rafmacalaba/datause-encoder-data huggingface dataset:
33 With that issue left unresolved, the "stylized facts" of firm demographics can be sorted into two categories, the clearly dynamic ones like the shakeout and everything else. In the latter category, some unknown mixture of quasi-equilibrium "turbulence" and the consequences of dynamic change produce the following patterns. Small firms tend to have short average lifetimes. Liquidation, rather than acquisition or other change of legal form, is the overwhelmingly important mode of disappearance of small firms. Rates of disappearance relative to the current population (hazard rates) at a given age decrease with size, and quite abruptly so in the smallest size ranges. Survivors in a given cohort of entrants tend to increase in average size. Gross rates of entry into an industry tend to be large relatively to net rates, but much more so in some industries than in others.

Quantitatively, many of these effects are quite impressive in the sense that economists unfamiliar with this empirical area often react to the facts with shock. A high-quality data set on Wisconsin firms with one or more employees for 1978-86 was created by Neuendorf and Shaffer, and its age-size relationships analyzed with sophisticated tools by Pakes and Ericson (undated). Looking at the 1979 birth cohort of 327 manufacturing firms, it turns out that over 50% of these firms had liquidated by 1986; however, the mean (employment) size of survivors at the end was 2.7 times larger than the mean size of the original cohort, so jobs were "created" by the cohort at a
_Mexico: The 1997 Pension Reform_ _25_ <!-- Start of picture text --> Assumptions: Insured/EAP Ratio<br>38%<br>36% - ---------- - - -- - ---- - 0 . _ _ ._ _--<br>32% --- 0--- 0-0-0 ---.------ - -<br>-30% ---- - -- NN<br>28% S ~---- -------- I------ ---..-.-.-<br>2 6 % -- - - - - -- -- --- - .-..-..--.--....<br>24% - -- -- - --- -.-- .. -.-- - - - -- - - - - - -<br>a)~~~~c aw gg o CN o-_N CN N4 N<br><!-- End of picture text --> These amounts ar exresseD as a ' ulIplen ofOtheminimu wae The avilbl daacmrie h

**E:stimating workers' salaries.** The wage of a person of a given age was determined by using the data for average salary history, i.e. the average age/salary couplets recorded by the IMSS for a given year. These amnounts are expressed as a multiple of the minimum wage. The available data comprised the salary histories reported for 15 years (1977-86 and 1990-94). To these observations a curve was fitted using a polynomial to describe the average salary history of IMSS members. Average salary history expressed as a multiple of the minimum wage is constant throughout the projection period but it is assumed that the minimum wage rises in real terms.

<!-- Start of picture text --> Assumptions: Increase in Real Salaries<br>15%<br>0%<br>--5°° -------- -------- -- 3 --------- - ------------- ~ ---- _ _ _--- ..<br>-25%<br>O C') OC 04 N uLD )0 t- 0 n N L co § W -- O<br>1- 0)0) t- r 0) r- 0) co 0) co 0) co 0) CD 0) 0) g; ) 8 00 0 81 0 8 8 0 0 o 0 -0 o o C8 0 0 0<br>- _ _ _ _ - _nm _ _ N N N N N N N N N N N<br>-4-_Minimum _--Assumption * Manufacturmng<br><!-- End of picture text -->
# Appendices Appendix 1. Alternative regression specifications

In the main text we used the Ordinary Least Squares (OLS) approach to estimate the reduced form equation: ∆Ycpt = γFDI_shockcpt + θFDI_empsharecpt−1 + θYcpt−1 + Xcpt−1′ β+ αpt + εcpt While using the Bartik shock in reduced form regressions is very common (Li 2018; Erten and Leight 2021), we estimate the following equation using Two-Stage Least Squares (2SLS) regression to demonstrate that FDI deregulation indeed led to higher FDI inflows and our baseline results remain valid. ∆FDI_empsharecpt is the actual change in foreign firms’ employment share in the manufacturing sector. We instrument it with FDI_shockcpt.

