rafmacalaba/datause-encoder-data huggingface dataset:
in raising the same revenue. **There** may, however, be some comparators that could **be** candidates for providing a g,.neral benchmark. One e"ample is a proportional consumption tax. Here the indicator is clearly the fraction of total consumption of all commodities accounted for by the poor. Another example is a poll tax. Here the indicator is simply the head count ratio - the fraction of tde population below the poverty line (see Besley and Ka,ibur, 1988).

Armed with this conceptual framework, we can now proceed to assess the regressivity, or otherwise, of petroleum taxation in Ghana. What we need to implement the rules of thumb is a "poverty profile" for Ghana, which gives us consumption patterns of the poor and of the economy as a whole. Such a poverty profile is presented in Boateng et al (1992), based on the Ghana Living Standards Survey. I will not here go into the details of the survey and how it was used to construct the poverty profile. Suffice it to say that the survey was conducted in 1987-88, and a poverty lin. of two thirds of mean per capita household consumption expenditure was used. On this basis, the survey showed that 36% of Ghanaians were below the poverty line, and their consunmption was 16% of total consumption hy all Ghanaians.

The following table presen., consumption patterns for non-food items, among which are gasoline and kerosene - the key items in fuel taxation (Boateng et al, 1992, present much more information on poverty in Ghana, including consumption patterns for food). The table makes interesting reading. The poor account for only 0.2% of total expenditure on gasoline. There is a clearly a prima facie case for the progressivity of taxing gasoline. However, the poor account for 25.3% of the consumption of kerosene. The poverty consequences of taxing this commodity are clearly more severe. In relation to the two "universal" indicators we see that while taxing kerosene is still more progressive than a uniform poll tax, it is less progressive than a uniform consumption tax. Another issue that has 7
27 earlier findings by Blau (1986) regarding self-employed urban males; on the other hand, Blau finds that the other sub-groups of own-account workers indeed had lower earnings (i.e. urban females, and rural males and females).<sup>1 4</sup> Although detailed information on the occupational codes is unavailable, the broad single-digit categories are known, and are included as explanatory variables in order to eliminate any omitted variable bias introduced by differential pay lines across sectors of production. The composition of employment is fairly disperse across the 10 occupation categories, as shown in Table 5. This dispersion is corroborated by data from the 1995 labor force survey (Malaysia Department of Statistics, 1996). The coefficients on occupations 1 through 9 are measured relative to the income of a worker with similar characteristics in occupation 0, which consists of the police and the armed forces. The returns to the various occupational categories are large in magnitude, especially for agricultural workers (occupation 6) and unskilled workers (occupation 9) who earn far less, indicated by negative coefficient values.

|**Tab**|**le 5: Employment Composition by Occupation**||
|---|---|---|
|**Occupation Category**|**Description**|**Share (%)**|
|Occupation 0|Police and armed forces|3.6|
|Occupation 1|Managers, senior officials|4.1|
|Occupation 2|Professionals|4.1|
|Occupation 3|Technicians, associate professionals|6.1|
|Occupation 4|Clerks|8.0|
|Occupation 5|Service workers, shop and market sales|9.1|
|Occupation 6|Skilled agricultural workers|18.0|
|Occupation 7|Craft-related workers|4.8|
|Occupation 8|Plant and machine operators|7.0|
|Occupation 9|Elementary occupations: sales, services,<br>agriculture, mining, construction, manufacturing|13.5|
|Total||100.0|

N.B. Data missing for 21.6 percent of sample.

Household size is an important explanatory variable, contributing 6:4 percent to household earnings (both labor and net income) per additional household member. Interpreting this result requires caution, since the causality between earnings and the number of household members is indeterminate. It

> 14 The different results for urban males might be explained by the fact that Blau's regression analysis accounts for hours and weeks worked, but does not include occupational category or household size among the independent variables, and considers Peninsular Malaysia only.
Policy Research Working Paper 9524

# **Abstract**

How do macroeconomic expectations affect consumer decisions? This paper reports results from a natural field experiment with 2,872 credit card customers from a large commercial bank to answer this question. Participants of the experiment first completed as survey that measured consumer expectations about future inflation and the nominal exchange rate. This survey was combined with an information-provision experiment that generated exogenous variation in respondents’ macroeconomic expectations. The survey and experimental data were then merged with detailed administrative data on participants’ credit card transactions and balances. The experiment was designed to test three standard predictions from models of intertemporal consumption choice: inflation expectations should affect spending on durables; exchange rate expectations should affect spending on tradables; and, holding constant the nominal interest rate, inflation expectations should affect borrowing. The analysis finds that the information provided to participants strongly affects subjective expectations. However, no significant effects are found on actual consumer behavior (as measured in administrative data) or self-reported consumption plans (as measured in survey data). The preferred interpretation is that consumers are not sophisticated enough to factor inflation and exchange rate expectations into their consumption decisions. The absence of a link between consumer expectations and behavior has potentially important implications for macroeconomic policies such as forward guidance.

