rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4: The distribution of log total adult equivalent consumption across years <!-- Start of picture text --> 6 8 10 12 -2 0 2 4<br>Log total AE consumption Log total AE consumption (after removing zone specific time trends)<br>2011 2013 2015 2011 2013 2015<br>.8 .8<br>.6 .6<br>.4 .4<br>kernel density kernel density<br>.2 .2<br>0 0<br><!-- End of picture text --> Note: This figure shows the distribution of log total AE consumption. The left panel shows the distribution of nominal values of log total AE consumption for each rounds of survey. The right panel removes zone specific trends to account for inflation at local level.

markets in Addis Ababa or Dire Dawa. Despite the above liberalization steps, the government still has a heavy hand in the coffee trade. First, following the creation of Ethiopian Commodity Exchange (ECX) in 2008, private traders are required to sell coffee through ECX (Gabre-Madhin, 2012). Other forms of government intervention include allocating licenses. For instance, the government frequently revokes export licenses from traders who hoard excessive amount of coffee (over 500 metric tons) (Tefera and Tefera, 2013), and closely monitors the domestic market to ensure that export-quality coffee is not being sold in local markets. Despite these government interventions, domestic coffee prices are highly correlated with international prices as shown in figure 2.

# **4 Empirical Strategy**

## **4.1 The pass-through of coffee price shocks to consumption**

To estimate the pass-through of international coffee price changes to household consumption, I use ESS data. ESS data allows me to measure household-level exposure to coffee price shocks using the share of household farmland allocated to coffee. The estimation equation is as follows: where _i_ indexes households, _γi_ is household fixed effect, and _γzt_ is zone-year fixed effect included to account for changes in food prices in response to demand changes following movement in 12
Behavior and Health Seeking among Tanzania’s Youth." _Studies in family planning, 53(2), 233-258_ .

- World Bank. 2012. Resilience, Equity, and Opportunity: The World Bank’s Social Protection and Labor Strategy 2012-2022.

- Wright, Nicholas A. 2021. "Need-based financing policies, college decisionmaking, and labor market behavior: Evidence from Jamaica." _Journal of Development Economics_ 150.

37
There are several approaches for defining inter-industry linkages: input-output based, labor skill based, and technology flow based.. Although these approaches represent different aspects of industry linkages and the structure of a regional economy, the most common approach is to use the national level input-output accounts as templates for identifying strengths and weaknesses in regional buyer-supplier linkages (Feser and Bergman 2000). The strong presence or lack of nationally identified buyer-supplier linkages at the local level can be a good indicator of the probability that a firm is located in that region.

To evaluate the strength of buyer-supplier linkages for each industry, we use the summation of regional industry employment weighted by the industry's input-output coefficient column vector from the national input-output account: where _Lir_ is the strength of the buyer supplier linkage, Q is industry i's national input-output coefficient column vector and _eir_ is total employment for industry i in district r. This is similar to the measure used in Koo (2002) to define the strength of buyer-supplier chains. The measure examines local level inter-industry linkages based on the national input-output accounts. The national 1-0 coefficient column vectors describe'intermediate goods requirements for each industry (i.e., inter-industry linkages). Assuming that local industries follow the national average in terms of their purchasing patterns of intermediate goods, national level linkages can be imposed to the local level industry structure for examining whether region r has a'right mix of supplier industries for industry-i. By multiplying the national input-output coefficient column vector for industry i and the employment size of each sector in region r (a district is used as a geographical unit for buyer-supplier linkage analysis), simple local employment numbers can be weighted based on what industry i purchases nationally.

