rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

Abraham, K.G., S. Helms, and S. Presser. 2009. “How Social Processes Distort Measurement: The Impact of Survey Nonresponse on Estimates of Volunteer Work in the United States.” _American Journal of Sociology_ 114, 1129-1165.

Behrman, J. and A. Deolalikar. 1989. ”Is Variety the Spice of Life? Implications for Calorie Intake.” _The Review of Economics and Statistics_ 71 (4), 666-672.

Berman, E., J. Felter, J. Shapiro, and E. Troland. 2013. “Modest, Secure, and Informed: Successful Development in Conflict Zones.” _American Economic Review_ 103 (3), 512-517.

Berman, E., J. Shapiro, and J. Felter. 2011. “Can Hearts and Minds Be Bought? The Economics of Counterinsurgency in Iraq.” _Journal of Political Economy_ 119 (4), 766-819.

Boyle, P., and O. Grada. 1986. “Fertility Trends, Excess Mortality, and the Great Irish Famine.” _Demography_ 23 (4), 5430562.

Buchanan-Smith, M., and S. Davies. 1995. “Famine Early Warning and Response: The Missing Link.” London: Intermediate Technology Publications, 1995.

Clay, D., D. Molla, and D. Habtewold. 1999. “Food Aid Targeting in Ethiopia: A Study of Who Needs it and Who Gets it.” _Food Policy_ 24 (4), 391-409.

Coll-Black, S., D. Gilligan, J. Hoddinott, N. Kumar, A. Taffesse, and W. Wiseman. 2012. In: Food and agriculture in Ethiopia: Progress and policy challenges, chapter 10 International Food Policy Research Institute (IFPRI).

Crost, B., J. Felter, and P. Johnston. 2014. “Aid Under Fire: Development Projects and Civil Conflict.” _American Economic Review_ 104 (6), 1833-1856.

Dando, W. 1981. “Man-Made Famines: Some Geographical Insights from an Exploratory Study of a Millennium of Russian Famines,” in _Famine: Its Causes, Effects and Management._ Ed.: J. Robson. New York: Gordon and Breach, 1981, 139-154.

Dell, M., B. Jones, and B. Olken. 2012. ”Temperature Shocks and Economic Growth: Evidence from the Last Half Century.” _American Economic Journal: Macroeconomics_ 4 (3), 66-95.

Dreze, J. 1990. “Famine Prevention in India,” in J. Dreze and A. Sen eds. _The Political Economy of Hunger: Vol 2._ 1990, 13-122. Dreze, J., and A. Sen. 1989. Hunger and Public Action. Oxford: Oxford University Press, 1990.

FAO. 2017. “Emergency Food and Nutrition Security Assessment.” Report, Food and Agriculture Organization of the United Nations, Rome, Italy.

FEWS NET. 2017. “Yemen Food Security Alert.” Famine Early Warning System, Sana’a, Yemen, June.

17
<!-- Start of picture text --> Kaplan-Meier survival estimates<br>0 10 20 30<br>Duration<br>Non-democratic Democratic<br>Kaplan-Meier survival estimates<br>0 10 20 30<br>Duration<br>Not fragile countries Fragile countries<br>1.00<br>0.75<br>0.50<br>Survival rate<br>0.25<br>0.00<br>1.00<br>0.75<br>0.50<br>Survival rate<br>0.25<br>0.00<br><!-- End of picture text --> 42
_Source: Authors’ calculations based on the Central Bank of Egypt data._

**Table 1.d - External Position Variables**

|_Period average_|**1987-90**|**1991-98**|**99-2004**|**2005-08**|
|---|---|---|---|---|
||_Percent o_|_f GDP (unle_|_ss otherwi_|_se stated)_|
|Trade Balance|-7.8|-14.8|-10.5|-12.4|
|Suez Canal dues|na|3.5|2.4|3.3|
|Tourism|na|4.4|4.7|6.7|
|Private remittances|5.0|5.1|3.4|4.9|
|Private remittances (Bn US$)|3.7|4.1|3.0|6. 1|
|Current Account Balance|-1.3|2.5|0.7|1.8|
|Foreign Direct Investment (Bn US$)|1.0|1.0|0.7|8.6|
|Foreign Direct Investment|1.3|1.9|0.8|6.7|
|Capital flows|_Billion_|_US$ (unless_|_otherwise_|_stated)_|
|Net International Reserves|1.7|14.1|13.6|25.2|
|Not Foreign Assets, annual inflows|1.1|1.2|-0.9|7.5|
|External Debt|42.2|31.8|29.5|31.1|
|External Debt to Exports (%)|583|215.5|156|78.3|

