rafmacalaba/data-use-mentions-extended huggingface dataset:
have NREGS cards, only 26.6 percent of the untreated do. Predicted political participation is low for both groups; six percent of non-participants attend village council meetings, while 3.9 percent of the untreated do.<sup>14</sup> The fact that all predicted outcomes are higher for non-participants than for the untreated suggests that _Mahila Samakhya_ generates sizable spillover effects for non-participants living in treated districts. Figure 3 tells us that 78.2 percent of participants can leave the house without permission, 53.7 percent participate in the village council, and 79.7 percent have NREGS cards. Compared to the predicted values for the untreated, these outcomes represent significantly higher levels of empowerment for participants. Participants are 28.8 percent more likely to go out with permission, 49.8 percent more likely to participate in the village council, and 53.1 percent more likely to have access to NREGS cards. The 2SLS estimates are similar in magnitude and significance to the ITTs and suggest that any omitted age-variable trends do not substantially change our findings.

Since our sampling strategy relied on networks, we do not have a randomly-selected sample. Statewide data<sup>15</sup> on NREGS show that in 2010, women in untreated districts used an average of 35 percent of the person-days of work generated by NREGS, while in treated districts, they used an average of 41 percent, which is consistent with _Mahila Samakhya_ having a positive effect on access to outside employment and suggests that our estimates reflect statewide trends.

# **7.3 PSM Results**

Table 11 presents the two sets of treatment effects discussed above estimated using PSM: the first comparing non-participants to the untreated, and the other comparing participants to the untreated. The upper panel of the table shows the results comparing non-participants to the untreated. These results tell us that a non-participant is not significantly more empowered by simply living in a treated district. Without matching, only the NREGS cards variable is significantly different, with non-participants being
: the agglomeration of exporters serving the same market with the same product has a large positive impact on the odds of survival and on the initial export value. This means that there are positive spillover effects from the activities of competitors, effects similar to external economies of scale.

There is also a very strong positive impact of having experience with the product (exporting it to many countries) and a negative one for lack of focus (having too many products, serving too many markets, being active in too many sectors). In an apparent paradox, the fact of having a Revealed Comparative Advantage for that product hurts the FPD‟s survival chances. Moreover, the coefficient of market attractiveness is not statistically significant, something we interpret as a possible landlocked-effect: being stuck with a few destinations, regardless of their appeal, simply because of their proximity. This effect is noticeable in the data for other landlocked countries.

Even though little can be done by policy makers to deal with a country‟s geography besides road and air infrastructure, thereby increasing the choice of destinations for exporters, export promotion is one tool that has been used to help firms reach larger markets outside their borders. It is in this area that the paper‟s most interesting conclusions rest, and accordingly we propose two key areas of focus for export promotion policy.

Firstly, our evidence suggests that export promotion support activities should focus on helping existing exporters with a strong track record of exporting a given product to diversify into additional markets. Where resources are finite, they should be prioritized to those activities with the highest returns. Our analysis of export survival suggests that focused firms that specialize in _Exporting from a small landlocked economy_ _23_
- 42 -

# 4. The Data

Table A.1 provides a full print-out of most of the data used in this study. The data on coups is as follows. Countries with one coug in 1965-73: Mali, Congo, Somalia, Sudan, Ethiopia, Madagascar, Burkina Faso, Rwanda, Zaire, Benin, Lesotho; one coup in 1974-82: Burundi, Ethiopia, Liberia, Congo, Nigeria; two coups in 1965-73: Burundi, Nigeria, Ghana, Sierra Leone; two coups in 1974-82: Chad, Burkina Faso, Mauritania; three coups in 1974-82: Ghana. Additional information is available from the author.
Where, _Di,t_<sup>_k_=1</sup><sup>_{_t-</sup><sup>_Gi_=k</sup><sup>_}_is defined as an event study dummy variable which takes the value</sup> of 1 if the unit i is k periods away from the start of the treatment period t, and zero otherwise. _Di,t_<sup>_<−K_</sup> and _Di,t_<sup>_>L_are defined comparably, such that,</sup><sup>_D_</sup> _i,t_<sup>0= 1 if the unit is first treated at time t, and</sup><sup>_D_</sup> _i,t_<sup>1= 1</sup> if one period has passed since the time of the first treatment (treatment lags) etc. Alternatively, we have _Di,t_<sup>_K_equal to 1 if the unit i is treated K times from t (treatment leads).Consequently, k=-1 is</sup> the reference period, _φs_ and _ωt_ represents state and time fixed effects and _ε_ st is the error term. In this paper, Equation 1 is estimated using both realized FDI and Domestic Direct Investment (DDI) data, with K and L limited to 4 lags and 5 leads respectively since this is quarterly data; and planned FDI data, with and Ł limited to 13 lags and 10 leads since this is monthly data.

