rafmacalaba/datause-encoder-data huggingface dataset:
not have panel data, our data fully include pre-crisis and crisis price levels, unlike Jensen and Miller (2008). We estimate the relationship between household-level food security and increasing food prices using a unique cross-sectional, temporally stratified, nationallyrepresentative survey. The data are from the National Risk and Vulnerability Assessment (NRVA) 2007/08, a sample of over 20,000 households from all 34 provinces of Afghanistan, conducted over a 13-month period. It is the first nationally-representative household survey in Afghanistan<sup>8</sup> designed to account for seasonal variations in consumption and other measures of wellbeing.<sup>9</sup> The most important feature of the design for this analysis is that the NRVA provides a comprehensive and representative portrayal of consumption patterns prior to and after the onset of the 2007/08 food price shock, providing substantial variation in prices and comparable sub-samples for our analysis.

A particular strength of the NRVA survey is its detailed information on the frequency and quantity of food consumption of 91 different food items, which allows us to observe how households change the composition of their diets in response to price changes and also allows us to create multiple measures of household food security. Food security broadly consists of four main dimensions: availability, access, utilization, and stability.<sup>10</sup> In this analysis, we concentrate on aspects of access and utilization. The former refers to a household’s ability to obtain food, which depends on income, prices, and market access; the latter refers to an individual’s ability to process nutrients and energy from food, which depends on many factors, including dietary diversity and nutrient absorption, intrahousehold allocation of food, and hygienic preparation. In our analysis we examine two aspects of utilization, namely measures of dietary diversity and consumption of a key macronutrient – protein.

Several salient findings emerge from the household-level food security analysis. Prior to and during the large increase in food prices from August 2007 to September 2008, the descriptive statistics reveal a decline in the real food consumption of approximately 33

> 8 While census data does not exist for Afghanistan, the pre-cursor to the census, a national household listing was undertaken from 2003 to 2005 by the Central Statistics Organization. This listing forms the basis for the current official population estimates and also serves as the sampling frame for the NRVA 2007/08.

> 9 The NRVA 2007/08 is the third round of the survey; fieldwork took place from August 2007 to September 2008. The first two rounds (2003, 2005) were each conducted during a single season, leading to seasonallybiased estimates, e.g., poverty estimates during or immediately after harvest will be lower than during lean periods. The NRVA 2007/08 was implicitly stratified over time during the year-long fieldwork so that estimates of wellbeing could account for seasonal variations.

> 10 FAO(2006)

4
# Table 3

## Productivity and Wage Trends in the Garment Industry by Plant Size

|Year<br>|Lab<br>(0<br>Smal|our Pro<br>00<br>of 19<br>l Mediu|ductivity<br>73 pesos)<br>m<br>Large All|A<br>(00<br>Smal|verag<br>0<br>of 19<br>l Mediu|e Wages<br>73pesos)<br>m<br>LargeAll|Aver<br>(00<br>Small|age La<br>0<br>of 197<br>Mediu|bour Costs"<br>3 pesoS)<br>m<br>Large All|
|---|---|---|---|---|---|---|---|---|---|
|1973|29.|8 38.1|49.3 40.|4 10.|5 13.9|17.0 14.|3 13.|0 17.5|23.1 18.7|
|1980|40.|1 51.4|75.5 56.|2 14.|0 15.7|20.5 16.|8 18.|6 21.9|30.7 23.9|
|1987|54.|9 76.1|77.2 72.|7 16.|8 1B.4|15.5 17.|4 24.|3 27.6|25.0 26.1|
|1990|na|na|na na|16.|4 17.6|20.1 18.|0 23.|8 26.6|31.1 27.1|
|1987|1.8|4 2.00|1.57 1.8|0 1.6|0 1.33|0.91 1.2|2 1.8|7 1.58|1.08 1.4|
|1987<br>197|/11<br>3|.84<br>2.00|1:57 1.8|0 1.6|0 1.33|0.91 1.2|2 1.8|7 1.58|1.08 1.4|
|1990<br>197|/<br>na<br>3|.a|na na|1.5|6 1.27|1.18 1.2|6 2.1|5 1.52|1.35 1.45|

a) Wages paid plus all fringe benefits.

