rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Quality across all Exports<br>i “iiitsearphieeny RE Seep tT wy «aE pei eS<br>ie« eooer es: F at. =<br>2 : ig a.<br>5 RS “he” eee 8, .<br><!-- End of picture text --> <!-- Start of picture text --> Unit Values across all Exports<br>--Co .<br>| Z<br>i- “y s bes Si xy i ae - SpihSe, ry<br><!-- End of picture text -->

<!-- Start of picture text --> io<br>0 10000 20000 30000 40000 50000<br>Exporter GDP per capita (2010 constant Dollars)<br>* LIC =—— Lowess Fit<br><!-- End of picture text --> <!-- Start of picture text --> $°<br>°<br>° 12000er GOPsapitnHy (2010 coratant Dollensy S0000<br>* LIC +——— Lowess Fit<br><!-- End of picture text --> <!-- Start of picture text --> Unit Values in Manufacturing ig ExportsExpo<br>«1:<br>—<br>. .<br><!-- End of picture text --> <!-- Start of picture text --> Quality in Manufacturing Exports<br>=~<br>a suet S35 teas ie Sieustgit cate? £10" 22 oO cmt<br>= ios ‘ 140 See otheees<br>Soe<br><!-- End of picture text --> <!-- Start of picture text --> = wae ge ets ans SE Seee alt hit Sette<br>og. See i<br>o<br>9 10000 20000 30000 40000 50000<br>Exporter GDP per capita (2010 constant Dollars)<br>“HIC “mic<br>= LIC —— Lowess Fit<br><!-- End of picture text --> <!-- Start of picture text --> S fi<br>ui?<br>;<br>010000 20000 30000 40000 50000<br>Exporter GDP per capita (2010 constant Dollars)<br>* HIC * MIC<br>*Lic ——— Lowess Fit<br><!-- End of picture text --> <!-- Start of picture text --> Unit Values in Agricultural Exports<br>nN es;<br>Sone|e5 *<br>S hae “i ta sihiys, «igs wees ie 26 Me ete<br><!-- End of picture text -->

<!-- Start of picture text --> Quality in Agricultural Exports<br>NZL<br>:<br>F : w'S O08 9 oor ep Mes ararsions 580 aan. ta?<br>41 a leacakeSPTae ea isMe RTSa ieeeny keete"<br>5 “oe Mee’<br><!-- End of picture text --> <!-- Start of picture text --> = agit eed eer .<br>o<br>ti) 10000 20000 30000 40000 50000<br>Exporter GDP per capita (2010 constant Dollars)<br>* HIC * MIC<br>*LiG —— Lowess Fit<br><!-- End of picture text --> <!-- Start of picture text --> Z<br>+3<br>0 10000 20000 30000 40000 50000<br>Exporter GDPper capita (2010 constant Dollars)<br>* HIC * MIC<br>*LiC —— Lowess Fit<br><!-- End of picture text --> <!-- Start of picture text --> nN Unit Values in non-ag. Commodity Exports<br>=< ee rE<br>=e 2 * . . . Ad s.<br>A Me rte<br><!-- End of picture text --> <!-- Start of picture text --> Oe] Quality in non-ag. Commodity Exports<br>7 lhe : = Se “ts me wees oo x cits < ,<br>‘ Avr) oo UE . Pecees MeTM 08 ote ¢<br>a Bee ey i es<br><!-- End of picture text --> <!-- Start of picture text --> Fi <a! at be Pa IMT AON ni Spas Bi,<br>*ee- er: x =) ee<br>o|¥*<br>0 10000 20000 30000 40000 50000<br>Exporter GDP per capita (2010 constant Dollars)<br>* HIC * MIC<br>“uc —— Lowess Fit<br><!-- End of picture text -->

<!-- Start of picture text --> PR aie tt<br>fs)<br>0 10000 20000 30000 40000 50000<br>Exporter GDP per capita (2010 constant Dollars)<br>" HIC * MIC<br>-uc —— LowessFit<br><!-- End of picture text -->
<!-- Start of picture text --> 7957 — Overall — Residual<br>74<br>3<br>Cc<br>6 69.72<br>5 66<br>><br>a<br>0<br>oO<br>: 61.0<br>= 58 55.7 .<br>a<br>o<br>=<br>2 |495<br>50<br>44.6<br>42<br>1993 1997 2001 2005 2009 2013<br><!-- End of picture text -->
in our data, since we were only able to collect enforcement data for 2002. Nevertheless, given that we rely mainly on cross-sectional variation (in distance and the availability of inspectors) to identify our main models, we believe this is not an important concern. The reduced form relationship between distance, availability of inspectors, and labor market outcomes does not suffer from this problem. Furthermore, the level enforcement is likely to be highly correlated over time within the same city, and so are labor market outcomes. We explain below that our estimates should be interpreted as long run (perhaps even steady state) effects of enforcement on labor market outcomes. Under this interpretation, measuring enforcement in 2002 instead of in 2000 should not be a substantial problem.