_Source: Authors’ calculations based on the Central Bank of Egypt and International Finance Statistics._ The current account, which represents the balance of saving of the economy with the rest of the world, shows a clear structural change in its level in the early 1990s. From being a capital importer from the rest of the world (negative current account), the country became a savings exporter in the early 1990s (positive current account). The structural shift coincides with the adoption of ERSAP. In 2009, the current account balance turned negative again, indicating a need for external savings in a period of crisis when the world financial system came to almost a complete halt.

The current account also shows an oscillating pattern around a given level. While that level was negative up to the late 1980s, it became positive since then. To examine the determinants of the current account behavior in Egypt we use a simple version of the Obstfeld and Rogoff (1996) intertemporal consumption smoothing model according to which the current account is determined by transitory deviations of income from its permanent level, by fluctuations of investment around their trend, and by transitory deviations of public spending. A simple regression shows that income and investment are statistically significant explanatory variables of the current account behavior in Egypt (see Annex 1). Figure 5 shows how investment is the main driver of the current account oscillation. In particular, the fall of investment up to the mid 1990s is associated with the change in the current account. The fall in public investment implicit in the economic reform program explains the fall in overall investment. The predicted values would be those that the consumption smoothing 8
# **_2.2 Technology adoption and innovation_**

Why is firm performance in Eastern Croatia lower relative to other parts of the country? One reason for the observed poor performance could be lower firm capabilities. This may manifest itself in the form of efforts to undertake innovation (process or products) and the adoption of sophisticated technology, for example for improving business processes, such as procurement or for tracking quality. The survey suggests that, while the share of firms that introduced new products and processes are similar in Slavonia relative to the rest of the regions, a much smaller share uses advanced technology for procurement and quality management (Figure 4).

When splitting the share of firms that introduced new processes versus products, firms in Eastern Croatia are more active in introducing new processes rather than innovative products. To this end the we consider the count of firms that have registered for trademarks, patents, utility models and industrial designs. Firms in Eastern Croatia are doing worse than their counterparts in Zagreb in introducing trademarks and patents. Impressively, firms in Slavonia are registering more for industrial designs than any other place in Croatia (Figure 5).

<!-- Start of picture text --> Figure 4: Firms in Slavonia lag behind in adoption Figure 5: Firms in Slavonia lag behind product<br>of advanced technology innovation but not with respect to industrial designs<br>Use of Technology and Introduction of New Products/Processes Registration for a New Innovation: Marks, Patents, Utility Models, and Industrial Designs<br>Croatian Firms, 2017 Croatian Firms, 2017<br>Other Regions Slavonia Zagreb<br>Other Regions Slavonia Zagreb<br>Marks Patents<br>Use of Technology: Control & Quality Managemen t New Goods or Processes Utility Models Industrial Designs<br>.8<br>.08<br>.6 .06<br>.4 .04<br>Proportion of Firms (%)<br>Proportion of Firms (%)<br>.02<br>.2<br>0<br>0<br><!-- End of picture text -->

# **_2.3 Skills_**

Another reason why firm performance in Eastern Croatia could be lower than the rest of the regions is perhaps found in the availability and quality of skills. One of the main problems, which has been identified by the firms across regions is the issue of skill shortage. We consider the top three shortage challenges with respect to skills among firms in Eastern Croatia, Zagreb and other regions. Ability to solve problems and initiative to participate in the job market are the two major problems cited by the firms for skill shortage across regions. The proportion of firms that perceive that the ability to solve problems is one of the core reasons for the skill shortage is highest in Eastern Croatia. Firms in Eastern Croatia additionally cite educational quality as the third most important problem, whereas firms in Zagreb and other regions do not perceive this as a big challenge; rather, problems relating to employees’ attitudes (e.g., discipline) could be a bigger constraint.