Note: Since there may be many temprory workers in this industry, itis possible thit'the reported wages per worker are seriously upward biased for the smaller plants...

34
**Table A25: Characteristics of Inspectors and Brokers Participating in the Corruption Scheme**

|**Dependent variable:**|**Average **|**excess inte**|**raction share**|
|---|---|---|---|
||**Panel A. Inspectors**|||
||(1)|(2)|(3)|
|Male|0.049*|0.048|0.010|
||(0.029)|(0.029)|(0.044)|
|Average tenure: 1-2 years|0.075***<br>(0.025)|0.076***<br>(0.027)|0.149***<br>(0.046)|
|Average tenure: 2-3 years|0.085**|0.080**|0.172***|
||(0.034)|(0.037)|(0.044)|
|Age||-0.002|0.002|
|||(0.002)|(0.003)|
|Management degree|||0.129**<br>(0.048)|
|Economics degree|||0.041<br>(0.038)|
|Law degree|||0.018<br>(0.044)|
|Observations|29|29|18|
|R-squared|0.201|0.222|0.624|
||**Panel B: Brokers**|||
||(4)|(5)|(6)|
|Based in Toamasina|0.059*|0.052|0.052|
||(0.035)|(0.037)|(0.037)|
|Importer acting as own br|oker|-0.050|-0.045|
|||(0.033)|(0.036)|
|Average market share|||-0.091<br>(0.657)|
|Broker tenure: 5-10 years|||0.044<br>(0.048)|
|Broker tenure: more than|10 years||0.046<br>(0.050)|
|Observations|63|63|63|
|R-squared|0.044|0.051|0.062|

_Notes:_ Standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. The average excess interaction share is measured as the share of declarations subject to significant excess interaction calculated using calibration methods (as explained in Section 4). The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

89
# **Three Macroeconomic Trends around the Onset of Armed Conflict in Developing Economies**

Daniel Lederman and Claudio J. Rojas<sup>1</sup> Office of the Chief Economist Middle East and North Africa Region The World Bank Group _Key words_ : Inflation, public debt, government consumption, armed conflicts.

_JEL codes_ : E6

> 1 Daniel Lederman is Deputy Chief Economist for the Middle East and North Africa, and Claudio J. Rojas is a consultant in the Office of the Chief Economist for MNA of the World Bank Group. The authors gratefully acknowledge the insightful comments from and discussions with Rabah Arezki, Abdallah Al Dardari, Kevin Carey, Shahrokh Fardoust, Adeel Malik and other participants in the First Authors’ Workshop of the Building for Peace in the Middle East project. The findings and opinions expressed in this paper do not necessarily represent the views of the World Bank Group, its Executive Directors or the governments that they represent. All errors are the responsibility of the authors.
usage rates reported by the head of household are slightly higher than those from the full enumeration treatments, though we cannot reject the hypothesis that the two rates are equal to one another. In all, however, the head of household reports information that is very similar to that generated by full enumeration.

This is good news because interviewing only the head is much cheaper than interviewing all adult members of a household, an issue we will return to below. However, some surveys with a financial services module, such as labor force participation surveys, are designed to interview all members of a household. Our results provide good news in those cases, too, in that the information generated via the full enumeration appears to be a reasonable substitute for that generated by the head of household. Because the household usage rates calculated from responses to institutionbased questions are comparable using either method, there is potential to expand comparisons across a much broader set of countries.

In contrast, a randomly selected adult from the household (who is not the head) does not provide information that is comparable to that generated by the head or via full enumeration. Randomly selected informants produce usage rates that are lower than those for the other two methods, and significantly lower for _banked_ , _indirect access_ , and _formal credit._ This pattern suggests that the random informant has substantially less knowledge about household use of financial services than does the head of household. We also note that the disparities are greatest for services provided by formal institutions. For both _informal savings_ and _informal credit_ , the usage rates produced by random informants are almost identical to those produced by the head of household or via full enumeration. This 19
# **Notes**

1 While the Republic of Korea viewed housing as a nonproductive sector and did not intervene directly in building housing in the early period of urbanization in the 1960s, it intervened quite heavily in land development. In the late 1970s, it began to intervene in the housing sector and had one of the more regulated housing development and finance systems.

