rafmacalaba/datause-encoder-data huggingface dataset:
displaced approximately 30 degrees clockwise or counterclockwise.

Gravity trips join a random origin with a destination in a random direction, at a distance that is drawn from a truncated Pareto distribution with shape parameter 1 and support between one kilometers and 250 kilometers. Both commutes and city trips in general have been shown to reflect this distribution in many contexts (Ahlfeldt, Redding, Sturm, and Wolf, 2015, Akbar and Duranton, 2018).

Amenity trips join a random origin with an instance of one of 17 amenities (e.g. shopping malls, schools, train stations) as recorded in Google Places. The particular establishment selected is based on a combination of proximity and “prominence” assigned by Google. The weighting across these amenity types is based on a mapping of amenities to trip purposes whose share we draw from the 2008 us National Household Transportation Survey (nhts) (Couture, Duranton, and Turner, 2018).

Using the sampling scheme above, we simulated 22,661,818 trip instances in Google Maps, covering 1,166,738 locations pairs and, hence, 2,333,476 trips across all cities and strategies, over 40 days between September and November of 2016.<sup>10</sup> For each trip, we record origin, destination, trip type, and length and estimated duration of Google’s recommended route under current traffic conditions (which we sometimes refer to as real-time travel time), as well as the time required for the same route without traffic and with “typical” traffic.<sup>11</sup> Google’s route selection and speed estimates are based on the location and speed of mobile phones using the Android operating system, as well as other phones running Google software, especially Google Maps. Accurate measurement thus requires that drivers are providing information. It is therefore possible that estimates are worse in cities with lower mobile phone penetration. This is unlikely to affect our results. There were 300 million smartphone users in India as of the 4th quarter of 2016.<sup>12</sup> In December of 2015, 71% of 10A further 115,733 trip instances were collected for Bokaro Steel City in December 2017 as the un database initially reported its location incorrectly. However, Bokaro is excluded from all results in section 6. We also describe the data we use for transit and walking trips below.

11While Google Maps does not report how it calculates travel time under regular traffic conditions, it generally provides the same answer for the same trip queried on different week days at the same time but not for the same trip queried at the different times.

12 Source: `http://www.counterpointresearch.com/press_release/indiahandset2016q4analysis/` . While not all smartphones use Android, in the second quarter of 2016, 97% of smartphones shipped in India did. Source: `http://indianexpress.com/article/technology/ googles-android-captured-97-indian-smartphone-market-share-in-q2-2016-report-2957566/` 5
# **Figure A2.1: Index of global prices of agricultural products, 1986-88=100**

<!-- Start of picture text --> 170<br>160<br>150<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>1985 1990 1995 2000 2005 2010<br>Year<br>Index<br><!-- End of picture text --> 66
**Figure 16: Sector specific administrative burdens**

<!-- Start of picture text --> 4.5<br>4.1<br>4.0<br>3.5<br>3.0<br>2.6<br>2.5 2.2<br>1.9 1.9 2.0<br>2.0<br>1.6<br>1.5<br>1.5<br>1.0 0.8<br>0.5<br>0.0<br>Romania EU15 OECD Bulgaria Slovak Hungary Czech MICs Poland<br>Republic Rep.<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, Turkey. Values refer to 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see appendix I.

# **_2.2 State control of economic activity_**

The degree of state control in the business sector has decreased substantially in Bulgaria since 1998. In the aftermath of the 1996-1997 crisis an ambitious program of macroeconomic stabilization and structural reforms placed Bulgaria on the course to EU accession. A crucial element of the reform package was the substantial reduction of the state’s presence in the economy. Between 1998 and 2002, most of the non-infrastructure enterprises and banks were privatized, thus reducing both the size and the scope of the public presence in the business sector. Currently, about 60 percent of all state-owned enterprises (valued at end-1995 prices), and 91 percent of all assets slated for privatization, have been privatized. At the same time, extensive price liberalization reduced the role of the state in many services.

The two measures of state control in the PMR system are public ownership and state involvement in business operations (through price controls and coercive, as opposed to incentive-based, regulation). A look at Figure 17 reveals that Bulgaria still remains quite far from OECD and EU levels with respect to both indicators, although it is on par with the pre-accession CEE (with the exception of the Slovak Republic).

