rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 0

# **Abstract**

This study examines the major constraints of rural business entry and performance in Yemen. The Yemen rural investment climate survey made it possible to analyze rural investment climate constraints for rural businesses. The survey was used to investigate both farm and nonfarm rural enterprises. The rural investment climate was assessed using a combination of subjective impressions related by rural entrepreneurs, and a more objective, empirical set of analyses that employed indicators to rank the constraints to _“doing business”_ in the areas surveyed. These empirical analyses included application of the entry model, the performance model, the closure model, and the migration model. The migration model was introduced to identify how the rural investment climate variables at the community level increase migration and economic activities. Based on the assessment of the rural investment climate, this paper identifies and explains four critical areas in which the rural investment climate in Yemen can be improved: market demand, access to markets, access to finance, and the provision of business services. Because farm and nonfarm businesses often experience common or similar constraints, the climate in which they operate can often be improved with the same measures and policies. Addressing the constraints that affect rural women entrepreneurs in particular, who play a vital role in rural nonfarm enterprises, warrants clear priority as a means to generate income and employment. Security and labor issues are identified as the key such constraints that disproportionately affect women.

This paper is a product of the Agriculture and Rural Enterprises Unit, Social Development Network. 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 author may be contacted at nsawada@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
# Iraq’s Universal Public Distribution System: Utilization and Impacts During Displacement<sup>1</sup>

Lokendra Phadera,<sup>2</sup> Dhiraj Sharma,<sup>2</sup> Matthew Wai-Poi<sup>2</sup>

**Keywords** : Public distribution system (PDS); Displacement; Welfare; In-kind food; Iraq **JEL Classification** :  H420, H430, O220

1 This document is an output from a project funded by the UK Department for International Development (DFID). The findings, interpretations and conclusion expressed in this paper are entirely those of authors. We are grateful for the guidance of the peer reviewers Nandini Krishnan and Rene Antonio Leon Solano. Maria Eugenia Genoni led the project in its early stage. We are also thankful to the Central Statistical Office (CSO) of Baghdad and Kurdistan Regional Statistical Regional Office (KRSO) for sharing the data.

2 Poverty and Equity Global Practice, World Bank. Please contact lphadera@worldbank.org, <u>dsharma5@worldbank.org, or mwaipoi@worldbank.org.</u>
available. We therefore calculate the present value of the orchard under the assumption that the orchards are halfway through their assumed lifetime.<sup>14</sup> For the expected price, we calculate a fiveyear moving average of actual domestic producer prices, converted to nominal dollars, centered on the current year (two periods forward and two periods lagged). The income per hectare is imputed forward in time (with discounting) for each crop and then aggregated. We use the U.S. interest and inflation rates to calculate a real interest rate, which serves as the discounting factor. Finally, we convert the result to constant (1990) dollars by applying the U.S. agricultural GDP deflator.

# **Data**<sup>_15_</sup>

The economies included in the agricultural investment data set are Australia, Austria, Canada, Cyprus, Denmark, Egypt, Finland, France, Greece, India, Indonesia, Italy, Kenya, Republic of Korea, Malawi, Mauritius, Morocco, the Netherlands, Norway, Pakistan, Peru, the Philippines, Sri Lanka, Sweden, Republic of Tanzania, Tunisia, Turkey, United Kingdom, United States, and Uruguay.

For most countries included in the data set on agricultural capital stocks, the series on the sectoral breakdown of fixed investment begin in the 1960s, thus sufficiently complete to estimate capital stocks beginning in 1967. All series on livestock and treestock begin in 1961. Looking forward, there is potential for the expansion of the data set on agricultural fixed capital to include more countries, but with a later starting date for the series.<sup>16</sup>

# **Evolution of capital stocks**

Having a data series on agricultural capital which is comparable across countries and over time allows us to examine the evolution of capital stocks and assists us in gaining insight on the process of agricultural growth. In Figures 2 and 3, we present the frequency distribution of growth rates of the capital components for the 30 countries in our sample.<sup>17</sup> Capital accumulation in agriculture since 1970 has been positive in all of the countries (Figure 2). The median growth rate of agricultural

> 14 Estimated lifetimes are assigned specifically for each tree crop, ranging from 20 years for coffee to 200 years for olives (see Appendix A.2). For a discussion of vintage in the evaluation of orchards, see Akiyama and Trivedi (1987).