2 World Bank 2010.

3 World Bank 2009.

4 World Bank 2009.

5 Tsenkova and Turner 2005.

6 Biderman 2008.

7 Razzaz 1992.

8 Payne, Durand-Lasserve, and Rakodi 2008; Lanjouw and Levy 2004.

9 Evidence from Brazil suggests that municipalities have a difficult time reconciling and keeping track of the multiple layers of rights through regularization programs and of historical systems of allocation, making for an opaque and even more cumbersome system of land administration.

10 Payne, Durand-Lasserve, and Rakodi 2008.

11 Payne, Durand-Lasserve, and Rakodi 2008.

12 Rojas and Medellin 2011.

13 The authors suggest that such arrangements may also be common in South Asian cities. There is evidence that similar arrangements existed in the bustees of Calcutta.

14 Oxley, Lishman, and Brown 2010.

15 Schuetz 2007.

16 USAID 2008.

17 Renaud 2004.

18 Buckley and Mathema 2007.

19 Whitehead, Stephens, and Munro 2005.

20 Ruprah 2010.

21 The program in Costa Rica that allowed much broader uses of the subsidy to purchase or improve existing units or build new housing had better results in both coverage and leakage. It initially managed to attract private developers, but it functions primarily through the nonprofit sector. However, budget constraints resulted in a reduction in resources available to support the program.

> 22 Rent subsidies ( _Bolsa Aluguel_ ) through rental allowances were created to allow a limited number of low-income families to access the private rental market in São Paulo. However, rental ceilings were set below available market rental rates, and the subsidy levels required were significant.

23 Whitehead 2011.

24 Whitehead, Stephens, and Munro 2005.

25 ODPM 2005.

26 For a comprehensive review of housing finance, including subsidies related to housing finance, see Chiquier and Lea (2009).

27 Chiquier 2006.

28 Filtering has not been studied in developing countries. An early study by Ferchiou (1988) of Mexico and Tunisia indicated that filtering was limited, with very short moving chains.

29 Barker 2004.

30 Biderman and Smolka 2011.

31 Hannah 1989.

27
developing countries increasing access to non-concessional financing as they continue to develop; (iii) or increasingly relying on more expensive domestic debt financing, as saving increases and the domestic markets develop. Under this scenario the countries with an upward shift in debt dynamics include Cambodia, and Tajikistan, among the LIDCs, with the debt to GDP of Tajikistan reaching above 50 percent, and Albania, Georgia and Malaysia among the EMs, with all countries with a debt to GDP ratio above 50 percent.

Figure 18. Scenario 2: Interest Rates at least at One Percent (Change in debt-to- GDP ratio, 2016-30) <!-- Start of picture text --> LIDCs EMs<br>20% 70% 20% 100%<br>90%<br>60% 15%<br>15%<br>80%<br>50% line<br>50% 10% 70%<br>10%<br>60%<br>40% 5%<br>50% line<br>5% 50%<br>30% 0% 40%<br>0% 20% -5% 30%<br>20%<br>-5% 10% -10% 10%<br>-15% 0%<br>-10% 0%<br>ALB MYS GEO<br>TJK KHM<br>Baseline BRI Debt to GDP ratio including BRI Baseline BRI Debt to GDP ratio including BRI<br>In percent of GDP In percent of GDP<br>Debt to GDP at end-2030 Debt to GDP at end-2030<br><!-- End of picture text --> Sources: WEO, WIND Database, LIC DSF DSAs and de Sources: WEO, WIND Database, LIC DSF DSAs and de Soyres (2019). Soyres (2019).

