rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 7 RPQs along spatial dimensions<br>1.60 ¢ 7a. a<br>140 ‘ ,<br>1.20i- rst) Pink.ae ~ ee”“4<br>_ s cur = 40<br>al<br>one<br>0.20<br>i 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>— = Addis Ababa ——— Agel. center<br><!-- End of picture text --> <!-- Start of picture text --> RPOS along other dimensions<br>2.00<br>1.80 1g1<br>160 wot<br>pspe . “a y1aK :1k% SS<br>1.00 a. = Bus 4 ow<br>oso |= MET = ~ 5 4 2<br>0.60 Ts cme OS, TMse : 7<br>0.40 \ Nee?” eee heetaiettet TY)<br>0.20<br>7<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>—sME === Young = = FDI —= Foodind<br><!-- End of picture text -->

|||Ethi|opia|||Addis|Ababa||
|---|---|---|---|---|---|---|---|---|
||Death|Survival<br>H|ighgrowth|Total|Death|Survival|Highgrowth|_ Total|
|Birth|46.7|42.4|10.9|100.0|44.3|42.1|13.7|100.0|
||(462)|(419)|(108)|(989)|(201)|(191)|(62)|(454)|
|Survival|24.1|65.9|10.0|100.0|23.5|65.8|10.8|100.0|
||(551)|(1509)|(229)|(2289)|(307)|(860)|(141)|(1308)|
|High growth|25.8|67.7|6.5|100.0|22.8|71.7|5.5|100.0|
||(64)<br>|(168)<br>|(16)<br>|(248)<br>|(33)<br>|(104)<br>|(8)<br>|(145)<br>|
|Total|30.5<br>|59.4<br>|10.0<br>|100.0<br>|28.4<br>|60.6<br>|11.1<br>|100.0<br>|
||(1077)|(2096)|(353)|(3526)|(541)|(1155)|(211)|(1907)|

Notes: Transition to High growth status using the OECD definition - Pooled Markov probabilities, frequencies in parenthesis. The table considers pooled transition probabilities for four periods (1997-2000, 2000-2003, 2003-2006, 2006-2009)
Demirguc-Kunt, A. and V. Maksimovic. (1999). "Institutions, Financial Markets, and Firm Debt Maturity," _Journal of Financial Economics._ Demirguc-Kunt, A. and R. Levine. (1999). "Bank-Based and Market-Based Financial Systems: Cross-Country Comparisons," unpublished manuscript.

Diamond, D. W. (1984). "Financial Intermediation and Delegated Monitoring," _Review of Economic Studies,_ 51(3), 393-414.

Easterly, W., R. Islam, and J. E. Stiglitz. (2000). "Shaken and Stirred: Explaining Growth Volatility," _World Bank Economic Review,_ forthcoming.

Erceg, C., D. W. Henderson, and A. T. Levin. (forthcoming). "Optimal Monetary Policy with Staggered Wage and Price Contracts," _Journal of Monetary Economzcs._ Fazzari, S.M., R. G. Hubbard and B. C. Petersean. (1988). "Financing Constraints, and Corporate Investment," _Brookings papers on Economic Activity,_ 1, 141-195.

Fischer, S. (1993). "The Role of Macroeconomic Factors in Growth," _Journal of Monetary Economics,_ 32(3), 485-511.

Gertler, M. (1988). "Financial Structure and Aggregate Economic Activity: An Overview," _Journal of Money, Credit, and Banking,_ 20 (3, Part 2), 25-50.

Gertler, M., and S. Gilchrist. (1994). "Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms," _Quarterly Journal of Economics,_ 109, 309-340.

Goldsmith, R. (1969). _Financial Structure and Development._ (New Haven, CT: Yale University Press.) Greenwald, B., and J. Stiglitz. (1993). "Financial Market Imperfections and Business Cycles," _Quarterly Journal of Economics,_ 108, 77-114.

Greenwood, J., and B. Smnith. (1997). "Financial Markets in Development, and the Development of Financial Markets," _Journal of Economic Dynamics and Control,_ 21, pp. 145-186.

