rafmacalaba/datause-encoder-data huggingface dataset:
- <u>Table 5B: Monthly median earnings for workers with post secondary education by occupation group</u>

|||||<br>Other wor|<br>kers with p|<br>ost-secondar<br>Services|<br>y school educat<br>Skilled|ion<br>Cf|Mhi|El|
|---|---|---|---|---|---|---|---|---|---|---|
||Teachers|Managers|Professionals|Technicians|Clerks|and sales<br>workers|agricultural<br>workers|rat<br>workers|acne<br>operators|ementary<br>occupations|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Burkina Faso|756|_1,152_|1,508|928|673|255|_139_|_255_|-|-|
|Côte d'Ivoire|852|636|627|572|763|297|222|220|475|191|
|DR Congo|109|136|145|130|182|127|167|170|245|200|
|The Gambia|286|581|362|408|328|265|-|281|_235_|_174_|
|Ghana|778|1,111|889|778|686|378|_249_|417|667|428|
|Liberia|231|648|498|608|_555_|393|_648_|_278_|_200_|_197_|
|Malawi|330|1,652|751|-|713|520|_419_|419|_384_|_118_|
|Namibia|2,532|3,978|2,893|2,210|1,266|1,422|_4,069_|1,686|1,655|_814_|
|Niger|374|_2,133_|1,444|1,304|800|551|_946_|_944_|-|-|
|Nigeria|483|796|828|598|525|_443_|_667_|828|_345_|374|
|Senegal|862|1,724|1,293|948|862|733|_1,293_|_754_|_216_|_1,185_|
|Sierra Leone|350|834|489|501|417|381|_1,669_|445|_620_|_195_|
|Tanzania|831|1,288|1,288|905|966|857|-|825|_805_|-|
|Uganda|336|541|494|494|395|355|_197_|415|-|276|
|Zambia|1,717|1,725|1,789|1,646|1,302|_537_|_1,413_|1,673|1,073|1,285|
|**Average **|**722**|**1,262**|**1,020**|**859**|**696**|**501**|**931**|**641**|**577**|**453**|

Notes: All the earnings are in PPP ($,2011). Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. Other wage workers include respondents who self-identified as a wage worker in other corresponding occupation groups; university faculty, special education teachers and other teachers were excluded from the group of other wage workers. ISCO-08 group definitions are used to classify occupation groups (ILO, 2012). Elementary occupations involve the performance of simple and routine tasks which may require the use of hand-held tools and considerable physical effort. Earnings in _italics_ are estimated based on fewer than 10 observations. Sample only includes workers with post-secondary education. The top and bottom 1% earnings are trimmed. Source: Surveys listed in Table 1.

38
# **Table 4. Income and Other Risk-Coping Mechanisms (Daily Shocks)**

||(1)<br>Total Income<br>from Sex Work|(2)<br>Total<br>Income|(3)<br>Total<br>Expend.|(4)<br>Medical<br>Expend.|(5)<br>Net Gifts + Loans<br>Friends &<br>Relatives|
|---|---|---|---|---|---|
|Mean of Dependent Variable<sup>1</sup>|663.34|751.86|429.11|13.23|-18.40|
|**Panel A. Household Sickness**||||||
|**_Household Sickness_**||||||
|Somebody in Household (other than|37.45|45.57|121.92|23.92|-11.19|
|respondent) Sick Today|(28.35)|(28.47)|(24.07)***|(4.81)***|(12.50)|
|Somebody in Household (other than|36.72|39.52|50.19|6.98|5.35|
|respondent) Sick Yesterday|(26.81)|(28.24)|(21.54)**|(3.45)**|(9.08)|
|Somebody in Household Sick|-11.71|-17.77|-140.15|-12.43|0.28|
|Today * Sick Yesterday|(40.21)|(40.63)|(31.71)***|(6.66)*|(17.61)|
|**_Own Sickness_**||||||
|Respondent Sick Today|-81.84|-81.73|103.59|43.08|-16.91|
||(25.46)***|(26.88)***|(22.07)***|(5.69)***|(12.04)|
|Respondent Sick Yesterday|27.18|23.04|46.67|6.75|-17.97|
||(24.18)|(24.90)|(18.05)**|(2.87)**|(8.98)**|
|Respondent Sick Today *|-46.17|-47.48|-120.91|-28.75|27.59|
|Respondent Sick Yesterday|(37.63)|(40.92)|(30.23)***|(6.32)***|(14.59)*|
|_p_-value for sum of household health|0.01***|0.01***|0.07***|0.001***|0.46|
|shocks = 0||||||
|_p_-value for sum of own health<br>shocks = 0|0.001***|0.001***|0.13|0.001***|0.29|
|Observations|11148|11148|11148|11148|11148|
|Number of Women|192|192|192|192|192|
|**Panel B. Death of Friend or Relative**||||||
|Death of a Friend or Relative Today|-65.74|-62.64|464.95|7.12|3.86|
||(67.20)|(68.78)|(69.85)***|(9.56)|(21.45)|
|Death of a Friend or Relative Yesterday|-25.68|-24.92|37.93|-1.99|13.69|
||(48.04)|(48.88)|(39.36)|(5.94)|(17.81)|
|Death of a Friend or Relative Today *|-262.18|-256.64|0.55|-8.27|12.14|
|Death of a Friend or Relative Yesterday|(160.86)|(167.54)|(154.90)|(17.37)|(34.49)|
|_p_-value for sum of shocks = 0|0.03***|0.05***|0.001***|0.82|0.19|
|Observations|7385|7385|7385|7385|7385|
|Number of Women|190|190|190|190|190|

