rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Empirical Strategy and Results**

## **3.1 Ordinary Least Squares**

### **3.1.1 Method**

Our first empirical strategy uses panel ordinary least squares (OLS) regressions that include year and country fixed effects and a battery of initial district-level controls. The OLS specification is _Sectori,c,t_ is the share of employment in agriculture, manufacturing or services for district _i_ in country _c_ , at year _t_ . _Paved Roadi,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ contains a paved road at year _t_ . _Electricityi,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ has more than 50% of its population with lights at night at year _t_ . _Interneti,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ has a node on the internet backbone fiber network at year _t_ . We add interaction effects to capture the complementarities between infrastructures. _Paved Roadi,c,t_ ∗ _Electricityi,c,t_ captures the interaction of road and electricity infrastructure, and _Interneti,c,t_ ∗ _Electricityi,c,t_ the interaction of Internet and electricity infrastructures. We do not include the isolated electricity variable alone and the interaction between Internet and roads.<sup>17</sup> _Controlsi,c,t_ represents the additional location-specific controls, which include initial district population, access to a main city, land size, distance to the coast, distance to the border, access to a city of more than 50,000 inhabitants, temperature, and elevation. _FE_ is the year and country fixed-effects. The coefficients _β_ capture the correlation between access to a type of infrastructure on the different sectoral employment shares, while the coefficients _γ_ capture the infrastructure interaction terms.

We run two other specifications. First we include a specification to study labor participation where the dependent variable is not the normalized sectoral share but the share of non-working individuals. Second, we include variables of infrastructure uses rather than presence of infrastructure to compare the results.

with _Electricityi,c,t_ the percentage of respondents in location i with access to electricity as indi-

> 17Too few observations have electricity but no paved road, and Internet but no paved road.

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6 the tax or distribution of its proceeds; or (2) when under provisions of the tax law they automatically receive a given percentage of the tax collected or arising in their territory; or (3) when they receive tax revenue under 3 tax law leaving no discretion to the collecting government." Under this definition, revenues collected by the central government but transferred directly to the local government in which the revenues were collected are classified as tax revenues of the beneficiary (local) government rather than as an intergovernimental grant. Such tax sharing arrangements are quite common in developing countries, particularly because higher levels of government may be able to collect the tax more economically than can local governments. Such arrangements are in essence intergovernmental transfers since, most commonly, it is the higher level of government which unilaterally determines this tax sharing arrangement. Because of this definitional issue, comparisons of the role of grants across cour.cries and even across time within a single country must be made cautiously.

Another conceptual issue that arises when making intercountry comparisons in intergovernmental transfers concerns the discretion which the local government has in spending the proceeds of the transfer. In some countries organizations called local governments are little more than agencies of the central government with all or nearly all public employees of these organizations being employees of the central government, with spending decisions made with little direct influence by locally-elected 5International Monetary Fund, A Manual on Government Finance Statistics, (Washington, DC: IMF, 1986), p.53.
# **The Changing Pattern of Returns to Education: What Impact Will This Have on Inequality?**

Harry Anthony Patrinos<sup>*</sup> JEL: I20, I21, I24, I25, I26 Key words: Rates of return to education, labor markets, poverty alleviation

> * Practice Manager, Education, World Bank. <u>hpatrinos@worldbank.org. Views expressed here are those of the</u> author and should not be attributed to the World Bank Group. This paper is dedicated to the memory of Christopher Colclough, supervisor, mentor, friend, and great development economist. I am forever grateful for the opportunity he gave me.
not just proxying for competing channels that might drive tax inspection policies, namely capital intensity and reliance on external finance (Gordon and Li 2009).

The IV estimated size-gradients imply that a 10 percentile increase in the WBES sizerank increases a firm’s probability of tax inspection by 2.3% (a 3.8% increase relative to a mean of 61%) and its tax compliance rate by 2.2% (a 2.7% increase relative to a mean of 81%). We estimate two main control models which allow non-parametrically for the tax inspection function to vary over firm characteristics or over two digit ISIC industries in every country-year. The latter specification exploits size variation between narrow ISIC3 industries such as “Manufacture of rubber products” (category 251) compared to “Manufacture of plastics products” (category 252), within the ISIC2 category 25. In addition, we find comparable coefficients in panel models which exploit within a country variation in the ranking of 3-digit ISIC industry over time. The size gradients mask non-linearities over the size distribution. Inspection and compliance appear concentrated among large firms, but no different for small and medium firms. The symmetry of results on inspection and compliance suggests that size-based tax inspection may explain part of the compliance behavior. Finally, we study heterogeneity across countries’ income levels: the size-gradient appears to fall with development and the size-gradient for countries with similar income level as the United States is zero and statistically different from that of low-income countries. A reliance on with decreasing size-dependent policies development is consistent with evidence showing that countries with weak fiscal capacity rely on production-inefficient tax instruments (Best et al. 2015, Bachas and Soto 2016).