Greenwood, J. and B. Jovanovic. (1997). "Financial Development, Growth, and the Development of Financial Markets," _Journal of Economic Dynamics and Control, 21_ (1), 20
# **Table of Contents**

# **1 Introduction 5**

# **2 Related Literature 6**

# **3 Research Questions 8**

- 3.1 Country Context: Political and Civil Liberties 9

- 3.2 Country Context: Dependence on Resource Revenues and Aid 11

- 3.3 Country Context: Programmatic Political Parties 12

- 3.4 Country Context: Prior Administrative Capacity 13

- 3.5 Country Context: Economic and Human Development 14

- 3.6 Reform Content 14

- 3.7 Reform Process Factors 15

- 3.8 Project Management Factors 16

# **4 The Data 16**

- 4.1 The Project Sample 16

- 4.2 Project Content Measures 17

- 4.3 Project Performance Measures 18

- 4.4 Country Context Measures 21

- 4.5 Process and Risk Measures 21

- 4.6 Project Management Measures 22

# **5 Identification Strategy 22**

- 5.1 Ordered Probit Regression Models 22

- 5.2 Heterogeneous Marginal Effect Estimations for PSM and Non-PSM Projects 22

- 5.3 Nearest Neighbor Matching 22

- 5.4 Matching Based on Subclassification 23

- 5.5 Validity Threats 24

# **6 Descriptive Statistics: Comparing PSM and Non-PSM Projects 25**

- 6.1 Comparing Project Performance 25

- 6.2 Comparing Country Targeting 26

- 6.3 Comparing Reform Content Factors 28

- 6.4 Comparing Process and Risk Factors 28

- 6.5 Comparing Project Management Factors 29

# **7 Estimation Results 29**

- 7.1 Country Context Correlates of PSM Project Performance 30

- 7.2 Reform Content Correlates of PSM Project Performance and PSM Project Targeting 34

- 7.3 Process and Risk Indicators 36

- 7.4 Project Management Correlates of Project Performance 38

- **8 Conclusion 39**
constraints on their growth, however. Entrepreneurs could lack the knowledge of how to write a business plan, be successful at obtaining financing, and run a successful business. Even with knowledge of how to obtain it, entrepreneurs may lack access to financing due to market imperfections. High taxes could limit the scope for internal financing of growth. The necessary managerial or employee human capital could be in short supply. And state bureaucrats could hinder growth by extracting bribes rather than protecting property rights and enforcing contracts. Evidence from a special survey will be employed to determine which of these factors are most important for growth.

Has the economy received any productivity benefit from the destruction, or has the worker dislocation been all pain and no gain? Many cases of direct subsidization and other forms of support for weak and failing enterprises are known to have occurred, while discriminatory taxes, bureaucratic interference, poor contract enforcement, and uncertain property rights protection have impeded those that are more successful (e.g., Frye and Shleifer, 1997; Aslund, Boone, and Johnson, 1996). Russia and Ukraine could thus be subject to “sclerosis” (Caballero and Hammour, 2000), in which less productive resources remain employed due to market imperfections and government policies, while the creation of more productive matches of resources and enterprises is impeded. Alternatively, less productive firms may have been pressured by competition and profit-seeking private owners to downsize, while more productive firms seized new profit-making opportunities and created jobs in the process. Such a scenario would positively contribute to aggregate productivity growth. Not only does the answer to this question affect productivity growth during the transition, but it is also important for future growth prospects. Productive firms should be able to respond more quickly to the recovery in demand, as well as be in a better position to compete in world markets, so future growth prospects will be brighter if more resources are concentrated among the most productive firms.

The productivity effects of firm turnover are of particular importance. Exit tends to be more costly to workers than job destruction in continuing firms – the concentrated nature of the destruction can make finding a new job even harder. So if firm turnover isn’t productive, that would be even worse for the economy than destructive continuing firm job flows. Productivity growth from firm turnover is not guaranteed. New entrants may lack the political connections and financing needed to 10
# _Wage Responses to Higher Food Prices_

In principle, the indicator of agricultural GDP used in table 3 includes farm wage incomes as part of value added. However, higher food prices might also result in increases in non-farm wages. Yet, as we noted in section II, only a small empirical literature has focused on these issues. Rarely has this literature used sufficiently high frequency data to accurately inform the question of how long it takes for wages to react to food price increases, and only one paper has examined the responses of urban wages to higher food prices (Headey et al. 2011). We therefore revisit short-run adjustment issues using high frequency data on rural and urban wage indices for Bangladesh from 2001 to 2011.<sup>15</sup> Although the data are monthly, the noise in the data prompts us to aggregate the data into quarters, which also more closely approximates Bangladesh’s different agricultural seasons ( _aman, aus, boro_ ). Our expectation was that labor demand and supply may be less likely to respond in the current season but may respond more substantially in subsequent seasons as planting decisions constitute an important behavioral response to higher food prices.