Note: All regressions are fixed effects regressions (at the daily level) with controls for the day of the week and the month of the year. Standard errors (clustered at the individual level) in parentheses. Sickness is an indicator variable equal to 1 if respondent reports a cough, fever, malaria, typhoid, diarrhea, cuts, burns, or other illnesses. Transfers are negative for inflows and positive for outflows. Exchange rate was roughly 70 Kenyan shillings to US $1 during the sample period.

1Mean of Dependent variable is mean when all shocks equal 0.

* significant at 10%; ** significant at 5%; *** significant at 1%
assumed to facilitate cross-border services trade (the number of secure Internet servers, fixedline teledensity, and the number of mobile subscriptions per 100 inhabitants, all taken from the WDI). Finally, we include sector and year fixed effects ( _ν_<sup>_k_</sup> and _μt_ , respectively) in order to capture the fact that the border estimates are identified relative to an average border<sup> ̄</sup> _bt_ . The change in the overall fit of the model when we move from the perfect-fit specification of column (1) in Table 6 to the specification described by equation (18) will be informative about how well these proxies do in terms of explaining the variability of border effects and in predicting internal trade.

Our main findings are presented in column (2) of Table 6. With 86% of variation explained the model fit is fairly high.<sup>23</sup> Hence, the observable characteristics do well in controlling for unobservable internal trade costs ( _tii_ ) and border barriers ( _bi_ ). In terms of predicted signs, factors that increase internal trade costs ( _Wi_ variables) will lead to lower internal trade, so we expect a negative relationship. Factors that increase the border barrier ( _Zi,t_ variables) would, _ceteris paribus_ , be associated with higher internal trade so we expect a positive coefficient.

The set of explanatory variables fits these priors very well; for instance, internal distance raises trade costs and thus comes in negative whereas business-friendly regulations lower trade costs and therefore exhibit a positive coefficient. Good digital infrastructure, in turn, reduces the deflective force of border barriers and leads to lower internal trade, thus coefficients of these variables generally exhibit a negative sign.

In column (3) we also consider the OECD’s Product Market Regulation (PMR) indicator, which has been widely used and enables us to account for the effects on trade flows of domestic regulatory barriers in service sectors. Consistent with expectations, we find that higher PMR values are associated with lower internal trade. Indeed, the negative coefficient on the PMR indicator is driven by the PMR subindex that captures (i) more complex regulatory procedures, (ii) administrative burdens and (iii) barriers in network sectors, all of which are relevant for internal trade costs. As such, we think that these results offer important

> 23The majority of explanatory power is in fact due to observable country characteristics, as sector and year fixed effects alone explain only 28.7% of the variation in log internal trade.