**Figure 17. Public ownership and state involvement in business operation a. Public ownership**

**b. State involvement in business operations**

22
# **3.3 Premia by Foreign Investor Characteristics**

The analysis in the previous section treated foreign firms as homogenous. The literature survey in section 2.1, however, showed that certain types of FDI seem to be more beneficial than others since actual FDI spillovers also depend on foreign firm characteristics. In this section, we therefore split the foreign investors into several groups to investigate if firms with certain characteristics have a larger FDI spillover potential than others.

We estimate the following equation: where subscript _i_ stands for firm, _s_ for the firm’s sector, and _c_ for country. α 0 designates the constant, _Dcs_ country-sector fixed effects, and ε _isc_ the idiosyncratic error term. _FC_ is a vector representing several foreign firm characteristics which take the value of 1 if a foreign investor fulfills a certain characteristic, and 0 otherwise. _potential_ is our measure of FDI spillover potential. Building on the theoretical discussion in section 2.1, we include the foreign investor characteristics shown in Table 5. The summary statistics are presented in Appendix 4.

**Table 5: Foreign Investor Characteristics, Definition**

|**Variable**|**Definition**|
|---|---|
|_own_|A firm’s percentage of foreign ownership|
|_age_fdi_|Number of years since a multinational has started its operations in the host country|
|_tech_|A foreign firm’s technology gap with its leading domestic competitor in the same sector, where 1<br>means “not existent” and 4 means “large”|
|_origin_SSA_|Dummy taking the value of 1 if the largest foreign investor’s region of origin is SSA, and 0 otherwise|
|_origin_Asia_|Dummy taking the value of 1 if the largest foreign investor’s region of origin is Asia (including South<br>Asia) and 0 otherwise|
|_motive_market_|Importance of access to (local and regional) markets, where 1 means “not important” and 4 means<br>“very important”|
|_motive_cost_|Importance of access to reduced labor and non-labor related costs, where 1 means “not important”<br>and 4 means “very important”|
|_motive_res_|Importance of access to raw materials and specific inputs, where 1 means “not important” and 4<br>means “very important”|
|_motive_asset_|Importance of access to skills and technology, where 1 means “not important” and 4 means “very<br>important”|

We apply four FDI spillover potential measures related to a foreign firm’s linkages with and assistance to domestic suppliers, as these are the categories where foreign firms lag behind domestic producers: (i) the percentage of purchased goods and services sourced from domestic 17
# Figure 1. Global factors and oil prices

## A. Global inflation factor

<!-- Start of picture text --> Percent Global inflation factor Average<br>1.5<br>0.5<br>-0.5<br>-1.5<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

## B. Global output growth factor

<!-- Start of picture text --> Percent Global output factor Average<br>6<br>3<br>0<br>-3<br>-6<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

## C. Global interest rate factor

## D. Global oil price growth

<!-- Start of picture text --> Percent Global interest rate factor Average Percent Oil price growth<br>2 50<br>1 25<br>0 0<br>-1 -25<br>-2 -50<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Note: All the global variables are demeaned. Shaded areas indicate global recessions (1975, 1982, 1991, 2009, and 2020). Global factors for G7 inflation, output growth, and interest rates are extracted from detrended national inflation, output growth, and interest rates (1<sup>st</sup> differenced), respectively, using a dynamic factor model. Inflation, output growth, oil price growth rate are based on month over month (seasonally adjusted) series.

