rafmacalaba/datause-encoder-data huggingface dataset:
# 3.3 The labor market

As much as 90 percent of the labor force in Pakistan operates in the informal sector (defined as individuals without formal employment contracts and without access to social security).

Firms are modeled as making the choice to engage in a formal or an informal contract after weighing the advantages and disadvantages of each employment type. Major benefits of the formal sector are access to financial services and government services. A major benefit of being in the informal sector is that firms can avoid direct taxation by the government.

This firm’s decision is captured in heterogeneous firm models as presented in Ulyssea (2018), where firms differ by productivity. Very productive firms tend to be larger than less productive firms and thus benefit from returns to scale. More productive and larger firms, therefore, tend to enter the formal market while less productive firms produce informally. In Appendix 1A, we present a simplified version of the model in Ulyssea (2018). This theoretical result has been supported by several empirical papers finding that reducing labor taxes increases formal employment (Rocha, Ulyssea, and Rachter 2018; Bosch and EstebanPretel 2012; Ulyssea 2010).

Integrating the model from Appendix 1A into PAKMod would be a conceptually convincing solution. However, any calibration would be very uncertain, and would require firm-level data on the distribution of firm sizes (both formal and informal).

Therefore, we follow Ahmed (2012) and assume that informal and formal labor can be combined in a 1 constant elasticity of substitution (CES) function to total labor, � . σσ FF<sup>σσ</sup> II II<sup>σσ</sup> 3.3.1 Data creation tt tt tt CC = �ββ<sup>FF</sup> CC + ββ CC The International Labour Organization provides data for the share of formal employment in total employment (ILO 2020b). This variable, denoted as , allows calculating the historical values for formal and informal employment, KK,II tt ss , FF KK,II tt tt tt CC = �1 −ss �∗CC, II KK,II where total employment, , is available in MFMod. CCtt = sstt ∗CCtt The budget constraint is given as CCtt , where is aggregate wealth, is the net wage paid for formal labor and is the wage paid for informal labor. The first order condition KK FF FF II II FF tt tt tt tt tt tt tt tt tt for the informal sector can then be written as WW CC = (1 + ττII )WW CC + WW CC WW WW tt WW . σσ �<sup>CCtt</sup> ~~�~~ II II tt II tt WW = ββ WW Based on the estimates in Table 1 of Ahmed (2012), we assume CCtt = 0.29 , = 0.71 and 2. ββ<sup>FF</sup> ββ<sup>II</sup> = 1 −ββ<sup>FF</sup> σσ= Available data for Pakistan, do not separate out direct taxes for labor and capital, so that we need to assume that the same tax rate applies to both labor and capital. The effective direct tax rate is thus 31
More generally, in column (4) we address the problem of potential unobservable policy or geographical factors that could affect the degree of fi rm openness and the propensity to innovate, by controlling for industry-region fi xed effects. Again our main fi ndings are robust. To the extent that the degree of competition is constant across industries and regions, this speci fi cation controls for the degree of competition faced by fi rms. However, it is possible that different fi rms face different competitive pressures. In particular, it is likely that fi rms operating in more competitive markets face stronger pressures to innovate and may also be more engaged in international activities. To address this possibility, we include an explicit measure of competition in the regression shown in column (5). Based on the total number of competitors, we construct dummy variables indicating whether the fi rm faces no competition, weak, medium, or strong competition in the domestic market. Even though we fi nd a positive effect of competition on technological innovation (stronger when the number of competitors is between 4 and 21 fi rms), the fi ndings for the main variables of interest remain robust.<sup>29</sup> A fi rm’s geographical location in the country’s capital city may enhance its innovation prospects due to the proximity to other fi rms, suppliers, clients, and other institutions, while also facilitating its access to global markets through exports and imports and increasing its attractiveness for FDI. The association between openness and innovation could therefore be spuriously due to location. However, the effects of minority foreign ownership, exports, and imports on innovation are qualitatively unchanged in column (6) when our main speci fi cation is estimated using the sample of fi rms located outside their country’s capital city. Similar results are found when considering only those fi rms located outside each country’s capital and second major cities.

The fi ndings in Table 3 show that industries with a higher degree of technological sophistication (high-tech) face and take advantage of more innovation opportunities than traditional industries with a lower degree of technological sophistication (low-tech). So far, we have controlled for such differences using industry fi xed effects. More generally, there may be differences in the role of technology diffusion through trade and FDI for fi rm-level innova-

> 29A non-monotonic relation between innovation and competition is also found for the UK. Aghion et al. (2004) show that the effect of concentration on innovation has an inverted "U"-shape.

