rafmacalaba/datause-encoder-data huggingface dataset:
- 25 -

# **Table** **<u>3. Conditional logit model with predicted crop prices</u>**

||Choice-<br>specific|Individual-spe|cific variables:|||||
|---|---|---|---|---|---|---|---|
||variables|Maize|Rice|Groundnuts|Tobacco|Cotton|Sesame|
|Transport cost|-0.009 ***<br>(0.003)|||||||
|Crop suitability|0.183 ***<br>(0.057)|||||||
|Crop price hat|-0.835 ***<br>(0.172)|||||||
|Access to fertilizer|0.073<br>(0.050)|||||||
|Access to irrigation|0.107 ***<br>(0.019)|||||||
|Access to improved<br>|0.866 ***<br>|||||||
|seed|(0.088)|||||||
|Area of plot||2.440 ***|2.324 ***|2.205 ***|2.478 ***|<br>2.536 ***|<br>2.469 ***|
|||(0.467)|(0.469)|(0.470)|(0.472)|(0.468)|(0.473)|
|Steep slope||0.247|-1.240 ***|-0.134|0.385|-1.030 **|0.416|
|||(0.348)|(0.382)|(0.374)|(0.493)|(0.488)|(0.448)|
|Land title||-0.816 *|-1.397 ***|-1.508 ***|-0.789|-1.072 *|-1.765 **|
|||(0.457)|(0.500)|(0.533)|(0.706)|(0.618)|(0.853)|
|Electricity access||0.772|0.965|0.471|0.969|-13.205 ***|<br>-0.419|
|||(1.063)|(1.080)|(1.118)|(1.299)|(640.693)|(1.474)|
|Mobile access||0.438|0.960 ***|0.760 **|0.537|0.569|0.565|
|||(0.355)|(0.370)|(0.378)|(0.516)|(0.436)|(0.462)|
|Distance to road||-0.098 **|-0.042|-0.069|-0.150 *|-0.161 *|-0.031|
|||(0.053)|(0.056)|(0.057)|(0.098)|(0.084)|(0.064)|
|Male household||-0.430|-0.743|-0.637|-0.041|-0.315|-0.783|
|head||(0.465)|(0.481)|(0.492)|(0.719)|(0.589)|(0.580)|
|HH head age||0.036 ***|0.027 ***|0.029 ***|0.020|0.016|0.030 **|
|||(0.010)|(0.011)|(0.011)|(0.015)|(0.013)|(0.013)|
|HH head's year of||0.108 *|0.103 *|0.017|-0.002|-0.058|0.214 ***|
|schooling||(0.068)|(0.071)|(0.073)|(0.100)|(0.084)|(0.091)|
|Household size||0.048|0.034|0.083|0.116 *|0.155 **|-0.045|
|||(0.067)|(0.069)|(0.069)|(0.076)|(0.072)|(0.087)|
|HH wage income||-0.033|0.017|-0.085|-0.159|-0.064|-0.017|
|||(0.107)|(0.105)|(0.179)|(0.620)|(0.205)|(0.185)|
|Obs.|17325|||||||
|Wald chi2|27029.9|||||||
|Pseudo R squared|0.5095|||||||
Policy Research Working Paper 5321

# **Abstract**

Firms in Africa report “regulatory and economic policy uncertainty” as a top constraint to their growth. This paper argues that often firms in Africa do not cope with policy rules, rather they face _deals_ : firm-specific policy actions that can be influenced by firm actions (such as bribes) and characteristics (such as political connections). Using Enterprise Survey data, the paper demonstrates huge variability in reported policy actions across firms notionally facing the same policy. The within-country dispersion in firm-specific policy actions is larger than the cross-national differences in average policy. The analysis shows that variability in this policy implementation uncertainty within location-sector-size cells is correlated with firm growth rates. These measures of implementation variability are more strongly related to lower firm employment growth than are measures of “average” policy action. The paper shows that the _de jure_ measures such as _Doing Business_ indicators are virtually uncorrelated with ex-post firm-level responses, further evidence that deals rather than rules prevail in Africa. Strikingly, the gap between _de jure_ and _de facto_ conditions grows with the formal regulatory burden. The evidence also shows more burdensome processes open up more space for making deals; firms may not incur the official costs of compliance, but they still pay to avoid them. Finally, measures of institutional capacity and better governance are closely associated with perceived consistency in implementation.

