rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3.1. Real macro indicators: level in 2012 (constant US$ million) and base growth by period (Percent per year)**

||**2012**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|
|**Absorption**|3,403.1|0.4|4.7|3.5|
|**Consumption - household**|1,550.8|5.6|5.4|5.5|
|**Consumption - UNMIL**|469.9|-84.5|-1.0|-40.8|
|**Consumption - government**|391.1|12.0|4.0|6.2|
|**Fixed investment - private**|717.0|-14.1|8.1|1.4|
|**Fixed investment - government**|274.4|7.8|-7.5|-3.5|
|**Exports**|697.9|21.6|5.2|9.5|
|**Imports**|2,253.3|2.7|4.6|4.1|
|**GDP at marketprices**|1,847.7|8.3|5.1|6.0|
|**GDP at factor cost**|1,638.7|8.8|5.2|6.2|
|**Total factor employment (index)**|100.0|5.9|3.2|3.9|
|**Total factorproductivity (index)**|100.0|2.9|2.0|2.2|
|**GNI**|1,686.0|8.4|5.0|5.9|
|**GNDI**|2,807.3|3.1|4.7|4.2|
|**GNI per capita (2009 US$)**|423.4|5.8|2.7|3.6|
|**GNDI per capita (2009 US$)**|704.9|0.7|2.4|1.9|
|**Real exchange rate(index)**|100.0|-0.7|0.8|0.4|
|**Unemployment rate (%)**|12.8|10.9||8.0|
|**MDG 1 -- poverty rate (%)**|60.2|54.2||38.6|
|**MDG 4 -- U5MR (per 1000)**|90.2|75.3||65.5|
|**MDG 7 -- Water access (%)**|70.8|75.7||77.6|
|**MDG 7 -- Sanitation access(%)**|19.6|23.7||25.4|

**Note:**

**Unless otherwise noted, the unit for the 2012 column is millions of US$ (at 2009 prices). Rows for unemployment and MDG indicators show levels in 2012 and at end of each period. Other rows and columns show average annual real growth during the indicated period.**

18
5

# **A** **_._** _Trade in goods and services_

Five general features of Turkey’s long term trade performance since 1985 stand out. First, except for contraction in 1999, the value of total exports of goods and services has been steadily expanding at the average rate of growth (least square estimate) of 10 percent over 19862004.

Second, imports were highly volatile over 1986-2004 and strongly correlated with GDP growth indicating that exchange rate crises and sharply falling incomes in 1994, 1999 and 2001 have been responsible for contraction in imports (Figure 1). Bursts or implosions in GDP growth rates in nominal terms accompanied similar changes in imports. The coefficient of correlation of annual changes in imports of goods and services and GDP growth rates in 1985-2003 was 88 percent. The same coefficient for exports of goods and services and GDP was 35 percent.

**Figure 1:** _Annual rates of growth in value of exports, imports and GDP in 1990-2004 (in percent)_

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>Imports of goods and services GDP Exports of goods and services<br>1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: Derived from the World Bank’s Development Indicators database.

Third, Turkey’s integration and dependence on global markets for goods and service has significantly increased over the last decade. Exports of goods and services as percent of GDP (both in current prices) almost doubled from an average of 16 percent in 1985-90 to 30 percent in 2000-2004. Trade in goods and services in terms of the GDP strongly expanded in the 1990s. The corresponding ratio of 60 percent were more than twice as high in 2000-03 as they were in 199093 (27 percent) indicating rapid increase in reliance of Turkey’s economy on external trade in goods and services.

The level of achieved integration into global markets for goods and services is relatively high by regional standards. Although respective values of two indicators expressed in terms of GDP—trade and exports of goods and services—are lower than for EU-8, other EU candidates and South East European (SEE) economies, the differences are not significant. They stem from the size of economy, tourist attractiveness and the level of GDP. Both SEE-4 and EU-8 economies are much smaller, except Poland, and some of them, on top of being much smaller, have also much higher GDP per capita. Both contribute to higher values of openness indicators. Taking into account the size of the economy, Turkey compares favorably with the largest economy among comparators—Poland. Exports of goods and services in percent of GDP are slightly higher in Turkey than in Poland and total trade slightly smaller (Table 1).
|Shareholding company?||2.025***||1.804***|
|---|---|---|---|---|
|||(0.647)||(0.294)|
|Access to a credit line?||0.651**|0.316**|0.0456|
|||(0.326)|(0.127)|(0.257)|
|Part of a larger group?||||1.383***|
|||||(0.313)|
|Experience a criminal attempt|||-0.374**||
|lastyear?|||(0.169)||
|Age|||0.0105**||
||||(0.00502)||
|% of firm owned by the largest||||0.0212***|
|owner(s)||||(0.00493)|
|N|1,669|971<br>285|541|378|
|R-squared|0.072|0.371<br>0.253|0.344|0.558|
|*** p<0.01, ** p<0.05, * p<0.1.<br>which a sufficiently large sample|Robust standa<br>of innovative|rd errors in parentheses. Showing<br>firms is available.|results only for|countries for|