> 15 The data set on agricultural capital stocks is available online at: http://www.farmfoundation.org/news/articlefiles/1725-capital_update_2000.pdf.

> 16 Since the dissolution of the Soviet Union, Czechoslovakia, and Yugoslavia in the early 1990s, many of the newly independent countries report data on agricultural gross fixed capital formation.

> 17 Average annual growth rates reported in this paper were obtained from trend regressions (natural log of variable on time).

8
Fourth, currency crises normally lead to a decline in a country's economic growth rate. This decline is caused by higher interest rates, as well as an erosion in public confidence and spending that typically accompanies capital flight. One factor that usually acts to lessen the decline in economic activity is an increase in exports, reflecting the improved short-term competitive position of the country due to the depreciation of its currency. In the Mexican currency crisis, GDP, which had risen 3.5 percent in 1994, fell 6.2 percent in 1995. In the recent Asian crisis, Korea's estimated growth rate in 1997 was 5.5 percent, but is currently forecast to be a negative 3-4 percent in 1998. Indonesia's growth rate is now forecast to go from an estimated 7.0 percent in 1997 to a negative 10.0 percent in 1998, and Thailand's from 0.5 percent to a negative 4.2 percent (J.P. Morgan 1998).

Fifth, curTency crises often result in an increase in domestic prices, in part because of the rise in import prices caused by the depreciation of the domestic currency. In Mexico, the consumer price index, which had risen at a moderate 7 percent in 1994, soared to 35 percent in 1995 (J.P. Morgan 1998). In the Asia crisis, Thailand, Indonesia, and Korea all had mid-range single digit rates of inflation in 1997 but seem likely to have inflation rates well into the double digits in 1998.3 Sixth, it is common for a currency crisis to result in a decline (often sharp) in real estate prices. This decline reflects both the fall in the level of economic activity and the rise in interest rates that typically accompany a currency crisis.

# _b) Effects on the iFinancial Conditions of Banks_

The macroeconomic and financial market effects discussed above can have several harmful effects on the financial condition of the banking system. This point is best illustrated by reference to the serious adverse effects that the Mexican currency crisis of 1994-95 had on the Mexican banking system.<sup>4</sup> 3. For example, J. P. Morgan has forecast Thailand's inflation rate for 1998 at 15 percent, Indonesia's at 60 percent, and Korea's at 12.5 percent (J. .P. Morgan 1998).

4. Material for these illustrations is derived from Sachs, Tomell, and Velasco (1996); Truman (1996); and intemal World Bank documents.

9
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# **Insert Table 2 here**

Table 2 presents summary statistics and correlations for our main variables from the formal manufacturing Census. Table 2 shows that after winsorizing, Firm Size ranges from 1 to 1616 employees with a mean of 121 employees. The mean Firm Size Ratio is 1.38 suggesting that the average firm in our sample is 1.38 times the size of its birth cohort. 62% of the firm-year observations are classified as small firm industries, 23% are classified as labor intensive and 42% are classified as being in industries that are highly dependent on external finance. 69% of the firms are in states with flexible labor regulations and 73% are in financially developed states. Panel B presents the correlation matrix between these variables. The sample correlations with age dummies show that older firms are larger and growing faster. When we look at the industry classifications we find that none of the correlations between the industry categories are larger than 0.19 suggesting that the three measures – Small Firm Industry, Labor Intensive, and EFD – are capturing three different aspects of industry technology. Overall we find that small firm industries are positively correlated with labor intensive industry and negatively correlated with external finance dependence. The correlations with Firm Size and Firm Size Ratio show that firms are larger and grow faster in large firm dominated industries, in capital intensive industries and in industries that are not highly dependent on external finance. The raw correlations also show that firms in financially developed states are larger and grow faster. On the other hand, while firms in states with flexible labor regulations are smaller, they are growing faster.