Figure 3 shows trends in these nominal rural and urban wage indices along with the nominal producer price of rice, which is easily the most important crop in Bangladesh. Nominal rice prices roughly doubled from July 2006 to July 2008 in close accordance with international prices. Nominal rural and urban wages also increased, but rural wages increased much faster than urban wages. Indeed, from July 2006 to January 2011, the ratio of rural to urban wages increased rapidly from 59 percent to 82 percent. This suggests two important conclusions. First, rural and urban wages (and hence labor markets) may not be very closely integrated in the short term. Second, it is plausible that higher food prices were the leading factor behind the declining rural-urban wage gap, though there are other possible explanations such as accelerated out-migration from rural areas (Zhang et al. 2013).

> 15. The rural wage index places most of its weight on the agricultural wage series for Bangladesh while the urban series includes wages for construction, masons, carpenters, plumbers, painters, electricians, and brick-breaking.

15
|China:Fujian Meizhou BayNavigation Improvement Project|Weimin Zhou|
|---|---|
|China:Fujian Meizhou BayNavigation Improvement Project|Weimin Zhou|
|China:Fujian Meizhou BayNavigation Improvement Project|Weimin Zhou|
|Support to Firm Data Generation Project|Jean-Pascal Nganou|
|Support to Firm Data Generation Project|Jean-Pascal Nganou|
|Support to Firm Data Generation Project|Jean-Pascal Nganou|
|BT: Improvingstatistical capacity|Carlos E. Sobrado|
|BT: Improvingstatistical capacity|Carlos E. Sobrado|
|BT: Improvingstatistical capacity|Carlos E. Sobrado|
|BT: Improvingstatistical capacity|Carlos E. Sobrado|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Nigeria Erosion and Watershed Management Project|Amos Abu|
|Hurricane Tomas EmergencyRecoveryLoan|Gaetano Vivo|
|Hurricane Tomas EmergencyRecoveryLoan|Gaetano Vivo|
|Hurricane Tomas EmergencyRecoveryLoan|Gaetano Vivo|
|Hurricane Tomas EmergencyRecoveryLoan|Gaetano Vivo|
|Hurricane Tomas EmergencyRecoveryLoan|Gaetano Vivo|
|Hurricane Tomas EmergencyRecoveryLoan|Gaetano Vivo|
|Hurricane Tomas EmergencyRecoveryLoan|Gaetano Vivo|
|EG-Strengtheningthe Capacityof SIOIRAFI inproject coordination,|MHusam Abudagga|
|EG-Strengtheningthe Capacityof SIOIRAFI inproject coordination,|MHusam Abudagga|
|EG-Strengtheningthe Capacityof SIOIRAFI inproject coordination,|MHusam Abudagga|
|EG-Strengtheningthe Capacityof SIOIRAFI inproject coordination,|MHusam Abudagga|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
|MekongIntegrated Water Resources Management Project- Phase|IICuongHungPham|
# **Table 8: Wage Changes Among All Workers Observed 2 Periods by Industry- and OccupationSpecific Exposure to Offshoring, 1983-2002**