24
Table 6: Hours Worked (OLS conditional on working)

||||Work Within|Market Work<br>Within the|<br>Household|||Agricultur|e|No|nagricultura|l activities|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Total Hours|Wage work|the Household|<br>Household|Productio|n Market Work|Wage work|Household|All|Wage work|Househol|d All|
||(1)|(2)|(3)|(4)|(5)|(6)|(1)|(2)|(3)|(4)|(5)|(6)|
|Ages 6-15|||||||||||||
|Rice Price|-11.574<br>[3.246]**|-50.153<br>[23.613]**|-10.857<br>[3.142]**|-11.345<br>[3.532]**|-6.155<br>[2.272]**|-9.959<br>[3.727]**|-44.763<br>[31.040]|-9.493<br>[3.545]**|-7.576<br>[3.686]**|0<br>[0.000]|-11.48<br>[15.531]|-29.44<br>[15.889]*|
|Rice Price*Male|3.88|-19.373|2.989|0.205|2.245|0.582|-19.706|-1.022|-1.628|0|3.269|12.07|
||[2.571]|[28.100]|[2.559]|[3.303]|[1.929]|[3.343]|[41.433]|[3.316]|[3.461]|[0.000]|[16.250]|[10.738]|
|Observations|6078|221|6040|3110|5264|3196|170|2954|3017|54|291|346|
|R-squared|0.39|0.66|0.38|0.38|0.22|0.37|0.73|0.4|0.39|0.91|0.65|0.62|
|Ages 16-24|||||||||||||
|Rice Price|-10.548|20.519|-14.659|-11.31|-3.104|-5.547|18.194|-11.442|-7.729|3.777|-2.012|2.762|
||[4.659]**|[7.869]**|[4.914]**|[4.333]**|[2.116]|[3.888]|[11.220]|[4.520]**|[4.512]*|[12.901]|[10.273]|[7.784]|
|Rice Price*Male|0.916|-10.651|2.568|-1.111|1.583|-2.967|4.067|-2.773|-3.588|-14.527|12.265|-5.94|
||[3.971]|[8.092]|[4.456]|[3.426]|[2.063]|[3.418]|[8.869]|[3.517]|[3.590]|[14.341]|[13.535]|[7.295]|
|Observations|5771|1551|5550|5023|4373|5446|786|4780|4961|782|797|1574|
|R-squared|0.29|0.29|0.31|0.29|0.31|0.27|0.36|0.35|0.32|0.38|0.36|0.29|
|Ages 25-64|||||||||||||
|Rice Price|-8.799|8.858|-12.11|-8.483|-2.371|-5.434|15.374|-9.421|-7.379|-1.117|-1.64|-0.463|
||[4.173]**|[4.781]*|[4.064]**|[3.483]**|[1.679]|[3.533]|[7.000]**|[3.654]**|[3.671]**|[7.581]|[5.653]|[4.270]|
|Rice Price*Male|2.62|-3.913|1.311|-0.793|0.783|1.041|-0.375|0.116|0.062|1.975|-3.167|-1.856|
||[3.153]|[5.502]|[3.282]|[2.590]|[1.591]|[2.470]|[8.679]|[2.716]|[2.850]|[8.110]|[5.526]|[4.405]|
|Observations|13141|3048|12958|12111|11286|12654|1428|11394|11656|1640|2518|4089|
|R-squared|0.28|0.22|0.27|0.25|0.24|0.27|0.33|0.32|0.29|0.28|0.24|0.23|

Note: See comments to table 3 and 5.
there social benefits associated with such gender-focused programs, in particular, through impacts on children's welfare as well as on women's fertility decisions and gender preferences?

The structure of the paper is as follows. In Section 2 we provide both an overview of gender-focused public policy in China and details of the nature of the community program being evaluated. In Section 3 we discuss the survey data used in our analysis and the methodology used to analyse this data. Section 4 presents the results from our empirical analysis. Section 5 concludes.

# 2. **Gender-Focused Public Policy and the Community Program**

In this section we first provide an overview of gender-focused public policy in China and its role in improving the well-being of Chinese women. We then describe the specific community program to be analysed in this paper.

# _2.1_ **_Gender-Focused Public Policy in China_**

As pointed out above, Chinese women have experienced a significant improvement in their well-being compared with women in many other low-income countries. This suggests that important policy insights may be gleaned from improving our understanding of the underlying factors that have contributed to higher women's welfare in China, a country ranked among the poorest countries in the world in the 1990s. To this end, a brief overview of China's history of gender-focused public polices is thus helpful.

The Chinese government has played an active role in implementing and enforcing gender-based public policies in key areas since as early as the 1950s. Specific public polices were designed particularly to: (I) improve women's education, health care, and employment 3One of the key organisations in the central government responsible for formulating population policies and their implementatio **n** ationwide.

6
whereas our chosen counterfactuals measure the costs of smoking in terms of current losses in productivity.