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 --> 2 The data<br>A major challenge in estimating the effects of fiscal policy in developing<br>countries using VAR methods is the availability of reliable data. This paper<br>continues the efforts made by IMV to catalogue quarterly fiscal data for<br>developing countries. While the focus in IMV is on the expenditure side<br>of the fiscal ledger, we assemble data on tax revenues in this project. We<br>also introduce new series (these at an annual frequency) of average marginal<br>tax rates for a sample of 28 countries. This extends work such as Barro<br>and Sahasakul (1983) to a broader set of countries, including a number of<br>developing countries. Finally, for the purpose of our SVAR analysis, we<br>estimate tax elasticities for the countries in our sample. To our knowledge,<br>this is the first attempt to assemble time series of average marginal tax rates<br>for a cross-section of developing countries. It is also the first to estimate the<br>output elasticities of tax revenues, using macroeconomic data, for a number<br>of developing countries.<br>We outline a few of the steps involved in obtaining the time series used in<br>the analysis. We obtained the entire distribution of corporate, personal, and<br>value added tax rates from the OECD tax database for OECD members, and<br>from PricewaterhouseCoopers’ “Individual Taxes Worldwide Summaries” for<br>non-OECD countries. As an example, Figure 1 compares the marginal per-<br>sonal income tax rate schedules for Peru in 1993 and in 1994: directly before<br>and directly after, respectively, a major tax reform intended at simplifying<br>the tax system. Due to historically high rates of inflation, tax brackets in<br>Peru are based on income in “tax units” rather than in local currency.<br>We then followed the OECD’s methodology, outlined in Giorno et al<br>(1995) and Van den Noord (2000), for calculating the average marginal tax<br>rate for each country and year. We calculated the earnings elasticity of<br>5<br><!-- End of picture text -->
**Table 4.2**

**Correlates of the highschool-dropout wage premium. 16 LA countries, 1989-2009**

||**Simple**|**Country**<br>**fixed**|||**Co**|**untry andy **|**ear fixed eff**|**Cl**<br>**ects**|**El VEN**|<br>**L**|
|---|---|---|---|---|---|---|---|---|---|---|
|||||||||**ontros,**|**xc.**|<br>**ong**|
||(1)<br>**OLS**|(2)<br>**effects**|(3)|(4)|(5)|(6)|**Controls**<br>(7)|<br>**min w.**<br>(8)|**CHL**<br>(9)|<br>**series**<br>(10)|
|**Relative supply**|-0.0759|-0.2048|-0.1198|-0.1099|-0.109|-0.1113|-0.1295|-0.1496|-0.1175|-0.1037|
||[0.0294]**|[0.0419]***|[0.0561]**|[0.0603]*|[0.0618]*|[0.0611]*|[0.0614]*|[0.0662]**|[0.0659]*|[0.0938]|
|**Log Net Barter Terms**||||-0.0594|-0.0628|-0.0583|-0.0533|-0.0474|-0.1235|-0.0653|
|**of Trade**||||[0.0479]|[0.0474]|[0.0456]|[0.0433]|[0.0471]|[0.0610]*|[0.0565]|
|**Aggregate**|||||0.2623||0.3255|0.6925|0.1334|0.5055|
|**unemployment rate**|||||[0.2886]||[0.2976]|[0.4263]|[0.3073]|[0.3068]|
|**Dropout**||||||0.6113|||||
|**unemployment rate**||||||[0.3732]|||||
|**High school**<br>**unmeployment rate**||||||-0.4127<br>[0.5183]|||||
|**Log minimum wage**<br>**index (1995+)**||||||||0.0304<br>[0.0432]|||
|**Constant**|0.4214<br>[0.0264]***|0.2975<br>[0.0402]***|0.4813<br>[0.0739]***|0.7559<br>[0.2620]**|0.7601<br>[0.2569]***|0.7377<br>[0.2484]***|-0.2807<br>[1.0656]|-1.0093<br>[1.2676]|1.0445<br>[0.3181]***|0.7386<br>[0.3106]**|
|**Observations**|209|209|209|209|209|209|209|178|186|141|
|**R-squared**|0.03|0.37|0.48|0.49|0.50|0.50|0.51|0.44|0.52|0.58|
|**Year effects**|No|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Country effects**|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Additional controls**|No|No|No|No|No|No|Yes|Yes|No|No|

Source: own computations based on SEDLAC (CEDLAS and World Bank) and WDI (World Bank). Notes: Robust standard errors in brackets, clustered at the country level for models 3-10. * significant at 10%; ** significant at 5%; *** significant at 1%. The additional control variables include the log of GDP per capita, the log of the real exchange rate against the US Dollar (both from WDI), and the log of a tariff index. Column 8 includes the log of a minimum wage index compiled by ILO (available only for 1995-2009). The regressions in column 10 only include the nine countries for which there is a relatively long, continuous and homogeneous data source: Argentina, Brazil, Chile, Costa Rica, Mexico, Panama, El Salvador, Uruguay and Venezuela.

