rafmacalaba/datause-encoder-data huggingface dataset:
We treat the 2006 and 2008 household surveys as repeated cross-sections and estimate the following regression specification of the difference-in-difference model for individual outcomes: where _Yijt_ is the HTC outcome of individual or couple _i_ living in facility _j_ ’s catchment area in year _t_ ; _P4Pj_ is a dummy variable that takes value 1 if facility _j_ belongs to Phase I (i.e. started receiving P4P in 2007) and 0 otherwise; α _j_ is a facility fixed effect; γ _2008_ is a fixed effect for 2008; _I2008_ is a dummy variable that takes value 1 if the year of observation is 2008 and 0 otherwise; the _Xkijt_ are individual characteristics; and ε ijt is a zero mean error term. We compute robust standard errors clustered at the district by year level to correct for correlation of the error terms across facilities within districts.

# **4. Results**

Table 5 reports the estimated P4P program impacts on HTC outcomes using the individual as the unit of analysis. We present analyses for the entire sample and then conduct sub-group analyses by marital status. In all of the estimated models, we control for age, education, and household assets and for gender and marital status when relevant. Assets are measured as the value of land, durables in the house, farm animals, farm equipment, and microenterprise equipment.

In column (1) of table 5, we find a positive and statistically significant impact at the 10% level of 6.1 percentage points in the probability to have ever been tested with respect to the comparison group. This represents a 10.6 percent increase over the control group. When we restrict the sample to individuals living in a couple (column 2), we find a positive impact of 10.2 percentage points that is statistically significant at the 5% level, representing a 14.5 percent increase from baseline. However, there is no impact on individuals not currently in a couple regardless of whether they are sexually active or not (columns 3 and 4). Those larger impacts of 11
<!-- Start of picture text --> 4000 4 8000 ==<br>7000<br>“ NNN<br>1600<br><!-- End of picture text --> <!-- Start of picture text --> 1600<br>+<br>1500 3000 =<br>c 1400 c =<br>So8©2500 1<br>a1300 a<br>= £<br>[s}ao°<br>Z1200 = 2000<br>1500<br>1000 |<br>900 1000} ==<br>800<br>1 2 3 4 5 1 2 3 4 5<br>Empirical MM5 Precis MMS5_ Precis Empirical MM5 Precis =MM5_ Precis<br>Data near near future future Data near near future future<br>future — future future —_ future<br><!-- End of picture text -->
> <sup>37</sup>

The omission of the average tenure of leaders may also generate other biases. For example, efforts by elected leaders to suppress political competition through vote fraud, the unbridled use of public funds for political purposes, or through outright legal restrictions on potential challengers, are difficult to observe but potentially influence both the elections variable and policy outcomes. Annex 2 provides evidence of close links between the years of competitive elections and the average tenure of elected leaders. Controls for the average tenure of leaders should effectively capture much of these unobserved anti-democratic political tactics.

Although there are numerous arguments to suggest that unobserved leader effects might bias the elections variable used above, none of these are borne out in practice. A variable controlling for the tenure of individual leaders, the average years in office of the chief executive over the democratic episode in question (from the _Database of Political Institutions_ ) was included in all of the specifications in Tables 2 and 3 . In every case, the estimated coefficient of the elections variable was highly robust. These results are not reported since, for most of the policies under consideration, the average years in office of the executive is insignificant.<sup>18</sup>

# **Conclusion**

The number of continuous years of competitive elections has a significant impact on seven policy areas widely identified as key to economic development. Only one explanation for this finds support in the foregoing tests: where political competitors are less able to make credible promises to voters, policies are distorted in the same fashion that we observe when we compare policy choices across younger and older democracies. Patron-client relationships in society are transported to the political realm with greater facility in such an

> 18 Years in office significantly increases the market share of government newspapers, as well as the rule of law and bureaucratic quality in two specifications, but is otherwise insignificant.
A consistent measurement of firm performance is crucial to our analysis. We use the total factor productivity (TFP) as our outcome of interest. To control for a possible simultaneity bias arising from the endogeneity of a firm‟s input selection, which will exist if a firm responds to productivity shocks unobservable to the econometrician by adjusting its variable input choices, we follow the method proposed by Ackerberg, Caves and Frazer (2006). Ackerberg et al. build on the widely used estimation procedures proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003). Unlike the latter method, their approach allows for more plausible assumptions about the timing of the firm‟s decision regarding input choices and optimization errors.