17
# **Table 9: Geographic and Absolute Convergence**

||`(1)`<br>`(2)`<br>`(3)`|
|---|---|
|`Dependent`<br>`variable`|`Change in log real income`|
|`North Sumatra`|`0.276`<br>`-0.037`|
||`(3.01)**`<br>`(0.46)`|
|`West Sumatra`|`0.147`<br>`-0.102`|
||`(1.22)`<br>`(0.92)`|
|`South Sumatra`|`0.467`<br>`0.019`|
||`(4.05)**`<br>`(0.19)`|
|`Lampung`|`0.585`<br>`-0.046`|
||`(5.08)**`<br>`(0.45)`|
|`West Java`|`0.308`<br>`-0.031`|
||`(4.09)**`<br>`(0.47)`|
|`Central Java`|`0.467`<br>`-0.044`|
||`(6.40)**`<br>`(0.63)`|
|`Di Yogyakarta`|`0.274`<br>`-0.178`|
||`(3.13)**`<br>`(2.05)*`|
|`East Java`|`0.408`<br>`-0.053`|
||`(5.36)**`<br>`(0.71)`|
|`Bali`|`0.360`<br>`0.027`|
||`(4.38)**`<br>`(0.35)`|
|`West NusaTenggara`|`0.436`<br>`-0.066`|
||`(4.29)**`<br>`(0.74)`|
|`South Kalimantan`|`0.108`<br>`-0.149`|
||`(1.17)`<br>`(1.78)`|
|`South Sulawesi`|`0.649`<br>`0.111`|
||`(5.92)**`<br>`(1.08)`|
|`Log Real Income`<br>`1993`|`-0.423`<br>`-0.422`|
||`(18.63)**`<br>`(19.87)**`|
|`Constant`|`-0.225`<br>`2.824`<br>`2.782`|
||`(4.26)**`<br>`(16.70)**`<br>`(20.34)**`|
|`Observations`|`4229`<br>`4229`<br>`4229`|
|`R-squared`|`0.01`<br>`0.18`<br>`0.18`|
|`Robust t statistics`|`in parentheses`|
|`significant at 5%;`|`** significant at 1%`|

38
rates. Our results do not provide evidence for either hypothesis. In column 5 we control for the level of education using data on enrollment into tertiary schools from the World Development Indicators. Again, the results are not altered and higher university enrolment is not associated with faster institution building. We also control for the EU accession process, since the prospect of future EU membership might have fastened institution building, both through political incentives and through assistance from the original EU member states. Surprisingly, the dummy variable for the countries that entered the EU in 2004 does not enter significantly, contrary to theoretical predictions by Roland and Verdier (2003) (column 6). Countries suffering from civil war during the 1990s were not more or less likely to experience institution building (column 7). Repressed inflation during the pre-transition era also does not alter the results (column 8). We control for price distortions by using the increase in deflated wages minus the change in real GDP over the period 1987 to 1990 (De Melo et al. 1996). However, initial price distortions are not significantly associated with institution building and controlling for them does not affect our results. The initial state of price and trade liberalization and importance of the private sector does not seem to be associated with institution building, and neither does the speed with which countries liberalized prices, open the trade regime and privatized (columns 9 and 10). To assess the sensitivity of our results to controlling for macroeconomic reforms we use indicators developed by de Melo et al. (2001) for price, trade and ownership liberalization, with annual variables for the period 1990-97. We use the values for 1990 to proxy for the initial condition of price, trade and ownership liberalization in transition economies and calculate a principal component indicator of these three indices. In column 10, we control for a principal component indicator of changes in price, trade and ownership liberalization, thus controlling for the speed of

> 20 This is also confirmed with a simple t-test between Institutional Development in FSU vs. non-FSU countries. Whereas this test is insignificant, the difference between the original FSU countries and all other countries is

25
the household, business and government sectors in thirteen countries. Of greatest interest is the ratio for business. It shows that this sector has had, on a-erage, a substantial net external financing need in all 35 countries. On average over a period of years, the weighted average for the thirteen countries comes to just over one-half, so that, as a rough rule of thumb we could say that the data indicates that about one-half of business's needs for financing capital accumulation are met by external 3 6 funding .

The self-financing ratios are based on data for gross saving and gross investment, as a share of GNP<sup>37</sup> . This data is of interest for its own sake, and is shown in Table 6 and Appendix Table 3. For the sample of countries and time periods covered, the household sector saved on average about one-half as much again as the business sector.