∆Ycpt = γ∆FDI_empsharecpt + θFDI_empsharecpt−1 + θYcpt−1 + Xcpt−1′ β+ αpt + εcpt Table A1 and A2 reports 2SLS results on structural transformation and school enrollment respectively. In both tables, FDI shock strongly predicts actual increase in FDI employment share. Increase in FDI employment share results in a significantly faster shift of employment and GDP from agriculture to industry and services. FDI inflows also leads to higher growth in high school and college enrollment.

Table A1. FDI inflows <u>and local structural transformation: 2SLS results</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Pan<br>|el A: Employm<br>|ent<br>||Panel B: GDP<br>|<br>|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆agri_gdps|∆indu_gdps|∆serv_gdps|
|∆FDI_empshare|-0.867**|0.426*|0.546**|-1.232***|0.470**|0.471**|
||(0.394)|(0.219)|(0.230)|(0.383)|(0.192)|(0.185)|
|L.FDI_empshare|-0.229***|0.182***|0.0686|-0.134*|0.0643|0.0376|
||(0.0628)|(0.0415)|(0.0457)|(0.0778)|(0.0450)|(0.0437)|
|||Panel C: F<br>|irst stage results<br>||||
|FDI_shock|2.093***|2.590***|1.927***|1.937***|2.039***|1.839***|
||(0.6318)|(0.7684)|(0.5368)|(0.5871)|(0.5934)|(0.5794)|
|First stage F statistics|576.7|548.23|565.86|2384.93|232.09|245.94|
|Observations|660|660|660|532|532|532|
|Prov-Year FE|Y|Y|Y|Y|Y|Y|
|Lagged city|||||||
|characteristics|Y|Y|Y|Y|Y|Y|

Note: This table reports the effect of change in manufacturing FDI employment share on city level structural transformation. Dependent variables are changes in city level agriculture, industry, and services employment share in Panel A and GDP share in Panel B. Lagged city characteristics include average years of schooling, population, average age, share of Han ethnicity. The regressions also control lagged sectoral employment or GDP share. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

36
# **1.2 Ground-truthing Landscan**

Here we attempt to ground-truth Landscan measures at the grid square level for Kampala and Nairobi, two cities where we have fine spatial resolution data on population and employment which is unavailable to Oak Ridge National Lab. We further attempt to replicate Landscan’s ambient population measure by grid square using an assignment algorithm on our own data. We will conclude that Landscan measures do well and seem superior to other commonly used measures which smear population into grid squares on the basis of built cover on the ground.

In the upper panels of Figures 1 and 2, we show the population and employment distribution for Kampala and Nairobi in 1 km grid squares. The population data for Nairobi are at the level of 2,213 enumeration units for 2009 contained in the 2015 built area of Nairobi defined in Henderson et al. (2018). For Kampala in 2002, population is at the level of 174 parishes within the administrative unit of Greater Kampala. We assign population levels from these survey units to the 1km grid square level by applying a weighted sum to the survey area numbers, where the weights reflect the share of land mass from each survey area(s) that falls within a 1km grid square. To make Kampala 2002 population comparable to 2011 employment numbers, we blow up the population in each grid square by an overall population growth rate of 3% per annum from 2002 to 2011. For employment, we use the economic census, which covers private and public employment for Kampala for 2011, and provides exact location points of firms across the city. One issue is that total employment in the census is far below known estimates; hence, given the age distribution in Kampala and labor force participation of urban Uganda, we have multiplied each grid squares employment by 2.761 to make up for the employment deficit.<sup>5</sup> The implicit assumptions in allocating growth and under-counting of employment to grid squares are obvious.

For Nairobi in 2009, we can quite accurately infer population of the grid square, based on fine-scale EA data. However, we do not know total employment, nor its distribution. We infer total employment based on Nairobi’s 2009 population, and labor force participation and age distribution numbers for urban Kenya. Since there is no economic census for

> 5The World Bank estimates that labor force participation of people aged 15 or more in Uganda is 0.71. There are 1,704,604 people of age 15+ in Kampala from the 2011 census. Thus, approximately 1,210,267 people should work out of the total city population of 2,957,505. The economic census only captures 438,374 of these.