Dependent Variable: Log Wage Change Between Periods

||Offsh<br>Industr<br>Ma|oring Measur<br>y-Specific Ex<br>nufacturingO|Non-<br>ed by<br>posure,<br>nly|Offsh<br>Occupati|oring Measu<br>on-Specific<br>All Sectors|Non-<br>red by<br>Exposure,|
|---|---|---|---|---|---|---|
|Variable|All|Routine|Routine|All|Routine|Routine|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged log of low income|-0.002|-0.003|-0.003|-0.015**|-0.018**|0.020|
|affiliate employment|(0.001)|(0.002)|(0.004)|(0.006)|(0.008)|(0.025)|
|Lagged log of high income|0.005*|0.009**|-0.004|0.013**|0.016**|-0.014|
|affiliate employment|(0.003)|(0.004)|(0.007)|(0.005)|(0.007)|(0.022)|
|Lagged import penetration|0.023|0.033|0.010|-0.033|-0.006|-0.660**|
|using 1979 weights|(0.016)|(0.025)|(0.043)|(0.040)|(0.045)|(0.256)|
|Lagged export share using|-0.042**|-0.058|-0.013|0.091*|0.049|0.415***|
|1979 weights|(0.020)|(0.039)|(0.052)|(0.051)|(0.063)|(0.129)|
|Number of observations|162,285|110,265|52,020|797,124|447,299|349,825|

_Source_ : Sample is composed of CPS MORG workers observed in two consecutive samples and employed in manufacturing in the first period.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. The classification of occupations into routine categories is determined by the proportion of tasks which are routine in each occupation, with routine being occupations with more than half. The regressions are run with the same controls as in Table 2. This includes controls for the worker's age, race, experience, union status, and sex. Fixed effects are included at the industry, year, and state levels in columns (1-3) and fixed effects for industry, occupation, year, and state in columns (4-6). Industry-specific regressions (columns 1-3) also control for lagged log price of investment, lagged total factor productivity, and lagged log real price of shipments. Controls for computer use rates are imputed by the worker's industry (columns 1-4) and by occupation (columns 4-6).
[Mukim (2011)] Megha Mukim. Industry and the Urge to Cluster: A Study of the Informal Sector in India. 2011.

[Nataraj (2011)] Shanthi Nataraj. The Impact of Trade Liberalization on Productivity: Evidence from India’s Formal and Informal Manufacturing Sectors. _Journal of International Economics_ , 85: 292-201, 2011.

[Paula and Scheinkman (2005)] A. De Paula and J.A. Scheinkman. The Informal Sector. _NBER Working Paper 13486_ , 2005.

[Ranis and Stewart (1999)] G. Ranis and F. Stewart. V-goods and the role of the Urban Informal Sector in Development. _Economic_ , 47 (2): 259–288, 1999.

[Sharma (2006a)] Gunjan Sharma. _Competing or Collaborating Siblings: An Investigation of the Relationship between Industrial and Trade Policies_ . PhD thesis, University of Maryland, 2006.

[Sharma (2006b)] Gunjan Sharma. Competing or Collaborating Siblings: Industrial and Trade Policies in India. _University of Missouri Working Paper 06-10_ , 2006.

[Sivadasan (2009)] Jagadeesh Sivadasan. Barriers to Competition and Productivity: Evidence from India. _The B.E. Journal of Economic Analysis and Policy_ , 9 (1 (Advances)), 2009.

[Todaro (1969)] M P Todaro. A Model of Labour Migration and Urban Unemployment in Less Developed Countries. _American Economic Review_ , 59: 138–148, 1969.

[Topalova and Khandelwal (2011)] Petia B. Topalova and Amit Khandelwal. Trade Liberalization and Firm Productivity: The Case of India. _Review of Economics and Statistics_ , 93: 995–1009, 2011.

[Xaba and Motala (2002)] J. P. Horn Xaba and S. Motala. The Informal Sector in SubSaharan Africa. _ILO Working Paper on the Informal Economy_ , (2002/10), 2002.

29
# 2 corresponds to the case where Mo _= M._<sup>_19_</sup>

The key feature to note is that here the exchange rate initially _appreciates_ at t _=_ 0. This is because inflation is lower at time zero than in later time periods. Since the effective price of consumption is low relative to future periods, the initial level of consumption rises relative to the old steady state. Since the money supply is constant, the cash in advance constraint implies that S must fall, i.e. the exchange rate appreciates. This illustrates the importance of the link between consumption and money demand in determining exchange rate dynamics. An attack will happen at time 0 only if consumption and real balances fall enough to drive government debt to a value greater than or equal to T.