27
3 The paper is divided into three parts that deal, respectively, with inflation, budget deficits and devaluations. In the first phrt the recent literature on the theory of inflation is .eviewed, and time series and crosscountry data are used to investigate the validity of a number of recent theoretical propositions. We report results that support the view that variables related to political factors help explain cross-country differentials in the inflation tax. The second part of the paper reviews the theory and th avidence on government budget deficits, and government borrowing. Here, the central finding is that more unstable political systems tend to be associated with larger borrowing. Finally, the third pa&t of the paper focuses on the stabilization episodes in developing countries. We argue that the failure of some of these episodes is rooted In both political weakness of the government and on political instability of the country. We analyze this proposition empirically by focusing on 39 stabilization periods. Our findings, based on nonparametric techniques, support the hypothesis that stabilizations tend to fail in those nations with more unstable politics. The last section contains brief concluding remarks and proposes new directions for future research. Finally, the paper has two appendices: Appendix 7 deals with tests of the optimal tax theory of Inflation; Appendix II deals with the measurement of political instability.

In this section we systematically analyze the theory and evidence on Inflation for a large group of developing countries. We start by providing a broad analysis of the data and then move to test whether political yariables help explain the observed cross country variability in the inflation tax. More specifically, we investigate the most important empirical
additional details on the survey design and sampling.<sup>15</sup> The harmonized WBES data capture substantial variation in firms’ behavior across countries and time periods spanning a significant range of financial development and inflation, making it possible to empirically identify the relation between inflation and firms’ innovation and financing activities. The data are thus well-suited to test the specific microeconomic mechanism underlying our model (cf. Proposition 1).

We proxy firms’ investment in advanced technologies with their innovation activity. The surveys distinguish between different dimensions of innovation, that is, between product and process innovation. Both are relevant for our analysis as they are expected to be more risky than conventional investments and to increase future productivity, and ultimately growth. We thus define three variables to measure innovation, indicating whether an establishment introduced a new product, a new production process, or both. Specifically, innovation is measured by a dummy variable equal to one if the establishment either introduced a new product or a new production process in the last three years and zero if not; analogous variables are defined for product and process innovation, separately.

The surveys also provide information on establishments’ sources of finance for their working capital expenditure. This is relevant since the idiosyncratic shock associated with the advanced technology (e.g., an unexpected cost overrun when introducing a new product or process) is captured as part of establishments’ working capital expenditure, which is financed via corporate liquidity in the form of internal funds or retained earnings. Specifically, we exploit the following survey question:

- _Over the last fiscal year, please estimate the proportion of this establishment’s working capital that was financed from each of the following sources? Internal funds/Retained earnings; Banks; Other._<sup>16</sup>

The dependence on internal funds/retained earnings to finance working capital expenditure can be regarded as a close counterpart to the requirement of using corporate liquidity holdings to meet unforseen expenditure needs associated with the advanced technology formulated in our

> 15The harmonized WBES data for all countries are available online at `https://www.enterprisesurveys. org/portal/index.aspx\#/library?dataset=Combined` .

> 16The category ‘other’ financing sources includes non-bank financial institutions, supplier and customer credit, friends and relatives.

15
**Table 3: Various Transfers in Relation to Economic Indicators in China, 2004**

|Correlation Coefficient between<br>and:<br>(provincial governments N=31)|Per Capita<br>Equalization<br>Transfers|Per Capita ad hoc<br>Transfers|Per Capita Specific<br>Purpose Transfers|
|---|---|---|---|
|a) Per Capita Provincial Gross<br>Domestic Product|-0.38|-0.36|-0.28|
|b) Per Capita Provincial Revenues|-0.31|-0.28|-0.22|
|c) Per Capita Provincial<br>Expenditures<br>d) Provincial Unemployment Rate|0.18<br>0.10|0.20|0.27|

Data for provincial gross domestic product, provincial population, provincial revenues, provincial expenditures, and provincial unemployment are from _China Statistical Yearbook 2005._

|**Table 4: The Equalization Impact of Central Transfers in China, 2004**|
|---|

|**Provincial Governments (N=31)**|**Mean**<br>**(per capita yuan)**|**Weighted Gini Index**|
|---|---|---|
|Provincial Revenues|904|0.365|
|+ The Equalization Grant<br>+ Ad Hoc Grants|906<br>1153|0.351<br>0.283|
|+ Specific Purpose Transfers|1070|0.295|
|Notes: The weighted Gini index, which weights each d<br>showed below:|ifference of per capita revenue by resp|ective population is calculated as|

_R_ is the national mean.<sup>_p_</sup> _i_<sup>and</sup><sup>_p_</sup> _j_<sup>are the population of province i and j respectively. P is the national population, and n is the</sup> _<u>pi</u>_ number of provinces. G varies from 0 for perfect equality to (1) for perfect inequality. _p_

