rafmacalaba/datause-encoder-data huggingface dataset:
The steady state is consistent with the growth of wages implied by the long-run equation (2). One implication for wages is that the equilibriurn value of nominal wages is smaller _ceteris paribus_ the larger the steady state growth rate of the unemployment rate when it is positive. Hence, the equilibrium value of the nominal wage _ceteris paribus_ is associated negatively with increases in the unemployment growth rate. Another implication of the steady state equation is that the equilibrium value of nominal wages is larger _ceteris paribus_ the larger is the steady state inflation rate when the latter is positive. The impact on wages from increased inflation is not one to one. Ten percentage points increase in inflation is associated with 4 percentage points increase in wages. Hence, inflation erodes wages in the long run.

Is there a wage determination model that fits the Zimbabwean data? First, we test whether the Zimbabwean data fit a real wage specification. The results indicate that real wages do not respond to the strength of demand in the labor market. Furthermore, the real wage hypothesis only holds if: (1) the restriction (w-p)=0 cannot be rejected; and (2) there exists a co-integrating vector involving all three variables w, u and p which has the sum of the coefficients equal zero. The first restriction cannot be rejected at a 1 percent significance level but is rejected at _5_ percent (F(1,20) = 4.84 [0.043*). Moreover, there does not exist a co-integrating vector containing all three variables. Hence, the real wage hypothesis can be rejected.

Second, we test other models of wage determination nested within the one presented in Table 4: (1) a first order error-correction model with wages adjusting towards a static equilibrium that depends on prices only (,B = X = p = 0) and this model cannot be rejected at a 1 percent level of significance but is rejected at a 5 percent level (F(1,20) = 4.85 [0.04]*); (2) a first order partial adjustment model with a target value of wages depending on unemployment only (1 = 8 = (p = 0) is highly rejected (F(3,20) = 6.89 [0.00]); and (3) a trend stationary real wage model (p = _6_ = 1 and 3 = X = 0), this hypothesis is highly rejected F(4,20) = 75.06 [0.00]); 21
- 24 -

choice among 10 such ports.<sup>52</sup> In addition, there are 139 ports of smaller scale along the national coastline, which extends over 5,000 km.

# Traffic handled during 1988 at India's Major Ports

|Port|Total Traffic<br>Container Traffic<br>........... (in million tons).|
|---|---|
|Bombay|29.567<br>3.130|
|Madras|22.819<br>0.903|
|Kandla|16.194<br>0.335|
|Visakhapatnam|15.371<br>C.0iO|
|Mormuago|13.332<br>-|
|Calcutta/Ealdia|12.903<br>0.620|
|Cochin|6.802<br>0.252|
|New Mangalore|6.107<br>-|
|Paradip|5.187<br>-|
|Tuticorin|4.146<br>0.082|

Source: Indian Ports Association 56. These major ports handled about 90 percent of the total national seaborne cargo throughput. While the total volume increased from just over 19 million tons in 1950 to almost 133 million tons in 1988, the magnitudes involved are modest by international standards for an economy of India's size.<sup>5 3</sup> This manifestation serves to stress again the consequences of India's prolonged inward-looking policies in the trade sector. But now that trade volumes are on an upward trend, the ports have quickly turned into major impediments to further trade expansion.

57. In most Indian ports, annual traffic volumes have reached levels which are uncomfortably close to capacity limits. Observed occupancy ratios at container and general cargo berths range between 70 and 90 percent in the major ports.

- 52/ Once inaugurated later in 1989, Nhava Sheva will be among the country's then 11 major ports.

> 53/ The annual incidence of seaborne cargo in Shanghai exceeds the **co .-tfve** cargo volumes of **all** Indian ports.
<!-- Start of picture text --> 4000<br>3500<br>¥ 3000<br>Vv H<br>= 2500 |<br>‘6<br>5 2000<br>rs<br>5 1500<br>2<br>1000<br>500 |<br>@ = inflection point: 21%<br>% 10 20 30 40 50 60 70 80 90 100<br>Percentage difference (%)<br><!-- End of picture text -->
**Plc,Planning, sndi Research**

**Agricultural Pollce**

Indonesia adjusted rapidly to sharply falling external terms of trade during the 1980s - using a classic package of currency devaluation, budgetary and monetary restraint, and regulatory relaxation.

