rafmacalaba/datause-encoder-data huggingface dataset:
businesses. Typically, they capture laws on the books that are only part of the story. Beyond the enforcement issue, macro datasets offer little information on how binding are for example, tax administration procedures on the private agents. A simple count of the number of tax procedures or the time it takes to comply with all tax procedures may not portray the full picture as to the sorts of countries that have more burdensome tax administration – it may depend on for example, the opportunity cost of time to the firms. We believe that our micro measure captures not just laws on the books but also their enforcement and how binding they are on the firms. Fourth, recent firm-level surveys such as the one used in the present paper offer a rich set of information about various economic phenomena that is typically not available in macro data. For example, the firm-level data we use contains detailed information about various firm characteristics such as age, size, ownership structure, gender of the owner, years of managerial experience and the quality of the broader business environment as experienced by the firm. Fifth, by utilizing firmlevel data, we are able to control for cross-country differences in for example, the composition of corporate sector and firm-size, which might cause spurious correlation in aggregate regressions.

Another novelty of the present paper is in exploring the possible non-linear effect of country size on the quality of tax administration. Specifically, we check if the strength of the proposed relationship between country size and tax administration is the same or does it vary across small vs. large countries. While there is little theoretical work on the issue, it is entirely possible that there could be increasing or decreasing returns to country-size. For example, if being large leads to more complicated tax rules and more wieldy tax administration (congestion costs), this effect may kick in only after a critical country size is reached or it may cease to matter at the margin once a country crosses a critical level in size. We treat the possibility of a non-linear effect of country size on tax administration as a purely empirical issue.

5
87 Another imporint policy issue related to th increase in petroleum product price li1he relaonsp bewem domKtc and border prices. Figue 7.2 shows ihe relationship betwom the domesic and border prices for diesel during the _1980s._ _Table 7.2: 7hrkey: Changes in the Production ofSelected Industrial Commodities (percen)_

||1986|1987|1933|1989|1990|
|---|---|---|---|---|---|
|Totalm<br>g|10.9|10.7|0.9|2.1|9.5|
|Food, bevrages, tobacco|-0.6|3.5|4.5|6.7|6.1|
|Textilelotn,la|11.9|8.9|1.8|3.2|2.3|
|Frsyproducb|10.1|4.4|-2.2|2.0|17.4|
|Paperandpriutiag|6.7|14.1|-7.3|2.6|15.3|
|Czicals,peroleum|12.4|_15.1_|<br>2.6|0.4|3.3|
|Soilpructs|16.3|12.9|6.1|4.7|3.8|
|Basic metals|18.5|12.1|-0.2|1.2|16.8|
|Machiny<br>ad anspot<br>equpment|10.9|_8.5_|-6.7|-1.2|31.8|
|Other<br>mauIcurnLna||11.4|<br>-17.4|72.9|34.3|

**_u_ OECDD 5coio** a **Syb nTui,. 1991.9**

_As_ figure 7.2 shows, domesic diesel prices during 1981-85 were a bit over te border pnices. Th price anges duig Ihe second halfofthe 1980s resulted in domestic prices dth were substanially higir an te border pnrces, thereby mobiliing much-needed rvenue for the govemment

**_Electrlcly_** _Price Change 1988:_ **_lhe_** _Household Sector_

Ihe mot mportant smr of comercil energ for Turkih households are coal ad decrily, fowed by LPG and keIsn 1be1 h of co-cial en expendibia for Mhe sn imeg rgms is bighr for wban households as compared to rural households As exected. the share of enery **:xpenditures fals** income **increases,** both for **rura** and urban **areas.**
with a large margin of error. Our preschool interaction is only significant at 15% (t-statistic is 1.61) in the larger sample (column 3). One additional preschool per 1,000 children appears to increase the number of female workers by 1 percent. The coefficient remains positive but becomes instead close to zero in the restricted TFP sample (column 4).

Second, we corroborate the finding that plants are more able to tap into female workers’ skills by using the data from SAKERNAS. We use repeated cross-section, spanning the years from 2007 to 2014, and estimate individual-level regression: where yidt is a labor market outcome for individual _i_ in district _d_ at time _t._ We analyze three binary outcomes: employment in manufacturing, employment in services and wage employment. Femalei is a dummy variable equal to 1 if the respondent is a woman, and Preschooldt is defined based on PODES data: number of preschools per child in district _d_ at time _t._ Xidt is a vector of time-variant variables, which include education, experience, and experience squared. γdt is a district-time fixed effect.