# **_Other Ad Hoc Grants_**

Looking at the relation between the ad hoc grants and selected economic indicators, we find that in general the grants bear a negative correlation with per capita provincial gross domestic product, and per capita provincial revenues (Table 3). Further, Gini coefficients of inequality reported in Table 4 suggest that the grants have an equalizing impact on 17
imported rice is estimated at Liberian $6.5 billion (about $100 million at the current exchange rate of Liberian $62 per US$), while that of locally produced rice is estimated at Liberian $ 4.7 billion (this includes the imputed value of locally produced rice for auto-consumption). In total, rice thus accounts for Liberian $10.2 billion in total consumption, a figure that can be compared to the total food consumption in table 3 estimated at Liberian $30.2 billion.

- The total value of rice imports in 2007, estimated in the survey at about US$ 100 million, is probably an overestimation of true imports, which is not surprising given that the 2007 CWIQ survey tends to overestimate consumption a bit (Backiny-Yetna et al., 2007).  At the same time, the order of magnitude of the estimation of imports is not unreasonable, since according to data from the International Monetary Fund, imports of rice were estimated at US$57 million in 2006 (the exchange rate between 2006 and 2007 has not changed dramatically), and it is quite possible that not all rice imports are reported in the government’s official statistics.

- The total production of local rice is estimated at approximately 103,000 tons, which may be on the low side, but is also of an appropriate order of magnitude given that according to table 1, production was estimated at 11,000 tons in 2004, and production has probably not increased dramatically since then.

- Locally produced rice is used mostly for auto-consumption, since only slightly more than one fourth of the locally producer rice is actually purchased.  This finding echoes similar results obtained from the 2006 Comprehensive Food Security and Nutrition Survey.

- The share of rice consumption in terms of the total estimated caloric intake per adult equivalents of households, at 50 percent, is even larger than the share of rice in total consumption, at about a third.  This underscores even more the fundamental role played by rice, including through imports, in the issue of food security in the country. Table 4 provides additional data on rice consumption as estimated in monetary terms

- using the CWIQ 2007 survey.  As expected, locally produced rice is consumed mostly by households in rural areas, and the capital of Monrovia is the area that is the most dependent on imported rice.  At the same time, an overwhelming majority of households outside of the capital also consume imported rice, so that for most households, local production is apparently not large enough to meet their own needs.
15 There are two village level variables besides social capital. First, the median distance of dwellings in the cluster to a market for crops included as a proxy for the market integration of the village'<sup>5</sup> . In addition, a set of dummy variables for six agro-climatic regions of Tanzania are also included to control partially for the economic and agro-climatic diversity of the country.

_Is asocial capital'capital?_ Column 1 of table 3 presents the results of OLS estimation of the partial correlation between a cluster's average household per person expenditures and social capital, controlling for this set of variables. The coefficient on the social capital index is empirically large and moderately statistically significant (p-level .08). Households in villages with higher levels of social capital have significantly higher expenditures.

However, 'social capital" or associational life may simply be a normal consumption good so that richer households consume more, that is, perhaps associational life is not 'capital' but "consumption" consumed more by households with greater income or leisure'<sup>6</sup> . If richer individuals live together then one would tend to find that richer villages are associated with higher village social capital. In the U.S., for example, the average income of neighborhoods would be associated with higher ownership of luxury cars, but this does not imply if poorer is In the above mentioned World Bank study of rural poverty, distance to market was interacted with an index of road quality and produced a strong income effect. In our case we could not replicate the road quality index with our HRDS sample and so used the simpler, but less appealing, measure of distance alone.

16 This argument is weakened by the fact that the social capital index is only partly a measure of associational _activity,_ as it more reflects the nature of groups of which individuals are members which is at least plausibly less related to income that is the magnitude of activity. To use Putnam's illustration, it may well be that richer individuals bowl more but less clear why they should bowl more in groups when they do bowl (Putnam, 1995).
**Table 11: Principal Components Analysis of Level 1 Variables (N=107)**