Restricting to the innovative firms, less managerial time spent with bureaucracy, not belonging to a foreign company, and access to credit lines are the main labor-productivity correlates shared by innovative firms across the examined EAP sample (Table 10).

_Table 10 What affects labor productivity? Focus on the manufacturing sector in each country, innovative firms_

||**China**|**Indonesia**|**Malaysia**|**Philippines**|**Vietnam**|
|---|---|---|---|---|---|
|Average years of education of the|0.0642***|||||
|typical worker|(0.0218)|||||
||-0.334***||||0.447|
|Power outages during the last year?|(0.0886)||||(0.410)|
|% management’s time spent on|-0.0365**||||-0.0272***|
|bureaucracy|(0.0142)||||(0.00865)|
||-0.00839**|||0.00996***|0.0172*|
|% of capacity used in production|(0.00367)|||(0.00356)|(0.00914)|
||0.220**|||||
|Have ISO quality certification?|(0.105)|||||
||||-1.655**||-0.976**|
|Belong to a foreign company?|||(0.824)||(0.397)|
|Access to a credit line?|||1.129**||0.578*|
||||(0.439)||(0.329)|
|% of sales spent on informal|||-0.0504***||0.0518*|
|payments to govt officials|||(0.0152)||(0.0300)|
|Age|||-0.0159|||
||||(0.0155)|||
|% of firm owned by the largest|||-0.00599|||
|owner(s)|||(0.0105)|||
|Use foreign technology in|||||1.354***|
|production?|||||(0.338)|
||||||-0.356|
|Water outages during the last year?|||||(0.400)|
|Shareholding company?||||0.597***||
|||||(0.169)||

16
house gas emissions. Other areas for negotiating rules of the game include public procurement, other non-tariff barriers, and subsidies, including investment incentives. The absence of international rules in these areas allows discrimination to be pursued with impunity. While many government policy responses to the crisis may be temporary, the effects of “buy national” or ‘buy local” could prove enduring if they result in emulation. It is noteworthy that some of the countries that are engaging in “buy national” policies are among those that have been most interested in seeing developing countries accede to the Agreement on Government Procurement. The signal that is being sent may have long term negative consequences for the realization of this objective. Finally, closure on Doha could also help launch a deeper reflection on how services trade negotiations could be pursued more fruitfully, and whether broader regulatory cooperation – e.g. on prudential regulation in finance and pro-competitive regulation in transport – is a necessary underpinning for market access negotiations at the WTO.

# **REFERENCES**

Abbott, R. 2009. “How to revive Doha with some chance of success,” ECIPE Policy Brief. Adler, M., C. Brunel, G. Hufbauer and J. Schott. 2009. “What’s on the Table? The Doha Round as of August 2009,” Peterson Institute for International Economics Working Paper 09-6, August.

Baffes, J. 2005. “The Cotton Problem,” _World Bank Research Observer,_ 20(1): 109-44.

Borchert, I. and A. Mattoo. 2009. “Is the Crisis Provoking Protection in Services?” In Evenett, S., B. Hoekman and O. Cattaneo (eds.) _The Fateful Allure of Protectionism: Taking Stock for the G8._ London: CEPR.

Bouët, A. and D. Laborde. 2009. “The Potential Cost of a Failed Doha Round,” IFPRI.

Bouët, A., D. Laborde, E. Dienesch and K, Elliott. 2009. “Who Would Benefit from Duty Free Quota Free Access?,” CGD working paper, forthcoming.

Bown, C. 2009a. “Global Antidumping Database, version 5.0 (at www.worldbank/trade) Bown, C. 2009b. “The Global Resort to Antidumping, Safeguards, and Other Trade Remedies,” In Evenett, S., B. Hoekman and O. Cattaneo (eds.) _The Fateful Allure of Protectionism: Taking Stock for the G8._ London: CEPR.

