rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Intended general and gender-specific discount distributions

|||Cond|itional Disc|ount|||
|---|---|---|---|---|---|---|
|General Discount|0|20k|40k|60k|80k|Total|
|0|6.7%|6.7%|6.7%|6.7%|6.7%|33.3%|
|20k|6.7%|6.7%|6.7%|6.7%|.|26.7%|
|40k|6.7%|6.7%|6.7%|.|.|20.0%|
|60k|6.7%|6.7%|.|.|.|13.3%|
|80k|6.7%|.|.|.|.|6.7%|
|Total|33.3%|26.7%|20.0%|13.3%|6.7%|100%|

The baseline price was TSh. 100,000 for a CRO, per parcel, regardless of size or other characteristics. Each cell shows the intended bivariate distribution of assignment to each combination of general and gender-specific discounts. Blank cells were not used to avoid offering a negative net price.

Following this survey, a ward-level meeting was held by a local NGO, the Women’s Advancement Trust (WAT), to explain the overall intervention and process of selection into treatment and control blocks. Using a town plan recently drawn up as a prerequisite for CRO distribution, we then divided land parcels into ‘blocks’ (contiguous groups of parcels), randomly assigning half of these into treatment and control groups.<sup>9</sup> All parcels in treatment blocks were subject to a cadastral survey and owning households were invited to participate in the program to obtain a land title, which required them to repay the cost of 100,000 TSh over roughly a six month period.

The second and third dimensions of the intervention were cross-cutting and randomized at the individual parcel level within treatment blocks. After treatment parcels were selected, owners were to be given up to two types of discounts on the price of a CRO, both redeemable at WAT’s office. The first type was an unconditional voucher, a simple discount on the 100,000 TSh price. The second was a conditional voucher, which could only be applied if one of the names registered on the CRO application form was a female household member. These conditions were carefully explained in Swahili on each type of voucher. If households elected to use a conditional voucher, names were checked at the time of application to ensure compliance with the requirements. Vouchers were assigned to a parcel, rather than to a particular owner, so as to remain impartial to the identity of the actual owner within the household and to prevent vouchers from being exchanged between households.

Vouchers could take on values ranging from zero to 80,000 TSh, in iterations of 20,000, so households could face subsidies between 0% and 80% of the total cost of a CRO. This variation will be crucial for our ability to estimate the price-elasticities of demand for both unconditional and conditional ‘prices’ of CROs. As shown in Table 2, every feasible combination of vouchers was given equal weighting in the randomization.<sup>10</sup>

> 9For Barafu, the total number of blocks was 10, for Kati it was 15.

> 10The net price of a title was restricted to be strictly greater than zero, so any voucher combination which would violate this restriction was excluded from the randomization.

10
because countries differ in the degree of enforcement of labor regulations, and other -- often informal – rules may be more important than those written in the labor codes.

An alternative approach to gauge the stringency of labor regulations is to rely on surveys of employers (e.g. Freeman, 2001; Di Tella and and MacCulloch, forthcoming). Business people presumably should know what aspects of labor laws actually affect business conditions as well as wages and employment. This approach is not without problems either. The questions raised in the surveys concerning labor regulation are often fairly vague and subject to different interpretations. Managers respond very differently depending on whether their company faces good or bad times even if underlying labor laws have not changed. Even more important, business perceptions are rooted in the country’s environment in which firms operate: what firms perceive as a constraining regulation in their own country may still be fairly liberal from an international perspective. Cross-country comparisons may therefore be difficult using business perceptions of labor regulations.

In this paper we bring together two novel sets of data from these two strands of empirical literature. The first draws from a review of legislation in different areas, including labor, for about 140 countries around the world in the late 1990s (World Bank, _Doing Business_ Database, 2004 _a_ ). These data allow the construction of synthetic indicators of the stringency of employment protection, summarizing different interrelated aspects of the legislation, such as notice period, severance payments, length of temporary contracts, etc. These synthetic indicators pass simple validation tests: for example they correlate well with other synthetic indicators produced by the OECD for its member countries, arguably the most complete measures available. The second set of data draws from two separate surveys of employers conducted by the World Bank in the past few years. The World Bank Business Environment survey (WBES) covers firms in more than 80 countries, but the sample in each country is fairly small and information on firms’ characteristics is limited. A more recent survey – the World Bank Investment Climate survey -- so far covers fewer countries (about 50) but many more firms in each country. This survey also allows for better control for firm characteristics and behavior.

With these data we address three main questions.

- Do employers in countries with relatively more stringent employment protection regulation tend to report these regulations as more constraining for their operation and growth plans?

- Which firms are more likely to report labor regulations as particularly constraining for their operation?

