rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9019

# **Abstract**

This paper studies the relationship between conflict and household welfare by using a detailed panel data set of household victimization across the most conflict-affected regions in Nigeria between 2010 and 2017, during a time characterized by a sharp increase in conflict. The North East region has been hardest hit with the recent Boko Haram insurgency. The North Central region has seen clashes between herders and farmers over land and resources. Several militant groups operate in the oil-producing Niger Delta region, where their aim is to extract resources by disrupting oil production. By exploiting the plausibly exogenous variation in the timing, intensity, and spatial distribution of victimization, this study finds that becoming a victim of conflict leads to higher food insecurity and decreased consumption. Since different types of actors have different motivations for their actions, the consequences of victimization might vary depending on the perpetrator. The study finds that events perpetrated by insurgents are the most detrimental to consumption, whereas food insecurity increases as a consequence of insurgent and criminal activity. This is in line with the results being strongest in the North East, which also has the highest intensity of conflict. The study also finds that property-related events are more detrimental to consumption and food insecurity than are violent events. Likewise, it finds suggestive evidence that violent events, as well as events perpetrated by insurgents and bandits, are detrimental to mental health. The findings highlight the importance of collecting nuanced information on victimization in conflict-affected areas.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mazad@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
# **Table 7(a): Baseline - 3 Lags VAR- Closed Economy Money Demand Model Cointegration**

# **Analysis with Johansen Test: sample 1959–2013**

|rank|eigenvalue|Log likihood<br>for rank|Trace test|Max test|Trace test|Max test|
|---|---|---|---|---|---|---|
||||[ Prob]|[ Prob]|(T-nm)|(T-nm)|
||||**109.70**|**52.21**|**79.78**|**37.97**|
|0||**4.707021**|||||
||||**[0.000]****|**[0.000]****|**[0.029]***|**[0.017]***|
|1|0.61296|30.81082|**57.49**|24.16|41.81|17.57|
||||**[0.023]***|[0.171]|[0.388]|[0.622]|
||||33.33|15.28|24.24|11.12|
|2|0.35550|42.89089|||||
||||[0.077]|[0.365]|[0.452]|[0.740]|
|3|0.24261|50.53262|18.05|12.20|13.12|8.88|
||||[0.098]|[0.180]|[0.362]|[0.459]|
||||5.84|5.84|4.25|4.25|
|4|0.19900|56.63454|||||
||||[0.210]|[0.210]|[0.388]|[0.387]|
|5|0.10078|59.55572|||||

P-values are in brackets, *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10% 47
- ii -

The text is accompanied by three case studies, which illustrate achievements and shortcomings of experimental lending operations in Argentina, Brazil, and Mexico. Some discussions of a more general character (e.g., on decentralization, revenue sharing, etc.) reflect current thinking on fiscal problems and are condensed in form of inserts (boxes). To support some of the points made in the text, Betty Alvarado has carried out the necessary elaboration of the statistics available from the IMF data base, including comparisons between LAC countries and industrial market economies.
# Contents

|1|Introduction|3|
|---|---|---|
|2|The Model|5|
||2.1<br>Consumption and Labor Supply . . . . . . . . . . . . . . . . . . . . . .|6|
||2.2<br>Final Good<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|9|
||2.3<br>Intermediate Goods . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|11|
||2.4<br>Design Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|12|
||2.5<br>Government . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|15|
||2.6<br>Market-Clearing Conditions . . . . . . . . . . . . . . . . . . . . . . . .|16|
|3|Dynamics and Steady State|17|
|4|Calibration|20|
|5|Public Policy|23|
||5.1<br>Provision of Basic Infrastructure . . . . . . . . . . . . . . . . . . . . . .|24|
||5.2<br>Training Subsidy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|25|
||5.3<br>Enforcement of Property Rights . . . . . . . . . . . . . . . . . . . . . .|25|
||5.4<br>Sequential, Composite Reform Program . . . . . . . . . . . . . . . . . .|26|
|6|Policy Implications|27|
|7|Concluding Remarks|29|
|A|ppendix A: Dynamics under the Mixed Regime|31|
|A|ppendix B: Dynamics under the Imitation and Innovation Regimes|35|
|Re|ferences|38|

2
commuting.