- Bown, C. 2009c. “The Pattern of Antidumping and Other Types of Contingent Protection,” _PREM Trade Note. 144_ , Washington DC: World Bank.

- Carrere, C. and J. De Melo. 2009. “The Doha Round and Market Access for LDCs: Scenarios for the EU and US Markets,” CEPR Discussion Paper 7313 (May).

- Deaton, A. and G. Laroque. 1992. “On the Behaviour of Commodity Prices,” _Review of Economics Studies_ 59(1):1–23.

- Decreux, Y. and L. Fontagné. 2009. “Economic Impact of Potential Outcome of the DDA,” CEPII Working Paper (May).

- Djankov, S., C. Freund and C. Pham, 2006, "Trading on Time", World Bank Policy Research Working Paper No. 3909.

- Elliott, K. and R. Soderquist. 2009. “Making Trade Preferences Work for Development: Recommendations for the Global Reform and Coordination of Trade Preference Programs,” Center for Global Development, mimeo.

27
question, which we will refer to this as the “corruption experiences” question was a new addition to the core GWP questionnaire in the 2008 wave of surveys. However, for reasons of timing and questionnaire space, it was asked in only 115 of the 124 countries covered in our sample of the GWP in 2008 and early 2009. This question was asked in most high-income OECD, Latin American, Asian and African countries, but coverage of Eastern Europe is scarcer. Nevertheless the breadth of GWP data still allows us to study the effects of corruption experiences in a much larger sample of countries and respondents than any previous work.

The GWP also asks a more generic question about the corruption perceptions of respondents that we will use alongside the experience question in this paper: _“Is corruption widespread throughout the government in this country, or not?”_ We refer to this as the “corruption perceptions” question. It was asked in 112 of the 124 countries in our sample. However, as the countries in which the corruption experience and perception questions were fielded do not match perfectly, the sample in which both questions were asked comprises 103 countries.<sup>5</sup> There are substantial conceptual and practical differences between the corruption experiences and corruption perceptions question. The former asks about a respondent‟s personal experiences with corruption, while the latter solicits the respondent‟s views about the prevalence of corruption, regardless of whether the respondent has witnessed or experienced any corrupt acts himself. We note first that one would naturally expect to see differences between the responses to the two questions. The corruption experiences question is potentially a good gauge of “petty” or administrative corruption that individuals might be likely to experience in their everyday lives: a policeman asking for a bribe to avoid a ticket, or a bureaucrat soliciting an irregular payment for a permit. On the other hand, the corruption perceptions question can potentially capture the prevalence of broader forms of corruption, particularly at higher levels of government. The downside of course of this latter question is that it does not draw on the respondent‟s personal experience, but rather is informed by the respondent‟s exposure to second-hand information about corrupt activities. Finally, as we argue in more detail in Section 4, a

> 5 See Table 8 for a complete list of countries used in this study.

8
<!-- Start of picture text --> Figure 19: Log per capita GDP and Expenditure on Transport, 2003<br>VNM<br>CHN<br>THA<br>CHL<br>LAO<br>KHM PHL<br>IND COLPER BRA MEXARG<br>IDN<br>6 7 8 9<br>Log(GDP per capita)<br>Source: Appedix A, Connecting East Asia a New Framework for Infrastructure, Calderon and Serven, 2008<br>and authors’ calculations<br>Figure 20: Log per capita GDP and Expenditure on Telecommunications, 2000<br>CHL<br>BRA<br>COL<br>PER<br>MEX<br>ARG<br>CHNPHL<br>IDN<br>KHMLAO<br>THA<br>IND<br>5 6 7 8 9<br>Log(GDP per capita)<br>6<br>4<br>2<br>Expenditure on transport (% of GDP)<br>0<br>1.5<br>1<br>.5<br>Expenditure on telecommunications (% of GDP)<br>0<br><!-- End of picture text --> _Source: Appedix A, Connecting East Asia a New Framework for Infrastructure, Calderon and Serven, 2008 and authors’ calculations_ 35
Table V: Aggregate Productivity, Management Mode and Regional Trust

||(1)|(2)|
|---|---|---|
|Dependent|ln_TFPQ_|ln_L_|
|FOFM|-0.248<sup>_∗∗∗_</sup><br>(0.0319)||
|Trust||-4.122<sup>_∗∗_</sup><br>(1.8913)|
|lnTFPQ||0.163<sup>_∗∗_</sup><br>(0.0636)|
|Trust_×_lnTFPQ||0.466<sup>_∗∗_</sup><br>(0.2026)|
|Controls|X|X|
|Clustering|Region|Region|
|No. Clusters|116|116|
|_R_<sup>2</sup>|0.35|0.46|
|_N_|73066|73066|

Note: All specifications include 3 digit SIC and country fixed effects. Controls include log firm age, log region gdp per capita and the region’s college share of residents. Complex is complexity measure defined in text. HighRol is a dummy for high rule of law countries Germany, Netherlands, Finland and Switzerland.