6
This section focuses on the sub-sample of international migrant households that were randomly selected for the more intensive household survey. It provides an assessment of several migration-related transactions and also the manner in which remittance is received and used by households. Except when otherwise stated, a migrant household is defined as one that has at least one member currently migrating to another country _and_ / _or_ has at least one member that migrated abroad in the last 10 years but has since then returned and is currently living with the household<sup>ii</sup> .

Table 4 presents data on the primary agent facilitating migration. In little more than one half the cases (54 percent) it was friends and relatives in the destination country that played the primary role. This factor is especially strong outside Dhaka and Chittagong more likely due to the tight-knit Bangladeshi community in the United Kingdom originally from Sylhet. In about 41 percent of cases it was a Bangladesh-based agent who facilitated the migration process.

[Insert Table 4] Employment agencies are mainly located in the Dhaka metropolis (Table 5) while, surprisingly, given the importance of remittances, the Chittagong metropolis plays a very minor role as a hub for processing overseas migration. In fact, about 40 percent of the respondents in the Chittagong region indicated that they had established contact with agents based in Dhaka. At the same time employment recruitment agencies appear to have quite a decentralized operation – in nearly half the cases the agencies were reported to have been located at the district or the village levels.

[Insert Table 5] 9
Table 1: Phaseout Categories of HS-8 Goods Imported to US from Mexico

|Classes|Number<br>Goods|1993<br>Scheduled<br>Tarif|Import<br>Share|Number<br>Goods|1999<br>Median<br>Tarif|Import<br>Share|
|---|---|---|---|---|---|---|
|A - 1 stage|438|5.5%|15.4%|367|0%|13.9%|
|B - 5 stages|760|9.0%|9.1%|695|0%|9.8%|
|C - 10 stages|641|6.5%|3.3%|498|2.6%|3.3%|
|C+ - 15 stages|68|19.0%|0.5%|66|10.4%|0.7%|
|D - Zeroed by 1993|4,814|0.01%|62.0%|4,249|0%|36.8%|
|Unclassifed|264|0.9%|9.7%|1,920|0.0%|35.6%|
|TOTAL|6,985|2.2%|100%|7,772|0.3%|100%|

Some of the tariff cuts took place immediately on January 1, 1994 and the rest were scheduled to be phased out gradually over various stages of tariff reductions. Step reductions took place on January 1 for up to 15 years. In fact, 96% of NAFTA’s tariff reductions were known at least one year in advance. Broadly, goods were classified into 5 categories, with class A to be immediately zeroed, classes B, C and C+ to be phased out over 5, 10 and 15 years and class D, goods with zero tariffs before NAFTA, to remain at zero. The annual distribution of US tariffs on imports from Mexico for each of these phaseout categories over the 1990s is illustrated in Figure 1.<sup>13</sup> As can be seen in Table 1, most of the HS-8 goods were already zeroed by 1993, providing little variation to estimate elasticities. Among those that were non-zero by 1993, around three quarters were scheduled to be phased out, while the rest became tariff-free the day NAFTA came into force. Given that there was considerable uncertainty regarding whether NAFTA would become effective, it is unclear that the latter reductions could have been anticipated.<sup>14</sup> But once signed, scheduled reductions of class B, C and C+ goods were certainly anticipated to become effective.<sup>15</sup> Before we document that indeed importers internalized this knowledge and anticipated upcoming tariff reductions during the early years of NAFTA, we describe our methodological approach to identifying anticipation in the next subsection.

> 13Although most tariffs were eliminated in equal annual stages, some schedules differed in the number of years with rate reductions and the size of each reduction. Because of this and different amount of goods per phaseout class, the median scheduled tariff rates declined non-monotonically during the 1990s.

> 14Given how the voting went, there was little political agreement in the US on NAFTA.

> 15We cannot ascertain at which degree agents internalized these scheduled reductions. However, a correlation of 87.42% between scheduled rates and applied duties at HS-8 level for goods that were phased out indicates that the scheduled reductions indeed materialized.