# **3. Data and descriptive statistics**

The data for our analysis come from the fourth round of Albania Living Standard Monitoring Survey (ALSMS) conducted in April-July 2005 by the Albanian Institute of Statistics with the technical support of the World Bank. The ALSMS uses a two-stage stratified clustered sample of 3640 households, and is representative at the national, urban, rural, and regional levels.<sup>8</sup> The ALSMS collects information on the demographic characteristics of the household members, their labor market activities, education, heath characteristics, and the household’s access to social services. The questionnaire also gathers information on individual and household income and consumption expenditures as well as on ownership of assets. A special section of ALSMS collects information about individual smoking behavior including: whether the respondent smokes on a daily basis or is an ex-smoker, age at which the respondent started smoking, number of cigarettes smoked per day, and whether the respondent’s father and mother ever smoked.

For the purpose of the current analysis we restrict our sample to employed primeage adults 25 to 60 years of age. By the age of 25 most of the adults in Albania have completed their education and are actively involved in the labor market. An individual is considered to be employed if he had worked in a permanent job in the week preceding the survey or was temporary absent from his permanent job due to vacation or illness. Our sample excludes employers, self-employed and individuals working in a household farm because the wage data is not reported for this group of respondents. The individual wages were measured as net monthly payments or earnings and bonuses.

Forty percent of males and about 5 percent of females age 25 to 60 reported to have ever smoked; 32 percent of males and less than 4 percent of females in our sample have categorized themselves as current smokers.<sup>9</sup> These numbers are comparable with

> 8 For further information on survey design and sample frame see: http://www.instat.gov.al/

> 9 That low proportion of smoking women may not reflect the actual prevalence of female smoking, as in Albania women are reluctant to admit they smoke because smoking is not seen as being socially desirable or acceptable for females (IMF 2006).

11
important variable as it sheds light on adequate cash flow management, which has direct consequences on the government’s ability to deliver services. These are just a few examples of the types of analyses that are possible. They all have in common however that access to the individual transactions is necessary, as aggregation in the FMIS across line items, spending units or programs hides the necessary level of detail.

This study pursues a very specific purpose: it develops a methodology based on transactions data on (i) how to estimate overall FMIS budget coverage for a given country, and (ii) how to develop an expenditure transactions profile for government. Data for Pakistan and Cambodia were kindly made available from state treasuries and are used illustratively to showcase how such data can be analyzed and interpreted to provide critical information for the expenditure management reform process.

# **2.1. Calculating Budgetary Coverage in FMIS**

Calculating FMIS budget coverage requires the summation of the value of all expenditure transactions routed through the FMIS in a given fiscal year, and subsequently dividing these by the total approved budget reported by government. This is presented in the equation below, where t represents the fiscal year and i the individual transaction.

From this, table 2 can be produced, which is populated by sample figures for illustrative purposes.

_Table 2 Sample Output of FMIS Budget Coverage Estimation_

||**2015**|**2016**|**2017**|
|---|---|---|---|
|Total approved budget|590,131,124|655,701,248|771,413,233|
|Total volumeprocessed through the FMIS|415,895,403|577,632,504|722,040,630|
|Percentageprocessed through the FMIS|70.5%|88.1%|94%|

_Source:_ Authors.

These figures can be calculated for general government as well as provinces and/or other subnational levels of government separately. These then give an idea of the geographic spread in coverage of the FMIS and the distribution of the usage of budgetary resources across the country, allowing the analyst to assess what percentage are spent at the center and what at other levels of government (e.g. provinces).

8
<!-- Start of picture text --> Real I US$<br>0~~~~~~~~~~C0c<br>W ~~3/1/2001 , v<br>3/11/2001<br>3/21/2001 4; 6<br>3/31/2001 m } < 0<br>4/10/2001<br>4120/2001 ii _<br>4/30/2001<br>°°<br>5/10/2001 .r<br>5/20/2001 -<br>5/30/2001 o<br>6/9/2001. ,<br>6/10/2001 00<br>6/29/2001<br>7/9/2001<br>X () C,,il of US$<br>millions of US$<br><!-- End of picture text -->
# Outcome Variable Collection Periods

||**Basline**|**Midline**|**Weekly**|**Endline**|
|---|---|---|---|---|
|**Psychological Well-being**|||||
|PHQ9|X|||X|
|Life Satisfaction Index|X|||X|
|Stress Index|X||X|X|
|Sociability (Total)|X||X|X|
|Sociability (Positive)|X||X|X|
|Self-Worth Index|X|||X|
|Locus of Control|X|||X|
|Allocation Decision Game||X||X|
|Stability Index||X||X|
|Physiological Well-being Index|X|||X|
|**Gender Dynamics**|||||
|Gender Perceptions - Work|X|||X|
|Gender Perceptions - Violence (IPV)|X|||X|
|**Financial Well-being**|||||
|Savings|X||X<sup>_∗_</sup>|X|
|Borrowing|X|||X|
|**Economic Decision Making**|||||
|Risk Preference||X||X|
|Time Preference||X||X|
|**Other Outcomes**|||||
|Cognitive Ability|X||X<sup>_∗_</sup>|X|
|Physical Health|X||X<sup>_∗_</sup>|X|