The selection of leads and lags is based on the time period of the data at the time of the analysis. Thus, if we have 13 months of data following the reform for planned investment for instance, then we select the pre-policy period to be an equivalent time period for comparison. The results will first show the average effect, then the effect period by period.

The primary identification issue is using aggregate data
ie, price changes are uniform across regions. Percent changes in demand for each of the 12 factors vary in addition by sector and region. Each adult in the PNAD microdata is identified by region and labour type; those employed are also identified by sector. Changes in microdata poverty levels are driven by wage changes and by the redistribution of jobs between sectors and regions (and hence between households).

15 In real terms, since the CPI is the model’s numeraire.

15
|**0.12**|**0.21**|
|_LL _|_−HH_|0.21|0.13|-0.02|0.36|0.19|0.24|
|_N_||1,210|242|452|181|181|154|

Table 1: Proportion of players who chose _Stag_ .

percentage points. These differences contributed to the divergence in pair outcomes, though the data indicate the particular importance of the last-period outcome ( _Stag_ , _Hare_ ).

Caste status is strongly correlated with wealth and education. We next consider whether these covariates explain the variation in behavior by caste status. We collected data in post-play interviews on land ownership, education, and housing.<sup>7,8</sup> Figure 4 shows, by caste status, the distribution of land owned by the households represented in our sample, as well as the proportions of subjects who completed high school and live in houses constructed only of mud (with thatch or tiled roofs). High-caste subjects own more land, are more likely to have a high school education, and are more likely to live in a house that is not made out of mud. However, there is substantial within-group variation, and the distributions of these characteristics overlap between high and low castes. For example, approximately 15 percent of the low-caste sample completed high school, compared to 18 of the high castes; 63 percent of low-caste subjects live in a mud house, compared to 19 of high-caste subjects.

> 7Houses were classified as either brick ( _pucca_ ), impermanent materials like sticks and mud, or a mixture of the two.

> 8Using data from the 1997-98 Survey of Living Conditions in Uttar Pradesh, we find that land ownership, high-school education, and housing type together explain between 30 and 40 percent of the variation in consumption for both high and low castes (Hoff, Kshetramade and Fehr, 2011).

13
The average employment ratio for CEE is presently about 64 percent, virtually the same as for the European Union (before the enlargement) and for the OECD. In the CIS it is even higher, above the OECD average. This somewhat surprising observation reflects the fact that under central planning the employment ratio tended to be at an unusually high level. The transition has been thus associated with a movement of employment rates towards a level characteristic of market economies. Such process is called a regression toward the mean. In many transition economies the current employment rates may be close to the equilibrium rates, which implies that there will be no return to the high employment rates prevailing before the transition.

Obviously, there is a substantial variation in employment ratios across countries. On the one hand, there are transition economies where the ratio is around 70 percent, which is high by international standards. These include the Czech Republic, Estonia, Lithuania in CEE, Belarus and Russia, and Azerbaijan and Kazakhstan among the other CIS (Figure 2).

# **Figure 2 Employment rates vary among transition economies**

## **Change in the Employment Rate, 1989-2001**

<!-- Start of picture text --> 0<br>CEE BRU 0ther CIS<br>-2<br>-4<br>-6<br>-8<br>1995-2001<br>1989-1995<br>-10<br>-12<br>-14<br>-16<br>-18<br>percentage points<br><!-- End of picture text --> Source: UNICEF TransMONEE database; Bank staff calculations On the other end of the spectrum are countries such as Bulgaria, Poland and Slovakia among the CEE countries, and Armenia and Tajikistan among the CIS, where the employment ratio is significantly below the OECD average.