How did the rountry fare in its efforts to alleviate poverty and undernutrition during that period?

It is difficult to measure poverty at one point in time - much less compare poverty in two - periods but Ravallion and Huppi answer that question using two large, comparable sets of household data for 1984 and 1987. They tested a wide range of possible poverty lines and - poverty measures and the sensitivity of key results to many of the underlying assumptions about poverty.

They found robust evidence that noverty continued to decline during the period.

Although caloric intake data are far from ideal, they found evidence that the extent of undemutrition also fell significantly. For a caloric intake level which 37 r -rcent of the population failed to attain in 1984, they found that only 27 percent of the population failed to attain it in 1987.

Why was this so? Gains to the rural sector contributed greatly to the alleviation of poverty. Gains to the urban sector and population shifts from the rural to the urban sector also helped but were quantitatively less important than dire gains to the rural poor.

Increases in average real consumption and an improvement in overall equity both helped to reduce poverty. In addition, Indonesia's recent economic history had created conditions favorable to alleviating poverty so long as modest growth in private consumption per capita could be maintained during the adjustment period.

This paper is a product of the Agricultural Policies Division, Agriculture and Rural Development Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cicely Spooner, room N8-039, extension 30464 (54 pages with figures and tables).

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research | Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
# **Table 7.3. OLS results**

_Dependent variable: Share of stages with unofficial payments in total (procedures No. 1, 2A, 2B,_ _<u>3A and 3B)</u>_

||Model 7.3.1|Model 7.3.2|Model 7.3.3|
|---|---|---|---|
|Constant|-0.010<br>(0.118)|0.075<br>(0.078)|0.099<br>(0.081)|
|Ln time required to complete a<br>procedure with a landplot|0.017<br>(0.019)|||
|Ln total time divided by number of<br>stages bylaw(templates)||-0.003<br>(0.017)||
|Ln time per document|||-0.013<br>(0.019)|
|Procedure 2A|-0.054<br>(0.047)|-0.029<br>(0.045)|-0.053<br>(0.047)|
|Procedure 2B|0.011<br>(0.047)|0.014<br>(0.045)|0.018<br>(0.046)|
|Procedure 3A|-0.062*<br>(0.032)|-0.067**<br>(0.032)|-0.069**<br>(0.033)|
|Procedure 3B|-0.095***<br>(0.033)|-0.093***<br>(0.032)|-0.099***<br>(0.033)|
|Kaliningrad oblast|0.169**<br>(0.080)|0.191**<br>(0.074)|0.210***<br>(0.079)|
|Saint-Petersburg|0.411***<br>(0.148)|0.280**<br>(0.114)|0.352***<br>(0.124)|
|Leningrad oblast|0.207*<br>(0.106)|0.220**<br>(0.104)|0.233**<br>(0.109)|
|Moscow|0.351***<br>(0.090)|0.338***<br>(0.082)|0.350***<br>(0.087)|
|Moscow oblast|0.232**<br>(0.100)|0.212**<br>(0.095)|0.222**<br>(0.098)|
|Sverdlovsk oblast|0.206**<br>(0.091)|0.194**<br>(0.084)|0.194**<br>(0.091)|
|Tomsk oblast|0.063<br>(0.081)|0.060<br>(0.076)|0.065<br>(0.079)|
|Khabarovsk krai|0.079<br>(0.081)|0.098<br>(0.076)|0.122<br>(0.081)|
|Irkutsk oblast|0.288***<br>(0.081)|0.309***<br>(0.076)|0.329***<br>(0.080)|
|Rostov oblast|0.484***<br>(0.084)|0.443***<br>(0.080)|0.444***<br>(0.082)|
|Perm oblast|0.022<br>(0.081)|0.041<br>(0.075)|0.072<br>(0.081)|
|Novosibirsk oblast|0.032<br>(0.080)|0.059<br>(0.075)|0.074<br>(0.080)|
|Nizhny Novgorod oblast|0.203**<br>(0.084)|0.216***<br>(0.077)|0.234***<br>(0.080)|
|Sakhalinsk oblast|0.038<br>(0.110)|0.060<br>(0.104)|0.100<br>(0.117)|
|Adj. R|0.274|0.242|0.240|
|N|358|398|373|
|Mean of the dependent variable|0.20|0.20|0.22|

