typically have a much narrower focus (i.e. on development aid, concessionary lending) or they start only in the 1970s.

We collect data on the lending transactions of 134 creditor countries and 56 international and regional financial organizations across 200 years, building on hundreds of sources, including international treaties, budget accounts, proprietary lending data by the World Bank, and archival material. In total, we identify more than 230,000 official loans, grants and guarantees from 1790 to 2015, with total commitments amounting to more than 15 trillion US Dollars (in constant 2015 terms). Using this data, we document the characteristics of official capital flows, link it to the occurrence of disasters (wars, financial crises and natural disasters), and study its determinants with a gravity model of bilateral lending since the 18<sup>th</sup> century. Our main findings can be summarized as follows.

Official lending by governments, central banks, and multilateral institutions is larger than commonly known. Indeed, over the past two centuries, official creditors have played a major role in international finance. Since 1800, official capital flows have repeatedly exceeded private cross-border flows. This is especially true during times of war, financial crises, or other disasters, when private flows decline and official actors become the chief international lenders.

The biggest surges in official lending occurred during major wars, in particular in WW1, WW2 and the Napoleonic wars. The amounts lent during these global conflicts were a multiple of the loans made during global financial crises or major natural disasters. This adds perspective to modern debates on the international financial system, as there is a tendency to think of official finance as a complementary type of capital flows in the context of development aid and crisis bailouts. But this view reflects the fact that much of the world has enjoyed 70 years of peace. Global wars have been the most disruptive force in international finance.

In peacetime, the main driver of official international lending was and continues to be financial crises. We show that international rescue lending has a long history, occurring repeatedly during banking panics and currency crises of the 19<sup>th</sup> and early 20<sup>th</sup> century, well before the creation of the IMF. The rescue loans extended in global crises were substantial, e.g. in the crises of 1861 or 1890, but these bailouts were ad hoc and aimed at bridging short-term liquidity problems. In contrast, since 2
91 Subsequent lifdng of some of h restrictions on trade union activity in early 1988 allowed even more labor discontent to surface. The two-year public sector wage agreement between the govemment and Turk-Is (the major trade union) in early 1988 was based on the undertanding tha further erosion of real wage would be avoided. Italso opened up some real wage increases. A 38 percent incrmase in wages in Janumy 1988 was followed by a fiurher 29 percent increase in the same year. An increase of 20 percent and 16 percent was stipulated for Januay and July 1989, respectively, and in case inflation tumed out to be higher hn expected provisions were made for supplementaiy wage increases to protect real wages. The legd minimum wage was also increased by S0 percent in July 1988. Private sector wage increases were geerally above those in the public sector and in some cases reached 100 percent As regards the revenue effects of price refonns, assuming unit elastic demand for diesel and kerosne, had ihe price reforms not been implemented during 1936-88, the estimnated shortfail in revenues in 1988 woud have been equal to TL 4805 billion or close to a quartr of central govenment revenues i he sme year.'<sup>2</sup> However, domestic diesel prices were already above the border prices before **the** price increases. As phnned, th increase in prices increased govenmerit revenues at **ihe** cost of higher-level distortions in ihese prices.

# Data Sources

Boratav, K., (1987): County Study _5:_ Turkey, Stabilization and Adjustment Policies and Programs, WIDER, Helsinki.

OECD (1989-1991): Main Economic Indicators, Department of Economics and Statistics, OECD, Pais. IEA (1991): Energy Balances of OECD Countries 1980-1989, OECD), Paris. World Bank, (1983): Turkey: Issues and Options in the Energy Sector, Report No. 3877-TU, ESMAP, Washington, D.C.

World Bank, (1987): Turkey. Countxy Economic Memorandu. Sustabiing *.e Adjustment Progrm, Report No. 6516-TU, EM2DA, Vol. 1,Washington, D.C.

World Bank, (1988): Turkey: Country Economic Memorandum. Towards Sustinable Growth, Report No. 7378-TU, EMENA Region, Washigton, D.C.

World Bank, (1992): World Development Report, Washington, D.C.

8 See **aumex** 1.7 **for deik rebted** to **calclatio of mroo effecti. See aTso discumion** of mChop. 1.

**mc isuact**
**Table 6. Moderate poverty headcount rates considering both pre‐ and post‐fiscal income aggregates, separately for households with distinct compositions in terms of headship structure and gender of the head**

|**Household group**|**Proportion of**<br>**individuals (%)**|**Pre-fiscal**<br>**poverty rate**|**Post-fiscal**<br>**poverty rate**|**Poverty change**<br>**(p.p.)**|
|---|---|---|---|---|
||**Panel A. H**|**eadship structure**|||
|Single-headed household|28.4|36.4%|33.3%|-3.1|
|Two-headed household|71.6|31.3%|31.7%|0.4|
||**Panel B. Gender**|**of the household h**|**ead**||
|Female household head|47.3|36.5%|36.0%|-0.5|
|Male household head|52.7|29.4%|28.7%|-0.7|
|**Panel C. Co**<br>|**mbining headship s**|**tructure with the g**|**ender of the head**||
|Single female household head|21.0|40.3%|37.7%|-2.5|
|Single male household head|7.4|25.6%|20.7%|-4.8|
|Two-headed household|71.6|31.3%|31.7%|0.4|

Source: authors’ estimations based on PNAD-C 2019. Notes: Pre-fiscal poverty indicates the poverty headcount rate considering market income plus pensions, while the post-fiscal poverty indicates the poverty headcount rate considering consumable income. The proportion of households indicates the number of households in each group as a share of the total number of households in PNAD-C. The household was considered two-headed if there is at least one member that declared to be the spouse of the household head.