9
# **Table A.5. E-commerce development and various categories of household expenditure per capita by household consumption level (Extended model 2)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|bottom 25%|25%-75%|top25%|
|Total household expenditure|0.245***|0.371***|0.142***|0.039|
||(0.062)|(0.078)|(0.047)|(0.050)|
|Observations|9,502|2,590|4,945|1,946|
|Adjusted R square|0.341|0.407|0.431|0.456|
|Cosmetics and beauty|0.406***|0.540**|0.214|0.177*|
||(0.125)|(0.272)|(0.146)|(0.098)|
|Observations|9,422|2,569|4,905|1,927|
|Adjusted R square|0.342|0.322|0.370|0.425|
|Food|0.289***|0.447***|0.226***|0.104**|
||(0.063)|(0.134)|(0.055)|(0.043)|
|Observations|9,398|2,551|4,895|1,931|
|Adjusted R square|0.326|0.395|0.331|0.457|
|Of which: Food at home|0.342***|0.502***|0.267***|0.203***|
||(0.067)|(0.170)|(0.071)|(0.056)|
|Observations|9,395|2,551|4,892|1,931|
|Adjusted R square|0.375|0.447|0.384|0.382|
|Of which: Dining out|0.349**|0.155|0.097|0.034|
||(0.146)|(0.245)|(0.155)|(0.118)|
|Observations|9,503|2,590|4,946|1,946|
|Adjusted R square|0.329|0.458|0.335|0.364|
|Clothes|0.211**|0.187|0.062|-0.054|
||(0.103)|(0.267)|(0.096)|(0.093)|
|Observations|9,297|2,536|4,833|1,907|
|Adjusted R square|0.321|0.325|0.374|0.353|
|Utilities|0.149***|0.337**|0.105***|0.051|
||(0.034)|(0.129)|(0.036)|(0.032)|
|Observations|9,469|2,584|4,935|1,929|
|Adjusted R square|0.298|0.321|0.303|0.349|
|Communications|0.190***|0.029|0.123**|0.094***|
||(0.045)|(0.135)|(0.052)|(0.034)|
|Observations|9,382|2,557|4,888|1,918|
|Adjusted R square|0.341|0.312|0.378|0.434|
|Local transport|0.364***|-0.053|0.181**|0.261**|
||(0.083)|(0.263)|(0.086)|(0.120)|
|Observations|9,335|2,555|4,855|1,904|
|Adjusted R square|0.299|0.339|0.315|0.302|
|Travel|0.704***|0.966**|0.376**|0.393**|
||(0.198)|(0.380)|(0.162)|(0.186)|
|Observations|9,478|2,586|4,936|1,935|
|Adjusted R square|0.296|0.420|0.350|0.277|
|Entertainment|0.528***|0.244|0.290|0.338***|
||(0.151)|(0.175)|(0.180)|(0.115)|
|Observations|9,455|2,580|4,921|1,933|
|Adjusted R square|0.289|0.332|0.308|0.321|
|Automobiles|0.384***|0.297|0.170|0.298*|

·· 37
percent during 2004–05 from 39 percent during 2001–02. This characterized a move from center-controlled borrowings to the autonomous NSSF but at higher cost (NSSF loans at 9.5 percent compared with cheaper market loans, weighted average of 8.39 percent during 2010–11). There is an inflexibility related with NSSF borrowings, since these are based more on availability and collection within the geographical territory of the state than the requirement by the state to borrow. The NSSF loans are also at higher interest costs and have been more asymmetrically beneficial for the center (Thirteenth FC, p. 144).

Given the Twelfth FC recommendations for greater autonomy and discontinuation of the financial intermediary role for the center, the lending policy was changed, with more market access for states.<sup>19</sup> Thus, states got more freedom, but also greater responsibility to manage their debt. A consequence of the new lending policy was the move to market discipline and transparency to enhance credibility among the market participants. Competition gradually increased among states to avail the best market terms and obtain credit ratings. There has been evidence of some variation in the spreads among states, with some states borrowing at slightly lower rates, although the overall range of the spreads has been narrow (table 1).