10
Table 6b: Contribution to the Unemployment Rate and Share of Formal Employment Volatility: Recessions

||Unemploym|ent Rate|Share of Form|al Employment|
|---|---|---|---|---|
||Mexico|Brazil|Mexico|Brazil|
|**Job Separations Rates**|**0.41**|**0.37**|**-0.19**|**-0.06**|
|I-U|0.24|0.19|-0.16|-0.09|
|S-U|0.09|0.15|-0.15|-0.14|
|F-U|0.09|0.03|0.12|0.17|
|**Job Finding Rates**|**0.20**|**0.21**|**0.34**|**0.69**|
|O-F|0.02|0.05|0.11|0.36|
|U-F|0.12|0.13|0.26|0.26|
|O-I|0.01|-0.01|-0.02|0.01|
|U-I|0.06|0.07|-0.05|-0.03|
|O-S|0.00|-0.01|0.00|0.06|
|U-S|-0.02|-0.02|0.04|0.03|
|**Job Reallocation Rates**|**0.05**|**0.07**|**0.94**|**0.89**|
|I-F|0.03|0.06|0.74|0.80|
|S-F|0.01|0.01|0.18|0.35|
|F-I|-0.01|-0.01|-0.01|-0.02|
|F-S|0.01|-0.01|-0.04|-0.25|
|I-S|0.00|0.02|0.09|0.05|
|S-I|0.00|0.00|-0.02|-0.04|
|**Exit of the labor force**|**-0.03**|**-0.08**|**0.00**|**-0.16**|
|I-O|-0.01|0.00|0.03|-0.01|
|S-O|-0.01|0.02|0.04|-0.06|
|F-O|-0.02|-0.10|-0.07|-0.09|
|**Unemployment-Inactivity**|**0.23**|**0.41**|**-0.08**|**-0.10**|
|U-O|-0.01|0.07|-0.01|-0.03|
|O-U|0.24|0.34|-0.07|-0.07|
|Total Contributions|0.86|0.98|1.00|1.26|

Notes: The table presents the contribution of the cyclical component of each flow to cyclical volatility of the unemployment rate an the share of formal employment for Mexico and Brazil in recessions following Shimer, 2007. We define recession as output below trend. We compute the covariance between the HP de-trended steady state unemployment rate (and share of formal employment) with the counterfactual of the HP de-trended series derived from setting all the possible flows at their average level and allowing only the flow of interest to vary. We present the covariance as a proportion of the variance of the series. O=Out of the Labour Force, U=Unemployment, E=Employment, S=Informal Self-Employed, I=Informal Salaried, and F=Formal Sector. Data for Mexico is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2.

46
Table 7: Dependent Variable: ln(number of export varieties)

|Sample|All|Dom private|Foreign|Multiple Ind|
|---|---|---|---|---|
|�2001|-0.0280|0.138|-0.0233*|-0.0272|
||(0.022)|(0.223)|(0.012)|(0.021)|
|�2002|0.0599|0.318|0.0712**|0.0729***|
||(0.042)|(0.221)|(0.029)|(0.020)|
|�2003|0.103**|0.479|0.107***|0.130***|
||(0.049)|(0.318)|(0.035)|(0.018)|
|�2004|0.124**|0.598**|0.126***|0.161***|
||(0.056)|(0.267)|(0.039)|(0.016)|
|�2005|0.210***|0.821***|0.210***|0.236***|
||(0.040)|(0.310)|(0.029)|(0.019)|
|�2006|0.286***|0.945***|0.283***|0.316***|
||(0.050)|(0.316)|(0.033)|(0.017)|
|�2007|0.275***|1.086***|0.267***|0.306***|
||(0.046)|(0.338)|(0.030)|(0.022)|
|�<br>P <sup>D</sup>M<sup>D</sup>+P <sup>I</sup>M<sup>I</sup><br>PY<br>�<br>it|0.0130|0.182|0.0110|0.0017|
||(0.018)|(0.266)|(0.017)|(0.018)|
|�wL<br>PY<br>�<br>it|-0.0474|-0.126|-0.0517*|-0.0606*|
||(0.059)|(0.726)|(0.028)|(0.031)|
|N|17871|858|16726|28925|
|R-sq|.0399|.121|.0388|.0486|

Note: Firm and year ...xed e¤ects are always included. Data set: merged NBS and customs data. Column (1) uses the whole sample; columns (2) and (3) include only domestic private and foreign-invested ...rms, respectively. Column (4) includes ...rms that operate in multiple industries as well. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

51
answering questions; and whether the respondents’ responses reflect their views on broader phenomena than the specific issue in the question. As subjective rankings implicitly combine an assessment of the issue being ranked with an assessment of how important that issue is to the respondent, endogeneity cannot be altogether eliminated. However, the concern that these responses simply reflect the individual’s own performance across the board (e.g. that poorly performing firms could complain more about everything) is a separate form of measurement error that can be addressed.

