rafmacalaba/datause-encoder-data huggingface dataset:
22 As noted earlier, workers no longer have an explicit role in management.8' Even the requirement that joint stock companies have workers' councils, which was contained in the 1988 law, remained only as option in the 1990 amendments (Article 45). Workers' councils are still required in socially-owned companies (Article 47), but the law envisions that such companies will soon be transformed into share-'ssuing joint stock companies in which workers' managerial rights will derive solely from ar.y share ownership they may have. Workers' rights in joint stock companies will henceforth be protected by collective bargaining agreements governed by republic-wide standards combined with enterprise-specific agreements.88 Characteristics of the two types of Dartnershir. The two types of partnership are the limited partnership" and the "company with unlimited Joint and several liability" (or general partnership) (Articles 109-119). Both forms are similar to analogous forms typical of market economies. In the limited partnership, the limited partners are passive investors and their liability is limited "o their investment (Article 109). The general partners manage the company and have unlimited liability (Article 109). Share capital can be transferred to third parties only with the agreement of the founders, unless otherwise specified in the articles of association (Article 112). In the general partnership, all partners are assumed to be active in management -nd are fully liable for the obligations of the company (Articles 115-117). ,s with the limited partnership, share capital can be transferred to third arties only with the agreement of all partners (Article 119).

# Procedures for Establishing a Companv

Setting up a company has typically been relatively easy and inexpensive in Slovenia. The founders must first prepare the articles of association and deposit the initial capital in a temporary account with the Service of Social Accounting (Company Law, Articles 81-84). The signatures of the firm's directors must be approved by the court (Article 184). Unlike many other European countries (including those in Eastern Europe), approval of the firm's

**87** Only workers in so: ially-owned enterprises continue to have an explicit role in management under the new law (Articles 63-75). If such enterprises invest jointly with private investors in mixed enterprises, their workers will have a management role in such mixed enterprises, but it will be strictly proportional to the amount of resources invested (Article 122).

88 See Zakon o temeljnih pravicah iz delovnega razmerja (Law on Basic Rights from Employment], Yugoslav Official Gazette, No 60 (1989) and Zakon o delovnih razmerjih (Law on Labor Relations), Official Gazette RS, No 14 (1990).

89 The partners have the option of raising capital through the issuance of individual shares, in which case the rules on share purchase provided for joint stock companies are applicable (article 114). This is similar to the "limited partnership divided by shares" found in some other European countries.
Policy Research Working Paper 8836

# **Abstract**

Business survey outcomes for micro and small firms are notoriously noisy, with multiple sources of measurement and recall error. This paper introduces a new survey methodology that combines automatic consistency checks of electronic data collection with triangulation and dynamic adjustment to arrive at more precise estimates of business performance. The methodology uses insights from behavioral science to lower the cognitive cost of initial recall and establishes salient and relevant anchors to allow for dynamic triangulation and adjustment toward a final estimate. The validity of this method is field tested against traditional performance measures as well as administrative data across three emerging markets: Ghana, Rwanda, and Uganda. The results show significant upward adjustment from traditional measures for both sales and profits, a lower coefficient of variation in the cross-section, and higher autocorrelation in panel data. Comparisons with administrative data further confirm a higher correlation and closer magnitude relative to traditional measures. This research reconciles recommendations for increased attention to survey design with a method to leverage electronic survey technology beyond consistency checks.

This paper is a product of the Development Research Group, Development Economics. 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 bzia@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
labor regulation index receives the highest loading, while the other six indices get only low positive or negative weights. Analogously, the third principal component loads heavily only on the tax regulation index. The close connection between the principal components and our regulation categories is clearly demonstrated by the pattern of correlation coefficients shown in Table 2.<sup>14</sup> Figure 1 provides a regional comparison of the overall, product market, fiscal, and labor regulation indices. In addition, figure 2 depicts scatter plots of these indices against the (the log of) GDP per capita of all countries in the sample. Looking at the first three panels, it becomes clear that employment regulation, fiscal burden, and product market regulation stand apart from each other. Indeed, while there seems to be little relation between a country’s average income and the strength of labor regulation, the relationship with income is positive and significant in the case of fiscal burden and clearly negative in the case of product market regulation. The overall regulation index is negatively related to per capita GDP, which is not surprising given the large weight of product market regulation in the overall index.