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 mkanz@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
world (with a trade surplus) or a net borrower (with a trade deficit) will have a significant effect on global macroeconomic balances as well as on the volume and patterns of trade. We do not contribute to the (sometimes heated) debate about the current Chinese trade balance and exchange rate policy. But our analysis does relate to the exchange rate issue in the long run. Whether China’s current account is likely to remain in significant surplus or shift to a deficit will naturally affect the equilibrium exchange rate (and in fact our analysis can be combined with other empirical analyses of the relationship between the exchange rate and the trade balance to form a view on future directions of the exchange rate).

The remainder of the paper is organized as follows: in Section 2 we introduce some basic stylized facts about China’s saving, current account, and net foreign asset position. It is well known that China has a high savings rate, reaching 50% of GDP in 2004. In light of this high savings rate it may not seem so surprising that in recent years China has had a current account surplus (excess of savings over investment). But introducing the data on the stock of foreign assets and liabilities highlights our puzzle: China has recently shifted to being a net creditor to the rest of the world, and that position is highly unusual for a poor country whose capital-labor ratio is well below the world average.

In Section 3 we draw on the model of North-South capital flows of Kraay, Loayza, Serven, and Ventura (2005) to develop a theoretical expectation of what China’s net foreign asset position should look like. In the model diminishing returns and production risk provide motives for cross border capital flows. In fact, these factors alone suggest very large capital flows – far beyond what is observed empirically. But introducing a realistic amount of concern about international default, based on historical experience, results in equilibrium net foreign asset positions close to what is observed in the data. In this model, a country’s net foreign asset position will depend on its per capita wealth and its productivity level relative to the rest of the world. We calibrate the model with the current Chinese data and examine China’s expected net foreign asset position. Our baseline calibration predicts that China should have a net foreign asset position of negative 17 percent of its wealth, in contrast to the small positive position that it has in reality. We show how sensitive this result is to reasonable changes in key parameters.

2
# **3.4. Omitted Variables, Agglomeration and Endogeneity**

A potential concern is that the decentralization decisions may be affected by particular circumstances faced by both the original oversight government and, in the case of decentralization, the final oversight government at lower level. In such cases, the estimated effect of distance may only reflect the impact of omitted local economic environment. We thus, for all three subsamples of central, provincial, and municipal SOEs, include fiscal revenue per capita, GDP per capita, and fiscal autonomy (i.e., the ratio of fiscal revenue to fiscal expenditure in the firm’s county)—for both the original oversight government and the government level immediately below it.<sup>15</sup> The results are shown in Table 7.<sup>16</sup> These additional oversight control variables barely matter in general, and the results on the distance are similar to those in Table 4. Omitted variables related to fiscal circumstances of the governments thus cannot explain the distance-decentralization link.

In China, political and economic centers tend to overlap. Beijing is not only the political center but also a top (economic) agglomeration center. Similarly, provincial capitals tend to be the largest cities in the provinces. Thus, a natural concern is that the distance to oversight government really measures the distance to major economic agglomeration. _A priori_ there are no strong reasons why proximity to economic centers would matter for whether an SOE should be governed in a decentralized way. Still, this is a relevant concern that we should take seriously.

If this concern is valid, we should expect the distance of an SOE to other major agglomeration centers in the same oversight region to have positive effect on

> 15 For the central SOEs sample, we do not include these additional variables for the original oversight government because there is only one central government, and these additional variables would be perfectly collinear with the year dummies, which we have already controlled for. 16 Since we don’t have fiscal data of prefectural and county governments in 2006 and 2007, regressions in Column 2 and 3 use the sample from 1998 to 2005.

20
private sector remain top priorities for policy. In a recent paper done for “Innovate UK”, the Technology Strategy Board of the UK government, Mazzucato (2014) advocates an activist role for government in supporting innovation in the economy. The paper argues that the government needs to forge strong _public‐private_ partnerships and play an active role (financial and otherwise) in research and innovation in order to support inclusive growth. They argue that government should be proactive on the direction of change, share funding, and share the risks and rewards of innovation. Brynholfssen and McAfee (2014) support federal funding of basic research to complement private sector R&D. Pfeiffer (1997) in his study of unemployment in West and East Germany, contends that measures to support R&D (including financing of it) and increased public investment were good policy choices for supporting employment growth over the medium‐term in West Germany, which had well‐functioning markets and institutions (though not in East Germany, where markets did not function well). In well‐functioning markets, (where resource reallocation is easier, and financial sectors are developed), public investment will complement private investment, public support to research will be more likely to spawn innovation and both will support output growth to bolster labor demand.