The World Bank’s Enterprise Surveys database (ES) provides an opportunity to address all of these concerns. It provides comparable data on 79,000 firms and 105 countries. There are four features of the data that are of relevance for this paper. First, there is a question that asks respondents to rank 17 dimensions of the business environment on how constraining they are to the operation and growth of their business. These provide the subjective perceptions data that are examined here. Second, the survey also has objective measures of the investment climate that correspond to these same issue areas.<sup>2</sup> Third, there is a rich set of firm characteristics and measures of firm performance in the dataset. Fourth, the same questionnaire is implemented in each country, with a standardized sampling methodology, making the data comparable across countries. This rich set of information on firm characteristics, including firm performance itself, allows

> 2  It may clarify the difference between the qualitative and quantitative data to provide an example. Looking at the issue of crime, the qualitative question asks respondents to rank on a scale of 0-4: ‘how constraining is crime to the operation and growth of your firm?’ In contrast, a quantitative question regarding crime would be “what share of sales is lost to theft or vandalism last year?”  To the extent all answers are reported by the respondent, they can be seen as having a subjective element to them.  The distinction we are trying to make, however, is that the quantitative questions are based on more objective criteria and ask for responses in terms of time or monetary costs, not a rating scale, and do not include any assessment as to whether the issue is of concern to the respondent or not.

3
Policy Research Working Paper 6647

# **Abstract**

The purpose of this study is to investigate the association between electronic filing (e-filing) and the total tax compliance costs incurred by small and medium size businesses in developing countries, based on survey data from South Africa, Ukraine, and Nepal. A priori, most observers expect that use of e-filing should reduce tax compliance costs, but this analysis suggests that the assumption should be more nuanced. In particular, policies that require business taxpayers to submit paperbased information in addition to their e-filing roughly negate savings that would otherwise be realized. In addition, adoption of e-filing requires an up-front investment by the business not only in capital assets, but also in the time, effort, and resources required to learn how to use e-filing properly and efficiently. Small businesses, in particular, are likely to face a steep “learning curve” and should probably not be forced to use e-filing before the majority of them have access to computers (with reliable electricity service) and have had a chance to become familiar with both computer use and the Internet.

This paper is a product of the International Trade and Investment Unit, Investment Climate Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at: fyilmaz@ucalgary.ca or jcoolidge@schoolbench.com.

_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
23 measure of privately owned banks. Thus, the exogenous component of privately owned banks is positively associated with economic growth.

These results suggest an economically large impact of financial development on growth. For example, India’s value of Private Credit over the period 1960-95 was 19.5 percent of GDP, while the mean value for developing countries was 25 percent of GDP. The estimated coefficient suggests that an exogenous improvement in Private Credit in India that had pushed it to the sample mean for developing countries would have accelerated real per capita GDP growth by an additional 0.6 of a percentage point per year. These types of conceptual experiments must be treated as illustrative because they do not account for how to increase financial intermediary development.

While these analyses confront the causality issue, problems remain. Besides the problems noted earlier with constructing accurate measures of financial development, these cross-country instrumental variable analyses only consider the endogenous determination of the financial development. They treat the other explanatory variables as exogenous. Furthermore, the cross-country instrumental variable studies do not simultaneously consider the independent role of equity markets.

# **2.4. Panel studies of finance and growth**

# **_2.4.1. Why use panel techniques?_**

In light of the problems associated with purely cross-country growth regressions, Levine, Loayza, and Beck (2000, henceforth LLB) use a GMM estimator developed for panel data (Arellano and Bond, 1991; Arellano and Bover, 1995). In comparison to purely cross-country approaches, the panel approach has three important advantages and one particular disadvantage. To see these, consider the panel regression specified as follows: y(i,t) = a _X_<sup>_1_</sup> (i,t) + b _X_<sup>_2_</sup> (i,t) + C(i) + T(t) + u(i,t)
**Skills** We argue the main theoretical predictions related to the impact of enforcement on skill composition depend on the relative cost to adjust labor across skill groups. It is plausible that at least some of the labor adjustment cost is proportional to the cost of labor, and would thus be higher for skilled (more experienced) workers than for unskilled workers. Hence, we conjecture that, following a technological shock, stringent enforcement of labor market regulations at the subnational level restrict needed labor adjustments relatively more for skilled workers than for unskilled workers in the formal sector. This is consistent with evidence presented in Montenegro and Pages (2004). The authors provide support for the idea that labor market regulations reduce employment rates of the unskilled at the benefit of the skilled workforce. If this effect dominates, in response to the same technology shock, industries located in strongly-enforced municipalities should increase the relative demand for skills by more than industries located in weakly-enforced municipalities.