# 4.2 Exogenous determinants

What exogenous factors determine the sectoral financial surpluses? The question of whether the foreign, or the government net lending are exogenous to the household and business sectors will be reviewed later. For this section "reduced form" regressions were estimated purporting to explain variations in each of the sector surpluses without reference to each other.

The exogenous factors for which data were collected are openness (measured by the sum of import and export shares in GNP), per capita income, GNP growth rate, real interest rate (measured as a differential over the real 35 Other than the Philippines. The Philippines data covers a rather disturbed period and records gross disinvestment by the business sector in some years: self-financing ratios have little meaning in such a context. 36 Ratios for a few industrial countries are also in Appendix Table 4. 37 Gross saving includes provision for capital consumption; gross investment includes changes in inventories.

35
future protectionism.<sup>17</sup> We do not address such critiques here. Instead, the next section focuses on how groups might use the tremendous amount of information compiled into the GTA and the Global Antidumping Database to improve the quality of information provided to developing country exporters in the WTO system regarding the potential infringement of their market access rights.

# **_The four questions that information generated from these new initiatives could help_**

# **_answer_**

The GTA and Global Antidumping Database provide raw data and information. Their main contributions to date were not to perform the technically demanding task of packaging the raw information into the format needed for exporters and their advocates in developing countries in the context of potential WTO dispute settlement. Here we address the question of how others can adapt the information now available to put it to good use.

Begin with the considerable GTA efforts of identifying trade-related measures, and its approach of ‘red flagging’ certain measures based on whether they are likely to have a negative impact on foreign commercial interests. For an exporting firm in a developing country and its advocates to make a sufficiently informed cost–benefit analysis of whether to use WTO dispute settlement to attempt to reopen a market that was closed off during the crisis, they require answers to at least four questions:

> 17 Criticisms of the GTA and the impact of such monitoring activity during the crisis include Rodrik (2009); see also the Reuters interview with Richard Eglin, Director of the WTO’s Trade Policies Review Division (Lynn, 2009).

17
Gender, Poverty, and Nonfarm Employment in Ghana and Uganda Constance Newman and Sudharshan Canagarajah* Constance Newman is with Rural Development and Poverty and Human Resources in the Development Economics Research Group (DECRG), World Bank, and Sudharshan Canagarajah is with the Social Protection Team in the Human Development Network (HDNSP), World Bank. The funding for this research came from the Poverty Research Report on Gender and the Poverty Reduction and Social Development Division in the Africa Region. We would like to express our thanks to Simon Appleton for his assistance, Patricia Zambrano and Ruchira Bhattamishra for excellent research assistance, and Andrew Mason for his valuable comments on an earlier version of the paper.
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 15: Required contribution rate needed to balance the scheme under two alternative mortality assumptions for teachers**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>variant 1 variant 2<br>2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073<br><!-- End of picture text -->

**Source** : WB calculations based on PROST

The required contribution rate for the civil servants sub-scheme starts from already a very high level of 36% which would not be sustainable even in a developed economy. Over time, it increases, then down and then up again driven by the changes in the system dependency rate. It reaches its lowest level of 17% and 19% respectively, depending on the two mortality variants, by the end of the period of accelerated coverage expansion but only for few years. After 2050 the coverage expansion slows down, the dependency rate increases and as a result the required contribution rate also increases. Due to increasing system dependency rates over time, the required contribution rate for civil servants would be even higher than what is reported in the graph if the simulation period were extended.

In the teachers sub-scheme, the required contribution rate is initially much lower than for civil servants due to a much lower starting dependency rate. By the end of the simulation period the contribution rate required to balance the teachers sub-scheme exceeds the one projected for civil servants. For both civil servants and teachers, future changes in the required contribution rates mimic projected changes in the dependency rate.

An alternative indicator to the required contribution rate is the “sustainable contribution rate”. This is an indicator conceptually very similar to the required contribution rate but it is defined as the fixed contribution rate that would balance the system over the whole simulation period. The sustainable contribution rate for civil servants is estimated at 30% and 31%, depending on the two mortality variants. The sustainable contribution rate for teachers is estimated at 36% and 39%, depending on the two mortality variants.