While we cannot establish a causal impact, we can explore associations between preschool availability and women’s employment. Table 10 shows that being a woman is negatively associated with employment in manufacturing, services and wage employment. However, the availability of preschools in the district appears to reduce this gender penalty. The coefficient on interaction between Femalei and our measure of preschool density is positive and strongly significant. An increase in one standard deviation (0.003 additional preschool per child) increases the likelihood of employment in manufacturing by 0.27 percentage points more for women.

We also limit the sample to women, and check whether preschool availability affects women’s employment differently, depending on the presence of children under 10 in the household.<sup>7</sup> We estimate:

> 7 SAKERNAS only establishes the presence of children in the household, not the relationship with female respondents. It also cannot be used to limit the children’s age to preschool eligibility age: under 10 is the best available proxy afforded by our data.

17
and 5 based on the clustering of standard errors at the area level. In this case, population density continues to remain significantly positive at the 1 percent level even after controlling for both the share of working-age population who possess higher education and market access.

Table 6: Results of regressions on the determinants of underlying productivity variations based on the single-stage approach<sup>a</sup>

|Nominal hourly wage (ln)|1|2|3|4|5|
|---|---|---|---|---|---|
|Population|0.057***|0.024***|0.012**|0.035***|0.017***|
|density (ln)||||||
|Average years of<br>schooling (ln)||0.636***|0.605***|||
|% of WAP with||||0.015***|0.014***|
|higher education||||||
|Market access (ln)|||0.013***||0.016***|
|Mean air temperature (ln)|0.013|0.018|0.02|0.018|0.022|
|Terrain ruggedness (ln)|0|0.002|0.006|-0.005|0|
|Total precipitation (ln)|-0.044***|-0.027***|-0.025***|-0.035***|-0.033***|
|Constant|-2.02***|-3.41***|-3.49***|-2.09***|-2.27***|
|n|4,000,142|4,000,142|3,766,690|4,000,142|3,766,690|
|_R_<sup>2</sup>|0.337|0.346|0.349|0.343|0.346|
|Adj. _R_<sup>2</sup>|0.337|0.346|0.349|0.343|0.346|

> a _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1. In all columns, country effects have been controlled for and standard errors have been clustered by location. In all columns, the dependent variable is the worker’s nominal wage, as shown in equation 6. Controls include observable worker characteristics within the broad sample (all wage/salary employees aged from 14 to 65 years old) and survey-year fixed effects. Worker characteristics include age, age-squared, marital status, gender, and years of schooling. We also include interactions between the worker characteristics and country fixed effects. The source of the population data is the Gridded Population of the World (GPW), v4.

# **4.2.1 Country heterogeneity**

The above results on the relative importance of agglomeration economies, human capital externalities, and market access are based on estimates for all 16 LAC countries pooled. However, there also exists considerable heterogeneity in estimated effects across the individual countries. Figure 5 shows the summarized coefficients for the 2nd stage regression when equation 5 is estimated separately for each country.<sup>34</sup> The coefficient reported is obtained from estimating equation 5 and the 95% confidence intervals are obtained from the coefficient distribution. There is a large difference in the magnitude of the estimated elasticities with respect to density, average years of schooling and market access (notice the figures use different scales for the vertical axes).

> 34Figure 5 does not show results for all 16 countries in our overall sample because of extremely wide confidence intervals or, as in the case of Panama, a lack of sufficient observations. None of the omitted coefficients is statistically significant. The points estimates are shown for all countries in table A3.