31
The lowest correlation coefficient in our sample is 0.53 for Greece, while the highest is 0.89 for Czechia.<sup>13</sup> These represent respectively the high and low mobility scenario, as the closer the coefficient is to one, the stickier incomes are. A further assumption then is that these imported coefficients are sufficiently extreme to form upper and lower bounds in the case of Malaysia. Given the variety of countries included, at various levels of economic development, we suggest that this assumption is not unreasonable.

# 2.5. Data

This study analyzes eight rounds of cross-sectional data from the Household Income Survey (HIS) collected by the Department of Statistics Malaysia. The data are for the years 2004, 2007, 2009, 2012, 2014, 2016, 2019 and 2022; we consider all pairs of adjacent survey rounds.<sup>14</sup> One requirement for analyzing welfare changes over time is that the measure of welfare, income in our case, is measured in a consistent and comparable manner. The Malaysian data are of high quality and satisfy this requirement. In addition, the sample sizes of the surveys are large, ranging from 36,000 to over 80,000 household-level observations per survey year.

The dependent variable in our analysis is the natural logarithm of monthly household pre-tax income per capita, which is evaluated against our poverty line to determine the poverty status of a household. We did not use post-tax income because few Malaysians are subject to personal income tax; it is thus unlikely to affect poverty or vulnerability status. Households report income over the twelve months preceding the interview. Notably, this means that households interviewed in 2022 also report over part of 2021.

> 13 In the case of Greece, this is the correlation of disposable household income between 2001-2004; for Czechia, it is the 2014-2015 correlation of disposable incomes.

> 14 Surveys may extend into the subsequent year. When we refer to ‘round 1’, this indicates the first round of any pair, not necessarily the 2004 survey (and similarly for ‘round 2’).

8
data, available in KLPS1, in which respondents provided every test score for as many times as they had taken the KCPE. Even if the most recent test score is endogenous with respect to the respondent’s type and the location of the cutoff, the first test score should not be. Appendix Figure A2 shows that although the problem is less severe in this restricted sample, even these first scores do not have a smooth density at the discontinuity. However, administrative data on scores in the region display no such irregularity at the cutoff score, so I conclude that the self-reports are, in many cases, incorrect, and administrative data must be matched to the KLPS2 dataset in order to use a regression discontinuity design.<sup>11</sup> To complement the KLPS data, I gathered an auxiliary dataset of 17,384 official KCPE scores from District Education Offices and, when the districtlevel offices did not have the records, directly from primary schools. The official records I was able to collect in 2009 and 2010 include roughly 88 percent of the KLPS schools during the years of interest in this study.<sup>12</sup> Based on name, year, and school, I am able to cross-check KCPE scores for roughly 77 percent of the KLPS respondents who report taking the KCPE. While many self-reported scores are in accordance with the official records, there is substantial misreporting.<sup>13</sup> Using the 88 percent coverage of the administrative data I could collect, a

> 11I show the distribution of regional 2008 test scores in Appendix A.3.

> 12 Every KLPS source school with missing records was visited at least once by me or another member of the data collection team; recent re-districting and political upheaval in Kenya, combined with local problems with record storage over the last 12 years, prevented the collection of the last 12 percent.

> 13I discuss the matching process and characterize misreporting in Appendix section A.1.5.

10
3 II Patterns of caTital flows in and out of Mexico. 2 The fifties and sixties saw conservative fiscal policies, steady growth and low inflation. The international environment was hospitable by later standards, with real interest rates well below Mexico's own rate of growth (Figure 1); as a result Mexico could sustain deficits on its non-interest current account throughout the period while maintaining its debt-output ratio at around twenty percent.