Debt dynamics in the medium term, during the construction phase of BRI infrastructure, signal that 12 countries (or 28 percent of the 43 countries considered) would experience heightened debt vulnerabilities. The medium-term analysis, which does not fully incorporate the impact of BRI investment on growth, concludes that 7 LIDCs and 5 EMs as a result of large BRI debt financing would quickly increase their debt-to-GDP ratio.

Debt dynamics over the long-term would worsen in 11 countries (or 37 percent of the 30 countries considered) due to BRI, but debt vulnerability would remain limited. Of the 30 countries included in the long-term debt dynamic simulations, 11 would experience a higher debt-to-GDP ratio under BRI compared to the baseline, but in only in 2 countries BRI debt financing would result in increasing debt dynamics, with the PPG debt increasing above 50 percent of GDP in 2030. Under less favorable financing conditions, 7 of these countries would experience a shift from a declining to an increasing debt-to-GDP ratio. In 5 of these 7 (1 LIDC and 4 EMs) increasing debt dynamics would result in a debt-to-GDP ratio above 50 percent.

# VI. Fiscal Risks

BRI investments may also represent fiscal risks, in addition to the guarantees that may be issued by a government. As in Brixi and Schick (2002), fiscal risk is defined as the source of increased financial requirements that a government could face in the future. In BRI-recipient countries, governments are 29
industrial countries were 16.8 percent of their total absorption in 2001/02 while their imports from other developing countries were only 8.8 percent (Tables 3 and 6). By 2007/08, these ratios were only 9.7 and 9.0 percent respectively. Our sample of developing countries have significantly reduced relative share of their imports from industrial countries and maintained their imports from developing countries. Thus, the sample developing countries’ import structure has become more balanced between industrial and other developing countries.

**Table 6: Developing Country's Manufacturing Imports from Developing Countries during 1991/92 to 2001/02 and 2001/02 to 2007/08**

||Share of<br>Imports|Developing<br>in Domestic<br>(%)|Countries<br>Demands|Annual Imp<br>From De<br>Countr|ort Growth<br>veloping<br>ies(%)|A<br>1991/92|nnual Import <br>- 2001/02|Growthdue t<br>2001/02|o<br>- 2007/08|
|---|---|---|---|---|---|---|---|---|---|
|||||91/92 -|01/02 -|Demand|Market|Demand|Market|
|Country|1991/92|2001/02|2007/08|01/02|07/08|Change|Share<br>Change|Change|Share<br>Change|
|Brazil|0.9|3.8|6.0|16.8|27.2|1.6|15.2|17.8|9.4|
|China|8.1|10.2|7.7|12.3|24.8|9.8|2.6|30.9|-6.1|
|India|2.1|4.6|8.8|14.0|36.9|5.4|8.6|23.0|13.9|
|Korea, Rep.|2.9|6.0|10.1|12.7|23.2|4.8|7.9|13.0|10.3|
|Malaysia|15.3|24.0|24.0|11.2|13.6|6.3|4.9|13.6|0.0|
|Mexico|3.3|11.1|20.1|22.3|21.3|8.3|14.0|9.8|11.5|
|South Africa|2.2|4.6|10.4|8.8|30.7|0.8|7.9|14.2|16.5|
|Turkey|3.0|7.1|13.1|11.5|36.7|2.1|9.4|23.6|13.1|
|Total: above<br>developingco.|4.6|8.6|9.0|13.0|24.7|6.2|6.9|23.7|1.0|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Sources: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

Acceleration of import growth from other developing countries to about 25 percent p.a. during the second period is especially dramatic. In all countries excluding China, share of imports from developing countries increased substantially along with higher import growth rates. In China also, import growth has increased to 25 percent per annum but the demand increase has been much higher than the import growth rates leading to declining shares of imports from developing countries in domestic demand. During the first period, excluding China and Malaysia, the contribution of market share changes are greater than demand increases. Even in the second period when demand increases dominate, absolute contributions of market share changes reach almost double digit levels in six out of eight countries. Thus China and to a lesser extent Malaysia are exceptions to the general trend of increasing share of imports from developing countries. While it has reduced its share of imports from other developing countries it 17
on the CCI interacted with another dummy for the post 2000 period. We estimate these regressions including either time fixed effects or a dummy for post 2000, with and without country fixed effects. Results quite decisively show that while the CCI and monetary policy procyclicality are positively correlated prior to 2000, their correlation is insignificant in the post 2000 period.