21
<mark>Kahn, Matthew E. "Demographic change and the demand for environmental regulation."</mark> _<mark>Journal of Policy Analysis and Management: The Journal of the Association for Public Policy Analysis and Management</mark>_ <mark>21, no. 1 (2002): 45-62.</mark> <mark>Kahn, Matthew E. "Environmental disasters as risk regulation catalysts? The role of Bhopal, Chernobyl, Exxon Valdez, Love Canal, and Three Mile Island in shaping US environmental law."</mark> _<mark>Journal of Risk and Uncertainty</mark>_ <mark>35, no. 1 (2007): 17-43.</mark> <mark>Kahn, Matthew E. "The death toll from natural disasters: the role of income, geography, and institutions."</mark> _<mark>Review of economics and statistics</mark>_ <mark>87, no. 2 (2005): 271-284.</mark> <mark>Kahn, Matthew E., and Matthew J. Kotchen. "Business cycle effects on concern about climate change: the chilling effect of recession."</mark> _<mark>Climate Change Economics</mark>_ <mark>2, no. 03 (2011): 257-273.</mark> <mark>Kleinnijenhuis, Alissa, Tobias Adrian, and Patrick Bolton.</mark> _<mark>The Great Carbon Arbitrage</mark>_ <mark>. No. 2022-07. Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, 2022.</mark> <mark>Knittel, Christopher R., and Shinsuke Tanaka.</mark> _<mark>Driving behavior and the price of gasoline: evidence from fueling-level micro data</mark>_ <mark>. No. w26488. National Bureau of Economic Research, 2019.</mark> <mark>Metcalf, Glibert.</mark> _<mark>An equitable tax reform to address global climate change</mark>_ <mark>. Discussion Paper 2007–12 (The Hamilton Project, The Brookings Institution 2007), 2007.</mark> <mark>Metcalf, Gilbert E. "On the economics of a carbon tax for the United States."</mark> _<mark>Brookings Papers on Economic Activity</mark>_ <mark>2019, no. 1 (2019): 405-484.</mark> <mark>Metcalf, Gilbert E., and James H. Stock. "Measuring the macroeconomic impact of carbon taxes." In</mark> _<mark>AEA Papers and Proceedings</mark>_ <mark>, vol. 110, pp. 101-06. 2020.</mark> <mark>Moran, Daniel, Peter-Paul Pichler, Heran Zheng, Helene Muri, Jan Klenner, Diogo Kramel, Johannes Többen et al. "Estimating CO 2 Emissions for 108,000 European Cities."</mark> _<mark>Earth System Science Data Discussions</mark>_ <mark>(2021): 1-23.</mark> <mark>Rausch, Sebastian, Gilbert E. Metcalf, and John M. Reilly. "Distributional impacts of carbon pricing: A general equilibrium approach with micro-data for households."</mark> _<mark>Energy economics</mark>_ <mark>33 (2011): S20-S33.</mark> <mark>Reiss, Peter C., and Matthew W. White. "Household electricity demand, revisited."</mark> _<mark>The Review of Economic Studies</mark>_ <mark>72, no. 3 (2005): 853-883.</mark> 19
household receiving the transfer or subsidy (urban-rural).<sup>48</sup> For example, the Más Familias en Acción (MFA) program, one of the programs with the greatest number of beneficiaries, is the one with the greatest impact on reducing inequality and poverty (see table 1 in Annex A), although this effect is concentrated in rural areas. Thus, while the coefficient of the contribution to the reduction of inequality is 0.004 for urban areas, it rises up to 0.019 for rural areas. Similarly, it is found that the contribution in the reduction of extreme poverty of this program is 0.0199 in rural areas, while in urban areas, it barely rises to 0.0032. On the other hand, transfers delivered under the Colombia Mayor program for the elderly are the most progressive as shown by the Kakwani coefficient (Figure V-7).

<!-- Start of picture text --> FIGURE V-7: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF DIRECT TRANSFERS<br>(FROM MARKET INCOME + PENSIONS TO CONSUMABLE INCOME)<br>0.9000 0.0080<br>0.8000 0.0070<br>0.7000 0.0060<br>0.6000<br>0.0050<br>0.5000<br>0.0040<br>0.4000<br>0.0030<br>0.3000<br>0.2000 0.0020<br>0.1000 0.0010<br>0.0000 0.0000<br>Kakwani Coefficient Marginal Contribution<br>Source: World Bank Group staff estimates based on ENPH 2017<br>Kakwani Coefficient Redistributive effect<br>Regressive <---------> Progressive Later Life Unequalizing <---------> Equalizing<br>Early childhood School Meals Program Unemployment subsidy Más Familias en Acción Familias en su tierra Jóvenes en Acción Victimization transfer<br><!-- End of picture text --> Subsidies for utilities, for their part, are progressive, as are the contributions made by households in strata 5 and 6. So, on the one hand, subsidies help to reduce inequality, while on the other, contributions redound to its increase (see table 1, Annex A). Finally, it merits noting that subsidies contribute to poverty reduction, given that the contribution coefficient for poverty reduction is positive, while, as expected, the contributions appear not to affect the reduction of poverty. (see table 1, annex A).