But oil discoveries in the late seventies followed by the second OPEC price explosion in 1979 triggered a strong fiscal expansion in Mexico at the same time that banks tried to recycle OPEC's surpluses. Real interest rates on international capital markets turned sharply negative while the strong fiscal expansion fueled by skyrocketing oil revenues pushed up Mexico's rate of growth. it is a measure of the extraordinary push into debt of the time that, in spite of the highly favorable turn in the output growth-foreign interest rate differential, Mexico doubled its debt-output ratio between 1974 and 1982. The true measure of this expansion is moreover masked by a strong real appreciation, slowing down the rate of increase in the debt-output ratio; while the latter only doubled, the real value of Mexico's external debt trippled in constant foreign prices. The monetary anti-inflation policies in the US and the UK in 1979 sharply turned the international environment against Mexico. Real interest rates turned abruptly positive, while oil prices, after the 1979 euphoria, collapsed almost as fast as they had gone up before (Figure 2). These adverse shifts triggered alarm bells about its borrowing strategy that went unheeded 2 Source for Figures in aLL sections was INEGI and Bank of Mexico.
perceived severity of shocks, measured through a simple yes/no question, apparently did not influence the decision to turn to forest resources in response to shocks.

**Table 9: Explaining the likelihood of extractive responses to self-reported shocks. Probit model**

||||Selection model|
|---|---|---|---|
|Dependent variabl<br>products to deal wi|e: used forest<br>th shock|or wild|Dependent variable: self-reported<br>climate-related shock|
||Estimate|SE|Estimate<br>SE|
|Shock severity|-0.027|(0.10)||
|Female headed<br>HH|-0.013|(0.11)|-0.093*<br>(0.05)|
|Age of HHH|0|(0.00)|-0.002**<br>(0.00)|
|Education of<br>HHH|-0.043***|(0.01)|-0.023***<br>(0.01)|
|HH size|-0.005|(0.01)|0.013**<br>(0.01)|
|HHH belongs to<br>ethnic majority|0.054|(0.09)|0.025<br>(0.06)|
|Cropland area|-0.079***|(0.03)|-0.004<br>(0.00)|
|Value of HH<br>durables|0|(0.00)|0<br>(0.00)|
|Financial assets|0|(0.00)|0<br>(0.00)|
|Forest income<br>share at village<br>level*|2.817***|(0.53)|0.066<br>(0.28)|
|Informal credit<br>facility in village|0.017|(0.12)|0.075<br>(0.08)|
|Formal credit<br>facility in village|-0.299|(0.31)|-0.075<br>(0.13)|
|Health center in<br>village|0.019|(0.13)|-0.154*<br>(0.09)|
|Distance to<br>district center|-0.002*|(0.00)|-0.001*<br>(0.00)|
|Distance to forest<br>edge|0|(0.00)|0.002**<br>(0.00)|
|Region (Africa)|0.284|(0.25)|0.412***<br>(0.09)|
|Region (Asia)|0.864***|(0.27)|0.147<br>(0.12)|

24
Lower fertility also produces benefits at the household level. Joshi and Schultz (2007) and Schultz (2009) study the effects of a long-run randomized family planning intervention in Bangladesh and find that the reduced fertility caused by the intervention (which amounted to approximately one child per woman) had significant and persistent positive impacts on women’s health, earnings and their households’ assets. In addition, children born in the treatment areas completed on average more years of schooling and were less likely to die before age five. Examining the expansion of a family planning organization in Colombia, Miller (2010) finds that family planning allowed women to postpone their first birth and reduce lifetime fertility, and that the ability to postpone the first birth enabled women to obtain more education and be independent later in life.

Given the potentially large economic payoffs from falling fertility, it is important to know what causes fertility to fall in the first place. This paper examines the sharp drop in fertility in Rwanda between 2005 and 2010 using data from two successive DHS cross sections. In a first step, we examine whether the drop in fertility is more related to the evolution of social and economic conditions simultaneously affecting women of all ages (so-called period effects) or to the shared socializing experiences of different cohorts (cohort effects). Using the hierarchical age-period-cohort (HAPC) method developed by Yang and Land (2006), we find that the decline in fertility has mainly been driven by cohort effects, with younger cohorts of women having significantly fewer children after controlling for the effects of age and period. These cohort effects are important as they represent the essence of social change and hint at further fertility reductions in the decade to come. In a second step, we decompose the change in fertility between 2005 and 2010 into a part that can be explained by changes in exogenous covariates and an unexplained part. We find that the drop in fertility for young women can to a large extent be explained by changes in covariates, most notably the sharp increase in educational attainment observed since 2005 and improving living standards at the household level. For older women the drop in fertility remains largely unexplained, though the explained part is in large part due to rising 4