In order to quantitatively evaluate the macroeconomic implications of the estimated size-gradients, we appeal to a standard general equilibrium model of firm dynamics. Our closest reference in the literature is a closed economy version of Atkeson and Burstein [2010]. It features three channels through which size dependent effective taxation can affect TFP: resource misallocation among incumbents, entry and exit of firms, and in4
in the late 1970s and the early 1980s the pattern of nlovement of th6 stock market is very different from that in the late 1980s.

Broadly speaking, there was a general upturn in the late 1980s in the size of the stock markets in the economies uader study. Figures 3 to 6 show the movement of MCY over time in these 19 economies grouped according to geographical areas. It can be seen from these figures that the pattern of movement of Market Capitalization as a proportion of GDP varied widely amon; ;t the 19 economies. In East Asian countries, the ratio remained rath - stable before the mid 1980s, but there were a sharp rise in Taiwan and Korea, and a moderate upturn in the Philippines from 1985. In South and South East Asia, the pattern of the movement of MCY in Malaysia was distinctively different from others. It had a higher starting point and it showed a discernible cyclical pattern. But the mid-1980s saw an uptum of the sizes of the stock markets in all five economies in this group. The pictures in the other two groups were more complicated.

The ratios had a much larger variation, especially in Latin America. But we can still see a general rise in the sizes of the markets since the mid-1980s in nearly all the economies in these two groups.

When the 1978-1990 period is divided into three sub-periods, the correlations in the period 1986-1990 are much more significant than in the two previous periods. This is evident from Table 5. We thus have to answer the question why there was a general upturn in the size of these markets after the mid-1980s and the correlations with the indicators of financial depth are more significant. Section V will provide some tentative explanations.

IV. Time-Series Correlations between the Indicators of the Development of the Stock Market and Financial Deepening This section analyses the correlations between MCY and other variables over time in different economies. Since for the economies which are not included in the IFC's Composite 6
# **Table 3: Balance tests**

This table reports the results of the balance tests. We limit the sample to consist of observations with the age of a city secretary in the range of 55-61 years old. The dependent variables are oncelagged labor productivity ( _Labor prod_ ), firm’s leverage ratio ( _Leverage_ ), the logarithm of total employees ( _Ln(employment))_ , and the return on sales ( _ROA_ ) respectively. The purpose of the balance tests is to show that the control variables vary smoothly around the age threshold, thus discontinuities around the threshold reflect the causal effect of the decrease of promotion probability. Definitions of other variables are the same as the previous tables. Standard errors clustered at the city level are reported in parentheses. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