8
_(Table C.4 continued.)_

|Personal income (Taka)|Personal income last month. This includes income from economic<br>activity, transfers and gifts, and other sources (e.g. government pro-<br>grams, prizes). Values are winsorised at a fraction of 0.01 in each tail<br>before beingsummed up.|
|---|---|
|Household<br>income<br>(Taka)|Total household income last month. This includes income from eco-<br>nomic activity, transfers and gifts, and other sources (e.g. govern-<br>ment programs, prizes). Values are winsorised at a fraction of 0.01<br>in each tail before beingsummed up.|
|Decision-making<br>(In-<br>dex), Ordinal (Index)|Index of woman’s involvement in seven decisions in the household:<br>1. Decision for her to take up work outside the home for income,<br>2. Decision for her to accept a promotion<sup>83</sup>,<br>3. Decision to purchase large household appliances,<br>4. Decision about large house repairs,<br>5. Decision to purchase clothing and jewelry for herself,<br>6. Decision for her to take a bus to run an errand,<br>7. Decision for her to visit a friend in the neighborhood.<br>For each decision, an ordinal variable was coded such that<br>4=Woman decides alone without needing permission,<br>3=Woman decides alone, but needs permission,<br>2=Woman is involved in joint decision-making,<br>1=Others decide, but need the woman’s permission,<br>0=Woman is not involved.<br>I create dummy variables for each ordinal category and decision, and<br>then compute a standardized index following Anderson (2008) and<br>O’Brien(1984).|

> 83For the selected or nominated women who had been offered a promotion in the wave 2 survey, these questions refer to the decision-making about the actual offer instead of the generic question.

56
26 horizon to use and of how to treat the incidence of capital<sup>investment</sup> expenditures.) In any case, the benefits will<sup>certainly not be equal. The</sup> methodology will tend to underestimate the disparities<sup>in how benefits are</sup> distributed. Policy implications will be affected as well.<sup>How to account</sup> for quality differentials when distributing benefits<sup>is an important area for</sup> further research. Finally, the methodology does not allow<sup>for the private</sup> costs of participation. These are likely to be correlated<sup>with living</sup> standards and so, could be important in assessing results<sup>and the implications</sup> of incidence estimates.

Implementation of the approach first requires calculation<sup>of the</sup> per unit costs for the various education and public health<sup>facilities. In</sup> education, government subsidies are made at the primary,<sup>secondary (lower and</sup> upper), and tertiary levels. In the health sector, the government<sup>spends on</sup> health care through hospitals and the primary health care system<sup>(health</sup> centers (puskesmas), subcenters (puskesmas pembantu),<sup>and the integrated</sup> health posts (posyandus)), as well as on training and<sup>communicable disease</sup> control. The study focuses on the apportionment of<sup>the benefits of</sup> expenditures on hospitals and public health centers<sup>for which utilization is</sup> identifiable from the household level data.

In estimating unit costs, this study (like the majority of studies of its kind) concentrates on variable and semi-variable<sup>or "recurrent" costs.</sup> It does not, therefore account for the costs of capital<sup>used in providing</sup> health or education services. This may lead to biases in<sup>the qualitative</sup> results. In his work on Malaysia, Meerman (1979)<sup>found that failure to</sup> account for public capital leads to serious underestimation<sup>of the total</sup> community resources used to provide medical care and<sup>education services. For</sup> example, accounting for imputed capital service cost<sup>per inpatient day for the</sup> Malaysia data increased total costs per inpatient day<sup>by 78 percent. In at</sup> least two cases, capital costs can be expected to<sup>be important to policy</sup> decisions. When public services require different levels<sup>of capital stock and</sup> are used by different income groups, there will be<sup>distributional</sup>
ECM is theory driven approach to estimating integrated data, but can also be used with stationary data.