# **High employment rates in slowly restructuring countries may not be sustainable**

High employment rates may imply a
Policy Research Working Paper 7758

# **Abstract**

Ghana is an exceptional case in the Sub-Saharan Africa landscape. Together with a handful of other countries, Ghana offers the opportunity to analyze the distributional changes in the past two decades, since four comparable household surveys are available. In addition, different from many other countries in the continent, Ghana’s rapid growth translated into fast poverty reduction. A closer look at the distributional changes that occurred in the same period, however, suggests less optimism. The present paper develops an innovative methodology to analyze the distributional changes that occurred and their drivers, with a high degree of accuracy and granularity. Looking at the results from 1991 to 2012, the paper documents how the distributional changes hollowed out the middle of the Ghanaian household consumption distribution and increased the concentration of households around the highest and lowest deciles; there was a clear surge in polarization indeed. When looking at the drivers of polarization, household characteristics, educational attainment, and access to basic infrastructure all tended to increase over time the size of the upper and lower tails of the consumption distribution and, as a consequence, the degree of polarization.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at vmolini@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
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in 2021 we find a statistically significant gender gap in business closures of 2.8 percentage points even after controlling for these firm characteristics.

The persistence of the gender gap in firm closures and performance even after accounting for firm level characteristics suggests there are other potential factors driving the gender gap that are not accounted for. Women entrepreneurs may be disproportionately affected by a

> 34COVID-19-focused phone-based surveys across 51 countries in 2020 that sample from national business registries find similar widespread negative impacts on sales and employment among small and medium enterprises (see Apedo-Amah (2020); Adian (2020)). The cross-country average suggests the probability of a business being closed was 26%, average sales reduction was -49%, and 57% of firms adjusted employment levels, evaluated after 6 weeks from the peak of the COVID-19 shock.

> 35This result is conditional on the business reporting a reduction in their workforce.

> 36This is slightly lower than the business closure rate in the previous survey wave in October 2020 where 14% of businesses were found to be closed.

21
WPS 3384

# **Local Conflict in Indonesia** **_Measuring Incidence and Identifying Patterns_**

Patrick Barron Kai Kaiser Menno Pradhan<sup>*</sup>

## **Abstract**

The widespread presence of local conflict characterizes many developing countries such as Indonesia. Outbreaks of violent conflict not only have direct costs for lives, livelihoods, and material property, but may also have the potential to escalate further. Recent studies on large-scale “headline” conflicts have tended to exclude the systematic consideration of local conflict, in large part due to the absence of representative data at low levels of geographic specification. This paper is a first attempt to correct for that. We evaluate a unique dataset compiled by the Indonesian government, the periodic Village Potential Statistics (PODES), which seeks to map conflict across all of Indonesia’s 69,000 villages/neighborhoods. The data confirm that conflict is prevalent beyond well publicized “conflict regions,” and that it can be observed across the archipelago. The data report largely violent conflict in 7.1 percent of Indonesia’s lowest administrative tier (rural _desa_ and urban _kelurahan_ ). Integrating examples from qualitative fieldwork, we assess issues in the measurement of local conflict for quantitative analysis, and adopt an empirical framework to examine potential associations with poverty, inequality, shocks, ethnic and religious diversity/inequality, and community-level associational and security arrangements. The quantitative analysis shows positive correlations between local conflict and unemployment, inequality, natural disasters, changes in sources of incomes, and clustering of ethnic groups within villages. The institutional variables indicate that the presence of places of worship is associated with less conflict, while the presence of religious groups and traditional culture ( _adat_ ) institutions are associated with conflict. We conclude by suggesting future areas of research, notably on the role of group inequality and inference, and suggest ways to improve the measurement of conflict in the village census.

### World Bank Policy Research Working Paper 3384, August 2004

_The Policy Research Working
rates of participation and attendance are consistent with our hypothesis that entrepreneurs in survival clusters are willing to learn management practices.

The treatment group and the control group share similar background attributes. A typical entrepreneur is male, about 45 years old, from the Ashanti region, where the cluster is located, went to school for a little more than 10 years, learned the skill of the trade as an apprentice from a master of either fabrication or machining for three to four years, and has been operating his own business for nearly 14 years. Fabricators are basically welders whereas machinists are basically lathe turners. Many workshops have both fabricators and machinists because their activities are complementary. We classify the entrepreneurs into these two types—fabricators and machinists—based on the original line of work that they were engaged in when they started their businesses.

Table 2 shows that the refusers tended to be older than the participants. Older entrepreneurs may have had higher time costs or lower expected benefits from the training than younger entrepreneurs. Another characteristic of the refusers is that none of them are from outside the Ashanti region. Local inhabitants would have greater involvement in extended family functions, community functions, and sideline businesses than those from other regions. If this was the case, their opportunity costs would be higher.

Table 3 reports the data on the adoption of recommended practices and three indicators of business performance. The data on the situations in 2000 and 2002 are recall data collected in 2004. The left side of the table shows the percentages of the entrepreneurs who visit customers periodically, kept records, and routinely analyzed the records in the specified year. Visiting customers is not a common activity in this cluster, and the majority of the sample firms do not keep records. Even if records are kept, they are seldom reviewed or analyzed. The data on the adoption of production management 12
to indigenous African entrepreneurs; Tanzania is the only country where these differences are not significant, except for title to property.