# **Regulation and governance**

The effect of regulation on firm dynamics and, ultimately, macroeconomic performance is likely to depend on the institutional context in which regulation is enacted. For example, countries with better institutions create regulatory environments that actually try to improve business conditions rather than privilege a few interest groups; they also limit the interaction between regulation and abuses of power in the public sector that place many firms in disadvantage, sometimes forcing them to operate in the informal sector (see for instance De Soto, 1986).

We use an index of “governance quality” in order to assess the quality of regulation itself and the general context that determines how regulation functions. We construct this index using measures from the International Country Risk Guide, which evaluates a country’s risk for international investment by looking at its social and political situation. Specifically, we average the values of indicators measuring the absence of corruption in

> 14 Other studies, such as Nicoletti et al. (2000) or Klapper et al. (2004) also use factor analysis to decompose regulation measures, and they further replace the measures with their principal components in the regressions.  Because of the strong correlation between the components and our indices (see Table 2), we choose not to use the principal components in our regressions.

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**TABLE 1. Effect of high-Skilled Emigration Rates on Wage, GDP, and TFP Differences between Countries**

||Dependen<br>**RelG**|t variable:<br>**DPij,t**|Dependen<br>**RelT**|t variable:<br>**FPij,t**|Dependen<br>**RelWa**|t variable:<br>**ge80ij,t**|Dependen<br>**RelW**|t variable:<br>**age90ij,t**|
|---|---|---|---|---|---|---|---|---|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|
|SMigij,t|0.1630***<br>(0.0276)||0.0830***<br>(0.0140)||0.2168***<br>(0.0490)||0.2290***<br>(0.0483)||
|SMigij,t-1||0.1386***<br>(0.0418)||0.0796***<br>(0.0198)||0.1645**<br>(0.0678)||0.1738**<br>(0.0699)|
|RelInvestijt-1|0.2331*|0.2317*|0.0333|0.0327|0.4989**|0.4975**|0.4356*|0.4341*|
|,|(0.1216)|(0.1215)|(0.0618)|(0.0617)|(0.2533)|(0.2533)|(0.2430)|(0.2430)|
|RelUrbanij,t-1|0.2113***<br>(0.0805)|0.2109***<br>(0.0806)|0.0617<br>(0.0432)|0.0615<br>(0.0433)|0.6594**<br>(0.3052)|0.6587**<br>(0.3054)|0.5761*<br>(0.3015)|0.5754*<br>(0.3017)|
|RelPrimSchoolijt-1|−0.3658|−0.3683|−0.4618|−0.4634|−1.0022|−1.0057|−0.5458|−0.5495|
|,|(0.7655)|(0.7668)|(0.3875)|(0.3882)|(2.2117)|(2.2127)|(2.0325)|(2.0336)|
|RelTertSchoolij,t-1|0.0046<br>(0.0028)|0.0047*<br>(0.0028)|0.0022*<br>(0.0013)|0.0022*<br>(0.0013)|0.0105<br>(0.0102)|0.0106<br>(0.0101)|0.0104<br>(0.0099)|0.0105<br>(0.0099)|
|(Intercept)|3.6064<br>(3.0786)|3.6211<br>(3.0845)|0.6013<br>(0.4408)|0.6045<br>(0.4415)|0.6731<br>(2.7047)|0.6802<br>(2.7058)|0.3170<br>(2.5903)|0.3245<br>(2.5916)|
|Origin FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Adj._R_<sup>_2_</sup>|0.9429|0.9428|0.9541|0.9541|0.8584|0.8582|0.8555|0.8553|
|N|2275|2275|1550|1550|1010|1010|1010|1010|