## **E.3 Auxiliary Data**

Since our study period overlaps with the COVID-19 pandemic period, we obtain COVID19 case data from Google Health. We also use the Indian Population Census 2011 to obtain district-level characteristics like population. We combine the two data sets and construct average daily new confirmed cases as a share of the population in a district. We include this variable in our regressions to account for the potential effects of COVID-19 on outcomes like labor market participation. To address further concerns that different states experienced different levels of mobility restrictions, we use data from the Oxford Covid Tracker on the share of days each month that state governments imposed a shutdown on public transportation.

# **F Empirical Strategy**

States that implemented the _Pink Slip program_ may differ from other states. Apart from including only the neighboring states of the treated state as controls, we employ a synthetic difference-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) to allay this concern further. The SDID re-weights the control units to make their time trend parallel to the treated units and then applies a DID analysis to the re-weighted panel. This method constructs a synthetic counterfactual for causal estimation. Since, in our setting, the treatment is applied at the state level, we construct a state-level panel using the CPHS data. With this panel, we can derive each control state’s SDID unit and A.1. time weights. A detailed explanation is provided in Appendix To estimate the impact of the _Pink Slip_ scheme on women (or households with women), we employ two regression specifications for individual (or household) _i_ in state _j_ and time _t_ weighted by the individual (or household) sampling weight, along with the SDID time and unit weights. Starting with a standard stacked event-study design where _Y_ are outcome variables such as household on _ijt_ monthly expenditure transport and individual time on travel. The variable _Treated_ takes the value 1 usage dummy _j_ if a state implemented the _Pink Slip program_ and 0 otherwise. **1** ( _Time_ = _t_ ) takes the 8
Policy Research Working Paper 5084

# **Abstract**

Size, age, sector, and productivity are commonly cited as factors determining a firm’s survival. However, there are several dimensions of the investment climate in which the firm operates that affect whether it continues in business or exits. This paper uses new panel data from 27 Eastern European and Central Asian countries to test the importance of five areas of the business climate on firm exit: the efficiency of government services, access to finance, the extent of corruption or cronyism, the strength of property rights, and the degree of competition. The paper finds that weaknesses in these areas do affect the probability of firm exit—largely in ways that undermine the Schumpeterian cleansing role of exit in raising overall productivity. Greater costs and regulatory burdens raise the probability that more productive firms exit, while less developed financial and legal institutions mitigate forces that would otherwise push less productive firms to exit. Thus, the more productive firms stand to gain the most from improvements in the investment climate, whether that is lowering transaction costs or improving market mechanisms. This holds both within countries and across countries. The impact of a particular investment climate measure can also differ significantly by type of firm, with the focus given to firm size. The differential impact on size can be significant at a size cutoff of 10 or more employees. As these are the firms that are near the threshold of many regulatory requirements, the implications are not just with regard to whether a firm remains in operation, but whether it does so in the formal sector.

This paper—a product of the Macroeconomics and Growth Team, Development Research Group—is part of a larger effort in the department to explore firm dynamics and the microeconomics of growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mhallward@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
<!-- Start of picture text --> 6<br>4<br>2<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Farm (+hired) Enterprise, Ag Enterprise, Ind<br>Enterprise, Ser Wages, Ag Wages, Ind<br>Wages, Ser Wages, Unkn<br>5<br>4<br>3<br>2<br>1<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Farm Enterprise, Ag Enterprise, Ind<br>Enterprise, Ser Wages, Ag Wages, Ind<br>Wages, Ser Wages, Unkn<br>Productivity ratio, output per psn per yr<br>Med. prod. ratio, output per psn per year<br><!-- End of picture text --> Figure 12: Productivity gap calculations including a predicted count of hired farm workers in addition to household members. Figure 12a (top) depicts the ratio between mean farm labor productivity per person per year and the mean labor productivity of other activities (i.e., NFEs and wage labor in different sectors). Figure 12b (bottom) depicts the median of intra-household productivity ratios between farming and other activities, where productivity is defined as output per worker per year.

32
1995 the job reallocation rates in the CEE countries are similar to those in mature capitalist economies (about 20 percent) with roughly equal job creation and destruction rates (Davis and Haltiwanger 1999).