We use panel cointegration tests developed by Westerlund (2007). The underlying idea is to test for the absence of cointegration by determining whether there exists error correction for individual panel member countries or for the panel as a whole. Following Westerlund (2007), we consider the following error correction model, where all variables in levels are assumed to be I(1): ′ + ∑ i + ∑ i i � nn nn iitt ii tt ii=1 iitt−ii ii=1 iitt−ii ii iitt−ii iitt−ii ∆CC = ∂∂ FF aa . ∆CC pp . ∆HH + aa �CC −pp HH where, ] and ] indicate first differences in consumption and housing of ith country between period t and t-1. The term diitt iitt t generates the trend components and the constant. The terms [∆. CC [∆. HH provides an estimate of the speed of error-correction towards the long run equilibrium = - ( iii / ) * for that series i. The methodology used by Westerlund provides four test statistics, iitt aa CC Ga, Gt, Pa and Pt. Ga and Gt tests the null hypothesis that H0: ii iitt i = 0 vs. i < 0 for at least one pp aa HH country. The other two test statistics, Pa and Pt test pool information over all the cross-sectional aa aa units to test H0: i = 0 Vs. i < 0 for all countries. Rejection of H0 should therefore be taken as evidence of cointegration for the panel as a whole. All 4 test statistics provide information of aa aa cointegration between consumption and housing investment at individual country as well as the region as a whole. We used 2 lags to comply with Average AIC selected lag length<sup>8</sup> .

Table 6 shows the evidence of trade-offs between housing investment and aggregate consumption expenditures. There is no evidence of a long term relationship across income groups but there is a short run adjustment for all income groups. And this result is significant.

**Table 6: Estimated long run trend and short run adjustment across income groups**

|LICs and LMICs|UMICs|HICs|
|---|---|---|
|Estimated long-run relationship|||
|Share of housing in GDP<br>-1.05|1.12|-0.51|
|Constant<br>178.86|-511.45|147.51|

> 8 Aggregate household consumption data is available since 1960 for most of the countries. However, for some countries the series contains missing values. We extrapolated these missing values based on the share of consumption in the nearest years.

28
Appendix Table 6 continued: RDD estimates of gaps in teacher characteristics associated with teachers hired pre- and post-FPE <u>(bandwidths of 5, 7, and 10 years)</u>

|Bandwidth||Urban|Age|Male|Born in<br>district|“Permanent<br>” contract<br>status|Seconda<br>ry or<br>more|Diploma<br>or more|Bachelor<br>s or more|Teaching<br>certificati<br>on|
|---|---|---|---|---|---|---|---|---|---|---|
|Mozambique<br>5|Coef|-0.070|-0.965|0.203|-0.168|-0.053|0.120|-0.263|-0.223|-0.408|
||SE|(0.185)|(1.865)|(0.172)|(0.178)|(0.158)|(0.070)|(0.120)|(0.121)|(0.181)|
||Pval|0.706|0.606|0.240|0.348|0.736|0.088|0.030|0.069|0.026|
|7|Coef|0.097|0.870|0.244|0.081|0.114|0.057|-0.140|-0.119|-0.233|
||SE|(0.148)|(1.764)|(0.146)|(0.154)|(0.130)|(0.062)|(0.097)|(0.093)|(0.150)|
||Pval|0.516|0.623|0.096|0.597|0.380|0.353|0.153|0.205|0.123|
|10|Coef|0.191|2.650|0.098|0.025|0.123|-0.017|-0.127|-0.120|-0.176|
||SE|(0.117)|(1.509)|(0.123)|(0.130)|(0.106)|(0.059)|(0.084)|(0.076)|(0.121)|
||Pval|0.104|0.081|0.426|0.850|0.248|0.768|0.133|0.114|0.147|
|Tanzania<br>5|Coef|-0.094|1.860|0.021|-0.016|0.001|-0.032|-0.069|-0.067|-0.004|
||SE|(0.088)|(0.838)|(0.100)|(0.082)|(0.017)|(0.042)|(0.074)|(0.053)|(0.005)|
||Pval|0.288|0.027|0.831|0.843|0.960|0.451|0.353|0.202|0.460|
|7|Coef|-0.036|2.762|0.045|0.053|0.002|-0.009|-0.064|-0.034|0.003|
||SE|(0.071)|(0.699)|(0.082)|(0.068)|(0.013)|(0.031)|(0.060)|(0.045)|(0.009)|
||Pval|0.607|0.000|0.586|0.435|0.867|0.766|0.286|0.450|0.706|
|10|Coef|0.037|2.557|0.114|0.073|0.007|0.001|0.048|0.031|0.001|
||SE|(0.056)|(0.599)|(0.071)|(0.057)|(0.011)|(0.024)|(0.051)|(0.038)|(0.008)|
||Pval|0.506|0.000|0.111|0.206|0.552|0.970|0.349|0.423|0.922|
|Togo<br>5|Coef|0.029|-1.475|0.039|0.100|-0.015|-0.034|-0.025|-0.070|-0.016|
||SE|(0.167)|(2.387)|(0.135)|(0.175)|(0.064)|(0.182)|(0.143)|(0.041)|(0.183)|
||Pval|0.861|0.538|0.773|0.567|0.810|0.854|0.860|0.086|0.931|
|7|Coef|0.058|-1.150|0.133|0.003|0.133|0.144|0.007|-0.043|-0.030|
||SE|(0.154)|(2.283)|(0.127)|(0.166)|(0.081)|(0.177)|(0.128)|(0.027)|(0.163)|
||Pval|<br>0.710|<br>0.616|<br>0.298|<br>0.987|<br>0.103|<br>0.417|<br>0.959|<br>0.115|<br>0.857|
|10|Coef|0.137|-1.825|0.129|-0.105|0.051|0.212|0.161|0.004|-0.126|
||SE|(0.132)|(1.883)|(0.116)|(0.140)|(0.075)|(0.140)|(0.108)|(0.010)|(0.130)|
||Pval|0.302|0.334|0.269|0.452|0.492|0.133|0.140|0.663|0.336|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-12
C23_reasonleave3 Why did you leave this job?