- Finally, how do firms react to constraining labor regulations?  For example, do they hire more temporary workers to circumvent rigid rules for regular workers? Do they invest more in training to enable their existing workforce to adjust to new technologies, instead of changing the skill composition of the workforce by hiring and firing workers?

2
# Table 7: Deep trade agreements and GVC integration in goods ‐sectoral estimations, PPML

||(1)|(2)<br>DVA|(3)|(4)|(5)<br>FVA|(6)|
|---|---|---|---|---|---|---|
|No. of provisions|0.00208***|‐0.000205|0.00293***|0.00127|‐0.00315|0.00230*|
||(0.000808)|(0.00471)|(0.00108)|(0.00103)|(0.00611)|(0.00134)|
|No. of provisions* VA share||0.00734|||0.0145||
|||(0.0140)|||(0.0185)||
|No. of provisions*High VA share {0,1}|||‐0.00252*|||‐0.00355**|
||||(0.00133)|||(0.00151)|
|BIT|0.122***|0.122***|0.122***|0.0848***|0.0845***|0.0848***|
||(0.0232)|(0.0232)|(0.0232)|(0.0253)|(0.0254)|(0.0253)|
|PTA (in force or inactive)|0.0368|0.0373|0.0354|0.0357|0.0365|0.0350|
||(0.0297)|(0.0297)|(0.0296)|(0.0362)|(0.0361)|(0.0361)|
|Observations|365,535|365,535|365,535|365,370|365,370|365,370|
|R‐squared|0.975|0.975|0.975|0.990|0.990|0.990|
|Country‐pair‐industry FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer‐industry‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter‐industry‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|p‐value F‐test No. ofprovisions||0.4472|0.6487||0.3600|0.1808|

Clustered standard errors at the country pair level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 26
Policy Research Working Paper 10553

# **Abstract**

This paper combines official subnational and remote-sensed data to uncover the relationships between business cycles in Türkiye and the corresponding changes in economic activity at lower levels of spatial aggregation. The objective is to document changes in the nature of growth within and across business cycles, with a focus on understanding how sectoral changes interact with within-country remoteness during each phase. The paper shows that: (i) the significant growth between 2010 and 2017 was bookended by recessions in which gross domestic product per capita fell more sharply the closer a province was to one of the two largest cities; (ii) the two recessions differed in terms of their sectoral impacts, with manufacturing declines inversely related to remoteness during the first recession and positively related during the second; (iii) there were large increases in the construction sector’s gross value added during the post-2009 rebound— consistent with unprecedented increases in nighttime light luminosity—with growth positively related to remoteness; and (iv) changes in nighttime light luminosity are correlated with changes in physical activity: a 10 percent increase in nighttime lights is associated with a 3.5 percent increase in construction output and a 1.5 percent increase in manufacturing output. Together, the results suggest that recessions and recoveries that may appear to be similar at a macroeconomic scale may be driven by very different changes at more disaggregated spatial scales and have varied impacts on regional convergence.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jbaez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> trading regime. Sri Lanka, having been essentially free of QRs before the mid 1980s,<br>moved towards liberality by reducing tariffs and export disincentives at the same time.<br>Since 1991, India and Bangladesh have moved toward liberality with radical reforms in<br>foreign exchange markets. Average tariff rates, although still high compared to those of<br>Chart 13<br>Trade Integration Before and After the Trade Policy Reform of the 1980s<br>(South Asia)<br>- - - - - L - - - - .6 -U -<br>a.a~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~- -- - - --<br>= C4. /2.0 _2. 4 / 0 Ct0 60 s o<br>Pakistan ,Sri Lanka India<br>{ Grour~~~~~etIb Itate of Import/GDP Ratio (percent)<br>| ~~~~~~~~Real Effective Exchange Rate<br>t ~~~~~~~~~~(1980-100)<br>I SO ~~~~~~~~~~~~~~~~~~~~ - - - - - - - - - - -=_Pkta- - - - - - - -<br>!60 - - -'- - - - - - - - - - --- - ------- - - - - - - -<br>40 -_-_____- __- __- ____- __- __- __- __- ____- __- __- __- __- ____-_ -<br>11980 19's1 IIS2 JIS3 1984 1915 1906 19Jo 1933 1929 1990 1991 1992 1913<br><!-- End of picture text -->

other regions, were reduced an average of 30 percent. QRs were reduced, on average, to less than half of the pre-reforrn levels. Although price distortions and foreign exchange 29
|Observations|665|665|
|---|---|---|
|R-squared|0.797|0.413|
|Prov-Year FE|Y|Y|
|Lagged city characteristics|Y|Y|

Note: This table reports the effect of FDI inflow shock on city level school enrollment. FDI_shock is FDI inflow shock calculated as the interaction of initial industry composition across cities and industry FDI growth explained by policy changes. Lagged city characteristics include population, average age, share of Han ethnicity. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# 4. Robustness checks

## 4.1 Additional control variables

The baseline regressions in Section 3 did not control for GDP per capita and the share of rural population because these two variables contain many missing values. GDP per capita and rural population share are gleaned from city statistical yearbooks. When rural population share is unavailable from city statistical yearbooks, we use population census data to calculate the share of population with rural Hukou. There is a difference between the share of population residing in rural areas and the share of population with rural Hukou, so we do not control rural population share in other robustness checks due to this caveat.