The general patterns evident in the BEEPS data are broadly in accord with these earlier findings of firm growth. Table III.1 shows the balance between growing and shrinking firms, i.e., the proportion of the sample of firms that is growing less the proportion that is shrinking (no growth firms are ignored). Here and below, growth is in real terms and covers the 3 years preceding the survey. Germany was in a period of macroeconomic stagnation during the period 2001-04, and this is apparent in the table, with the share of firms with growing sales exceeding the share of shrinking firms by only 5 percentage points. The cohesion countries, by contrast, were growing rapidly, with the number of firms with growing sales exceeding the shrinking share by 30 percentage points. The EU8 look very similar, and also show a moderate slowdown over the full period covered by the BEEPS surveys. An acceleration in growth is very apparent in the poorest countries/slowest reformers (middle income and low income CIS), going from near stagnation in 1996-99 to rapid expansion in 2002-05.

The picture in terms of employment is more muted. The number of firms with expanding employment in the EU8 has barely exceeded the number of downsizing firms from the very start of the BEEPS surveys. This share is, moreover, low compared to that in the cohesion countries. Here we see the first evidence of a possible failure of convergence: evidence of possible stagnating job growth in the EU8 members. We will return to this point below. The pattern in the other regions is quite different: in SEE, firms expanding employment have markedly outnumbered firms shedding labor since 1996-99; and in the middle income CIS and low income countries, stagnation in 1996-99 is replaced by largescale expansion in more recent years.

Part of the explanation can be attributed to continued downsizing of employment and restructuring of state-owned and privatized firms. By 2005, these firms are sharing in the output expansion in TEs with new private firms, but are still shedding labor. Privatized 13
**Table 6 – The regional growth induced by a hypothetical 10% enhancement of the** **<u>performance of different provinces (coastal vs. inland)</u>**

||Natio<br>|nal<br>||Coa<br>|stal<br>||Inla<br>|nd<br><br>|Wes<br>|tern<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Province|Aver|age<br>|Province|Aver|age|Province|Average<br>|Province|Average|
|1|Ningxia||0.02|Shanxi||...|_Beijing_|...|_Beijing_|...|
|2|Xinjiang||0.03|Nei Mongol||...|_Tianjin_|...|_Tianjin_|...|
|3|Qinghai||0.06|Jilin||...|_Shanghai_|...|_Hebei_|...|
|4|Guangxi||0.15|Heilongjiang||...|Ningxia|0.04|_Liaoning_|...|
|5|_Tianjin_||0.19|Anhui||...|Xinjiang|0.07|Jilin|...|
|6|_Beijing_||0.21|Jiangxi||...|Qinghai|0.12|Heilongjiang|...|
|7|Guizhou||0.21|Henan||...|_Fujian_|0.24|_Shanghai_|...|
|8|Shanxi||0.22|Hubei||...|Guangxi|0.33|_Jiangsu_|...|
|9|Yunnan||0.25|Hunan||...|Guizhou|0.46|_Zhejiang_|...|
|10|Heilongjiang||0.27|Guangxi||...|Shanxi|0.48|Anhui|...|
|11|Jiangxi||0.31|Sichuan||...|Yunnan|0.55|Fujian|...|
|12|Gansu||0.36|Guizhou||...|Heilongjiang|0.58|Jiangxi|...|
|13|Jilin||0.43|Yunnan||...|Jiangxi|0.67|_Shandong_|...|
|14|Anhui||0.54|Shaanxi||...|_Jiangsu_|0.75|Ningxia|0.06|
|15|Nei Mongol||0.55|Gansu||...|Gansu|0.78|Shanxi|0.19|
|16|Shaanxi||0.55|Qinghai||...|Jilin|0.93|Xinjiang|0.20|
|17|Hunan||0.68|Ningxia||...|_Zhejiang_|0.95|Qinghai|0.33|
|18|_Shanghai_||0.69|Xinjiang||...|_Liaoning_|1.15|Nei Mongol|0.48|
|19|_Liaoning_||0.72|_Tianjin_||0.34|Nei Mongol|1.18|Guangxi|0.51|
|20|Henan||0.78|_Liaoning_||0.36|Shaanxi|1.19|Henan|0.53|
|21|Sichuan||1.12|_Beijing_||0.40|Anhui|1.20|Guizhou|1.00|
|22|Hubei||1.38|_Guangdong_||0.53|Hunan|1.48|Shaanxi|1.23|
|23|_Zhejiang_||1.60|_Shanghai_||1.26|Henan|1.71|Yunnan|1.47|
|24|_Fujian_||1.72|_Shandong_||1.70|_Shandong_|2.07|Gansu|1.90|
|25|_Shandong_||1.87|_Zhejiang_||2.15|_Hebei_|2.13|Hubei|2.57|
|26|_Guangdong_||1.93|_Fujian_||2.95|Sichuan|2.42|Hunan|2.63|
|27|_Jiangsu_||2.42|_Hebei_||3.09|Hubei|3.02|_Guangdong_|2.74|
|28|_Hebei_||2.64|_Jiangsu_||3.84|_Guangdong_|3.59|Sichuan|3.69|

Note: Figures are presented in 1/10000. " ... " stands for the figures inferior to 0.0000005. For example, a 10 percent increase annual growth rate in Hebei province leads to a 2.64 × 10<sup>-4</sup> increase of the growth rate of the whole China. Coastal provinces are marked in italics.