**[DO NOT READ CHOICES BELOW. LISTEN TO SPONTANEOUS RESPONSE AND SELECT APPROPRIATE CODE FROM LIST.]**

## **[MARK ALL THAT APPLY]**

1. Terminated

2. Firm closed 3. Job completed 4. Found preferred job 5. Decided to start my own business 6. Decided to work for family business 7. Retired 8. Still work at this job but it is no longer main job 9. Other (specify) 888. Don’t know 999. Refused

# **ENUMERATOR:**

**IF C2_emptype3=8, 9, 10 OR 11 (THAT IS, IF THE RESONDENT INDICATED THAT PREVIOUS MAIN JOB #2 IS IN SELF-EMPLOYMENT OR AS A PAID OR UNPAID FAMILY WORKER IN A HOUSEHOLD BUSINESS), FILL OUT C24-C31 FOR PREVIOUS MAIN JOB #2.**

# **OTHERWISE GO TO SECTION D.**

C24_selfemptime3 How long did you run or work in this business?

1. Up to 6 months 2. 7-12 months 3. 1-3 years 4. 3-5 years 5. 5-10 years 6. More than 10 years 888. Don’t know 999. Refused

C25a_registered3 Was the business registered in any national or local agency?

1. Yes 2. In the process of being registered 3. No à **Go to C26** 888. Don’t know à **Go to C26** 999. Refused à **Go to C26**
<!-- Start of picture text --> 5.0<br>4.0 High income<br>3.0 Upper middle income<br>Lower middle income<br>2.0<br>Low income<br>1.0<br>0.0<br>2004 2005 2006 2007 2008 2009<br><!-- End of picture text -->

**Figure 5: Entry Density by Region, 2004-2009**

We also calculate the one-year growth rate of new firms from 2005 to 2009 (Figure 6).<sup>12</sup> The data show that a period of high-growth in entrepreneurship (2005 to 2007) was followed by a quick and sharp drop in new firm creation, and by 2009, the impact of the crisis can be seen across all income-groups.

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>Low Income<br>15<br>Lower-Middle Income<br>10<br>Upper-Middle Income<br>5<br>High Income<br>0<br>‐5 2005 2006 2007 2008 2009<br>‐10<br>‐15<br>% Change in New Firms<br><!-- End of picture text -->

**Figure 6: One-Year Growth (Percent Change) in New Firms, by Income Group, 2005-2009**

> 12 One year growth rates are trimmed for outliers at 5% and 95%.

13