We use the Ackerberg et al. method to estimate sector-specific production functions and obtain the TFP as the residual from this estimation.<sup>15</sup> We group some smaller industries together in order to facilitate the estimation.<sup>16</sup> Following the advice of Ackerberg et al., we use value added as the dependent variable in the production function. Value added is defined as the sales of firm _i_ in year _t_ less the value of material, services and energy inputs. All components of value added are expressed in real terms. Capital and labor inputs (expressed in real terms) are included as independent variables. Material and services inputs (in real terms) are used to proxy for the productivity shocks.

Nominal output is deflated by a set of wholesale price indices disaggregated at the 2-digit level, while capital inputs are calculated from detailed data on net values of land, buildings, machinery and computers, all deflated by the relevant sector deflators. In the absence of data on the number of workers employed, the labor input is calculated by normalizing the wage bill of each firm by the average wage prevailing in a given 2-digit sector in a given year. Materials are deflated by input-output coefficient weighted sector deflators based on the wholesale price index. Energy inputs are deflated using National Accounts Statistics price indices for “Fuel, Power, Light and Lubricants.” Services inputs are aggregated from detailed data on reported expenses on travel, transport, legal services and accounting, and non-interest banking expenses. These items are deflated using a weighted average of services sector deflators from the national accounts statistics. Given that our interest is in upstream services reform, a proper accounting for services inputs at the firm level is essential to control for changes in the intensity with which firms use

> 15 We are grateful to Carolina Villegas-Sanchez for sharing with us a STATA routine implementing the procedure. 16 The industry groupings are: food and tobacco; textiles; garments and leather goods; wood, paper and printing; petroleum products and chemicals; rubber and plastics; non-metallic minerals, iron and steel; metal products; machinery, office, electrical and communication equipment; lifting, medical and industrial equipment; motor vehicles and other transport equipment.

18
# **N Tables**

Table N1: Decomposing variance in percentage changes in synthetic benefits per population, Farm Bill and ARRA

|||(1)|||(2)||
|---|---|---|---|---|---|---|
|VARIABLES|% change in IV, FB|Covariance share|R2 share|% change in IV, ARRA|Covariance share|R2 share|
|Log average gross income|7.105***<br>(1.402)|0.260|0.101|6.851***<br>(0.946)|-0.0978|0.148|
|Log average rent|-1.433|-0.0711|0.025|-2.285***|0.260|0.065|
||(0.932)|||(0.464)|||
|Log average household size|4.656<br>(2.945)|-0.0795|0.028|1.769<br>(1.747)|0.0534|0.045|
|Share elderly/disabled|5.219|0.0680|0.027|-1.251|0.00691|0.019|
||(3.239)|||(1.703)|||
|Share homeless|13.95|-0.00325|0.005|-0.415|0.000459|0.007|
||(13.04)|||(7.692)|||
|Log SUA|-2.114***|-0.0594|0.039|-3.318***|0.831|0.453|
||(0.570)|||(0.307)|||
|% change in SUA|0.159***|0.685|0.458|-0.00602|0.0254|0.036|
||(0.0150)|||(0.00846)|||
|Other Controls||-0.0198|0.045||-0.0227|0.087|
|Energy Controls||0.179|0.271||-0.138|0.141|
|Residual||0.0406|||0.0819||
|Observations|48|||48|||
|R-squared|0.959|||0.918|||
|Prob _>_ F|0.000|||0.000|||