|Dependent Variable:|(1)<br>|(2)<br>|(3)<br>|(4)<br>|
|---|---|---|---|---|
||_Laborprodt-1_|_Leveraget-1_|_Ln(employmentt-1)_|_ROAt-1_|
|_Dum(Age≥58)_|0.005|0.014*|0.027|-0.004|
||(0.026)|(0.008)|(0.026)|(0.003)|
|_Age-58_|-0.021*|-0.006*|0.021*|-0.000|
||(0.011)|(0.003)|(0.013)|(0.001)|
|_(Age-58)* Dum(Age≥58)_|-0.004|0.014*|-0.093|0.004|
||(0.054)|(0.007)|(0.056)|(0.005)|
|IndustryFE and CityFE|Yes|Yes|Yes|Yes|
|N|43,591|43,591|43,591|43,591|
|R-square|0.313|0.111|0.172|0.094|

``` 33 ```
estimate that China's share of the world capital stock as of 2000 is about 8 percent. We choose units such that the world capital stock to one in the theory. We therefore set (1⋅ η) a*=0.08, or a=1.15 and a*=0.4 implying that the rest of the world has per capita wealth that is roughly three times higher than in China.

> σ * φ ⋅ σ We next calibrate production risk by equating V = and V = with the

> η 1− η

standard deviation of rest-of-world and Chinese real per capita GDP growth. For this calculation we define the rest of the world as the 98 countries in the PWT6.1 with continuous annual data on real GDP per capita between 1960 and 2000. The standard deviation of real per capita GDP growth for the world excluding China over this period is 1.4 percent, so we set V=0.014 which implies σ=0.013. For China we focus on the post1978 period where the standard deviation of real per capita GDP growth was 3.7. We set V*=.037 which implies φ=1.32. For sovereign risk we follow KLSV and set α=0.03, who argue that this roughly corresponds to the frequency of episodes of widespread default over the past 150 years.

We also need to calibrate China's relative productivity advantage or disadvantage, π. We use a very simple cross-country development accounting exercise to retrieve π from the data. Assuming a common Cobb-Douglas production function, the share, A and A* are total factor productivity, and K/L and K*/L* are capital stocks per worker. In the theory, the ratio of the expected marginal product of capital in North corresponds to κ=0.75. This can be justified by interpreting K as a broad concept of capital. We next retrieve estimates of A and A* as residuals from this production function, setting κ=0.75. To implement this we use data on GDP and capital stocks per worker for China and for an aggregate of the rest of the world in 1996, which is the year that maximizes our cross-country data coverage. For China, GDP per worker and capital per worker were $4,908 and $7,408 in 1996 PPP terms. For the rest of the world the corresponding figures are $17,697 and $39,137 respectively. This implies a relative 14
by private traders in the open bidding market in India. On the supply side, farmers cannot very easily switch from non-spinnable cotton to spinnable cotton, because of the different soil conditions. On the demand side, _deshi_ cotton is not consumed by downstream textile industries but used for other industrial purposes. All the initial values used in the simulation are shown in **Table 11.**

**Table 11:** **_Initial Values_**

|Commodity|Supply<br>India<br>(rnkg)|Demand<br>India<br>(mkg)|Export<br>India<br>(mkg)|Supply<br>World<br>(mkg)|Demand<br>World<br>(mkg)|Producer<br>Price<br>(Rs/kg)|Consumer<br>Price<br>(Rskg)|World<br>Price<br>ts/kg)|
|---|---|---|---|---|---|---|---|---|
|Cotton|1,900|1,800|100|16,500|16,600|53|_55_|61|
|Cotton Yam|1,500|1,300|200|16,600|16,800|91|97|106|
|Cone Yarn|1,050|_850_||||93|1oo||
|Hank Yam|450|450||||83|89||
|Cotton Textiles|1,400|1,000|400|9,600|10,000|217|221|221|
|Powerloom|960|600|360|||216|||
|Handloom|440|400|40|||219|||

Sources: EICA (1995), ICAC (1994), Office of the Textile Conmmissioner (1995) etc.

# _Assumption on Hank Yarn_

Another assumption is in regards to hank yarn. In the Indian yarn market, two kinds of cotton yarn - cone yarn and hank yarn - are traded. Cone yarn is consumed by the powerlooms and composite mills while hank yam is exclusively processed by handlooms. No hank yarn is exported from India as there is no world market. It is assumed that so far as the current hank yarn obligation policy is not subject to change, spinning mills will keep winding 30 per cent (i.e., current rate) of their domestic cotton yam supply in hank form.

If the hank yarn obligation is phased out, spinning mills will supply only cone yam. This assumption is reasonable because the production process of hank yarn is exactly the same as cone yam except for the final process - winding in either cone form or hank form, which is why it is easy for mills to switch from hank yarn production to conce yarn production. With no hank yarn in the market, handloom weavers will have to purchase cone yarn in the domestic market and rewind it to hank form by paying extra rewinding costs.

# **_Price_** _Elasticities_

This multi-market model computes some of the price elasticities endogenously. Although various empirical studies attempted to measure price elasticities for cotton and textiles in the past (Goswami, 1990, Ministry 33
developing countries. Overall, these comparisons suggest that the lending environment is more important in shaping bank financing to SMEs than firm size or the bank ownership type.

The rest of the paper is organized as follows. Section II describes the survey used to gather our data. Section III examines banks’ perceptions of the SME segment, focusing on the factors driving and impeding SME finance. Section IV gets into the nitty-gritty of banks’ involvement with SMEs by describing the business models banks use to serve SMEs. Section V presents and analyzes data on the extent, type, and pricing of banks financing to SMEs across countries and across bank ownership types. Finally, Section VI concludes.

# **II. The Survey**

To gather information on bank financing to SMEs around the world, we designed a survey with 56 questions focusing on three main areas. First, the survey examines how banks perceive the SME segment. In particular, the survey asks banks about the drivers and obstacles to SME financing. Also, the survey inquires about banks’ perception of the role of government programs to support SME finance and of banking prudential regulations. Second, the survey includes questions related to the business models used by banks to serve SMEs. In particular, the survey asks whether banks have separate specialized departments to manage their business with SMEs; whether sales decisions are separated from risk management; whether SME loans are processed at the branches or at headquarters; whether credit scoring is used to approve SME loans; about the types of criteria considered when evaluating SME loans; and the types of collateral most frequently used in SME lending. Finally, the survey tries to gauge the extent, type, and pricing of bank financing to small, medium-sized, and large enterprises. To measure the extent of banks’ involvement with SMEs, the survey gathers data on the share of lending to small, medium-sized, and large enterprises vis-à-vis total loans. To characterize the type of financing, the questionnaire collects information on the share of loans devoted to investment purposes and the ratio of loans that are collateralized or secured. To describe the pricing of SME loans, the survey gathers data on the fees and interest rates charged on these loans.