These studies highlight a number of areas in which policy will need to come together to provide a holistic response to digitization‐labor interactions. These policies and their impacts, for example a universal basic income, labor subsidies, government financing of entrepreneurship, incentives to provide for basic R&D, among others, remain to be further studied for application in specific country contexts.

# **‐** **<u>Demographics and Technology: Aging and Shrinking Labor versus Labor Saving Technology</u>**

Finally, a word on demographics. Much of the developed world (and even some of the emerging/developing world) faces aging and/or shrinking populations, a phenomenon that has raised concern about how to maintain output growth in many countries. For example, EU studies on demographics and growth highlighted the need to find alternative ways to maintain output and growth 34
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1943; Evans and Leighton, 1990; Storey, 1991 and 1994). The empirical evidence suggesting the important role of job losses in fostering entry is indeed quite robust (see Storey and Jones, 1987; Santarelli, Carree and Verheul, 2009). Using a panel of Italian data, Audretsch and Vivarelli (1995 and 1996) found that job losses represent an important ‘push factor’ in spurring new firm formation at the regional level (together with other factors such as the local industrial structure and the presence of agglomeration and external economies). At the end of the ’90s, in the UK the incidence of people starting a firm not because of a market opportunity but just because they had no better choice was about 22% (see Small Business Service, 2001, p.6). Likewise, unemployment has been found to be one of the most important determinants of ‘latent’ entrepreneurship in the stagnating Japanese economy of the second half of the ’90s (see Masuda, 2006)<sup>13</sup> .

Thus entry may be determined by a set of different environmental factors including some ‘progressive’ determinants such as profitability and promising technological opportunities, but also ‘regressive’ determinants such as low wages and the actual condition of being (or the fear of becoming) unemployed. In determining new firm formation, these environmental drivers interact with the potential entrepreneur’s personal traits (see next section).

# **3.2** **<u>The personal characteristics of the entrepreneur</u>**

New firm founders differ with regard to characteristics such as previous work experience, family tradition, financial status, personal motivation. To start with, the founder of a new firm is heavily influenced by his/her own background, with particular reference to his/her previous job experience; as already discussed in the previous section, on the one hand the importance of previous job experience explains sectoral inertia in entrepreneurship, while on the other the loss of (or the fear of losing) the previous job may trigger the start-up of a

> 13 In a series of my previous studies using different Italian datasets (Vivarelli and Audretsch, 1998; Arrighetti and Vivarelli, 1999; Vivarelli, 2004), the state of actual unemployment or an impending state of unemployment were never found to be a top crucial motivation in determining the decision to start a new business. However, although rather low in the average rankings, the motivation ‘escape from unemployment’ emerged as being quite important in about 15-20% of the examined cases (see also Thurik _et al_ ., 2008, for a study where the role of unemployment in fostering start-ups and the possible job creation effect of new firms are simultaneously considered).

11
depending on their existing level of performance. The paper assesses whether program impact was different among high performing schools that may have had to exert less effort to obtain the performance component of the school grant.

3. **What impact did the additional funds have on student learning in schools that received the performance component?** The paper also compares the impact on learning outcomes between government schools that received the performance grant and schools that did not.

# **_Empirical Strategy_**

In order to explore the first two questions, the paper starts out using a difference-in-differences (DD) approach to compare changes in educational outcomes of Jakarta government schools before and after the announcement of the school grant in 2014 with analogous changes in comparison schools that are not eligible for the grant. To estimate the impact of the introduction of the grant on all eligible schools, changes in UN scores between government schools and non-government schools in Jakarta are compared.<sup>8</sup> The approach is illustrated in Table 4 using the raw average scores for the main comparison groups used in the paper. For example, in 2014, before the introduction of the program, the difference in examination scores between government and non-government junior secondary schools was 4.2 percentage points. After the introduction of the program this raw difference increased to 6.8 percentage points in 2015 and 8.6 percentage points in 2016. The difference in difference from these raw scores suggests that the program may have increased scores in 2015 by approximately 2.6 percentage points and by around 4.4 percentage points in 2016.

# **Table 4: Difference in government-non-government gap in examination scores in Jakarta**

|||Estimating<br>20|impact in<br>15|Estimatin<br>20|g impact in<br>16|
|---|---|---|---|---|---|
||_Pre_|_Post_|_[Post -_|_Post_|_[Post -_|
||_(2014)_|_(2015)_|_Pre]_|_(2016)_|_Pre]_|
|**1. E**|**ffect on all g**|**overnment sch**|**ools**|||
|**a. Primary**||||||
|Jakarta government|70.9|70.2|-0.7|68.1|-2.8|
|Jakarta non-government|72.9|72.7|-0.2|71.6|-1.3|
|[government - non-government]|-2|-2.5|**-0.5**|-3.5|**-1.5**|
|**b. Junior secondary**||||||
|Jakarta government|75.0|77.2|2.2|65.0|-10.0|
|Jakarta non-government|70.8|70.4|-0.4|56.4|-14.4|
|[government - non-government]|4.2|6.8|**2.6**|8.6|**4.4**|

Note: UN scores are expressed as percentages. Figures in bold are the difference in differences of interest.