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. % change in IV refers to the percentage change in synthetic benefits per population, FB refers to the Farm Bill in 2008m10, ARRA refers to the American Recovery and Reinvestment Act in 2009m4. Covariance share refers to the share of the variance in the outcome explained by each variable. R2 share refers to the share of R-squared explained by each variable following Huettner and Sunder (2012). This measure uses Shapley and Owen values, which calculates the average marginal contribution of each regressor over all possible orderings. All variables are measured in 2006 except for two variables. Log standard utility allowance (Log SUA) is measured the month before the event of interest, and the percentage change in the standard utility allowance (% change in the SUA) is measured at the Farm Bill only because the SUA did not change during the ARRA. Other controls include annual state-level changes in housing prices, unemployment rate, average wage, tobacco tax, average SNAP recipient household gross income and rent, and average personal current transfer receipts from various government policies. Energy controls refer to annual changes in state-level total energy, electricity, LPG, and natural gas quantities consumed and prices for the residential sector. We sum up the covariance share of these variables to obtain their total covariance shares.

72
about 27 percent of employment in commercial agriculture and agribusiness being in microenterprises. Moreover, SME vs. larger firms split of employment for UBR2010 was 14:3 (or 28% vs. 6%) in the non‐ traded sector as compared with 20:13 (or 40% vs. 26%) in manufacturing and 44: 19 (or 44% vs. 19%) in commercial agriculture and fishing.

In turn, the rise in the employment share of microenterprises was likely to have had a bearing on whether or not the structural change we see in the rise in the employment shares of the non‐traded sector between UBR 2001 and UBR2010 was productivity enhancing since technology and productivity would normally vary across size groups even within narrowly defined industry groups. Indeed, if productivity happens to be lower in microenterprises than it is in SMEs or larger businesses within each industry, as typically the case, then the structural change at issue would involve some loss of productivity on that score alone even if there were no significant inherent differences in productivity between the traded and non‐traded sectors.

# 2.4.2. Within the traded sector: Comparative advantage industries

Although the employment shares of manufacturing and of the traded sector as a whole declined over the decade, the 2000s were years of fairly rapid expansion and growing formalization of manufacturing employment and output in Uganda. There were 4,262 manufacturing SMEs and 101 larger plants in the country according to UBR 2010. These had a combined workforce of 350,000, as compared with 46,000, who worked in some 28,000 micro craft shops of an average of two people each. The number of manufacturing SMEs had nearly doubled relative to what it was in 2001. The combined SME workforce had also quintupled, making up scores of times over for the decline in the workforce of large manufacturing plants by about 8,000 and exceeding several times over the growth of small crafts shop workers from about 19,000 to about 46,000.<sup>15</sup> Compared to that in manufacturing employment in commercial agriculture and commercial fishing has been quite small and did not expand much in the 2000s. It ranged between 25,000 and 32,000 people working on sizeable farms, and did undergo significant restructuring in the form of the downsizing of larger scale farms. There were 43 such farms and agribusinesses on UBR 2001 with a combined workforce of more than 20,000 at the time. More than half of those farms and agribusiness had dissolved by 2010, shedding in the process a little over 12,000 jobs. However, the job losses were just about made up for through the creation of some 1, 300 small farms and scores of mid‐sized farms and fisheries, which between them employed about 16,000 workers in 2010. This added to about 8,000 in the remaining large scale commercial farms, to bring the total sectorial workforce back to the size it had in 2001. By 2010, more than half the sector’s workforce was employed in small and mid‐sized farms and

> 15 See Annex Tables 3 and 4 for details.