# 4.1 Does fiscal policy have the same welfare impacts across racial groups?

Racial disparities are historically high in Brazil and fiscal policies hold the promise of contributing to mitigate such gaps. Therefore, a relevant question is to determine whether the most vulnerable racial groups are better off after taxes and transfers. Table 7 presents the poverty rates for households headed by whites, Afro-Brazilians and non-white, non-Afro-Brazilians. We observe that the impact of the fiscal system is similar across groups, particularly among households headed by Afro-Brazilians and whites. This result indicates a somewhat different trend compared to the previous evidence for Brazil relative to 2008/2009 that the fiscal system used to contribute more to reduce poverty among households headed by whites than Blacks and pardos (Pereira 2016).

**Table 7. Moderate poverty headcount rates considering both pre‐ and post‐fiscal income aggregates, separately by household head’s race**

|**Race of household head**|**Prop. of**<br>**individuals(%)**|**Pre-fiscal**<br>**poverty**|**Post-fiscal**<br>**poverty**|**Change in poverty**<br>**(p.p.)**|
|---|---|---|---|---|
|White|39.9|20%|19%|-0.5|
|AfroBrazilian|58.8|42%|41%|-0.7|
|Non-white,non-AfroBrazilian|<br>1.3|30%|31%|0.5|

Source: authors’ estimations based on PNAD-C 2019. Notes: Pre-fiscal income considers the household-level market income plus pensions, while the post-fiscal income considers the household-level consumable income. Afro-Brazilians are those declared to be either Black or Pardos. Proportion of households indicate the number of households in each group as a share of the total number of households in PNAD-C.

34
Zambian farmers. But before doing that, we review the poverty trends observed in Zambia during the 1990s.

# **1.3 Poverty Trends**

In spite of the significant reforms adopted by the Zambian government and of the significant intervention in international agricultural markets, Zambia is one of the poorest countries in the world. Furthermore, poverty rates tended to increase during the 1990s.

Our description of the poverty trends is based on three household surveys, the 1991 Priority Surveys, and the 1996 and 1998 Living Conditions Monitoring Surveys.<sup>6</sup> The Priority Survey of 1991 is a Social Dimension of Adjustment (SDA) survey. It was conducted between October and November and covered a total of 9,886 households. Sample sizes were increased to 11,750 and 16,800 households in the 1996 and 1998 LCM Surveys.

Table 6.1 reports the poverty dynamics. In 1991, the poverty rate at the national level was 69.6 percent. Poverty increased in 1996, when the head count reached 80 percent, and then declined towards 1998, with a head count of 71.5 percent. In rural areas, poverty is widespread; the head count was 88.3 percent in 1991, 90.5 percent in 1996 and 82.1 percent in 1998. Urban areas fared better, with a poverty rate of 47.2 percent in 1991, 62.1 percent in 1996 and 53.4 percent in 1998. In what follows, our focus is on rural areas.

Poverty trends by provinces are reported in Table 6.2. Zambia is a large country, and provinces differ substantially in basic characteristics like land quality, distance to the capital, roads, etc. In particular, cotton, the commodity under investigation here, can be produced (due to soil characteristics) in only the Southern, the Central, and the Eastern provinces. At the national level, poverty increased from 1991 to 1996 and then declined in 1998. Poverty trends in Lusaka,

> 6The Zambia Central Statistical Office, CSO, is in charge of conducting the surveys. The data from the 2001 LCMS was under preparation when this paper was written.

8
labor-intensive manufacturing sectors. The cheap labor and good infrastructure in the SEZs, as well as the Open Door policies coupled with generous incentives, provided a great opportunity for FDI to flow into China from the diaspora. Given the culture, language, and location advantages, such investments were dominant in the beginning stage, especially for the early SEZs (see table 1.4 for the FDI inflows to these SEZs).