_Note:_ All dependent variables are expressed in logs and represent relative differences between countries j and i. SMigij,t denotes the stock of high-skilled emigrants from country i living in country j divided by the stock of high-skilled residents in i. RelInvestij,t-1, RelUrbanij,t-1, RelPrimSchoolij,t-1 and RelTertSchoolij,t-1 denote the lagged relative investment share, relative urbanization share, relative primary school enrollment, and relative tertiary school enrollment between j and i. Table A1 in the appendix provides additional information on all variables. Robust standard errors are in parentheses and clustered for migration destinations. *** indicates a significance level below 1 percent; ** indicates a significance level between 1 and 5 percent; * indicates a significance level between 5 and 10 percent.

32
Table 3. Nominal and Effectivc Raics of Protection, 1991-92 Trcnd Valucs (Pcrccntngc)

|Item, Location|NRP|ERP|
|---|---|---|
|Wheat, Gujranwala|-39|46|
|Basmati, Gujranwala|-54|-59|
|Coarsc Ricc. Larkana|-30|-39|
|Sugarcanc, Faisalabad|53|70|
|Cotton,Multan|**_-54_**|-63|

Sourcc: Longmire and Debord, Tabic II. These measures wcrc constructed using frce-tradc cxchangc rates.

More important is the role of non-traded inputs. The combined effects of output and input pricing on incentives can be summarized by the Producer Subsidy Equivalent (PSE), which measures the subsidy to, or from, producers resulting from output and input price policy (Tables 4 and 5). To highlight the role of non-traded inputs in Pakistan, Table 4 presents producer subsidy equivalents and effective rates of protection side by side. Important measured subsidies on nontraded inputs include the lack of complete recovery of operations and maintenance costs on the irrigation system, and interest rate subsidies on loans.

<!-- Start of picture text --> 'PcOr PnceV in......... ..<br>0.r~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..<br>M~~~E -B InS<br><!-- End of picture text --> 6
As the data generated by _W(it)_ is cleaned from the time and country effects, they should best represent the more stable technology, referred to here as the core implemented technology.

# **III. Data**<sup>15</sup>

# **<u>Output and inputs</u>**

We estimate a cross-country agricultural production function where agricultural output depends on inputs, agricultural technology, and the state of the economy. In this analysis we use a measure of agricultural capital which revises and updates the previously constructed data set from Larson, Butzer, Mundlak, and Crego (2000). The inclusion of agricultural capital is one of several aspects which differentiate this study from most studies of agricultural production functions based on a panel of countries.

Agricultural output is measured as agricultural GDP in 1990 US dollars. We choose the GDP variable rather than the more often used agricultural production because it comes from the national accounts used for the construction of the fixed capital variable. Inputs to agricultural production include land, capital, labor, and fertilizers. Hectares of agricultural area are used for the measure of land. This includes arable land, land under permanent crops, and permanent pastures. Agricultural labor is defined as the economically active population in agriculture. Fertilizer consumption is often viewed as a proxy for the whole range of chemical inputs. The data on agricultural capital consists of two components: fixed capital, consisting primarily of structures and equipment, and capital of agricultural origin, consisting of livestock and trees. The two components differ in the method of construction, and also in terms of markets and pricing.

# **<u>Technology</u>**

As the available technology is unobserved, what we can do in empirical analysis is to identify variables associated with variations in the implemented technology. In the case of agriculture, there is a natural variable to measure the level of technology for a given

> 15 For a description of the construction of the data, see Mundlak, Larson, and Butzer (1997). While the methodology is the same, sources have been updated and in some cases revised. Details can be obtained from the authors.