**Table 1: Weighted Average Spreads during 2010-11**

|**Weighted Average**<br>**Spread**<sup>b</sup> **(Basis Points)**|**General Category States/UTs**|**Special Category States**<sup>a</sup>|
|---|---|---|
|**1**|**2**|**3**|
|30-40|Puducherry, Gujarat, Goa,<br>Rajasthan, Tamil Nadu, Bihar|Manipur, Nagaland,<br>Meghalaya, Tripura|
|40-50|Kerala, Andhra Pradesh,<br>Madhya Pradesh, Punjab, West<br>Bengal, Uttar Pradesh,<br>Karnataka, Haryana, Orissa|Assam, Himachal Pradesh,<br>Jammu and Kashmir|
|50-60||Sikkim, Uttarakhand|

Source: Rakshitra, various issues, The Clearing Corporation of India Ltd.

Note _:_ a. Special category states are all the North-eastern states, along with Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. They have distinct characteristics: a low resource base, cost disabilities due to their physical geography, sparse terrain, remoteness, and historical circumstances. These states account for only 5 to 6 percent of all states‘ gross state domestic product.<sup>b</sup> Over the Center‘s benchmark.

> 19 The Twelfth FC stated that ―...as regards the future lending policy, the central government should not act as an intermediary and allow the States to approach the market directly.‖ The practice of on-lending from the center has been discontinued since then (recommendations of the Twelfth FC were accepted in May 2005).

10
First, the absence of long-run positive effects associated with the subtler reintegration benefits could be due to the low predictive power given the small number of officially demobilized soldiers. Our analysis of spillover effects does not suffer from this caveat, as the combination of household panel data and the variation in the number of demobilized combatants across collines allowed us to quantify these effects both in the short and the longer run. Second, our analysis focused on an area in north-western Burundi in which ex-combatant density was above the country average, and it should not be seen as representative of the country. The theoretical framework highlighted that the impact of cash and in-kind transfers may vary according to the context and the way the program is implemented. More research is needed to assess the external validity of our findings. Finally, our interpretation of the evolution of the impact of the DDR program on CNDD+ demobilized combatants relies on the indirect observations of a short-run positive impact identified in the literature (Ndayiziga et al, 2008; Verwimp and Bundervoet, 2009; Willems et al, 2010). More research is needed to test the relevance of our interpretation and, more generally, to assess how impacts of demobilization programs evolve over time.

In spite of its limitations, we believe that our study sheds light on some important effects of the DDR program on both ex-combatants and civilians. We hope that our study, together with other analyses of the effects of the DDR program, can pave the way towards innovative designs for evaluating the impact of such important programs.

# **References**

- Ajayi, O. (2006) Experiences in the application of the core welfare indicator questionnaire (cwiq) survey technology in africa: the journey so far. _The African Statistical Journal_ 3, 189–210.

- Alderman, H., Behrman, J., Kohler, H.-P., Maluccio, J., Watkins, S. (2001) Attrition in Longitudinal Household Survey Data. _Demographic Research_ 5(4), 79–124.

- Alderman, H., Hoddinott, J., Kinsey, B. (2006) Long term Consequences of Early Childhood Malnutrition. _Oxford Economic Papers_ 58(3), 450–474.

- Altonji, J., Elder, T., Taber, C. (2005) Selection on Observed and Unobserved Variables : Assessing the Effectiveness of Catholic Schools. _Journal of Political Economy_ 113(1), 151–184.

- Angelucci, M., De Giorgi, G. (2009) Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles’ Consumption? _The American Economic Review_ 99(1), 486–508.

- Angrist, J., Pischke, J.-S. (2008) Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press, Princeton, USA.