The measures for attracting FDI included streamlined administrative control; concessionary tax rates, breaks, and exemptions; preferential fees for land or facility use; reduced duties on imports; free or low-rent business accommodation; flexibility in hiring and firing workers; depreciation allowances; and favorable arrangements pertaining to project duration, size, location and ownership (Ge 1999). For FDI, the corporate tax rate was especially generous—15 percent as opposed to 30 percent for domestic firms—plus exemption from local income tax.<sup>14</sup>

**<mark>Table 1.4 FDI Inflows in Five Comprehensive Special Economic Zones, 1978–2008</mark>**

|_Year_|_Shenzhen_|_Zhuhai_|_Shantou_|_Xiamen_|_Hainan_|
|---|---|---|---|---|---|
|||_Exports (billi_|_oncurrent US$)_<br>|||
|1978|0.009<sup>a</sup>|0.009<sup>a</sup>|0.251<sup>b</sup>|0.082|—|
|1990|8.152|0.489|0.84|0.781|0.471|
|2000|34.564|3.646|2.595|5.880|0.803|
|2006|135.959|14.843|3.484|20.508|1.376|
|2007|168.542|18.477|3.912|25.555<sup>c</sup>|1.838<sup>c</sup>|
|2008<sup>d</sup>|163.780|19.730|3.278<sup>e</sup>|26.970|—|
|||_UtilizedFDI(m_|_illioncurrent US$)_|||
|1978|5.48<sup>a</sup>|n.a.|1.61<sup>b</sup>|—|0.10<sup>b</sup>|
|1990|389.94|69.1|98.09|72.37|100.55|
|2000|1961.45|815.18|165.61|1031.50|430.8|
|2006|3268.47|824.22|139.60|954.61|748.78|
|2007|3662.17|1028.83|171.62|1272<sup>c</sup>|1120<sup>c</sup>|
|2008<sup>d</sup>|3929.58|1138.49|—|1955.63|—|

_Sources:_ Yeung et al. 2008; Yeung, Lee, and Kee 2009. _Note:_ — = <mark>not</mark> available. a. 1979. <mark>b. 1980. c</mark> . <mark>Preliminary figures</mark> . <mark>d</mark> . January–Nov <mark>ember</mark> . <mark>e</mark> . January–Sept <mark>ember</mark> .

Empirical evidence shows that FDI inflow is indeed positively linked with the expansion of output, employment, and labor productivity in the SEZs. Several figures based on the Shenzhen case illustrate this relationship. Figures 1.4 and 1.5 show that the trend of foreign investment in the secondary and tertiary sectors (where most of the FDI goes) appears to be closely correlated to the changing pattern of production, with some time lags.

Figure 1.6 shows that the rapid expansion in labor employment, especially in the 18
PoiLicY' Rx SEARCH WORKING PAPER 2891

# Abstract

Vietilam has achiieved remarkably high rates of school enrollmient and has mainitained good social indicators (infant and under-five mortality rates, life expectancy, fertility rate, child nutritioni, and access to basic services) compared to other coLintries with similar low income per capita.

Nga documlienits and analyzes chaniges in enrollimenit and education finance itn Vietnam from 1993-98. Enrollment rates increased substantially, but the increases were not eqLially spread across different income groups, regions, gender, and ethnic groups. The higher the level of education, the larger the gap in school enrollilenit among different socioeconomilic groups. Althoughi school fees were no longer compulsory at the primary level, households paid for many other school-related items, suchi as books, uniforms, private tutors, Iliuch, and transportation. These costs are a significanit financial burden on the poor.

On the other hanid, there is considerable variation in public spending per student across regions that, when cotipled with variation in enrollment rates across regions, resulted in a not pro-poor public spending pattern, although public spending on primary education was neutral in 1998.

Finally, Nguyen investigates whether rates of returin to edUcationl in the private wage sector changed in the 1990s. She concludes that returns to schooling increased substantially between 1992-93 and 1 997-98, especially at the tipper sccondary education and university levels.

This paper-a product of Macroeconomliics and Growth, Developmenlt Research Grotip-is part of a larger effort<sup>in</sup> the group to study household welfare and poverty reductioni in Vietnami. Copies of the paper are available free from the Woricl Bank, 18 18 H Street NW, Washiligton, DC 20433. I'lease contact Emily Khine, room MC3-347, telephonie 202-473-747 1, fax 202-522-3518, email address kkhine@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. Thc author may be conltacted at nnga@t,worldbank.org. September 2002. (28 pages) _Thec Policy, Rescarch WZorking Paper Series dissedmibyates t fieadiis Res of work io prygress to encourage tae excfafige of ideas aitt_ **_deuelo pmolent_** _isslze s._ **_Anl_** _objvectieve of tIe serie s is to get th)e finldinzgs_ **_o)zit_** _qnickIv. even if the presenztations are less_ **_zIbaii_** _fidlyv polisbeed. Th)e papers carry the_ **_nlames_** _of-tbe authors_ **_aii(d_** _sbouldt beL cited accordingly. The 1/iiditigs, initerpretattioiis, anid coircltfsionzs expressed in this parper are enrtire.1v those o)f tlve auxtbrs. Tbley do_ **_no(t_** _ntecesscarily, represenzt tl)e vietv of the Il/ovrld_ **_Bazik,_** _its Execi(tive l)irectors. or the coitittries tbe), repiresent._ Procluced **hiy** the Researchi Advisorv Staff