11
- 25 -

# Table 3: Panel Data **Regressions** (Annual **Data)** With Added Variables

# Dependent variable: annual growth rate of income

|Method of Estimate|Pooled OLS|Fixed-effects|Random-effects|
|---|---|---|---|
|_Sf/Sf_|0.058<br>(0.007)|0.058<br>(0.007)|0.058<br>(0.007)|
|_Sf/S(t-1)_|0.024<br>(0.007)|0.023<br>(0.007)|0.024<br>(0.007)|
|S/<br>(t - _2)_<br>_f_<br>J|0.017<br>(0.007)|0.016<br>(0.007)|0.016<br>(0.007)|
|_Si/Sfft-3J_|0.011<br>(0.007)|0.007<br>(0.007)|0.010<br>(0.007)|
|_S,/Sh_|0.017<br>(0.004)|0.016<br>(0.004)|0.016<br>(0.004)|
|_N/N_|0.901<br>|1.170<br>|0.908<br>|
||(0.2176)|(0.510)|(0.271)|
|INFLAT|-0.023|-0.023|-0.024|
||(0.005)|(0.006)|(0.005)|
|EXCHPREM|-0.022|-0.010|-0.014|
||(0.005)|(0.004)|(0.004)|
|TOT|0.030|0.028|0.030|
||(0.011)|(0.011)|(0.011)|
|SCHOOL|0.00008|-. 00027|-0.00004|
||(0.00008)|(0.0002)|(0.0001)|
|EXPORT|0.056|0.045|0.051|
||(0.009)|(0.009)|(0.009)|
|DEASIA|<br>0.005||0.006|
||(0.006)||(0.008)|
|DSAS1A|0.0006||0.0005|
||(0.007)||(0.010)|
|DLATIN|-0.0097||-0.009|
||(0.0052)||(0.007)|
|DSAFRIC|<br>-0.017||-0.018|
||(0.006)||(0.008)|
|C|<br>0.025||0.028|
||(0.010)||(0.013)|
|_R2_|<br>0.325|0.262|0.286|
|_1?2_|0.310|0.192|0.218|
|Countries:|44|44|44|
|Observations:|772|772|772|
|Test of Restrictions:||F(43,614)= 1.96|X<sup>2</sup>(16)=28.37|

Note: Standard errors are in parenthesis.
# **Table 16: Computation methods of the “conversion factor”**

This table shows the results of the so-called _conversion factor_ using three different methodologies. The conversion factor is calculated, for each country for which household data are available, as the total number of deposit accounts divided by the number of individuals who have savings/deposit accounts in formal financial institutions. This factor is a proxy for the average number of accounts per account holder. Such proxy is not perfect, since the figure for the total number of accounts include not only individual accounts, but also business and government accounts. For _reported totals only,_ the sample was restricted to countries with reported account data and household survey data. In the _reported and predicted_ row, we use countries with household survey data and account data, regardless of whether it were reported or predicted data. Finally, in the last row, labeled _comm. bank accounts_ , we use a sample of countries with household survey data and commercial bank reported account data. The columns present the mean, as well as the 90% confidence interval.

|**Conversion Factor Calculation**||**Conversion**<br>**Factor**|**90% C.I.**|
|---|---|---|---|
|**Method**|**# Obs.**|**Mean**|**-**<br>**+**|
|Using reported totals only|15|3.0|2.0<br>3.9|
|Using reported and predicted|28|3.2|2.6<br>3.9|
|Usingreported comm. bankaccts.|17|2.5*|1.6<br>3.4|

Notes: *The conversion factor from reported commercial bank accounts is not necessarily comparable to the others since it will use generate the number of banked adults from the number of commercial bank accounts only.