**Table 1: Monetary Policy and Currency Cyclicality Index over Time**

|Dependent Variable|(1)<br>Monetary<br>Policy<br>Cyclicality|(2)<br>Monetary<br>Policy<br>Cyclicality|(3)<br>Monetary<br>Policy<br>Cyclicality|(4)<br>Monetary<br>Policy<br>Cyclicality|
|---|---|---|---|---|
|CCI x Dummy pre 2000|0.25***|0.25***|0.20**|0.22***|
||(3.24)|(3.14)|(2.70)|(3.03)|
|CCI x Dummy post 2000|0.09|0.04|0.01|0.07|
||(0.79)|(0.31)|(0.07)|(0.58)|
|Dummy post 2000||0.02|-0.08||
|||(0.28)|(1.13)||
|Constant|-0.38***|-0.38***|-0.35***|-0.31***|
||(7.02)|(9.24)|(18.92)|(4.84)|
|Observations|910|910|910|910|
|Time Effect|Yes|No|No|Yes|
|Fixed Effect|No|No|Yes|Yes|
|R-squared|0.05|0.04|0.03|0.05|
|Number of Countries|40|40|40|40|

Robust t statistics (clustered by country) are in parentheses. ***, **, * indicate significance at 1, 5 and 10 percent levels respectively. Since the rolling observations introduce serial correlation, we cluster our standard errors by countries in the estimations using these data In an endeavor to understand the factors behind the trends observed in the conduct of monetary policy and its relationship with the cyclical currency movements we make three more observations. First, the countries with procyclical currencies and negative net foreign currency positions (using the Lane and Shambaugh’s, 2010, measure of net foreign currency position) are the one that pursue procyclical monetary policy.<sup>21</sup> This is evident in Chart 12, Panel A and B, where we find a positive and significant relationship between the net foreign asset position and the cyclicality of monetary policy for countries with a positive CCI but no distinct relationship for countries with a negative CCI. This is consistent with the hypothesis that the countries with procyclical currency and currency mismatches in their balance sheet fear large movements in the exchange rate and use monetary policy to tame them. This may imply tightening their monetary policy in the midst of an economic

> 21 Lane and Shambaugh (2010) calculate net foreign currency position as the weighted sum of net foreign assets to capture the sensitivity of a country’s external balance sheet to a uniform movement of its domestic currency against all foreign currencies. The variable takes values between -1 and 1, where a value of -1 corresponds to a country that has zero foreign-currency foreign assets and it only holds foreign-currency foreign liabilities, a country afflicted by an extreme form of original sin. A value 1 corresponds to a country that has foreign assets only in foreign-currency and foreign liabilities only in domestic-currency. The figures we use are those updated by Benetrix et al. (2014).

17
<!-- Start of picture text --> Congo-Brazzaville<br>Democratic Republic of Congo<br>1992-Stable lights<br>MM 1-10<br>HM 11-20<br>[21-30<br>[31-40<br>(41-50<br>[51-60<br>HM «1-62<br>1:450'000 Mi cs<br><!-- End of picture text --> <!-- Start of picture text --> Congo-Brazzaville<br>ey L Democratic Republic of Congo<br>2009-Stable lights<br>HM 2-10<br>HM 11-20<br>[21-30<br>[J 31-40<br>[J 41-50<br>(51-60<br>HM «1-62<br>H:450'000 ma<br><!-- End of picture text -->
# **Table A.1.6. Barth, Caprio and Levine (2004) survey questions** (continued)