Table 4 presents the results on structural transformation and school enrollment with additional controls. We only report results on sectoral employment share as employment is the primary indicator to measure structural transformation, and we have many more observations on sectoral employment than sectoral GDP. We include initial city level GDP per capita in natural logarithm and initial rural population share as additional controls. As expected, cities with higher initial income level experience a faster structural transformation and college enrollment growth. Cities that have a higher share of rural population at the start of the period are slower to move into services and experience lower growth in college enrollment rate. The coefficients on FDI shock are very close to baseline results in column (1) to (4). The coefficient on FDI shock becomes smaller in column (5) as lagged GDP per capita and rural population share explain a proportion of changes in college enrollment rate. Overall, controlling GDP per capita and rural population share does not affect our estimates significantly.

Table 4. Robustness <u>check: additional controls</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Panel A:|structural transf|ormation|Panel B: schoo|l enrollment|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆Highschool|∆College|
|FDI_shock|-1.855**|1.175*|0.728**|1.073***|1.110***|
||(0.763)|(0.636)|(0.313)|(0.198)|(0.218)|
|L.FDI_empshare|-0.105***|0.119***|-0.0109|-0.0867***|0.0238|
||(0.0363)|(0.0334)|(0.0343)|(0.0282)|(0.0258)|
|L.GDP per capita|-0.0498***|0.0316***|0.0199***|0.00711|0.0175***|
||(0.0111)|(0.00729)|(0.00750)|(0.00638)|(0.00528)|

17
|**Table 11: IP**|**As with Provision**<br>|**on Test Data**<br>|**Confidentiali**<br>|**ty for Pharm**<br>|**aceuticals(Bi**<br>|**lateral Trade,**<br>|**36 IPAs)**<br>||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Low-IP|(2)<br>AI|(3)<br>BIO|(4)<br>CHEM|(5)<br>ICT|(6)<br>MED|(7)<br>PT|(8)<br>Other|
|**Exports**<br>log(GDPi)|0.312***<br>(0.075)||||||||
|Sector × log(GDPi)||0.441***|0.042|0.211**|0.241**|0.399***|0.361***|0.271***|
|||(0.084)|(0.112)|(0.084)|(0.100)|(0.092)|(0.105)|(0.069)|
|Sector × LI × IPAi|–0.086|0.182|0.285|–0.484|–0.524|0.507|0.780|0.678|
||(0.306)|(0.513)|(1.033)|(0.622)|(0.782)|(0.579)|(0.865)|(0.490)|
|Sector × LMI × IPAi|–0.433*|0.764*|0.889**|0.452|0.691|0.830*|0.853***|0.673***|
||(0.222)|(0.440)|(0.449)|(0.318)|(0.574)|(0.455)|(0.272)|(0.190)|
|Sector × UMI × IPAi|–0.133|0.867**|0.366|–0.305|0.297|1.351***|0.408|0.555***|
||(0.201)|(0.438)|(0.451)|(0.333)|(0.626)|(0.475)|(0.302)|(0.192)|
|Sector × HI × IPAi|–0.082|0.126|0.231|0.167|0.437|0.295*|–0.058|–0.084|
||(0.234)|(0.246)|(0.325)|(0.177)|(0.310)|(0.156)|(0.237)|(0.108)|
|**Imports**|||||||||
|log(GDPj)|0.543***<br>(0.046)||||||||
|Sector × log(GDPj)||0.112***|0.028|0.001|–0.044|0.093***|0.042*|0.025|
|||(0.023)|(0.063)|(0.030)|(0.064)|(0.020)|(0.023)|(0.026)|
|Sector × LI × IPAj|–0.041|–0.048|1.200**|0.160|–0.533|0.810***|0.152|0.407***|
||(0.145)|(0.223)|(0.495)|(0.397)|(0.373)|(0.201)|(0.267)|(0.130)|
|Sector × LMI × IPAj|0.069|0.093|–0.055|–0.094|–0.053|0.018|–0.132|–0.045|
||(0.127)|(0.130)|(0.250)|(0.131)|(0.181)|(0.141)|(0.104)|(0.092)|
|Sector × UMI × IPAj|0.046|–0.549***|–0.662|–0.415**|0.032|–0.841***|–0.423***|–0.061|
||(0.166)|(0.169)|(0.422)|(0.210)|(0.276)|(0.321)|(0.158)|(0.088)|
|Sector × HI × IPAj|<br>0.123|<br>0.090|<br>0.092|<br>–0.145|<br>0.095|<br>0.188|<br>0.060|<br>–0.099|
||(0.089)|(0.126)|(0.210)|(0.155)|(0.177)|(0.153)|(0.104)|(0.060)|
|Observations||||||||1,025,080|
|Number of country pairs||||||||26,923|
|Pseudo_R_<sup>2</sup>||||||||0.951|