*, **, *** Significance level of 10%, 5%, and 1%, respectively

30
# Debt Vulnerability Analysis: A Multi-Angle Approach<sup>*</sup>

Doerte Doemeland Marcello Estev ̃ao Charl Jooste James Sampi Vasileios Tsiropoulos _Keywords_ : Debt, Default, Factor Model, Prediction.

_JEL classification_ : H12, H63, C32

> *World Bank Group. Ideas in the paper are those of the authors and not that of the World Bank. We thank Timothy Kehoe and Juan Conesa for their help and for sharing their code to simulate our theoretical framework. The ideas in this paper benefited from remarks by Paloma Casero, Andrew Burns, Ivailo Isvorski, Lea Hakim, Jorge Araujo, Aart Kraay, Pablo Saavedra, Gallina Vincelette, Franziska Ohnsorge, Norbert Fiess, Stefano Curto, Naoko Kojo, Gregor Schwerhoff, and Alexander Haider. Author contacts: ddoemeland@worldbank.org; mestevao@worldbank.org; cjooste@worldbank.org; jsampibravo@worldbank.org; vtsiropoulos@worldbank.org
Dillon, A., Gourlay, S., McGee, K., and G. Oseni. (2016). “Land Measurement Bias and its Empirical Implications: Evidence from a Validation Exercise”, _World Bank Policy Research Working Paper 7597_ . Washington, DC: The World Bank.

FAO (1982). Estimation of crop areas and yields in agricultural statistics, FAO, Rome.

- Fasbender, D., C. Lucau (2012). “Identifying and modelling sources of uncertainty in GNSS area measurements.” _Proceedings of the 17_<sup>_th_</sup> _GeoCAP Annual Conference_ .

- Fermont, A., T. Benson (2011). "Estimating yield of food crops grown by smallholder farmers: A review in the Ugandan context.” _Uganda Strategy Support Program Working Paper_ No. USSP 05, IFPRI.

- Goldstein, M. and C. Udry (1999) ‘Agricultural Innovation and Risk Management in Ghana’, _Unpublished, Final report to IFPRI_ , IFPRI, Washington DC.

- Hofmann-Wellenhof, B., H. Lichtenegger, E. Wasle. (2008). “GNSS – Global Navigation Satellite Systems.” Springer-Verlag: New York, NY.

- IFAD (2010). _Rural poverty report 2011 New realities, New challenges: New opportunities for tomorrow's generation_ . Rome, Italy: IFAD.

- Istat (2008). _Farm Structure Survey 2009/2010. Survey on agricultural production methods 2009/2010. National Methodological Report (NMR)._ Rome: Istat.

- Joseph, George Gheverghese (1997). Geometry in India. In H. Selin. “Encyclopaedia of the History of Science Technology and Medicine in Non-Western Cultures”, Springer.

- Keita, N., and Carfagna. E. (2009). “Use of modern geo-positioning devices in agricultural censuses and surveys: Use of GPS for crop area measurement.” _Bulletin of the International Statistical Institute_ , the 57th Session, (2009), Proceedings, Special Topics Contributed Paper Meetings (STCPM22), Durban.

- Keita, N., Carfagna, E., and Mu’Ammar, G. (2010). “Issues and guidelines for the emerging use of GPS and PDAs in agricultural statistics in developing countries.” _The Fifth International Conference on Agricultural Statistics_ ; Kampala, Uganda.

- Kelly, V. and C. Donovan (2008). “Agricultural Statistics in Sub-Saharan Africa: Differences in Institutional Arrangements and their Impacts on Agricultural Statistics Systems A Synthesis of Four Country Case Studies.” _Michigan State University International Development Working Paper_ No. 95, East Lansing, Michigan.