33
**Table 14. Reasons for Training. Ma tching group: Age 17-65. Three nearest neighbors bas ed on propensity**

|_Cohort_|_Prerequi_<br>_Control_<br>**_Gr_**|_sitefor ent_<br>_CONAL_<br>**_oupDifeE_**|_ering a job_<br>_Diferece_<br>**_nceGroup_**|**_ontrol_**|_Update_<br>_CONAL_<br>_EP_|<br> **_Difference_**<br>_fern_|_Control_<br>**_Group_**|_Self-inter_<br>_CONAL_<br>_EP_|_est_<br>_Differece_<br>_Dee_|_W_<br>_ontrol_<br>**_Group_**|_as Reque_<br>_CONAL_<br>_EP_|_sted_<br>_Dffeence_|_ontrol_<br>_Gop_|_Other_<br>_CONAL_<br>_E_|<br>_ff_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|90-93|<br>9.1|<br>7.1|<br>-1.9|31.8|60.0|<br>28.2|<br>22.7|15.7|-7.0|<br>27.3|10.0|-17.3|<br>9.1|7.1|-1.9|
|91-94|9.1|8.3|-0.8|40.3|53.3|13.1|24.7|18.3|-6.3|20.8|11.7|-9.1|5.2|8.3|3.1|
|92 - 95|7.4|6.5|-0.9|39.7|53.2|13.5|22.1|24.2|2.1|26.5|8.1|-18.4|4.4|8.1|3.7|
|93 - 96|17.0|17.3|0.3|35.8|51.9|16.1|13.2|9.6|-3.6|30.2|7.7|-22.5|3.8|13.5|9.7|
|94-97|9.1|16.7|7.6|31.8|43.3|11.5|27.3|30.0|2.7|27.3|6.7|-20.6|4.5|3.3|-1.2|
|Total'|10.1|10.2|0.2|36.4|53.6|17.3|22.1|18.6|-3.5|26.0|9.1|-16.8|5.5|8.4|2.9|
|_ENECE_**_992_**|10.7|||37.9|||22.0|||24.6|||4.8|||
|_ENECE 99, LS_<sup>_3_</sup>|30.7|||11.4|||27.7|||25.4|||4.8|||
|_ENECE 99, US_|_'_<br>13.9|||27.2|||27.5|||26.9|||4.5|||

1. **Sample: Workers in the matching group.**

2. **Sample: All workers.**

**3. Sample: Workers with lower-secondary complete and** 3 **years of experience (18 and 19 years old).**

4. **Sample: Workers with upper-secondary complete and 1-5 years of experience (22-26 years old).** 37
specific recommendations for small farmers and other poor people. In this regard, PROINDER has demonstrated that: (i) strategies need to be tailored to the regional and local characteristics of the rural poor and that (ii) interventions aimed at increasing the productivity and sustainability of poor small farmers are viable provided that there is institutional support available to them. PROINDER’s data suggest that the approach followed has yielded positive results, both in terms of the welfare status of the families participating in the program as well as from the institutional side, with the establishment of the Comisión de Desarrollo Rural and the drafting of provincial rural development strategies.

Finally, in Argentina rural development is a small part of the Ministry of Agriculture. In order to serve other sectors than agriculture a new model is called for with a clear rural strategy or a national policy to address rural issues including rural poverty.<sup>18</sup> One option is to create a Sub-secretariat or Secretariat of Rural Development, as has been implemented in other countries in the region, for example in Brazil. This together with increased coordination of programs would increase the impacts individual programs can achieve. Moreover, the government should establish clear and efficient mechanisms for NGO collaboration. Emerging NGO consortia provide one mechanism, which should be explored for fostering greater coordination, dialogue, and joint planning with the government.