Panel B: Depositor compensation processing time and coverage at banking failure

|**Country name**|**On average, how**<br>**long does it take**<br>**to pay depositors**<br>**in full?**<br>in months<br>|<br>**What was the longest**<br>**that depositors had**<br>**to wait in the last 5**<br>**years?**<br>in months<br>|<br>**Were any deposits not explicitly covered by**<br>**deposit insurance at the time of the failure**<br>**compensated when the bank failed (excluding**<br>**funds later paid out in liquidation**<br>**procedures)?**<br>yes=1 no=0|
|---|---|---|---|
|Albania|||No cases|
|Algeria|||0|
|Argentina||9|1|
|Austria|3|3|0|
|Bahrain||||
|Belarus|24||0|
|Belgium|12|24|0|
|Bolivia||54|1|
|Bosnia-Herzegovina|6|18|1|
|<br>Brazil|1|1|0|
|Bulgaria|1.5|1.5|1|
|Canada|1|0.25|0|
|Chile||||
|Colombia|48|60|0|
|Croatia|12|24|0|
|Cyprus||||
|Czech Rep.|3|5|1|
|<br>Denmark|3|3|1|
|Ecuador||60|1|
|El Salvador|2||0|
|Estonia|9|9|0|
|Finland||||
|France|||1|
|Germany|||1|
|Gibraltar||||
|Greece|3||0|
|Guatemala|||1|
|Honduras|0.17|0.17|1|
|Hungary|3|3|0|
|Iceland||||
|India|5|5|0|
|Ireland||||
|Isle of Man|||0|
|Italy|0|5|1|
|<br>Japan|0|0|0|
|Jordan||||
|Kazakhstan|0|0|1|
|Kenya|12|18|0|
|<br>Korea|3|6|0|
|Latvia|||0|
|Lebanon|84|120|0|

43
**Table 4: Regressions for Sophistication in Service Exports, 1991, 2000 and 2006**

||(1)|(2)|(3)|
|---|---|---|---|
||1991|2000|2006|
|log GDP per capita|0.588*|0.0480|0.462|
||(0.314)|(0.221)|(0.545)|
|(log GDP per capita)<sup>2</sup>|-0.0293|0.00178|-0.0202|
||(0.0180)|(0.0129)|(0.0305)|
|Control for size|Yes|Yes|Yes|
|EU15|0.0168|0.0632|0.0645|
||(0.0327)|(0.0570)|(0.0562)|
|NMS|-0.0210|0.00358|0.00881|
||(0.0313)|(0.0282)|(0.0277)|
|ACC|0.0834**|-0.0490|-0.112**|
||(0.0356)|(0.0517)|(0.0459)|
|Constant|5.785***|8.522***|6.784***|
||(1.365)|(0.964)|(2.429)|
|Observations|116|126|120|
|0% level ** Signific<br>R-squared|ant at 5%<br>0.380|level ***<br>0.306|Significant at 1%<br>0.428|

_Note:_ * Significant at 10% level ** Significant at 5% level *** Significant at 1% level. t-statistics are in parentheses. Size is population size.

With a descriptive picture of the level of service export sophistication in the three European country groups, we will now look at the extent to which their level of sophistication is associated with their level of economic growth.

# **4 RESULTS FROM GROWTH REGRESSIONS**

We now turn to the analysis of the relationship between service export sophistication and growth, and in particular if there are any differences in how the level of sophistication affects growth in Europe compared to the rest of the world. Mishra, Lundstrom and Arnand (2011) analyses the relationship between _Service EXPY_ and economic growth through a number of estimations using fixed effect panels and GMM techniques. The data includes up to 103 countries over the time span of 1990-2007, and the result show a strong positive association between service sophistication and growth. The result stands given a number of different model specifications.<sup>9</sup> In this section we use the same approach but take a closer look at Europe.

The baseline empirical growth model consists, in addition to the variable of interest, of four determinants of economic growth - initial income level, rates of physical and human capital accumulation, trade openness and institutional quality. We use data on income per capita from WDI. To account for differences in human capital accumulation we use latest available data on years of schooling from Barro and Lee (2010). We use M2 (as percent of GDP) as a proxy for

> 9 Apart from the control variables presented in the base model, they control for Goods EXPY, test various time periods and country groupings and drop outlier such as India, and the result still holds.

16