28
|Surplus<br>NFHS<br>(11)<br>10.5-12.7<br>10.5-12.7<br>10.5-12.7<br>10.5-12.7|
|---|
|n of disease,<br>e<br>Mid(=IMR)<br>Low<br>(9)<br>(10)<br>106<br>45<br>95<br>40<br>81<br>35<br>68<br>29<br>57<br>24<br>46<br>20<br>42<br>18<br>-64<br>- 27<br>-49<br>-21<br>-15<br>-6<br>, National Sample<br> Columns 2 is the<br> is missing in the<br>dairy, and pulses.<br>ic intake between<br>7 are the authors’<br>the calorie-disease<br> India (per 1,000<br> 10 multiply IMR<br>et caloric surplus|
|burde<br>Diseas<br>High<br>(8)<br>184<br>165<br>141<br>118<br>99<br>80<br>73<br>-111<br>-86<br>-26<br> 3 and 4<br>edule 1. <br>8 as this<br>cereals, <br> in calor<br>s 6 and <br>ents for<br>MR) for<br>ns 8 and<br> is the n<br> S.8).|
|Table 4: Comparison of daily Total Energy Expenditure (TEE), caloric intake, caloric <br>and caloric surplus implied by weight gain<br>Consumption<br>TEE<br>Real mpce<br>NSS calories<br>NSS cal. 7-day<br>Share 7-day<br>Authors’ cal.<br>Pop TEE<br>Adult TEE<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>(7)<br>1983<br>302<br>2190<br>2307*<br>0.37<br>2186<br>2136<br>2557<br>1988<br>305<br>2202<br>2337*<br>0.42<br>2180<br>2134<br>2553<br>1994<br>321<br>2133<br>2269*<br>0.43<br>2139<br>2158<br>2553<br>2000<br>350<br>2151<br>2290*<br>0.44<br>2169<br>2155<br>2531<br>2005<br>355<br>2040<br>2184*<br>0.48<br>2083<br>2172<br>2524<br>2010<br>386<br>2000<br>2140<br>0.48<br>2017<br>2159<br>2471<br>2012<br>479<br>2087<br>2225<br>0.49<br>2044<br>2153<br>2446<br>∆1983-2012<br>-102<br>-82<br>-142<br>17<br>-111<br>∆1983-2005<br>-150<br>-123<br>-102<br>36<br>-34<br>∆2005-2012<br>47<br>41<br>-40<br>-19<br>-78<br>_Source_: Authors’ analysis based on data from 1998-1999 India Time-Use Survey, National Family Health Survey rounds 2, <br>Survey rounds 38, 43, 50, 55, 61, 66, and 68, with independent estimates by other authors cited below.<br>_Note_: Column 1 is the authors’ calculation of real mean per capita expenditure (mpce) using National Sample Survey sch<br>ofcial estimate of caloric intake from (NSSO 2013) using the 30-day recall period (with (Deaton and Dr`eze 2009) for 198<br>report). Column 3 is the ofcial estimate using the new schedule type 2, which uses a 7-day recall for all food except <br>* indicates our imputation, based on the share of expenditures on the 7-day recall items (column 4) and the diference <br>the 30-day and 7-day schedule. Column 5 is the authors’ independent estimate of caloric intake (see appendix). Column<br>population and adult Total Energy Expenditure (TEE) estimates. Columns 8 through 10 are based on the reported coefci<br>slope in (Duh and Spears 2017), where disease is proxied by infant mortality. Column 9 reports infant mortality rate (I<br>live births) from the World Development Indicators and the mid-level estimate based on a slope coefcient of one. Colum<br>by 1.74 sand 0.426, the high and low end estimates reported in (Duh and Spears 2017) Table 8. Column 11 “Surplus” <br>consistent with average weight gain using the National Family Health Survey (NFHS) data (see text and Appendix Table|

32
**Table 11 Percentage changes in**<sup>**market shares and in the level of T**</sup>