**Notes:** The “Baseline” survey was conducted with respondents before treatment assignment was revealed. The “Midline” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “Weekly” surveys were conducted after each week of work (if any). The “Endline” survey was conducted after the end of the eight week engagement and all work had ceased.

> _∗_ Physical Health, Savings, and Cognitive Ability are measured differently during the weekly surveys than at baseline or endline.

66
services firms tend to be more reliant on non-technological forms of innovation (Pires et al. (2008), Tether et al. (2007)). In other words, for the same expenditure in innovation, one should expect less innovation outcomes in a regular technological innovation survey that aims at measuring “hard innovations” with questions on new products or services rather than in surveys which also cover more “soft” (non-technological) types of innovation such as organizational changes (Hauknes (1998)). Also, given the fact that expenditure in innovation is still heavily associated with technological innovation one should expect that, _ceteris paribus_ , under the traditional measures of innovation expenditures, services firms should appear to spend less in innovation, relative to manufacturing ones. Fifth, innovation in services tends to be driven by an external technological ‘pull’ – consumer/client led- rather than an internal ‘push’ –science and technology led. This implies that external sources of information, particularly cooperation with clients, should be relatively more important for services firms, while pure science-and-technology inputs (especially R&D) should be less important (Kanerva et al. (2006); Love and Mansury (2007); Nijssen et al. (2006)). Finally, although still important, size appears to be less relevant for innovation in services than for manufacturing (Pires et al. (2008), Sirilli & Evangelista (1998)).

# **(b) Export orientation of service firms**

When it comes to the analysis of the determinants of exports in the service sector and its differences with the manufacturing sector, the literature is much thinner. As far as we know, there is no theoretical analysis of how the particularities of the services sectors lead to different predictions with respect to export status, except by applying the concepts of traditional models based on manufacturing. This is also reflected in the absence of disaggregated micro level data on services exports, especially for developing 7
that is hit with a tariff cut faces a large drop in wages and the wage never recovers. As workers move out of the import-competing sector, labor supply increases in recipient sectors and thus nominal wages tend to decline. With lower prices due to tariff cuts, real wages instead increase. Workers in the nonimport-competing sector unequivocally gain from the trade reform. Workers in the import-competing sector can also benefit from the liberalization because the high volatility of their idiosyncratic shocks combined with rising real wages in other sectors implies higher option values, and this effect can dominate the direct loss from the lower wages in their own sector. This happens in the U.S. simulations (Artuc, Chaudhuri and McLaren, 2010) and in the Argentine simulations (Bet, Brambilla, and Porto, 2011) but not in the Turkish simulations (Artuc and McLaren, 2010).

Given the information provided in these papers, I cannot recover the estimates of the TACs using the formulas from section 2. However, an idea of the magnitude of the costs can be glimpsed with the examination of the evolution of the present discounted values (bottom panel of Figure 3). As discussed, the values for all workers increase due to trade liberalization. However, there is an important difference is the dynamics of values for workers initially in the protected sectors vis-à-vis workers in all the other sectors. Lifetime utilities for workers in the non-manufacturing sectors increase at the announcement of the trade policy and then gradually and slightly decline towards the steady state. Instead, the lifetime utility of initially protected workers increases to a lesser extent at the time of the announcement and then continues to increase towards the steady state. The reason, as explained above is the option value. Note that this implies positive TACs attached to workers in the initially protected sector, but negative TACs for all other workers. This is because of the overshooting of wages in non-protected sectors. From the plots in Figure 3, it appears that the trade adjustment costs borne by manufacturing workers dominate the short-run exceptional gains (which are given by the size of the overshoot in values) in all other sectors. But this is not necessarily true, which raises the possibility of negative adjustment costs.

11