16
25 Serra, T., D. Zilberman, B.K. Goodwin and A. Featherstone (2006) ‘Effects of Decoupling on the Mean and Variability of Output’, _European Review of Agricultural Economics_ 33(3): 269-88.

Tyers, R. and K. Anderson (1992), _Disarray in World Food Markets: A_ _Quantitative Assessment_ , Cambridge and New York: Cambridge University Press.
rather than individuals, ten out of twelve SHGs in the treatment-credit group joined DrumNet, compared to only five out of twelve from the treatment group without credit. This provides some evidence that, at a minimum for increasing take-up, credit is perceived by farmers as an important factor for cultivation of export-oriented crops.

Table 2 shows the determinants of participation in DrumNet. Column 1 examines both treatment groups and includes an indicator variable for the credit treatment. Columns 2 and 3 show the determinants of take-up for the credit and no-credit groups separately. Since the results in Columns 2 and 3 do not differ much, we focus here on the results from Column 1.

We examine a few hypotheses regarding the take-up decision. First, is offering credit an important determinant? We find that the credit indicator is positive but not significant statistically. When the same specification is run including only the credit indicator (i.e., none of the other covariates), we find that it is significant at the 10 percent level (result not shown in tables).

Second, are farmers who join more educated? If education is required to understand the potential benefits of DrumNet, we would expect a positive correlation. On the other hand, if educated farmers are already more advanced, accessing export markets, they may see no additional value in the DrumNet services and refuse the offer to join. We find that literacy, as defined by the self-reported ability to read and write, is positively correlated with joining DrumNet.

Third, does household income predict take-up? This is particularly important to examine for the treatment groups separately, to examine whether DrumNet without credit 12
_COTE D’IVOIRE: Volatility, Shocks and Growth_

# **VAR Estimation Procedure**

We use vector auto regression (VAR), a non-theoretical statistical “tool-kit” which focuses on variables as predictors of each other rather than parameter estimation or hypothesis testing. The series were all integrated of order one, and this model is in the first differences, using two lags. The series passed a stability test. Tests for Granger causality were inconclusive (and provide no empirical evidence about ordering). The model seems stable, and results do not change much consequent to various changes in specification (such as lags, ordering, etc.). Some notes on our estimation procedure:

- Using the AIC statistic we fitted the VAR with 2 lags and a constant.

- The Granger test didn’t give much indication on causality. Our variables causing dln(Y/L) and the impulse-response functions were rather robust to the ordering.

- We ran the ADF test on the time series and ran the VAR in differences as every variable was integrated of order 1 ( I(1) ) .

- The innovations in the impulse response functions are orthogonal to each other with unit variance and pair wise uncorrelated.

For VAR models, it is customary to report the forecast error variance decomposition (FEVD). See Table 2: VAR Forecast Error Decomposition of the difference of the natural logarithm of GDP per worker in response to cocoa price shocks and real exchange rate shocks. Here we present two elements of the FEVD analysis, which pertain to two of the IRFs of interest. One pertains to the IRF for the impact of the cocoa price on output, while the other pertains to the IRF of a change in the real exchange rate on output. Interpretation: A one standard deviation shock to cocoa prices would account for %10.55 of the observed variability in the growth of GDP per worker one year out, reaching up to 25% in 3 or 4 years. A one standard deviation shock to cocoa prices would increase GDP per worker growth rate by 1.02% in the first year out, and 1.91% two years out, receding thereafter.