# **3. Data and Descriptive Statistics**

Our main data are administrative records from Brazil for formal-sector workers linked to their employers. We characterize the tasks required in each worker’s occupation, based on data describing the activities of similar occupations in the United States. We then aggregate the information on employment and skill intensity by each industry-municipality-year cell. We merge these outcomes of interest to city-by-time information on the provision of internet services, to industry-specific information on the use of information and communications technology, and city-by-sector-by-year information on the enforcement of labor market regulations.

The sample period for analysis covers a significant expansion of digital technologies throughout the country, between 1996 and 2006. The fact that Brazil’s internet rollout and regulatory enforcement vary across municipalities and over time allows us to examine the 11
44 Lin, J. Y., 2012 a. _New Structural Economics: A Framework for Rethinking Development and Policy_ , Washington D.C., World Bank.

Lin, J. Y., 2012b. _The Quest for Prosperity: How Developing Economies Can Take Off_ , Princeton, N.J., Princeton University Press.

Lin, J. Y., 2011. _From Flying Geese to Leading Dragons: New Opportunities and Strategies for Structural Transformation in Developing Countries_ , Policy Research Working Paper no. 5702, Washington D.C., World Bank, June.

Lin, J. Y. and C. Monga, 2011. “Growth Identification and Facilitation: The Role of the State in the Dynamics of Structural Change” _Development Policy Review_ , 2011, vol. 29, no. 3, pp. 259-310.

Lindauer, D. L., and A. D. Velenchik, 1994. “Can African Labor Compete?,” in: D. L. Lindauer and M. Roemer (eds.), _Asia and Africa: Legacies and Opportunities in Development_ , San Francisco, Institute for Contemporary Studies, pp. 269-304.

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Monga, C., 2012a. “Shifting Gears: Igniting structural Transformation in Africa,” _Journal of African Economies_ , vol. 21 (Supplement 2), pp. ii19-ii54.

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Monga, C., 2011. “Post-Macroeconomics: Lessons from the Crisis and Strategic Directions Ahead,” _Journal of International Commerce, Economics and Policy,_ Vol. 2, No. 2, pp. 1–28.

Monga, C., 1997. “A Currency Reform Index for Western and Central Africa,” _The World Economy_ , vol. 20, no. 1, January, pp. 103-125.

Page, J., 2012. “Can Africa Industrialise?,” _Journal of African Economies_ , vol. 21 (Supplement 2), pp. ii86-ii124.

Perkins, D. H. and M. Roemer, 1994. “Differing Endowments and Historical Legacies,” in: D. L. Lindauer and M. Roemer (eds.), _Asia and Africa: Legacies and Opportunities in Development_ , Cambridge and San Francisco, HIID and Institute for Contemporary Studies, pp. 25-58.

Roemer, M., 1994. “Industrial Strategies: Outward Bound,” in: D. L. Lindauer and M. Roemer (eds.), _Asia and Africa: Legacies and Opportunities in Development_ , Cambridge and San Francisco, HIID and Institute for Contemporary Studies, pp. 233-268.

Spence, M., 2011. “The Impact of Globalization on Income and Employment: The downside of Integrating Markets,” _Foreign Affairs_ , July-August, pp. 28-41.

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adult males in the 11% census. All three categories are regarded as upper castes. As we see from Table 1, migrants are much more likely to be upper caste than non-migrants.

The census distinguishes 84 di ff erent languages. The main ones are Nepali and Maithili, spoken by 58% of the population. In Table 1 we see that work migrants are much more likely to speak Nepali, the main language in the country. While the Nepalese population is heterogeneous in terms of ethnicity and language, it is relatively homogeneous in terms of religion: 81% of adult males are Hindu and 11% are Buddhist. We see in Table 1 that work migrants are predominantly Hindu.