Kilic, T., Zezza, A., Carletto, G., and Savastano, S. (2013). “Missing(ness) in Action: Selectivity Bias in GPS-Based Land Area Measurements,” _World Bank Policy Research Working Paper 6490_ . Washington, DC: The World Bank.

- Lyman, John, and John Wilfrid Wright. 2015. “Surveying”. In _Encyclopaedia Britannica. Encyclopaedia Britannica Online Academic Edition._ Encyclopædia Britannica Inc., 2015. Web. 20 Mar. 2015.

30
Table 2: INHlE6TICN **lESTS** FOR **7HE LCONG-Ui:** FIRST DiFFEENC DATA

|||Unit Root Test||**No.of**Lags<br>In AOF|Serial Correlation<br>Test|
|---|---|---|---|---|---|
|Series|CRDW<br>(crit.<br>va!ue<br>99%: 0.532|DX<br>(crit.<br>value<br>99S: -4.07|AiX<br>(crit.<br>value<br>99%: -3,77|n<br>_Zll_YAst-i|_.2_<br>WM3):3<br>forADF|
||95%: 0.386|95%-3.37|95%: -3.17|||
||90%: 0.322)|90%<br>-3.03)|90%: -2.84)|||
|ALnICP33|1.939|-5.892|-4.255|3|2.056|
|ALnICPAG|2.030|-6.177|-4.237|3|2.044|
|ALnICPMM|1.558|-4.737|-3.555|3|0.304|
|ALnICPAGF|1.950|-6.604|-3.919|3|0.152|
|ALnlI:PAGWF|<br>2.116|-6.472|-5.817|3|2.396|
|ATBR|1.706|-5.246|-3.341|3|0.898|
|ALn IP|2.389|-7.988|-3.783|3|4.781|
|A2LnCPIG7|0.420|-2.384|-1.540|5|0.454|
|ALnCPIG7T 1/|<br>1.867|-9.906|-4.111|3|4.336|
|ALnPOIL|1.787|-5.399|-3.606|1|0.762|
|ALnE)t|1.048|-3.606|-4.140|3|0.862|
|A LnGN'7|2.057|-6.562|-2.084|6|2.006|
|aLnhS|0.807|-3.915|-1.698|3|2.240|

- 1/ It was not necessary to Include the trend In the first and second order intogration test for the CPIG7, because it was not significant.

Notes: For variable definitions see Table 1.

a Indicates that the first difference of the variables has been taken. A Indicates second differencing.
# **3.4 Multiple Hypothesis Testing**

We estimate many different treatment effects given our use of multiple outcomes, four treatments, and two rounds of surveys. We use three different approaches for dealing with multiple hypothesis testing. First, we aggregate different outcomes in a domain into pre-specified standardized aggregate indices. In particular, we use an aggregate index of 41 different business practices, and an overall sales/profit index that aggregates our four measures of firm sales and profits. This enables us to examine whether the average impact of a given treatment on these key main outcomes is significant or not. Second, since we are also interested in the individual outcomes, for each treatment we take the set of all 26 main outcome-survey round estimates across Tables 4 and 5, and calculate sharpened q-values that hold constant the false discovery rate (Anderson 2008). These estimates are reported in Appendix Table 9.1. Similarly, we correct for the full set of 26 mechanism outcome-survey round estimates in Tables 6, 7 and 9, and provide sharpened q-values in Appendix Table 9.2. Third, following Young (2019), we conduct a Wald omnibus test of overall experimental significance. This tests the joint hypothesis that all treatment effects on business practices and firm growth are zero. The corresponding p-values are 0.006 for survey round 1, and 0.008 for survey round 2. That is, we can reject the null that all treatments are jointly irrelevant.