19
# PRE Working Paper Series

||io|Authbgt||Contact<br>for paper|
|---|---|---|---|---|
|WPS675|Are Buybacks Back? Menu-Driven<br>Debt-Reduction Schemes with<br>Heterogenous Creditors|Ishac Diwan<br>Mark M. Spiegel|May 1991|S. King-Watson<br>33730|
|WPS676|Higher Education in the Republic<br>of Yemen: The University of Sana'a|Viswanathan Selvaratnam<br>Omporn L. Regel|May 1991|C. Cristobal<br>33640|
|WPS677|On Economic Transformation in East<br>Central Europe: A Historical and<br>International Perspective|- Andres Solimano|May 1991|E. Khine<br>39361|
|WPS678|Economic Growth: A Review of the<br>Theoretical and Empirical Literature|David Renelt|May 1991|R. Luz<br>34303|
|WPS679|Poverty Alleviation in Mexico|Santiago Levy|May 1991|M. Stroude<br>38831|
|WPS680|On Hunger and Public Action|Martin Ravallion|May 1991|C. Spooner<br>30464|
|WPS681|Political-Economy Arguments for<br>UniformTariff|Arvind Panagariya<br>Dani Rodrik|May 1991|K. Cabana<br>37947|
|WPS682|Intertemporal Substitution, Risk<br>Aversion, and Private Savings in<br>Mexico|Patricio Arrau<br>Sweder van Wijnbergen|May 1991|S. King-Watson<br>31047|
|WPS683|Vocational Schooling, Occupational<br>Matching, and Labor Market Earnings<br>in Israel|Shoshana Neuman<br>Adrian Ziderman|May 1991|C. Cristobal<br>33640|
|WPS684|The Value of Intra-household Survey<br>Data for Age-based Nutritional<br>Targeting|Lawrence Haddad<br>Ravi Kanbur|May 1991|J. Sweeney<br>31021|
|WPS685|Children and Intra-household<br>Inequality: A Theoretical Analysis|Ravi Kanbur|May 1991|J. Sweeney<br>31021|
|WPS686|Lending for Learning: Twenty Years<br>of World Bank Support for Basic<br>Education|Adriaan Verspoor|May 1991|C. Cristobal<br>33640|
|WPS687|Brazilian Frozen Concentrated<br>Orange Juice: The Folly of Unfair<br>Trade Cases|Carlos Alberto Primo Braga<br>Simao Davi Silber|May 1991|N. Artis<br>37947|
|WPS688|Macroeconomics of Public Sector<br>Deficits: The Case of Zimbabwe|Felipe Morande<br>Klaus Schmidt-Hebbel|May 1991|S. Jonnakuty<br>39074|
Table 3. Number of households (HH) in the sample

||Host<br>Community|Settled Migrant<br>HH<br>(>4 years in<br>Turkey)|Recent Migrant<br>HH<br>(<=4 years in<br>Turkey)|Settled Migrant<br>HH<br>(>3 years in<br>Turkey)|Recent Migrant HH<br>(<=3 years in<br>Turkey)|
|---|---|---|---|---|---|
|2009|133,710|2,127|54|2,142|39|
|2010|141,597|2,216|58|2,223|51|
|2011|142,203|2,101|57|2,111|47|
|2012|143,756|2,086|68|2,098|56|
|2013|143,572|2,363|120|2,374|109|
|2014|147,175|2.523|359|2.553|329|

_Source_ : LFS 2009-2014. World Bank author’s calculations.

The rate of increase in the recent migrant population of labor force surveys between 2011 and 2013 is parallel to observed trends in the government and UNHCR data regarding the SUTPs (Figure 2 in the annex). At the level, the LFS is not comparable to the government and UNHCR data since the sampling structure was not planned to represent the entire SUTP population. However, Figure 2 shows that the change between 2012 and 2013 in the recent migrant population of the LFS has a similar slope to the increase in the SUTP population from administrative data. The trend in the recent migrant population near the Syrian border is almost identical to the movement in the UNHCR and government numbers.

A caveat to the analysis in this paper is the small sample of foreign-born households in the LFS. In the 2009 survey, there are only 58 foreign-born households, and only three are located near the Syrian Border. Applying sampling weights, these three households represented 1,368 people in the population. By 2013, the number of surveyed foreign households increased substantially to 120, with 34 households in the NSB region. In 2014, the sample of foreign-born almost tripled from the year before and the estimated foreignborn population over the age of 15 reached 1.3 million. One very important change in the survey that cannot be emphasized enough is that the sampling of the LFS changed in 2014. Therefore the population estimates in 2014 are only of the population aged 15 and over. From 2009 to 2013, estimates reflect the entire population. However, estimates of the number of households should not be affected by this sampling change.