# **5. Empirical Strategy**

Our main empirical specification is the following: where _Yij_ is the outcome of interest in city _i_ and state _j_ , _Eij_ is enforcement in city _i_ and state _j_ , _Xij_ is a vector of city level controls,  j is a state fixed effect, and _uij_ is the residual.  is the parameter of interest and measures the impact of enforcement on labor market outcomes. The main outcomes we consider are the share of informal workers in the city, poverty, inequality and unemployment, and earnings and employment of formal, informal, and self-employed workers. Enforcement is measured with the logarithm of the number of inspections per firm in the city (computed as the number of visits by labor inspectors plus one, divided by the number of firms).

For some labor market outcomes (such as the proportion of formal workers), it is possible to relate  to equations (15) or (27) (one important difference being that in (27)  is a function of _T_ , which means that (33) should be nonlinear in _T_ ). There are, however, two concerns. First, for some outcomes such as poverty or education we do not have an explicit model anywhere in the paper. Equation (33) should be seen as a reduced form equation, but we can easily interpret the resulting estimates. Second, as mentioned before, in this section we consider two types of informal workers: informal wage earners and self-employed. This distinction is important empirically and may be justified with models of duality within the informal sector (referenced above), but the theoretical model of section 3 is not rich enough to capture it (lumping together all informal workers in a single sector), and introducing this in the model would complicate it. It 19
# **Appendix Figure 1E - United Kingdom The main variables during 1997-2010 for firms from United Kingdom**

<!-- Start of picture text --> 10.00% Gross Savings 12% Gross Capital Formation<br>9.00%<br>10%<br>8.00%<br>7.00% 8%<br>6.00% 6%<br>5.00%<br>4%<br>4.00%<br>3.00% 2%<br>2.00% 0%<br>4%<br>Excess Savings Non-Operating Assets<br>40%<br>3%<br>35%<br>2%<br>30%<br>1%<br>25%<br>0% 20%<br>-1% 15%<br>-2% 10%<br>-3% 5%<br>0%<br>-4%<br>-5%<br>-5%<br>65% Leverage 12% Liquidity Holdings<br>60% 10%<br>8%<br>55%<br>6%<br>50%<br>4%<br>45%<br>2%<br>40% 0%<br>Median<br>Asset-Weighted Average<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Excess Savings, Gross Savings, Gross Capital Formation, Non-Operating Assets, Leverage, and Liquidity Holdings, during the period 1997-2010, according to median and asset-weighted average, for the sample of manufacturing firms from United Kingdom.
_<u>Table 8. Innovation Impact on Output, GMM, and Predicted Value</u>_

|Dep. Var.<br>ln revenue|(1)<br>All|(2)<br>Internal vs. external|(3)<br>Synergies|
|---|---|---|---|
|ln_labor|0.041<br>(0.473)|-0.112<br>(0.340)|-0.162<br>(0.167)|
|ln_capita|-0.070<br>(0.137)|0.009<br>(0.906)|0.050<br>(0.495)|
|ln_material|0.247<sup>***</sup><br>(0.000)|0.215<sup>***</sup><br>(0.000)|0.189<sup>***</sup><br>(0.001)|
|Innov_hat|0.857<sup>***</sup><br>(0.000)|||
|Inter_hat||-0.275<br>(0.180)|-0.230<br>(0.249)|
|Exter_hat||1.374<sup>**</sup><br>(0.001)|1.477<sup>***</sup><br>(0.000)|
|Int_ext_hat|||-0.026<br>(0.062)|
|Export_stat|-0.529<sup>***</sup><br>(0.000)|-0.768<sup>**</sup><br>(0.002)|-0.870<sup>***</sup><br>(0.000)|
|FDI_stat|0.530<sup>***</sup><br>(0.000)|0.652<sup>***</sup><br>(0.000)|0.643<sup>***</sup><br>(0.000)|
|# of obs.|12,764|12,764|12,764|
|# of group|3,210|3,210|3,210|
|Sum of coefficient|1.075|1.211|1.298|
|AR2 (p-value)|0.000|0.001|0.003|
|Hansen(p-value)|0.000|0.000|0.001|