# **4. Impacts on Main Outcomes**

We begin by examining effects on business survival. This is not because business survival was expected to be one of the main outcomes of the program, but rather because our interpretation of impacts on all other outcomes will be affected if there are large survival differences across treatment groups. Survival rates were high, with 97.7 percent of control group firms interviewed in our first follow-up survey and 90.2 percent of those in our second follow-up surviving. The interventions do not appear to have had large effects on either the selection of which firms survive (Appendix Table 3.3 shows balance on baseline observables for the second round survivors); or on the survival rate (Appendix Table 3.4 shows statistically insignificant treatment impacts of 2 to 4 percentage points). Nevertheless, we also carry out robustness checks which condition on survival to allow for any potential effect of these small survival differences.

18
most important issues in the Republic of Yemen and in many conflict-affected countries (e.g., OCHA 2018; World Bank 2017a; etc.), there are a number of other welfare dimensions that need to be better tracked. Information on adequacy of housing, access to basic services like health and education, and psychological well-being are all important areas that are unable to be addressed in the above analysis.

Second, the needs of the displaced population without access to mobile phones are not addressed. Although mobile phone penetration is high in the country (even among displaced populations), other survey modalities need to be used to reach this population. Face-to-face interviews that are representative of the entire population are difficult in this setting, but they could at least account for some of this difficult-to-reach population.

And lastly, it is important to note that the consequences of displacement might be very sensitive to the characteristics of different conflicts and contexts. For example, it is unclear whether some of these results generalize to settings in which only part of a country is in conflict, to settings in which households migrate to foreign countries, or to settings in which the pre-conflict baseline was not as poor as in the Republic of Yemen (e.g., WFP 2012). More investigation as to which results are most likely to generalize to other settings is also an important issue to address in future work.

22
expenditure in average for the previous three years. Investment expenditure is normalized in different ways. We consider, in turn, the ratio of expenditure to the labor force and the ratio of expenditures to GDP; and in addition, we consider the logarithm of each of these two ratios. As mentioned above, publicly available Egyptian historical data has not (until recently) disaggregated between the transportation and telecommunication sectors;<sup>28</sup> therefore, we use the infrastructure index that combines those two sectors. For electricity, on the other hand, there is sector specific expenditure data; and therefore, we use its own infrastructure index. The results are presented in Table 6 for electricity and Table 7 for transport and telecommunications.<sup>29</sup>

**Table 7.  Transportation and Telecommunication Expenditures and Improvement**

|**Transportation and Telecommunication**<br>_Estimation Method: Quantile regression_<br>**Change in**|**Dependent v**<br>**Transportation**<br>**Infrastructu**|**ariable:**<br>**& Telecommunic**<br>**re Index**|**ation**|
|---|---|---|---|
|**[1]**|**[2]**|**[3]**|**[4]**|
|Ratio of expenditure to labor force<br>0.002 ***||||
|(expenditure on transportation & telecommunication per 100,000 workers)<br>[14.22]||||
|Ratio of expenditure to labor force|0.038 ***|||
|(expenditure on transportation & telecommunication per 100,000 workers, in logs)|[9.46]|||
|Ratio of expenditure to GDP||0.002 ***||
|(expenditure on transportation & telecommunication / 1,000 GDP)||[5.08]||
|Ratio of expenditure to GDP|||0.061 ***|
|(expenditure on transportation & telecommunication / 1,000 GDP, in logs)|||[3.96]|
|Constant<br>-0.016 ***|-0.076 ***|-0.036 **|-0.18 ***|
|[3.84]|[6.16]|[2.08]|[3.16]|
|Observations<br>45|45|45|45|
|R-squared<br>0.47<br>_Notes:_<br>_All the expenditure variables are the moving average of expenditures in the last three years._<br>_Numbers in brackets are the corresponding t-statistics._<br>_* significant at 10%; ** significant at 5%; *** significant at 1%_|0.44|0.19|0.22|

28 As stated in footnote 24, disaggregated investment data for transportation and telecommunication has recently become available for the period 1983-2007. It is not used here, however, because of its limited time coverage and because it has not undergone the necessary quality controls.

29 These regressions are estimated using the Quantile (Median) estimator, rather than the simpler and more common Ordinary Least Squares procedure. Although the results from both methods are similar to each other, in this case we prefer the Quantile estimator given its lower sensitivity to outlier observations.