However, if we look closely into the distribution of these positive spillover effects from the coastal locomotives at regional level, the results of the last three columns suggest that the induced growth mainly concentrates in the coastal region and their effects on the inland region (and western region) are very limited. The resulting widening regional development gap is inconsistent with the objective of balanced growth. To generate the optimal spillover effects on the catching-up of the inland/western provinces, development aid should be targeted towards the inland hubs, Hubei and Sichuan, for their rapid growth might not only has large spillover effects on the inland/western regions, but also on the entire Chinese economy.

23
# **Table 16: Impact on the Regions of Russia of Emission Intensity Standards to Reduce Carbon Emissions by 20 Percent, with decomposition of carbon emission effects. (Results are Percentage Change from Base Year unless otherwise indicated.)**

||**Overall**||**St. **||**North-**||||||**Far **|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**average M**|**oscow**|**Peters.**|**Tumen**|**west**|**North**|**Central**|**South**|**Urals**|**Siberia**|**East**|
|**Aggregate welfare**||||||||||||
|Welfare (EVas % ofconsumption)|-1.7|-1.5|-1.6|-6.9|-1.4|-2.0|-1.2|-1.6|-1.4|-1.6|-1.9|
|Welfare (EVas % ofGDP)|-0.8|-0.8|-0.7|-1.5|-0.6|-0.8|-0.6|-0.8|-0.6|-0.7|-0.9|
|**Carbon Dioxide Emissions and Deco**|**mposition**|||||||||||
|CO2price (ruble per tonofCO2)||0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|CO2emissions, decomposedinto:|-20.0|-19.4|-20.1|-24.1|-21.0|-18.5|-20.6|-19.8|-20.4|-17.2|-17.7|
|Outputeffect(% ofCO2)|-1.8|-1.5|-1.5|-4.2|-1.7|-1.8|-1.5|-1.7|-1.6|-1.5|-1.6|
|Compositioneffect(% ofCO2)|-1.0|1.5|1.3|-13.4|1.0|-0.1|1.1|0.7|0.6|-0.7|-0.2|
|Technique effect(% ofCO2)|-17.1|-19.4|-19.9|-5.6|-20.4|-16.7|-20.3|-18.8|-19.5|-15.1|-15.9|
|**Non-Carbon Dioxide Emissions**||||||||||||
|Sulphur Dioxide|1.0|4.9|3.8|-0.2|-0.6|1.1|2.6|2.9|0.5|0.6|3.1|
|NitrogenOxide|2.0|5.0|2.9|-1.6|5.0|2.5|5.7|3.6|1.2|1.6|2.5|
|Hydrocarbons|-3.6|-5.9|-5.6|-2.6|-5.3|-4.3|-5.4|-4.3|-3.1|-4.9|-6.2|
|ParticulateMatter|1.1|4.9|4.1|-1.4|6.8|2.6|5.5|4.9|0.8|0.7|0.8|
|Volatile Organic Components|-1.4|-1.3|-1.7|-1.6|0.7|-1.0|-0.6|-1.6|-1.1|-1.0|-0.7|
|Carbon Monoxide|-1.2|1.2|-0.6|-1.7|-2.9|-0.7|-2.1|-1.3|-1.4|-0.3|0.2|
|**Aggregate trade**||||||||||||
|Regional terms of trade (% change)|-0.6|2.5|2.4|0.9|2.4|2.3|2.6|2.4|2.4|2.5|2.4|
|Regionalexports (% change)|-1.3|-1.2|-1.3|-2.3|-1.3|-1.2|-1.2|-1.2|-1.0|-1.3|-1.3|
|Realexchangerate (% change)|-0.3|-0.2|-0.3|-1.1|-0.3|-0.4|-0.3|-0.4|-0.3|-0.3|-0.3|
|Internationalexports (% change)|-0.4|-2.9|-2.1|1.8|-2.7|-0.4|-3.4|-0.4|-0.7|-0.6|-0.9|
|**Returnto primary factors (% change)**||||||||||||
|Unskilledlabor|0.0|0.3|0.2|-0.8|0.2|-0.2|0.2|-0.1|-0.1|-0.1|-0.1|
|Skilledlabor|0.3|1.2|0.9|-2.7|1.0|0.0|1.0|0.7|0.7|-0.2|0.0|
|Nationalcapital|-1.1|-1.0|-1.0|-1.8|-1.0|-1.1|-1.0|-1.1|-1.1|-1.1|-1.0|
|Regional mobile capital|-0.7|-1.0|-1.0|0.7|-1.1|-0.8|-0.9|-0.7|-0.7|-0.7|-0.9|
|Crude oil resources|-2.4|||-2.9|-1.8|-1.3||-1.5|-1.4|-1.2|-1.0|
|Naturalgasresources|-42.7|||-44.3|-25.2|-26.9||-28.0|-27.6|-27.0|-24.9|
|Coal resources|-7.9|||||-8.1|-8.3|-8.8|-8.8|-7.9|-7.7|
|Specific capital indomesticfirms||-0.9|-1.0|-1.8|-1.4|-1.3|-1.2|-1.5|-1.5|-0.9|-0.8|
|Specific capital in multinational firms||-0.8|-0.9|-2.2|-1.1|-1.1|-0.7|-0.5|-0.4|-0.7|-1.0|
|**Factor adjustments**||||||||||||
|Unskilledlabor(% changing sectors)|0.7|0.7|0.7|1.1|0.8|0.6|0.7|0.7|0.7|0.7|0.7|
|Skilledlabor(% changing sector)|1.1|0.9|0.9|2.1|1.0|1.0|0.9|0.9|0.9|1.2|1.2|
|**Source: Authors' estimates**||||||||||||