_Note:_ Year and industry dummies were included but not reported. Standard errors are clustered and robust (White’s correction for heteroskedasticity); _p_ -values in parentheses. Significance level: * = 5 percent, ** = 1 percent, *** = 0.1 percent Like table 5, overall innovation (innov_hat) and external innovation have positive and statistically significant effects on final output, while internal innovation and the synergy between two sources of innovation are statistically insignificant. Surprisingly, the magnitude of the coefficients became much larger when using the predicted value, so that the coefficient of overall innovation is 0.857 in column (1), and external innovation is 1.374 in column (2) and 1.477 in column (3), which makes the 1 percent increase in either external or overall innovation investment have a larger effect on final output than the 1 percent increase of other factors, such as intermediary inputs.

50
# **B Regression Discontinuity Appendix**

Figure B.1: Raw Data with Outlier in April 2014 <!-- Start of picture text --> A. Raw Trends B. Transformed Trends<br>I. Number of Card Transactions<br>⇐ August 2014 ⇐ August 2014<br>VAT Rebates of up to VAT Rebates of up to<br>4 ppt Enter Into Effect 4 ppt Enter Into Effect<br>2011m12 2012m12 2013m12 2014m12 2015m12 2011m12 2012m12 2013m12 2014m12 2015m12<br>Year-Month Year-Month<br>II. Volume of Card Transactions<br>⇐ August 2014 ⇐ August 2014<br>VAT Rebates of up to VAT Rebates of up to<br>4 ppt Enter Into Effect 4 ppt Enter Into Effect<br>2011m12 2012m12 2013m12 2014m12 2015m12 2011m12 2012m12 2013m12 2014m12 2015m12<br>Year-Month Year-Month<br>3 3<br>2.5 2.5<br>2 2<br>Log Number of Card Transactions Log Number of Card Transactions<br>1.5 1.5<br>10 10<br>9.5 9.5<br>9 9<br>Log Volume of Card Transactions Log Volume of Card Transactions<br>8.5 8.5<br><!-- End of picture text --> Notes: This figure shows that the months of April and May 2014 constitute outliers in terms of the number of card transactions and the volume of transactions, with a short-lived drop in both outcomes in April 2014 and a strong recovery in May 2014. We hypothesize that this might be due to consumers temporarily postponing purchases in anticipation of the passage of the financial inclusion reform. The VAT rebate provisions were indeed widely debated in the media and consumers might have falsely expected those provisions to enter into effect imminently. After realizing that the rebates would not enter into effect until August, they conducted in May the purchases they had initially postponed in April. To account for this, we average these two outcomes over April and May 2014 in Figure 1. No change is applied to the data used in the regression discontinuity estimations, as these are run on weekly data.

49
**27**

**PHN 1992 Sector Review**

control systems. The hospital fee policy will be reviewed one year into the project's implementation with a view to refining and expanding it to other facilities.

3.48 Municipalities participating in the India Family Welfare (Urban Slums) project must generate resources from direct beneficiaries of the facilities and programs being developed under the project, in order to support a portion of operating and maintenance costs of those activities. City Health Plans, to be developed by those municipalities, will thus set out financing plans, a regime of fees, and plans for insurance and subsidization. During the course of the design and development of the India AIDS project, the Bank and MOHFW initiated a collaborative study on health financing to identify gaps in financing of sectoral interventions and how they can be effectively met in light of the escalating costs of caring for AIDS patients. Under the India Safe Motherhood and Child Survival project, the government has committed itself to maintain levels of planned budgetary allocations for the national MCH program for fiscal years 91-95, as agreed with the Bank.

reflects on this achievement, notes some areas of relative weakness in poverty-related work of the PHN sector, and suggests ways of further strengthening this aspect of PHN work, based on some of the better designs of this year's projects and on implementation experience and lessons learned, documented during the year.

3.49 Currently available data, which are sparse and somewhat unreliable, indicate a drastic reduction in social sector public expenditure in Sao Tome and Principe over the past decade or so. As a part of the Health and Education project, the government will prepare a report, based on more reliable data, on social sector allocation and expenditures from 1987 to the present. For each of the subsequent fiscal years, the government will present to IDA and discuss the adequacy of future budgets. The government, having recently adopted an essential drugs policy, will also initiate with project assistance cost recovery from the sale of drugs, which should also assist in generating increased resources for health.