> 18 PROINDER has an institution-strengthening component promoting the development of provincial rural development plans and it also encouraged dialogue among main rural development actors in the country to design a national rural development plan. So far, 16 provinces have designed their rural development plans and a national dialogue is taking place at the National Commission for Rural Development, created on the initiative of PROINDER.

31
India where states that were initially poorer have grown more slowly, resulting in unconditional divergence in both absolute and relative terms.<sup>7</sup> This is apparent in figure 2, which plots the average annual growth rate of real per-capita state GDP against a state’s initial per-capita GDP relative to the poorest state. India’s poorer states are still experiencing positive growth, but the high growth rates, post reform, have been elsewhere.

**_Figure 2: Growth rates at the sub-national level in China and India_**

<!-- Start of picture text --> 14.0<br>Indian states Chinese provinces<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0<br>Per-capita GDP of province(state) in 1978(1980)<br>relative to poorest province(state)<br>1978/1980 and 2004<br>capita state GDP between<br>Annual growth rate (%) of per-<br><!-- End of picture text -->

**_Source:_** China Statistical Yearbook (various years); Central Survey Organization, Government of India

In China, provinces that were initially poorer have managed to keep pace with the initially wealthier provinces in terms of aggregate growth rates (figure 2). That has meant no divergence in relative terms, but absolute differences across provinces have increased. There have also been signs of divergence regionally between the coastal and inland areas of China.<sup>8</sup>

# **...and that has meant uneven progress against poverty**

The spatial unevenness of growth has contributed to uneven progress against poverty in two ways. Firstly, because household-income growth has been closely associated with poverty reduction at the sub-national level in both India and China,<sup>9</sup> the fact that growth was geographically uneven has meant that progress against poverty was uneven as well, with some states and provinces seeing far more rapid reduction in poverty than others. In China, the coastal areas fared better than inland areas. The trend rate of decline in the poverty rate between 1981 and 2001 was 8 percent per year for inland provinces, versus 17 percent for the coastal provinces. In India, most of the western and southern states—peninsular India (with the exception of Andhra Pradesh)—did comparatively well, while the more backward BIMARU

> 7 Econometric tests indicating more marked growth divergence for India in the post-reform period can be found in Ghosh (2006). 8 See Chen and Fleisher (1996), Jian et al. (1996), Sun and Dutta (1997), Raiser (1998) and Kanbur and Zhang (1999).  Milanovic (2005) describes the regional inequalities within five federations, including China and India. 9 This is clearly documented for India by Datt and Ravallion (1996, 2002) and Deaton and Dreze (2002), and by Ravallion and Chen (2006) in the case of China.

7
# **Annex 2. Public vs. Private Sector Compensation across working categories**

An alternative approach to investigate the existence of compensation differentials between the public and the formal private sector is to use ENAHO data to distinguish employees based on their responsibilities and the service provided and verify the existence of compensation differential within each group. In particular, compensation differentials in Peru can be analyzed after dividing employees into the following categories: professionals, technical experts and so-called “auxiliaries” (i.e., support staff or ‘auxiliaries’) (SERVIR, 2010).

In order to test the robustness of the results obtained by this study, we used the same approach followed by SERVIR (2010) to disaggregate the data sample and estimated the log-wage equation (Rees and Shah, 1995) for each occupational group. Table A2.1 shows some descriptive statistics useful to contextualize the analysis. The majority of respondents belong to the Technical experts’ category. As for professionals, the large part of respondents is employed in the public sector (with a proportion close to 4:1) whereas the majority of technical experts and auxiliaries work in the formal private sector. Average values for total compensation are consistent with our expectations. When we focus on total compensation, we notice that compensation is significantly for professionals and, generally, for those one working in the formal private sector.

# **Table A2.1. Descriptive statistics**

|**Population Characteristics**|**Professionals**|**Technical**<br>**Experts**|**Support**<br>**staff**<br>**(****_auxiliares_)**|
|---|---|---|---|
|Number of observations|1633|2178|1892|
|Number of observations (sub-sample: public sector)|1282|783|505|
|Number of observations (sub-sample: private sector)|351|1395|1387|
|Total Hourly Compensation<br>(includingin-kind compensation and bonuses)|12.96|9.74|7.24|
|Total Hourly Compensation (sub-sample: public sector)|12.47|9.37|5.45|
|Total Hourly Compensation (sub-sample: private sector)|14.77|9.95|7.90|

Regression analysis provides a further insight. Total compensation is significantly higher in the formal private sector for technical experts and support staff ( _auxiliares_ ). The empirical evidence is less clear for professionals. The sign of the public sector dummy suggests a lower compensation for public sector professionals but the difference is not statistically significant. This result is rather surprising since we expected a more pronounced private sector premium for professionals. The coefficients estimated for the rest of the explanatory variables are consistent with our expectations. Education matters, especially 24
opening of a country’s borders to foreign investment. Understanding what drives the aftermath of liberalization, such as the impact of a change in FPI, may offer insight into the debate on liberalization.