Table 6 through Table 9 highlight statistics on host community and foreign-born households and population. An interesting trend is the movement of the local population away from the East. In 2013, 16.6 percent of host community households were located near the Syrian border. This value dropped to 15.7 percent in 2014. The recently (<=4 years in Turkey) arrived foreign households, are also mobile and able to move inland. In 2013, 25.7 percent of recent foreign households were located Near the Syrian Border compared to 22.2 percent in 2014. However, the sampling frame of the LFS also changed in 2014 which might have impacted population trends.

9
We distinguish between the use of different types of funds, while drawing comparisons across sectors and support sources. We look at both the level of use and the degree of usefulness of various forms of financial support, measured on a scale of 1 (low) to 5 (high).

Startup finance: Consistent with our first hypothesis, our survey indicated that among the sample SMEs, the main sources of finance at start-up were the firm's existing capita!, as well as capital from informal sources such as relatives and friends (Table V.5). Private loans were another source for at least six sample SMEs, as were parent firms for a few others. As expected, collective sources of support (sources 1 to 4, 11, and 12) were lightly used at start-up, tapped by only 6 of the 122 surveyed firms. Looking across the four sectors, users of collective financial support were limited to the relatively high tech sectors of electronic parts and factory automation.

A surprisingly high 21 sample SMEs, or about one-sixth of the sample, reported using general bank loans at start-up (i.e. source 5, "other' bank loans). However, over half of the users of such loans are in the woven textiles sector, which has several features that may make such loans more feasible. First, these textile firms have the highest degree of geographical concentration (around Taegu), fostering the development of information networks conducive to credit monitoring. Woven textile producers also have the lowest rate of technological change, perhaps making them relatively low-risk. Finally, these firms also have a proven record of growth in both sales and exports.

The last column of Table V.5 also shows the average usefulness of the various sources of financial support. With the exception of two rarely-used collective sources, (governmentsupported venture capital firns and the SME Bank's Restructure Fund), all sources averaged high scores of at least 4 on a 5-point scale.

**Expyrt** Finance: Table V.6 provides data on the use and usefulness of eleven different sources of export support among sample SMEs, and then aggregates these sources into three general categories: pnvate, commercial, and parent firm.<sup>29</sup> Almost all exporting SMEs made use

29 Support for overseas investment is included in these aggregates since in each case this involves finance for an exporting SME. Nevertheless, since these funds were used by only four SMEs, their exclusion would have little effect on the results.

36
3 In this paper we use nighttime lights (NTL) radiance-calibrated annual image composites, for 1996, 2000 and 2010, to provide a standardized characterization of the urban form of 919 Latin American cities. We make a descriptive analysis of the urban form variables, and their relationships with the potential factors that affect the urban form. We then look for differences in the data across countries and regions and identify the trends of change over time.

The rest of the paper is organized as follows. Section 2 presents a literature review. Section 3 describes the data used in this analysis, and the definitions of urban form variables and factors. Section 4 shows the descriptive analysis of the set of LAC cities, the differences across countries, and the interplay with the factors that affect urban form. Section 5 shows the space-time analysis of urban growth in the region and Section 6 presents the main conclusions of this work.

# **2 Literature review**

The importance of urban form has been extensively recognized in the urban economics literature. The form of cities affects their economic performance (Parr, 1979), sustainability levels (Breheny, 1992; De Roo and Miller, 2000), quality of life (Squires, 2002), commuting costs (Wheeler, 2001), costs of transporting intermediate goods (Ciccone and Hall, 1996), the level of knowledge spillovers through human interactions (Lynch, 1981; Jaffe et al 1993 and Glaeser 1998), and the level of matching of firms and workers (Ciccone and Hall, 1996; Rosenthal and Strange, 2004; Cervero, 2001). All this together makes urban form a key factor from an urban policy standpoint.

However, the literature on urban form is not extensive, and it is mainly focused in North American cities (Hasse and Lathrop, 2003; Ewing et al., 2002; Filion, 2001; Tsai, 2005); European cities (Schwarz, 2010; Kasanko et al., 2006, Antrop, 2004; Cheshire, 1995; CEC, 1992; Champion, 1992), and Asian cities (Deng and Huang, 2004; Lin, 2002; Sorensen, 2000). In these studies, the main focus has been to measure the spatio-temporal patterns of urban sprawl.