Our questionnaire includes quantitative and qualitative questions. For quantitative questions, we report averages across banks. Information on the qualitative questions is conveyed by reporting the percentage of banks that chose each option provided under each question.

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Policy Research Working Paper 8717

# **Abstract**

This study examines recall bias in farm labor by conducting a randomized survey experiment in Ghana. Hours of farm labor obtained from a recall survey conducted at the end of the season are compared with data collected weekly throughout the season. The study finds that the recall method overestimates farm labor per person per plot by about 10 percent, controlling for observable differences at baseline. Recall bias in farm labor per person per plot is accounted for by the fact that households in the recall group report fewer marginal plots and farm workers, denoted here as listing bias. This listing bias also creates a countervailing effect on hours of farm labor at higher levels of aggregation, so that the recall method underestimates farm labor per plot and per household and overestimates the labor productivity of household-operated farms. Consistent with the notion that recall bias is linked to the cognitive burden of reporting on past events, the study finds that recall bias in farm labor has a strong educational gradient.

This paper is a product of the Development Data 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/research. The authors may be contacted at at igaddis@worldbank.org and apalacioslopez@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
(and total labor supply) is updated exogenously between periods. It is also possible to add updating mechanisms for other variables such as public expenditure, transfers, technological change or debt accumulation. Below we present a brief description of the static and dynamic aspects of the model. [A complete list of equations and variables is available from the authors upon request.

_Static Module._ In each sector there is a representative firm, which earns capital income, transfers a share of profits to households and foreigners and pays direct income taxes to the government. We adopt a nested structure for production. Sectoral output is a _Leontief_ function of value added and total intermediate consumption. Value added is in turn represented by a CES function of capital and composite labor. The latter is also represented by a CES function of two labor categories: skilled labor and unskilled labor. Both labor categories are assumed to be fully mobile in the model. In the different production activities we assume that a representative firm remunerates factors of production and pays dividends to households.

Households earn their income from production factors: skilled and unskilled labor, agricultural and non-agricultural capital. They also receive dividends, intra-household transfers, government transfers and remittances and pay direct income tax to the government. Household savings are a fixed proportion of total disposal income. Household demand is represented by a linear expenditure system (LES) derived from the maximization of a _Stone–Geary_ utility function. The model includes nine household categories according to characteristics of the household head, as identified in the HES household survey. Five of these categories correspond to rural households and four are reserved for urban households. Minimal consumption levels are calibrated using guess-estimates of the income elasticity and the _Frisch_ parameters.

We assume that foreign and domestic goods are imperfect substitutes. This geographical differentiation is introduced by the standard _Armington_ assumption with a constant elasticity of 6
primary question of interest in this paper, which concerns the size and source of downward biases present in measures of corruption that do not take reticent behavior into account. We focus here on estimates of effective corruption, i.e. the likelihood that a firm is visited by a tax inspector and a bribe request takes place. The standard way to estimate effective corruption without taking reticence into account would be to simply calculate the proportion of all respondents who admit to both a visit and a bribe request. The population frequency of this is in the notation of Section 3. Note that the standard estimate of effective corruption differs from the standard estimate of corruption conditional PP[DD= 3] on a visit occurring, which is described in Equation (2) above and is commonly reported in analysis of the Enterprise Survey data.

The left-hand side of this expression is the ratio of measured corruption to true effective corruption, which is less than one if there is reticent behavior. The first term on the right-hand side is the ratio of the population value of the standard estimate of the probability of a visit to the true probability, and so measures the downward bias in reported visits due to reticence. The second term on the right-hand side is the ratio of the population value of the standard estimate of bribery conditional on a visit to the corresponding true probability, and so measures the downward bias in reported bribe interactions conditional on a visit being reported.

These two sources of bias are different in the two models we have considered. In particular from Equation (1) we see that the bias in reported visits is: In Model A we assumed there is no reticence in responding to the visit question so there is no bias relative to the true incidence of visits. However in Model B reticent respondents who experienced a bribe behave reticently with probability , and so there is a downward bias, i.e. . We report standard and reticence-adjusted estimates of the probability of a visit, and the ratio of the two, in the qq 1 −rrqqgg< 1 first part of Panel B of Table 11. This ratio is equal to 1 for Sri Lanka, Turkey, and Ukraine where Model A is the preferred model. However, for the remaining four countries this ratio is less than one, and substantially so in India (67%) and Bangladesh (73%). On average, for the four countries where Model B is the preferred model, reticent behavior implies that only 81% of visits that actually occur are acknowledged by respondents.

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