While Table 4 illustrates a simple comparison of mean scores, a more robust difference-indifferences model outlined in equation (1) is estimated. This is estimated on a sample comprised only of Jakarta schools.

> 8 The DD analyses exclude schools in Jakarta’s Kepulauan Seribu district since this district does not have any nongovernment schools for comparison. Moreover, all schools in Kepulauan Seribu were given an equity-based grant in addition to the performance-based incentive starting in 2015, which may make it difficult to separately identify the impact of the performance component.

10
Table 6 reports the number and percentage of these 366 households whose electricity consumption was in each of the IBT’s seven blocks. We can see that the electricity consumption by only 2% of these households qualified for the lowest block and only 11% for the two lowest blocks. The majority (69%) of these households consumed 100–400 kWh per month, placing them in the third, fourth, or fifth block of the IBT.

**<mark>Table 6. P</mark> ercentage of Sub‐sample of Private Connections in Each Tariff Block** **<u>of the Electricity IBTs (Addis Ababa, 2016)</u>**

||**Electricity**|**Average price**|
|---|---|---|
|**Block No.**|**No. Observations**<br>**Percentage**|<br>**paid by households in**<br>**this block**|
|**1 [0 – 50 KWh]**|8<br>2%|0.013|
|**2 [>50 – 100 KWh]**|34<br>9%|0.015|
|**3 [>100 – 200 KWh]**|89<br>24%|0.018|
|**4 [>200 – 300 KWh]**|89<br>24%|0.020|
|**5 [>300 – 400 KWh]**|77<br>21%|0.022|
|**6 [>400 – 500 KWh]**|39<br>11%|0.023|
|**7 [>500 KWh]**|30<br>8%|0.025|
|**Total**|366<br>100%||

Table 7 presents the corresponding information for households’ water use. In contrast to electricity, 50% of the 366 households’ water was in the first (lifeline) block and 91% was in the two lowest blocks. None of these households’ water use was in the fifth, sixth, or seventh block.

18
Looking at Table 1, the differences across samples in most of the variables can be explained in terms of the age-gender composition of the samples, while there are no hints of a heavy selection on unobservables. For instance, it is natural to expect that “sons” and “head/bros” show different characteristics because they differ quite a lot by age, given that individuals in “head/bros” are typically one generation older than in “sons”. The same is true when we compare “siblings” with the “full sample”, because by construction we have removed most heads, all heads’ spouses and other relatives. Thus average age in “siblings” is much lower than in the “full sample”. Compared with the “full sample”, we observe that the percentage of males is much higher in “siblings”, indicating that females are more likely to exit from their original family, to enter in the husband’s family or to establish a new family. Alternatively, males are more likely to marry and stay in their original family or to exit at later ages.

**Table 1 Sample description**

||||Siblings||Full sample|Only sons|
|---|---|---|---|---|---|---|
|||**Sons**|**head/brothers**|**total**|||
|observations||5900|1200|7100|27186|1045|
|age||23.74|32.44|25.20|35.30|25.70|
|age [min-max]||16-57|16-64|16-64|16-64|16-58|
|% male||68.3|74.9|69.4|49.5|64.3|
|years of education||8.31|6.77|8.05|6.58|8.34|
|% reporting chronic<br>and disabilities|diseases|11.1|19.3|12.5|24.8|15.2|
|%<br>reporting<br>bad|general|6.3|11.9|7.2|12.9|8.8|
|health conditions|||||||
|% married||15.62|45.0|20.5|67.0|22.39|
|% employed||55.2|70.3|57.7|49.5|56.26|
|% informal sector||53.0|46.3|51.9|47.2|56.2|
|hours<br>of<br>work|supplied|<br>43.42|45.91|43.91|40.95|42.96|
|(among employed)|||||||
|hourly<br>wage|[pounds]|<br>1.45|1.28|1.41|1.79|1.35|
|(among employed)|||||||
|obs. per household||2.98|2.85|2.96|3.62||
|obs. per household||2-8|2-8|2-8|1-19||
|[min-max]|||||||

A useful benchmark against which to compare “sons” is the sample “only sons”. Their composition in terms of age and gender is similar, so that by comparing them, it is possible to guess if members of “sons” are systematically different in terms of education, health conditions, employment, wage and labor supply. By looking at the summary statistics we observe that the two samples are very similar in terms of education 11