When comparing the distributional effects of direct transfers and subsidies in Colombia, with other countries in the region, it is found that, although direct transfers in Colombia are not as progressive as in other countries of the region, the social programs that provide direct transfers to Colombian households are the ones that most help to reduce inequality. As far as subsidies are concerned, it is observed that, in Colombia, subsidies are highly progressive, compared to the region, and contribute more to the reduction of inequality. However, this result must be read very carefully, given that the lack of information made it impossible to include fuel subsidies.

> 48 The authors of this paper intend to produce a document in which the differences in the incidence of spending and taxes between urban-rural areas, and between different regions (departments) of the country are explored in greater detail.

38
**Figure 5: Government debt and sovereign bank exposures in EMDEs**

A.Government debt (% GDP) 2017 vs. Banks’ exposures to the sovereign in EMDEs (% GDP) 2016 <!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>0 10 20 30 40 50 60 70<br>Banks' exposures to the sovereign (% GDP)<br>General government gross debt (% GDP)<br><!-- End of picture text -->

- B. Government debt (% GDP) 2017 vs. Holdings of government securities by banks (% Total assets) 2016

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>0 5 10 15 20 25 30 35 40 45 50<br>Holdings of government securities by banks (% assets)<br>General government gross debt (% GDP)<br><!-- End of picture text --> Sources: International Financial Statistics, IMF; Fitch Connect. Own calculations.

47
(Schofield, et al. 1961). In contrast, the Colombian study found no difference in neonatal tetanus mortality rates between births whose mothers received 2 versus 3 tetanus injections; both groups experienced no recorded tetanus cases or deaths during the study period of more than fcir years (Newell, et al. 1966). Finally, data in the Matlab study by Rahman, et al. (1982) allow the comparison of the following 4-14 day mortality rates:

|Tetanus<br>Immunization Status|4-14 Day Mortality<br>Rate (per 1000)|(N)|
|---|---|---|
|2 doses (1978-79)|10.7|(934)|
|2 doses (1978-79), 1 dose|(1974)<br>15.6|(64)|
|2 doses (1978-79), 2 doses|(1974)<br>7.0|(142)|

Although the number of cases is small, the results provide no evidence of a consistent further reduction in 4-14 day mortality rates with either 3 or 4 doses of tetanus toxoid, relative to the group who received only two injections while pregnant in 1978-79, during the interval of the study period.

Further corroboration of the Matlab findings through serological studies would be desirable, particularly with respect to the effects of a single dose of tetanus toxoid, given the uncertainties which persist. One limitation common to all of these studies has been the use of 4-14 day mortality as an indicator of tetanus mortality. While most neonatal tetanus deaths are known to be clustered in this period, recent work in Matlab has shown that tetanus may account for only roughly half of all deaths during this age period (Koenig, et al. 1991a), leading to considerable imprecision in this measure as an indicator of tetanus-related mortality. It should also be recognized thesu studies demonstrate significant reductions in, but not necessarily the elimination of, neonatal deaths due to tetanus-- the stated objective of the Expanded Program on Immunization, and one which might lead to quite different immunization strategies. The recent findings from Matlab nevertheless provide strong evidence that current maternal tetanus immunization strategies may confer significant levels of protection for a much more extended period than has previously been believed. A single injection of tetanus toxoid may also confer a significant degree of protection against tetanus for at least several years, while two injections may provide significant protection throughout much of a woman's reproductive period.

28
**FIGURE 7: Housing Stock Level in Selected Countries, 1991**

<!-- Start of picture text --> 450/<br>400 350 * - f _ _ _ _<br>300 * f<br>250 * * * * * __<br>200 * * * f<br>150 fl * * * * f -<br>1001 * * l<br>50 *l * f<br>0<br>Estonia Hungary Bulgaria CSFR Romania Poland Russia<br><!-- End of picture text --> _Source. UN-ECE, Annual Bulletn of Housingj & Building Statistics, November 1994_

# **_3.2 Housing is Caught Between Plan and Market_**

Today, housing in socialist economies is caught between plan and market. Managing the transition toward markets requires the simultaneous pursuit of two very distinct set of policies which should match the dualistic structure of the housing system. One set of policies is needed to privatize the large inefficient stock of public housing, and the other to encourage the rapid growth of the very small new private sector.