_Notes_ : Robust standard errors two-way clustered by exporter and importer in parentheses. Includes exporter- and importer-specific time trends, exporter- and importer group-sector year fixed effects, and bilateral pair fixed effects. TRIPS compliance by country included not reported. Observations at 5-year intervals: 1995, 2000, ... 2015. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

31
<!-- Start of picture text --> 100%<br>86%<br>80% 71%<br>62%<br>60% 4 55%<br>40% 39% ay<br>19% 19%<br>30%<br>0% . :<br>oo ro6 ee so or ofgh od4B al= ee ;oeMe oeHe oogh<br>wg ae an wg ge a<br>v eat s oP<br>goar gowa?<br>acce eNon-Gcc<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>80% boss<br>60%<br>40% 30%<br>ws : Fil<br>0% |i fj BE 6%<br>ogoe xoS oe iM gt oo gv ool go aoti go ao oei]<br>a a ggat ogggt oeyh<br>eet ge ew oo<br>Fe ao<br>ot .<br>g = State 3° » Private<br><!-- End of picture text -->
Policy Research Working Paper 9018

# **Abstract**

The Phillips curve is central to discussions of inflation dynamics and monetary policy. In particular, the New Keynesian Phillips Curve is a valuable tool to describe how past inflation, expected future inflation, and real marginal cost or an output gap drive the current inflation rate. However, economists have had difficulty applying the New Keynesian Phillips Curve to real-world data due to empirical limitations. This paper overcomes these limitations by using an identification-robust estimation method called the Tikhonov Jackknife instrumental variables estimator. Data from Morocco are used to examine the ability of the New Keynesian Phillips Curve to explain Moroccan inflation dynamics. The analysis finds that by adding more information to the hybrid version of the New Keynesian Phillips Curve model by increasing the number of moment conditions, the inflation dynamics in Morocco can be well-described by the New Keynesian Phillips Curve. This framework suggests that the New Keynesian Phillips Curve would be a strong candidate for short-run inflation forecasting.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at vtsounguibelinga@worldbank.org, and m.doukali@bkam.ma.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **c. Married Couple with Second Earner Moving into Employment**

A second set of scenarios in the TBM considers earning incentives for second earners in the household when the first is already working. Take a household with 1 child where the principal earner income is set at the MW: in the model, the family net income schedule shifts according to the wage of the second earner, who, in 24 percent of the cases in Poland, tends to be the mother.<sup>15</sup>

**Table 2: Gender of highest earning member by family type (for families with two earners)**

|**Sex**|**Overall (%)**|**Married, with 1 child**<br>**(%)**|**Married, with 2 children**<br>**(%)**|
|---|---|---|---|
|Male|74|72|75|
|Female|26|28|25|

_Source:_ Authors’ calculations with HBS 2014 data.

This allows representing the trade-offs faced by families when deciding if the second earner should stay home looking after the children or enter the labor force. Under the current set of taxes and benefits, Figure 7 shows that for the second earner, moving into work leads to only minimal welfare gains for the household, even before accounting for childcare and other costs. In the most extreme cases, when the second earner makes between 21 percent and 46 percent of the AW (which is equivalent to a full-time MW job), the household will be actually worse off financially if the mother works rather than if she stays home.<sup>16</sup> If the second earner moves from inactivity to MW employment, there is a moderate surplus in family income (PLN 4,800 per year), yet this does not account for the fact that it is likely that additional costs such as childcare and transportation will be incurred by the family.

Accounting for childcare costs, the moderate surplus from moving into work is almost completely wiped out (Figure 8). This means that most of the time a mother has no financial incentive to return to the labor market. While this concern has long existed, the introduction of the 500+ benefit intensified it. Over half of the lost income in transfers (50.5 percent of MW) when the second earner moves into employment is accounted for by the withdrawal of the 500+ benefit (27 percent of MW).

> 15 In Poland, this case is typically characterized by the man working full time with the woman staying home having to assume caretaking duties. This can be confirmed when examining Labor Force Survey (LFS) data on causes of inactivity. Please refer to Figure 8.

> 16 At 22 percent of AW earnings for the second earner, the annual household income drops to PLN 28,830. With the secondary earner staying at home, the annual income is PLN 29,660.

14