41
1. Estimates for NO2 and CO pertain to a nine week window, and for PM 2.5 to a seven week window.

Std. errors are clustered at one week. * indicates significantly different from zero at 10 percent level, ** at 5 percent level, and *** at 1 percent level.

**Table S1.3a Effect of Delhi Metro on Air Quality: Lagged Outcome Controls,**

**ITO**

|**Metro Extension**|**NO2**|**CO**|**PM2.5**|
|---|---|---|---|
|<br>|<br>(Percentage<br>pollutant)<br>|<br>change in le<br>|<br>vel of<br>|
|**Yellow Line First Extension (Jul 03, 2005)**|-2.64|-47.81***||
|Std. Error|(9.42)|(10.14)||
|First Lag|0.53***|0.03***||
|Std. Error|(0.05)|(0.004)||
|Second Lag|0.15***|0.004**||
|Std. Error|(0.04)|(0.002)||
|Third Lag|0.11*|0.006**||
|Std. Error|(0.06)|(0.002)||
|Fourth Lag|0.02|0.004*||
|Std. Error|(0.02)|(0.002)||
|Cumulative Effect|-7.24|-50.21||
|Observations|1457|1497||
|**Blue Line Introduction (Dec 31, 2005)**|-11.58*|||
|Std. Error|(5.92)|||
|First Lag|0.23***|||
|Std. Error|(0.01)|||

60
# **Figure B22: Wheat**

<!-- Start of picture text --> Log scale<br>7.5<br>Log price Permanent component<br>7.0<br>6.5<br>6.0<br>5.5<br>5.0<br>4.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>0.75<br>Medium-term cycle Transitory component<br>0.25<br>-0.25<br>-0.75<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 63 —_
Study databases covering primarily developed countries (LIS). Household income data are organized in “spells,” i.e. income changes between two survey years, calculated as average annual log differences.<sup>8</sup> These are calculated for average income, income at the bottom 20 percent and the bottom 40 percent. We focus on those non-overlapping spells that are at least five years long, which provides 299 spells for 117 countries with a median spell length of 6 years, which is the preferred sample of Dollar et al. (2016).

# **_Control Variables_**

Building on the existing literature, our model also includes two sets of control variables that serve as proxies for a variety of policies and institutions that might matter for growth and be relevant for changes in the incomes of the poorest segments of the population (a complete list with sources and descriptive statistics is given in Appendix A). The first group includes variables capturing policies that may matter for the distribution of income:

- The mean income growth rate to take into account the strong link between mean income growth and income growth at lower parts of the distribution (Dollar and Kraay, 2002);

- Inflation and inflation variation as two measures to control for the macroeconomic environment, reflecting the idea that a stable macroeconomic environment is important for economic agents to plan ahead and thus a relevant enabling factor for income generation, especially when access to financial insurance is limited, such as for the poor (e.g. Easterly and Fisher, 2001);

- Telecommunication connectivity (use of land and mobile lines) as a proxy for infrastructure, which has been shown to be an important driver of growth and income generation, especially for low-income countries (Calderon and Chong, 2004; Araujo et al., 2014; Moller and Wacker, 2015);<sup>9</sup>

- School enrollment and life expectancy (alternatively child mortality) are included as standard variables in most growth regressions, as they reflect the level of human capital and the potential to generate income (see Weil, 2007 and 2014 for the health angle); and

- Financial development measured by the ratio of credit over GDP (Ghura et al., 2002; Lopez, 2004; Singh and Huang, 2015).

The second group of control variables reflects characteristics which may be less easy to change through policies although potentially important for economic growth and income distribution:

> 8 POVCALNET data are either income or consumption, LIS data are disposable income. We still refer to “income” in our paper.

> 9 We considered alternative infrastructure indicators, such as access to electricity, improved water sources, or paved roads. Their coverage is, however, more limited. They are nevertheless highly correlated with our telecommunication connectivity variable (0.8 for instance in the case of improved water sources).

-8-