||Germany|Sim<br>France|ulation I<br>Italy|Japan|UK|
|---|---|---|---|---|---|
|Sc. Int.|-0.8|-5.6|-1.9|-2.9|-7.7|
|Spec. Sup.|-0.7|-5.2|-1.9|-2.7|-8|
|<br>Sc. Based|-0.6|-5.3|-4.8|-3.24|-10.5|
|T|-5.7|-6.25|-5.38|-4.9|-10.95|
|||Sim|ulation 2|||
||Germany|France|Italy|Japan|UK|
|Sc. Int.|-10.04|-27.9|...|...|-17.3|
|Spec. Sup.|-7.62|-27.7|...|...|-17.3|
|<br>Sc. Based|-7.62|-27.9|...|...|-26.8|
|T|-10.88|-32.7|...|...|-25.7|
|||Si|mulation 4<br>|||
||Germany|France|Italy|Japan|UK|
|Sc. Int.|<br>_0.5_|1.8|0.56|3.7|0.9|
|Spec.Sup.|0.45|1.4|0.65|2.7|1.4|
|<br>Sc. Based|0.36|1.9|0.19|3.52|1.4|
|T|5.4|2.05|5.47|5.6|7.7|
|||Si|mulation_5_|||
||Gernany|France|Italy|Japan|UK|
|Sc. Int.|<br>-1.7|-1.5|-0.8|-1.8|-1.7|
|Spec. Sup.|-1.8|-1.2|-0.9|-1.1|-1.8|
|<br>Sc. Based|-1.6|-1.5|-2.7|-I.4|-2.4|
|T|2.2|5.7|0|0|7.3|

# 7. **Conclusions**

In this paper we have examined the<sup>properties of a dynamic disequilibrium</sup> model focused on trade specialization<sup>and knowledge accumulation. The</sup> sectoral breakdown, which includes<sup>four export and two import sectors,</sup> follows Pavitt's (1984) taxonomy which<sup>is particularly appropriate to deal</sup> with empirical analyses of the relationship<sup>between innovation activities</sup> and production and export performance.

Steady state analysis shows that under<sup>perfectly balanced growth -</sup> which implies no change in trade<sup>specialization and a uniform growth of</sup> knowledge, international diffusion<sup>of knowledge is irrelevant for growth.</sup> Unbalanced growth obviously implies<sup>changes in the specialization</sup> structure; the process of structural change<sup>may be enhanced by the effects of</sup> domestic spillover on domestic knowledge<sup>accumulation as innovation</sup> activities in one sector generate positive<sup>externalities on the rest of the</sup> economy. The analysis also clarifies<sup>the ambivalent effect on domestic</sup> 38
_Carrera Magisterial_ self-selection problem. The probit equation for computing the Mill's ratio was specified as follows: Defining vj=1 if the jth teacher is in Carrera Magisterial and vj=0 otherwise. Geographical variables as state and stratum, as well as classroom size, teacher characteristics and school characteristics explain this probability. "Teacher's opinion about _Carrera Magisterial_ program" is proposed as the trigger variable for measuring the differences in the application of this program, which might affect the probability of participation. The probit estimation results are shown in Section 1 of the Appendix. Interestingly, the age, region, stratum, classroom size, gender, school level, experience in 5th grade, supervision and household size are important in explaining the probability of enrollment in _Carrera Magisterial_ . Being female increases this probability by 14% while age and experience increase it by 5% and 4.3% respectively. Selectivity bias is significant in urban areas but not in rural areas (see the Annex).

In Section 4, the EEEP is used in order to measure the effect of school variables on a student’s performance. An exploratory analysis was performed of the school variables, which are likely to constrain, empower and motivate teacher's performance. For this purpose, schools were grouped into learning achievement quintiles. In addition, compound indexes of some of these school variables were constructed. Examples of school variables used were teachers’ performance, school principal's supervision, schools' facilities, directorial supervision at classroom level, teacher's training, career opportunities available to the teacher ( _Carrera Magisterial_ ), experience, and school equipment, all by public/private institution and stratum. Section 5, which also uses the EEEP, presents the estimates of school and family effects on learning achievement by means of several multivariate models<sup>7</sup> . Section 6 offers conclusions.

# **4. THE EFFECT OF SCHOOL VARIABLES ON STUDENT PERFORMANCE: A DESCRIPTIVE ANALYSIS.**

Mexican education literature is rich in ethnographic studies of schools in various parts of the country. In contrast, there are hardly any econometric studies that quantify the effects of school factors on student learning. There are some econometric studies (World Bank 1998; Lopez-Acevedo 1999) analyzing _Programa para Abatir el Rezago Educativo_ (PARE) data, but they too are limited to a few states. This section presents a national/urban/rural and public/private analysis of the EEEP measuring students’ performance. The purpose

> 7 The methodology used for such a part is presented with more detail in section 5.

8