_3.50_ This chapter shows that the Bank's PHN sector operations have been responsive to the Bank's restated emphasis on poverty alleviation as a fundamental development objective. Chapter V
Likewise, to reduce the number of geographic entities, the 32 states are aggregated into 5 regions similarly as in Hanson (1997).<sup>23</sup> Optimally, those five regions aggregate states with analogous regional economies and integrated labor markets. This assumes that labor markets clear at this more aggregated regional level instead of by state. Moreover, workers are assumed to supply different types of labor services depending on the educational level they have attained and the region where they reside. Arguably, these assumptions are reasonable in the case of a developing country such as Mexico.<sup>24</sup> To summarize, the model is estimated with 5 goods (agriculture, food products, textiles and apparel, household equipment, and other manufacturing), 5 regions (US border, north, center, Mexico City area, and south), and 2 levels of wages (skilled and unskilled). To better capture the effect of prices on wages, the data from the six household surveys are stacked together, therefore compensating for the fact that prices may not vary significantly within one single survey to allow the estimation of such price-wage elasticities. Finally, to reduce the effects of measurement error, observations with the lowest or highest 0.25 percent of wage values for each year are dropped.

The estimation equation also includes several control variables. Specifically, I include the age and gender of the worker, a dummy variable that control for his or her status as household head, and a categorical variable to capture wage premiums for skilled individuals that have completed secondary education or college. Furthermore, two dummies control for job characteristics (permanent or temporary position and employment in agricultural work). Year dummy variables are included in the regression to control for fixed year effects. Finally, region-specific issues are captured by the inclusion of a variable for average regional expenditure (which controls for the varying cost of living) and a variable capturing the regional labor endowments (which controls for the supply of skilled/unskilled labor in the region). The estimation is run for all individuals reporting salaried wages between 18 and 65 years of age.<sup>25</sup> The estimation

> 23 Figure A.1 in the appendix illustrates the 32 Mexican states and their geographical aggregation.

> 24 Further details on the aggregations are discussed in the data appendix.

> 25 To limit the effect of measurement error, the estimation is restricted only to employed individuals. Self employed individuals reporting imputed wages are not included in the estimating sample.

18
# Future prospects

There are now a wide variety of survey instruments that are being designed for or adapted to measure income or consumption among FDP populations. Home visits initially designed by the UNHCR to question FDPs for protection purposes are being revised to ask questions on income and consumption, becoming in this way viable instruments for poverty measurement. Large home visit exercises are conducted every year in countries such as Jordan and Lebanon and these data have been used to conduct poverty assessments of Syrian refugees (Verme et al., 2016). The WFP conducts vulnerability assessments that have been used by the WFP, UNHCR and World Bank to make gross poverty estimates using the consumption modules of these surveys, even if these modules are typically very short, with few items. The UNHCR conducts Multisector Needs Assessment such as the one conducted in Cox’s Bazar, Bangladesh, in 2018, Socio-economic assessments such as the one conducted in Zimbabwe in 2017, or nutrition surveys such as the one conducted in Tanzania in 2017.<sup>12</sup> All these surveys contain some information on income, consumption, or expenditure that is being used to assess the well-being of FDPs. The World Bank has conducted welfare assessments of Venezuelans in various Latin American countries including Colombia, Peru and Ecuador, with a forthcoming study expected for Chile. The welfare of Afghan refugees has now been studied in their main host country, Pakistan, and in Afghanistan upon return, providing elements to compare the living conditions of this population in the two locations. Again, all these surveys and studies are either very recent or were not used before for poverty/welfare analyses.

Whereas individual and household data on refugees are now more systematically collected, data collection for IDPs remains extremely scarce when compared to refugees, mostly limited to head counting. Refugees and IDPs are very different in that they have a different legal status, which leads to different access to public services, labor markets, government, and international assistance. Surveys for IDPs are often more difficult to conduct because the host country might be linked to the cause of displacement, and a registration system like UNHCR’s proGres is absent, making it more challenging to obtain a representative sample. Given the large number of IDPs (58 percent of all FDPs), the real challenge will be to collect microdata on IDPs systematically in all those countries that are home to large numbers of IDPs. So far, the only country that collects data on IDPs systematically is Colombia and this country has shown that, when quality microdata are available, research on IDPs flourishes.

While many of the discussed issues need more attention and research, some processes are in place that should lead to the establishment of guidelines that address some of these questions. A recent process

> 12 All these surveys can be found in the World Bank microdata library at: https://microdata.worldbank.org/index.php/catalog?sort_by=rank&sort_order=desc&sk=refugees.