The dependent variable Mis<sup>hinourmainregressionofinterest,regression (3),isconstructed</sup> as follows. We begin by creating, for each of the 16850 work migrants h identi fi ed in the 11% census, 75 Mis<sup>hobservationscorrespondingtoeachofthepossible75districtdestinationss.We</sup> set Mis<sup>h= 1ifmigranthmovedfromdistrictitodistrictsinthe5yearsprecedingthecensus,</sup> and 0 otherwise. We then drop Mii<sup>hsincewefocusonmigrants.Byconstructionamigrant</sup> reside in one district. For each migrant, variable Mis<sup>hthustakesvalue1onceandvalue073</sup> times.

Since the migrant can only move to a single destination, the 74 Mis<sup>hobservationsarenot</sup> independent and residuals in (3) are correlated. Dependence across Mis<sup>hobservationscombines</sup> negative and positive correlation. To illustrate this point, imagine for a moment that all destinations are equivalently attractive to the migrant. The probability Pr(Mis<sup>h= 1) of selecting one of</sup> them is thus 1/74. Further assume that one of them is selected at random; for this observation, we have u<sup>h</sup> is<sup>=1−Pr(M</sup> is<sup>h=1)=73/74.Forallotherobservations,theresidualuh</sup> is<sup>=−1/74.</sup> We see that, for individual h, the observation in which Mis<sup>h=1isnegativecorrelatedwith</sup> observations in which Mis<sup>h=0.WealsoseethatobservationsinwhichM</sup> is<sup>h=0arepositively</sup> correlated with each other. This combination of positive and negative correlation means that a 14
The Cote d'Ivoire Living Standards Survey was designed to collect information on expenditure patterns, and a host of other socio-economic variables, for a nationally representative sample of households (for a detailed description of the survey, see Grootaert, 1986). It therefore satisfies one of the informational requirements for the construction of a regional price index. However, the price data that it collects, while national in coverage, do not have adequate commodity coyerage. Glewwe (1990) gives an excellent account of the problems to which this gives rise: "The price data were recorded on a separate price questionnaire which was administered in the local markets of the sampling areas from which CILSS households were drawn. Prices were obtained for 18 food items and 4 nonfood items... The prices of the four non-food items (domestic cloth, plastic sandals, enamel bowl and menthol) varied widely, and further investigation led to the conclusion that [they were] unusable for the construction of a price index... Since variation in the price of non-food items is largely due to transportation costs, it seemed advisable to use some of the food items which were relatively non-perishable and found throughout the country. The only food item to meet these criteria was canned tomato paste. Thus the price of cans of tomato paste are used as a proxy for the prices of non-food items." The Glewwe index was used by Glewwe (1987, 1990), and by Kanbur (1990) to analyze poverty and welfare in Cote d'Ivoire. The problems with the index should be obvious. A more recent attempt to derive a regional price index (McKay, 1992) addresses the oroblem by simply excluding, non-food items and restricting attention to only food items, which is hardly a satisfactory solution.

The basic problem is thus the inadequate commodity coverage of prices in the CILSS. The only way to resolve this problem is to have access to a data base with adequate geographical and commodity coverage of prices. The ICP data base makes this possible and we turn to that now.

3
**Table 2: Calorie availability and hunger prevalence**

|Module|Mean Kcal per<br>capita<br>(95% CI)|Hunger<br>Prevalence<br>(95% CI)|
|---|---|---|
|1. Long list of 58 food items; 14 day recall|1793<br>(1722-1864)|0.684<br>(0.640-0.728)|
|2. Long list of 58 food items; 7 day recall|2129<br>(2055-2203)|0.481<br>(0.432-0.531)|
|3. Short list of 17 food items; 7 day recall^|2066<br>(2001-2131)|0.484<br>(0.435-0.534)|
|4. Long list of 58 food items; usual 12 month recall|1909<br>(1823-1995)|0.594<br>(0.546-0.642)|
|5. 14 day household diaries with frequent visits|2413<br>(2341-2486)|0.268<br>(0.223-0.313)|
|6. 14 day household diaries with infrequent visits|2520<br>(2446-2594)|0.230<br>(0.186-0.274)|
|7. 14 day individual diaries with frequent visits|2677<br>(2600-2756)|0.188<br>(0.148-0.228)|

^The 17 foods account for 77 percent of the food budget, so calorie availability is scaled up by 1/0.77.

20