So far, the privatization of the public stock has been slowed by several factors. _First_ , privatization has been complicated by land property restitution policies and inadequate or unclear administrative processes. There was a contradiction between the profound desire for a return to a legally based civil society and the use of rough-andready procedures to return property to former owners. _Second,_ technical problems are also serious. Socialist housing systems have uniformly poor land registration and cadastre. The concept of real estate property combining land and buildings has to be recreated -- or, in Russia, understood for the first time. Much of housing privatization, especially as it is practiced in many republics of the former Soviet Union is merely the privatization of apartments units to their occupants without clear rules about the ownership and maintenance of the public spaces, and no delineation nor registration of land boundaries. Hence, the ubiquitous preoccupation with condominium laws and their practical implementation. A _third_ impediment to rapid privatization is the inability of the various public owners of housing to achieve full cost recovery of housing maintenance costs -- without speaking of capital costs which are typically ignored and treated as sunk costs. Under such conditions, _why would occupants want to buy their units when_ **_it_** _is so much cheaper to rent than to own?_ The implicit rent windfall is used for the purchase of

**I I**
16

# **Comparison of Moscow trends with Yaroslavl**

Of considerable interest for comparison with the privatization situation in Moscow are the findings of a survey conducted in 1993 in Yaroslavl, a historical city and major industrial center with a population of 628,O00.9 The survey revealed much the same trends as in Moscow. I'he rates of housing privatization varied substantially by location from the city center, and a comparison of the age of dwellers living in privatized and in separate, state-owned apartments showed that an overwhelming majority of privatizers are older tenants.

# **1.2.2. The privatization processes in provincial Russian towns**

The findings of sociological surveys conducted in late 1996 in Vladimir, an oblast center, and in Gorodets, a small town in Nizhny Novgorod oblast, permit an analysis of the privatization process in provincial locations that is analagous to the analysis for Moscow described above.

In 1992-1993, Moscow was the national leader in the proportion of privatized/already private housing. By late 1996, the share of privately-owned housing in Vladimir caught up with that in Moscow, and the share in Gorodets exceeded the Moscow rate by 1.5 times, constituting 48% and 66% respectively (Table 1.12). This can be explained, in part, by the presence of private single-family "houses there long before privatization began (in Vladimir, 6% and in Gorodets, fully one-third of the housing stock) l O. At the same time, the privatization rate of the multi-unit housing stock that used to belong to the state and to enterprises also exceeded the rate in Moscow, rising to 47% in Vladimir and to 50% in Gorodets (Table 1.13). The data on the privatization rate of multi-unit housing stock reflects the role of quality and location of housing in the households' readiness to privatize.

**Table 1.12.** Vladimir and Gorodets, housing stock byt.e of ownership,,1996 **(%).**

|NType<br>of housing|Vladimir|Gorodets|
|---|---|---|
|Total privately owned|48|66|
|o/w: Privatized housing|42|33|
|o/w: Already private single-family houses|6|33|
|State & municipal|52|35|
|Total|100|100|

Source: Vladimir and Gorodets Longitudinal Household Survey, December 1996.

**Table 1.13.** Vladimir and Gorodets, share of private housing by type of building, 1996 __%/ of total_in each grou)

|Type of**housing**|Vladimir|Gorodets|
|---|---|---|
|Separate apartments, average|47|50|
|of which:|||
|Old low-rise stock|43|36|
|Khrushchev-era buildings|62|64|
|Modem brick buildings|47|50|
|Modem panel buildings|38|60|
|Old single-family private houses|100|100|
|Average|48|66|

Source: Vladimir and Gorodets Longitudinal Household Survey, December, 1996.

In Vladimir, the quality factor proved insignificant: even in the worst dwelling category-the old low-rise apartment stock-43% was privatized. Location was more important: the rate of apartment privatization

> 9 Lehmann and Ruble (1994). Tihe sample contained 1,430 respondents.

'<sup>0</sup> In most Russian towns, the share of such privately.owned houses is higher than in Vladimir and lower than in Gorodets.