**Table 2: Finance Characteristics of SMEs**

||**Kenya**|**Tanzania**|**Zambia**|**Zimbabwe**|
|---|---|---|---|---|
|**_Pct. receiving_**|30.3|11.8|19.2|66.4|
|<br>**_Supplier Credit_**|||||
|<br>**_Avg. years of_**|8.5|7.9|8.6|12.0|
|<br>**_supplier relation_**|||||
|<br>**_Pct with title to_**|37.4|37.1|47.9|43.2|
|**_property_**|||||
|<br>**_Pct. rec. any bank_**|24.6|8.2|11.4|11.2|
|<br>**_loan at startup_**|||||

_Source: Enterprise Surveys 1990s, Regional Program on Enterprise Development, World Bank_ 7 It should be noted that the RPED sample was drawn on the basis of employment rather than on the basis of the number of firms.

13
electronics industries. There are no innovative firms in the automobile industry in Nepal, and a small proportion of firms are innovative in Pakistan.

Figure 2. Technological and Non-Technological Innovations in Key Sectors <!-- Start of picture text --> Technological and Non Technological Innovation Rates by Specific Sectors<br>Bangladesh (% All Firms) India (% All Firms)<br>Food Apparel Electronics Automobiles Food Apparel Electronics Automobiles<br>Nepal (% All Firms) Pakistan (% All Firms)<br>Food Apparel Electronics Automobiles Food Apparel Electronics Automobiles<br>Product & Process Organizational Product & Process Organizational<br>1 1<br>.75 .75<br>.5 .5<br>.25 .25<br>0 0<br>1 1<br>.75 .75<br>.5 .5<br>.25 .25<br>0 0<br><!-- End of picture text --> Source: Author’s elaboration from Enterprise Survey (2014)

# 5.4 Degree of Innovativeness or Novelty

Although innovation rates are relatively large for some countries in the region, this does not necessarily mean that the degree of innovativeness is high. Indeed, one pattern found for several developing countries is that a limited number of firms engage in disruptive innovative activities such as introducing new products to the country or even better new products to the world, while the majority of them conduct incremental innovations or pure imitation - either by upgrading the quality of existing goods or introducing new products to the firm (Cirera and Sabetti, 2019).

When firms decide among alternative innovation choices, several factors affect this decision. The market structure is one important determinant. When product market differentiation is low and the degree of competition is high, the entrance of new firms in a market reduces incumbents’ market shares by much more than in the case where products are imperfect substitutes like in the case of monopolistic or oligopolistic
crops, such as China, India, Southeast Asia and Eastern Europe, could face food supply reductions of 3.5-6.1% from the baseline by 2020.

# **References**

Narayanan, B. G. and T. L. Walmsley, Editors. 2008. Global Trade, Assistance, and Production: The GTAP 7 Data Base, Center for Global Trade Analysis, Purdue University.

- Banse, M., H. van Meijl, A. Tabeau, and G. Woltjer. 2008, Will EU Biofuel Policies Affect Global Agricultural Markets. European Review of Agricultural Economics. 35(2):117141.

- Burniaux J-M. and Chateau J., 2010. ”Background Report: An Overview of the OECD ENVLinkages model”, OECD.

- Birur, D., Hertel T., and Tyner W., Impact of Biofuel Production on World Agricultural Markets: A Computable General Equilibrium Analysis, GTAP Working Paper No. 53, Department of Agricultural Economics, Purdue University.

- Choi S., 2004. "The Potential and Cost of Carbon Sequestration in Agricultural Soil: An Empirical Study with a Dynamic Model fo the Midwestern U.S." Ph.D. Thesis, Department of Agricultural, Environmental, and Development Economics, Ohio State University.

- Farrell A.E., R.J. Plevin, B.T. Turner, A.D. Jones, M. O‟Hare and D.M. Kammen. 2006. Ethanol Can Contribute to Energy and Environmental Goals. Science 311: 506–8.

- Jarrett P. and Torres R., 1987. “A revised supply block for the major seven countries in INTERLINK”, OECD Department of Economics and Statistics Working Papers No. 41.

- Ma H., Oxley L., and Gibson J., 2010. “China's energy economy: A survey of the literature”, Economic Systems, Elsevier, vol. 34(2), pages 105-132.