20
Figure 14: Contribution of Regulation-Based Entry Barriers and Idiosyncratic Distortions to TFP Gains <!-- Start of picture text --> TFP Gain Relative Full Reform TFP Gain Misallocation Only TFP Gain Entry Barrier Only<br>Kenya<br>SalvadorPortugal KenyaSalvadorPortugal<br>EthiopiaGhana<br>BulgariaIndiaRomaniaPeruColombiaMalaysiaChile Belgium EthiopiaGhanaBulgariaIndiaRomaniaPeruColombiaMalaysiaChile Belgium<br>Hungary Hungary<br>LatviaSpain F Italyranceinland LatviaSpain F Italyranceinland EthiopiaKenyaGhanaSalvadorBulgariaIndiaMalRomaniaPeruPortugalColombiaHungLatviaChileSpBelgium aF Itysi r inl a anceylyin a nd<br>5 6 7 8 9 11 5 6 7 8 9 11 5 6 7 8 9 11<br>log GDPpc log GDPpc log GDPpc<br>1.6 1.6 1.6<br>1.5 1.5 1.5<br>1.4 1.4 1.4<br>1.3 1.3 1.3<br>1.2 1.2 1.2<br>Relative to Initial SS Relative to Initial SS Relative to Initial SS<br>1.1 1.1 1.1<br>1.0 1.0 1.0<br>0.9 0.9 0.9<br><!-- End of picture text --> Note: The figure decomposes the total gains in TFP resulting from the withdrawal of both entry barriers and idiosyncratic distortions into those stemming from removing a single distortion while holding the other one fixed. Entry barriers are taken from the World Bank’s Doing Business Indicator (DBI). The left-most panel reproduces the overall gains. The middle panel computes the gains from removing idiosyncratic distortions, setting _γi_ = 0 while keeping the corresponding entry barriers _τi_<sup>_E_.Theright-most</sup> panel sets the entry barrier to zero, _τi_<sup>_E_</sup> = 0 , keeping the productivity elasticity of distortions _γi_ fixed.

firm size to discipline the inference of distortions. Exploiting the countervailing forces exerted by each type of distortion on the average firm size, and the independence identifying idiosyncratic distortions from equilibrium variables, the methodology exploits the equilibrium conditions of our model of the firm size distribution to infer the entry tax rate that rationalizes a country’s ratio of average firm size with respect to the US under the measured misallocation.

We identified sizable entry barriers around the world. In developed economies, such as France, Spain, and Italy, the model-based entry barriers are closely captured by the World Bank’s DBI, a prominently used regulation-based indicator of barriers to entry. In middle- and low-income countries, however, the model- and regulation-based estimate start to diverge, finding significantly higher entry barriers in the model. The differences between the model- and regulation-based estimate have notable micro and macro implications. We showed that not only is the DBI unable to replicate the cross-country differ50
- 15 -

The influence of chgnge ln labor costs on manufacturing employment is *lgnific nt ( nd negatlve) in most of the countries where w were *ble to *stim te * labor deaand functlon (Table 3a) Thi suggest that sost of the observ d ch nges tn labor cost levelr would be *ssociated vlth changes in lzbor productlvity ln fact in the tatistical *sercise included in th *ppendix the *ffect of * veriable ludic-ting dintorticnzry intervention in th labor sarket -- the ratio N"C to per caplt- inco e -- protucet slgalflc-ntly B gatSve paraDeters only in th cases of M*slco Colo bla ladia nd Glgeria It is interosting to note thgt th se countrles *re char cterised by tiffernt trends ln labor costs through tlDe not necessarlly by * incr asing on The *ffect of NWC of labor on non-tratltlonal *xport is anoth r isportant lssu So th estent th t hlgh r NWC r^y reflect * tlstortion which i 8ativ 1y *ffects *sport growth lacreasing labor costs would l^t to deteriorating esports nd _ loytent An empirical cros-country *nalysis on -hir issue is presentet in Rlv ros (1989) wherc the degree of distortion is easur d by th rgtlo NWC to per czpit inco Thur b1 pooling countrler in * group chargeterlsed by a restrictive labor market regime -- *s measured by th burden impced throu p job security regulation -- versus nother group of countries characterized by * more llberal regime the concluslon is that th *ffect of hi p r distortionary NWC n g tiv 1y gffccts *xports Thls result is ladlcativ of th import nce of N"C of labor in *nzlyzing the relstionshlp between non-tr ditional *ports *nd labor sarkets *nd *lso sugg sts th t statisticsl *nalysec must *ccount for th *ntire set of regulations nd fin ncial oblig tions *sisting in LDCs labor markets