# **Table 17: Estimation of the number of banked people using the three different conversion factors**

This table shows the estimation of the banked population using three different methodologies to estimate the socalled conversion factor. The conversion factor is calculated, for each country for which household data are available, as the total number of deposit accounts divided by the number of individuals who have savings/deposit accounts in formal financial institutions. For _reported totals only,_ the sample was restricted to countries with reported account data and household survey data. In the _reported and predicted_ row, we use countries with household survey data and account data, regardless of whether it were reported or predicted data. Finally, in the last row, labeled _comm. bank accounts_ , we use a sample of countries with household survey data and commercial bank reported account data.

|**Conversion Factor Calculation**<br>**Method**|**Region**|**Estimate for**<br>**Number Banked**|**% Adult Pop.**<br>**Banked**|
|---|---|---|---|
||World|1.78 Bn|38%|
|Using reported totals only|High Income|0.70 Bn|81%|
||Developing|1.08 Bn|28%|
||World|1.91 Bn|41%|
|Using reported and predicted|High Income|0.74 Bn|86%|
||Developing|1.17 Bn|31%|
||World|1.71 Bn|37%|
|Using reported comm. bank accounts|High Income|0.65 Bn|75%|
||Developing|1.06 Bn|28%|

51
# **Figure 13. The private sector expects low returns. MPK is low**

<!-- Start of picture text --> Government<br>Macroeconomic Regulatory uncertainty, Rules and<br>concerns and anticompetitive practices, and regulations<br>fiscal adjustment (volatility in) corruption protect<br>oligopolies<br>Barriers to Entry<br>Firms<br>Traditional Sectors: Non K-intens. Sectors:<br>Oligopolies Competition<br>Low tax collection • Agriculture • Electronics<br>• Transport assembly<br>• Power & cement • Call centers -<br>• Mining & banking BPOs<br>Low Public Savings<br>Pricing power<br>2bi. Insufficient Public Investment 2bii. Expensive Inputs<br>Low MPK<br><!-- End of picture text --> _Source:_ Author.

# **2bi. Insufficient public investment**

**Low public investment is a bottle-neck for private investment.** Compared with its Asian neighbors, the country ranks low in transport comparisons (railroads, port, and air), and educational achievement. The declining quantity and quality of public investment, especially in infrastructure and education, provide little incentives for private investment and constitutes a bottle-neck to long-term economic growth. For example - according to the recent Investment Climate Assessment (ICA) - high transport costs have weakened the private sectors’ willingness to invest in agriculture; and in manufacturing, inadequate infrastructure

18
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30
Estimates tend to be more similar between the two surveys for people in Gaza Within the overall pattern of differences, estimates disaggregated by region are more similar between the PPCS and the Facebook survey in Gaza than in the West Bank for six of the seven core indicators calculated for both regions (conflict exposure is excluded here because it is calculated only for Gaza). For these six indicators, the greater degree of similarity is substantial: the relative difference between the two surveys tends to be about half as much in Gaza as it is in the West Bank. (For the one indicator in which estimates are more similar in the West Bank, the differences in estimates are close between the two regions.)

**Figure 2: The relative difference between Facebook and PPCS estimates is smaller in Gaza than in the West Bank**

This may be in part because there is more homogeneity in Gaza around geography, socioeconomic characteristics, and exposure to conflict than there is in the West Bank. For example, the West Bank is more than 15 times the size of Gaza and more varied in the distribution of its population over type of location (urban, rural, or camp). Per capita welfare is also more variable in the West Bank than in Gaza: the sample standard deviation of the most recent per capita welfare aggregate is about 1.5 times larger in the West Bank than in Gaza. Estimated poverty rates vary more within the West Bank, with a difference of about four percentage points between the poorest and wealthiest governorates in Gaza compared to about 10.5 percentage points between the poorest and wealthiest governorates in the West Bank (this difference in variability increases at lower levels of disaggregation).<sup>9</sup> Experience of violent conflict also tends to vary more across the West Bank than it does in Gaza, as suggested by the pattern of events recorded by the Armed Conflict Location and Event Data Project (ACLED) during the May 2021 violence. Over a period of 15 days, ACLED recorded fewer than 15 total events involving violence in four West Bank governorates, while the highest governorate-level event counts in the West Bank in that same period

> 9 For further detail on this, see Atamanov & Palaniswamy (2019).

11