# 4.4. **Sensitivity Analysis**

Table 2 describes the effect of perturbations to the Korean benchmark parameter values on the timing of a speculative attack and the present value of seignorage revenues. Four key results emerge here. First, if the increase in the deficit reflects a rise in government purchases rather than an increase in transfers, then t* falls. The reason for this is as follows. In the benchmark model, time 0 consumption rises by 2 percent relative to its initial steady state level. Correspondingly, there is an increase in the demand for money and a reduction in the government's debt. Other things equal, the reduction in bo _-_ fo delays the time at which bt _- ft_ reaches its threshold level, **T.** Because of the rise in future government purchases, the household's wealth declines relative to its initial steady state value. Consequently time 0 consumption does not rise by as much as in the benchmark model. Now c is roughly equal to the initial steady state level of consumption, c, and there is no initial improvement in the government's debt position. Other things equal, 19 We also experimented with setting _Mo = M,_ as well as chosing _Mo_ so that the government's intertemporal budget constraint is satisfied. These perturbations made very little difference to the results.

23
price averaged ₦216.25 and ₦229.75, respectively; similarly, the price of rice averaged at ₦612.25 and ₦615.88, respectively. Focusing on yellow maize, high variability was observed in the daily mean of crowdsourced data (₦156 to ₦296), as compared to enumerator range (₦120 to ₦374). However, the observed variability attenuated as the submissions were averaged into weekly and monthly datapoints (Figure 3b-c). By averaging both datasets into coarser temporal granularity (i.e. daily =>weekly =>monthly), the cohesion and similarity of price signals became more evident, and correlation coefficient improved (36% increase) from 0.69 at daily timestep to 0.94 at monthly timestep. Further, at monthly timestep (Figure 3c), 86% of the variability inherent in ground truth prices is represented in the crowdsourced data (p<0.001, α=0.05). In the corollary, the co-variability explained at weekly and daily timesteps are lower, likely owing to data noisiness at daily and weekly time intervals.

**Table 1:** Relationship between Ground truth reference prices from enumerators (Gr), Crowdsourced (Cr), and Artificial Intelligence (AI) estimated prices for maize and rice. The GrCr price data comparison was based on intraday data submissions over 8-months period (Mar - Oct 2021), while the Cr-AI data comparison was based on 3-years data (2019-2021) monthly average Cr prices juxtaposed with monthly AI-estimated closing prices. The values in the table represent the overall mean prices for each period, the corresponding correlation coefficient (R), and coefficient of determination (r<sup>2</sup> ), indicating the direction and strength of the relationship between the paired price datasets.

|||**8-month**<br>**Gr (**₦**/kg)**|**s Validatio**<br>**Cr (**₦**/kg)**|**n**<br>**Gr ~ Cr**<br>**_R_**|<br>**_r_**<sup>**_2_**</sup>|**3-Year Co**<br>**Cr (**₦**/kg)**|**mparison**<br>**AI (**₦**/kg)**|**Cr**<br>**_R_**|**~AI**<br>**_r_**<sup>**_2_**</sup>|
|---|---|---|---|---|---|---|---|---|---|
|aize|Yellow|219.94|232.76|0.94|0.86<sup>**</sup>|160.72|131.15|0.99|0.98**|
|M|White|212.68|227.05|0.94|0.86<sup>**</sup>|||||
||Thailand|649.31|625.50|0.78|0.55<sup>*</sup>|||||
|Rice|Indian|575.34|606.13|0.96|0.91<sup>*</sup>|434.72|429.85|0.94|0.88**|

* denotes significant relationship, _P-value_ <0.01; ** denotes significant relationship, _P-value_ <0.001. All tests performed at α = 0.05.

7
Policy ReseaRch WoRking PaPeR 4918

# **Abstract**

This paper examines how households trade off migration and savings when subject to exogenous violence. The authors propose that households under violence decide jointly on migration and saving, because a higher assetstock is more difficult to carry to a new place. When confronted with exogenous violence, households are expected to consider migration, and reduce their assets, both in order to reduce their exposure to violence, and to make migration easier. In some cases, after a migration decision has been taken, savings can increase as a function of violence to ensure a minimum bundle to carry. Empirical evidence from rich Colombian microdata supports the conceptual framework for violence that carries a displacement threat, such as guerrilla attacks.

This paper—a product of the Human Development Division, Middle East and North Africa Region—is part of a larger effort in the region to understand the impact of violence on household decisions. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at rgrun@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team