55
## **(b) Country Income Classification (between 2002-2011)**

<!-- Start of picture text --> Poverty Data Point Total Surveys Average Ratio<br>High Income 5.8 16.8 2.5<br>Upper Middle Income 4.4 19.0 5.6<br>Low Middle Income 3.5 19.3 7.7<br>Low Income 1.8 16.6 10.8<br>0 5 10 15 20 25<br><!-- End of picture text --> This observation leads us to suggest that attempts to improve the coordination or planning across different types of surveys would increase the availability of poverty data (and other data as well). To facilitate the coordination across different survey initiatives, PARIS 21 and development communities encourage countries to prepare National Strategies of Developing Statistics (NSDS). The NSDS typically include plans to undertake household surveys within five years or so, and if the NSDS are implemented as intended, the frequency of poverty data can be increased without increasing survey implementation costs. It will be very useful to evaluate the performance of NSDS in reducing the gaps between the number of household surveys in the IHSN and in the database used for developing this note.<sup>18</sup>

# **7. Concluding Remarks**

This paper analyzes the availability of poverty data using World Development Indicators, which include both national poverty data measured at country-specific poverty lines and international poverty data measured at the $1.25 or $2 a day poverty line. The WDI database in principle includes comparable poverty estimates that are vetted by the World Bank’s poverty measurement specialists. Since efforts in global poverty monitoring such as the Millennium Development Goals and the World Bank Group goals use this database, the WDI database is appropriate for analyzing the availability of poverty data.

This paper defines the status of data availability in the following way:

- if a country does not have two or more poverty estimates in ten years, the country is categorized as ”data deprived”;

- if a country has two poverty estimates in ten years but the interval of surveys is more than five years apart, then the country is categorized as “vulnerable to data deprivation”; and

18 Writing in the context of Sub Saharan Africa, Devarajan (2013) places some of the blame of the failure in the NSDS on the donors: “Many donors, including the World Bank, undertake statistical activities without ensuring that they are consistent with the NSDS. Why? Because donors may want the data for their own purpose—to publish a report, for example. Their incentive is to collect the data as quickly as possible, whereas building statistical capacity takes time. Even though the country owns the data, donors often behave as if they do. They often publish the data without any recognition of the source of the data”. In a different context, Sandefur and Glassman (2014) point out how a coordination failure between donor and statistical agencies can lead to flawed data being produced (with there being a frequent disconnect between statistics produced from household surveys and from administrative data).

20