||Eigen-<br>Sum|Diff-<br>mary Stati|stics||Coeffi|cients for fi|rst five prin|cipal comp|onents|
|---|---|---|---|---|---|---|---|---|---|
||value|erence|Prop.|Variable|Comp1|Comp2|Comp3|Comp4|Comp5|
|Comp 1|**3.86**|1.97|0.23|**SDI variables**||||||
|Comp 2|**1.89**|0.11|0.11|Share of time teaching and learning|0.22|**0.39**|0.15|0.11|-0.28|
|Comp 3|**1.78**|0.17|0.10|Good Teacher Demeanor|0.16|-0.03|0.23|**0.42**|**-0.30**|
|Comp 4|**1.61**|0.37|0.09|Good Pedagogical Practice|0.14|-0.21|0.07|**0.40**|-0.20|
|Comp 5|**1.24**|0.16|0.07|Classroom environment|0.04|-0.03|**0.57**|**-0.39**|-0.12|
|Comp 6|**1.09**|0.09|0.06|Materials and infrastructure|0.08|0.28|0.21|0.13|**0.35**|
|Comp 7|**1.00**|0.25|0.06|Use of materials|0.16|-0.13|0.22|**0.37**|-0.02|
|Comp 8|0.75|0.06|0.04|**Stallings variables**||||||
|Comp 9|0.69|0.06|0.04|Share of time teaching and learning|0.23|**0.48**|0.06|0.01|-0.09|
|Comp 10|0.63|0.04|0.04|Materials and infrastructure|0.14|0.22|0.02|0.05|**0.55**|
|||||Classroom environment|0.08|-0.16|**0.58**|**-0.33**|-0.02|
|||||**CLASS variables**||||||
|||||Emotional Support|**0.41**|-0.17|-0.17|-0.25|-0.04|
|||||Classroom Organisation|0.30|0.26|-0.06|-0.18|0.05|
|||||Instructional Support|**0.41**|-0.19|-0.21|-0.15|-0.10|
|||||Student Engagement|**0.42**|-0.16|-0.21|-0.19|-0.08|
|||||**Teach variables**||||||
|||||Share of time teaching and learning|0.24|0.29|-0.06|0.03|-0.09|
|||||Classroom culture|0.21|-0.12|0.10|0.11|**0.52**|
|||||Instruction|0.26|-0.10|0.08|0.24|0.06|
|||||Socioemotional Skills|0.18|**-0.37**|0.10|0.10|0.21|

Note: Eigen values greater than 1 and component loadings greater than 0.3 (in absolute value) are in bold.

51
– the better off the _comuna_ , the further it is situated to the right in the figure. The figure shows negative bars towards the left (the poorer _comunas_ ) and positive values to the right (the better-off _comunas_ ). Hence, the spatial distribution of social expenditures in Cali was antipoor and perpetuated spatial inequalities. The same result emerged when examining other expenditure categories.

Obviously, the criterion applied here is a very strict one: it implies that the spatial distribution of expenditures would ideally follow the distribution of poverty in the city. For many, especially bulky investments which benefit the whole city, this criterion will not be appropriate. However, an anti-poor bias emerged in all expenditure categories and this needed to be addressed for the city to become a more integrative and equal place for its inhabitants. One reason for the observed anti-poor distribution of city expenditures originated with the formula guiding _comuna_ allocations. The first component of the formula distributed expenditures equally across _comunas_ , irrespective of their population and poverty levels. The second component took into account the fiscal effort of the area, clearly creating a bias against poorer _comunas_ . Only the third component, an assessment of housing needs, worked in favor of poorer areas.

For a select number of programs, we could use the EPSOC survey to compute the distribution of benefits by income group. In the absence of data on individual benefits of education or health services rendered, we made the common, but restrictive, assumption that ‘access’ was equal to ‘benefits’.

Most public health and education expenditures in Cali were progressive. Table 5 reports that the largest share of public expenditures in these sectors benefited the poorest two income quintiles the most. Since the poor were the most intensive users of public primary and secondary education, a higher portion of the benefits of these expenditures flowed to them – and the same held for public health centers and public hospitals. Although we had observed above that the coverage of SISBEN (providing access to subsidized social programs) was relatively small among the poor,<sup>20</sup> benefits derived from being assigned to the system flew in a large proportion to the very poor – 43 percent to the poorest 20 percent of Caleños. Subsidies in higher education, however, went largely to the better-off income groups.

> 20 (7.2%), 18 (7.2 %), 13 through 16 (2.4%), 6 (2.4%) and 7 (2.4%).

> 20 Becerra (1997, p.12) reports that focus group discussions in poor neighborhoods have also criticized SISBEN for its low coverage.

19