**Table 2: VAR Forecast Error Decomposition of the difference of the natural logarithm of GDP per worker in response to cocoa price shocks and real exchange rate shocks.**

|**Forecast Error Variance Decomposition (FEVD**<br> <br>|**) Variable:**<br>|**D.LnYL**<br>|
|---|---|---|
|**Forecast Step**<br>**Cocoa Price Shock**|**SE**|**OIRF**|
|**1**<br>10.55%|0.09|1.02%|
|**2**<br>13.83%|0.10|1.91%|
|**3**<br>24.98%|0.13|0.57%|
|**4**<br>25.64%|0.13|-0.20%|
|**5**<br>25.23%|0.13|0.08%|
|**6**<br>25.00%|0.13|0.51%|
|**7**<br>25.57%|0.13|0.40%|
Mexico MFA 9 Table 2. Summary data for Apparel Exports

|Group|Average<br>Quota|Average<br>Percent of|Average<br>Percent|Type|Average<br>Percent|
|---|---|---|---|---|---|
||Utilization|MFA under<br>9802|Value<br>Added|of<br>Quota|Special<br>Regime|
||(1981-90)|(1987-90)|inU.S.||(1988-90)|
|1. Sweaters<br>|38.10|1.20|33.00|cm|00.00|
|2.Trousers|70.70|83.10|67.30|di/sI|83.70|
|3. Men's Coats|42.90|88.20|65.50|dl|4.60|
|4. Women's Coats|51.80|80.10|70.10|di/sl|29.20|
|_5._Woven Shirts|63.40|89 70|57.70|sI|74.20|
|_6._Underwear_|59.30|82.30 _|78.30|dl/sl|59.70|

S,urce: World Bank data tapcs and U.S. Department of Commerce data.

cm-consultation mechanism; dl-designated level; sl=specific limit.
**Decoupling and recoupling**

returns in the host country. As internal instruments we use the first lag of the dependent variables. Results are in line with our previous estimations (only for currencies we fail to recover a significant effect): indeed, the interaction terms for equity and credit gest bigger at the expense of the simple betas, suggesting that the amplifying effect could be even larger than reported in the previous tables.

Evidence of asset co-movement induced by the incidence of benchmarked international funds can also be seen in the investment decision of fund managers. Country weights tend to remain close to their benchmarks, and at any rate they adjust slowly even in times of turmoil. Alternatively, flows in and out of individual countries mimic those stable weights, which, in times of liquidation, induce correlated sales and price action.

The underpinnings of benchmarking can be illustrated based on EPFR ́s individual fund data on flows and country weights. For the sample of emerging market-dedicate funds, panel regressions of today ́s country weights against last period ́s country weights and country returns (with and without time effects, and with country-time effects) show that the time correlation of weights is close to one ( **Table 12** ). This correlation reappears almost as strongly when we compute weights using the funds aggregate (which includes EM and country-dedicated as well as global funds –the same sample used in the decoupling regressions of **Table 10** ): monthly weight changes tend to be quite small. As can be seen in columns 2 and 6, the persistence of country weights is not altered in crisis periods.

In turn, it is easy to show how the influence of market weights on the cross-country correlation of fund flows increases in times of massive swings in fund allocations. After netting out monthly fund returns, we can decompose individual fund flows to a given country as: which, following the results of Table 6 and expressing !!!!!!!!"!!!!"#$%&'#!!"!!!!!!!!!, yields with _f ́_ <0, _g ́_ >0, where _Flow i,j, t_ , _AUM i,j, t_ and _wi,j,t_ are flows, assets under management and weights in fund _i_ to country _j_ at time _t_ . The first term corresponds to re-allocation flows due to <u>16</u>
also emphasizes that financing (at least to the extent we can measure it) does not account for the majority of the increase in entry. It is clear that more women-owned establishments with financial backing enter after the reforms, but this increase is only part of a general expansion for female entrepreneurship, rather than the sole driver.