The papers most related to mine are Harrison, McMillan, and Love (2004) and Laeven (2003).<sup>4</sup> Although similar in intent, these papers differ from mine on many dimensions. Harrison et al. (2004) focus on the impact of foreign direct investment on the financial constraints of firms.<sup>5</sup> They examine foreign direct investment cash flows as a proportion of all foreign investment rather than the size of the market in question.<sup>6</sup> Laeven (2003) examines the impact of liberalization (reform policies that open financial markets to foreign direct and portfolio investment) on financial constraints rather than the specific cash flows resulting from said reformation. My paper differs from these in that I speak to access to capital (versus financial constraints) that can be directly measured by issuance data and accounting levels.

Further, my papers differ from these with regard to data. Both Harrison et al. (2004) and Laeven (2003) use the Worldscope database. Since my emphasis is on small firms, I create a unique database of over 250,000 firm year observations across 43 countries merging two datasets to circumvent the larger firm bias from which many existing international databases suffer.

The rest of the paper is organized as follows. Section 2 describes the mechanism by which FPI may be beneficial to small listed firms. Section 3 describes the empirical methodology. Section 4 describes the data. Section 5 provides the results, Section 6 the robustness analysis, and Section 7, the conclusion.

# **2. Small Firm Access to finance**

Small firms tend to be dependent on bank lending to finance their growth (Cull, Davis, Lamoreaux, and Rosenthal, 2005). A portion of small firms reach the capital markets and might use public financing to grow their businesses. Those who do access public capital markets (“listed” firms) might still rely, at least in part, on bank lending to finance their growth. As such, I examine two ways in 3
lend, and to zero otherwise. Each of the 2,203 observations represents one Village Fund; the independent variables may be grouped into those related to other sources of VF capital, objective characteristics of the VF committee, the size and organization of the village, characteristics of the households in the borrowing area, and the attitudes of VF committee themselves. The estimation results are shown in Table 13.

On the supply side, the only “objective” variable that matters is the size of the initial injection of government funds; when more funds were made available, VFs borrow _more_ to on-lend, perhaps because they are seen as having more to lose (and hence being more bankable). On the other hand, VFs in larger villages, or rural areas, or with better-educated committee members, or with computerized accounts, are not more (or less) likely to borrow in order to on-lend.

On the demand side, areas with more bank borrowers have VFs that are more likely to borrow to on-lend, perhaps because these populations understand the nature of banking better. It is perhaps surprising that VFs in areas with more-affluent residents are _less_ likely to borrow to on-lend; one possibility is that the need for VF-style credit is weaker in such areas, both because there are good alternatives, and because there are fewer credit-hungry farmers in these localities.

The variables that matter most strongly are the attitudes of VF committees. VFs that say they would borrow more if funds are short (rather than ration existing funds), that say they could safely lend another million baht, and/or that want to borrow more funds, are more likely to actually be on-lending.

This social entrepreneurship may be hard to relate to objective characteristics of VFs or villages, so we cannot tell how these attitudes arise, but there appears to be a virtuous circle whereby VFs borrow to on-lend, so they are more willing or able to borrow to on-lend, so they borrow to on-lend. It is possible that familiarity with borrowing to on-lend demystifies the process, and makes it easier to sustain. If so, encouraging VFs to borrow even modestly for on-lending may ultimately help nudge some to a “higher” equilibrium, moving them from social intermediaries to true financial intermediaries.

Despite the fact that one in five VFs borrow to on-lend, there is a strong reluctance to become a financial intermediary. Most village funds say they would like to lend more; in the 2010 survey of Village Funds, 75% said they want more funds to lend, and 72% said they could lend an additional million baht (about US$29,000) safely. When asked to specify the ideal size of their loan portfolio, the median response was 2 million baht (compared to median actual lending of 1.14 million baht). On the other hand, only 13% would be willing to borrow more from the BAAC (or GSB or KTB) in order to expand their borrowing, despite 22