The NSSO collects data on external financing of unorganized establishments.<sup>2</sup> A baseline descriptive point is that only 2%-3% of female-owned businesses in a typical year report using external finance (9%-10% for male-owned businesses). This statistic is again informative. While it is possible that many latent women entrepreneurs are precluded from entry due to poor financial conditions, we should also not be surprised if much of the entry occurs in a way that does not display a connection to external finance, at least as our data can measure it.<sup>3</sup> The first econometric evidence is in Table 3 when we segment entrepreneurs into groups based upon whether or not they are using external finance. The group not reporting external financing both accounts for most of the sample and also has the higher elasticity response. While we do observe some tentative evidence of an increase in firms supported by external finance, the bulk of the entry is occurring elsewhere.

A second approach tests whether the entry response is disproportionately stronger in industries that were more dependent on external finance in 1994. This approach has been used frequently since Rajan and Zingales (1998) to test for the importance of external finance channels in economic outcomes. It again has the advantage of not requiring direct observation of loans, just the correct ordering of industries. These regressions are reported in Column 7 of Table 4. There is a positive elasticity of 0.1 with a t-statistic greater than one. There thus appears to be more traction to this story than the previous two, but at the same time, the financing interactions are far less than those for the other industry dimensions emphasized in Table 4.

Finally, the aggregate entry rates potentially mix two things: 1) ability to get loans conditional on establishment type with 2) changes in the type of establishments. Especially as

> 2 External finance is defined to include any loans from central- and state-level term-lending institutions, governments (central, state, and local bodies), banks and societies (public sector, commercial, and co-operative), other institutional agencies, money lenders, business partner(s), suppliers, contractors, friends and relatives, and lenders not otherwise specified.

> 3 Moreover, the share of new female businesses reporting external financing declines from 7% in 1994 to 4% in 2005. The share of male entrants reporting external financing grew over the same period, from 16% to 17%.

5
WPS3550 FEEDBACK LINKS BETWEEN ECONOMY-WIDE AND FARM-LEVEL POLICIES: Application to Irrigation Water Management in Morocco Terry Roe<sup>1</sup> , Ariel Dinar<sup>2</sup> , Yacov Tsur<sup>3</sup> , Xinshen Diao<sup>4</sup>

# ABSTRACT

This paper focuses on policy interventions for improving irrigation water allocation decisions by including both macro and micro considerations in a unified analytical CGE framework. The approach is demonstrated, using the case of Morocco, by analyzing selected policy (top-down and bottom-up) interventions and external shocks that affect the water sector. Both direct and indirect effects of these interventions are identified. The top-down (macroto-micro) links are of a trade reform type. The bottom-up (micro-to-macro) links pertain to changes in farm water assignments and the possibility of water trading. We find that water productivity is strongly influenced by these policies, with the general equilibrium (indirect) effects modifying and sometimes reversing the partial equilibrium (direct) effects. We also find that the impacts of the two reforms we assessed are different, with trade reform having an absolute impact of a higher magnitude than the water reform. Finally, we show that the sequence of introducing the policy reforms has different consequences.

# World Bank Policy Research Working Paper 3550, March 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1University of Minnesota, St Paul USA; 2World Bank, Washington DC USA; 3Hebrew University of Jerusalem Israel;<sup>4</sup> International Food Policy Research Institute, Washington DC USA.

This paper is the result of the first phase of the research project “Macro-Micro Linkages of Irrigated Water Management” supported by funding from RSBRA and DECRG of the World Bank. Review comments by Erol Cakmak, Rachid Doukkali and Rashid Hassan are acknowledged. Suggestions by Gershon Feder, and comments from participants of the Workshop Irrigation Water Policies: Micro and Macro Considerations, Agadir Morocco, June 15-17, 2002, and from participants of the ABCDE World Bank Conference May 3-4, 2004 in Washington DC, are greatly appreciated.