- 44 -
|Variable|Definitions and Sources|
|---|---|
|Trading Partners Growth|External Demand Growth ; Source: IFS & Balance of Payments Statistics, IMF|
|Unemployment Rate (in %)|Share of labor force including people available for and seeking employment.Source:<br>International Labour Organization [ILO]|
|int1. Index|Index int1|
|∆%M2|Percentual Change of M2|
|M2|Money + quasi_money; Source: IFS|
|LGDP_HP_cycle|Hodrick Prescott cycle of natural logarithm of GDP|
|LGDP_BK_trend|Baxter-King trend of natural logarithm of GDP|
|LGDP_HP_trend|Hodrick Prescott trend of natural logarithm of GDP|
|LGDP_BK_cycle|Baxter-King cycle of natural logarithm of GDP|
|int1. Index negative|Index of negative int1 values|
|int2. Index negative|Negative change in the ratio of foreign assets by the Central Bank and M2|
||Percentual Change of the Herfindal index of export value; Source: Lederman &|
|∆%ExportHerfin|Maloney (200X)|
|Saving nominal/GDP|Nominal gross domestic savings as a share of GDP; Source: WDI|
|Interest Rate|Nominal interest rate; Source: IFS|

34
- Most lagging regions in income terms are also lagging in terms of having higher than average incidence of poverty and/or poorer human development indicators (Figure 6, Maps 2 and 3,  and Table 1).

**Table 1: Population Mass, Economic Mass and Poverty Mass: Bangladesh Districts** **<u>Bordering India’s North East and West Bengal (2000)</u>**

|**Districts**|**Population**|**Per Capita**<br>**Income**|**Economic**<br>**Mass**|**Human**<br>**Poverty**<br>**Index**|**Poverty**<br>**Mass**|**Literacy**<br>**rate (age**<br>**7+)**||
|---|---|---|---|---|---|---|---|
|_Bordering_<br>_North East_||US$|US$|||Both<br>Sexes|Female|
|Bandarban|298120|339|101062680|39.77|118562|31.66|23.67|
|Brahmanbaria|2398254|304|729069216|37.65|902943|39.45|36.68|
|Comilla|4595557|266|1222418162|26.72|1227933|45.98|42.63|
|Feni|1240384|262|324980608|28.15|349168|54.26|51.18|
|Habiganj|1757665|299|525541835|34.45|605516|37.72|33.62|
|Jamalpur|2107209|277|583696893|41.87|882288|31.80|28.02|
|Khagrachari|525664|239|125633696|37.58|197545|41.80|32.65|
|Kurigram|1792073|282|505364586|39.42|706435|33.45|27.55|
|Lalmonirhat|1109343|265|293975895|35.63|395259|42.33|36.25|
|Maulvibazar|1612374|280|451464720|32.69|527085|42.06|38.45|
|Mymensingh|4489726|305|1369366430|34.70|1557935|39.11|36.26|
|Netrokona|1988188|303|602420964|37.06|736822|34.94|31.88|
|Nilphamari|1571690|261|410211090|38.50|605101|38.84|32.58|
|Panchagarh|836196|277|231626292|35.03|292919|43.89|37.33|
|Rangamati|508182|365|185486430|35.74|181624|43.59|34.21|
|Sherpur|1279542|277|354433134|42.98|549947|31.89|28.55|
|Sunamganj|2013738|262|527599356|39.44|794218|34.37|30.47|
|Sylhet|2555566|315|805003290|35.06|895981|45.59|41.51|
|_Bordering_<br>_West Bengal_||||||||
|Thakurgaon|1214376|329|399529704|35.87|435597|40.32|35.87|
|Dinajpur|2642850|311|821926350|33.31|880333|36.24|33.31|
|Joypurhar|846696|323|273482808|35.70|302270|37.23|35.70|
|Naogaon|2391355|305|729363275|32.32|772886|36.91|32.32|
|Nawabganj|1425322|255|363457110|39.66|565283|41.68|39.66|
|Rajshahi|2286874|339|775250286|33.57|767704|35.98|33.57|
|Kushtia|1740155|320|556849600|35.78|622627|36.79|35.78|
|Meherpur|591436|318|188076648|36.01|212976|36.91|36.01|
|Chuadanga|1007130|305|307174650|32.11|323389|34.02|32.11|
|Jhenaidah|1579490|317|500698330|32.37|511281|35.74|32.37|
|Jessore|2471554|357|882344778|28.20|696978|30.77|28.20|
|Satkhira|1864704|309|576193536|31.74|591857|35.53|31.74|
|**_Dhaka_**|**8511228**|**758**|**6451510824**|**26.51 **|**2256327**|||
|**Bangladesh **|**124355263**|**355**||||||

Note: Population data refer to 2001, per capita income data refer to 1999/2000, and human poverty index refers to 2000.

_Source_ : Massum 2008 13
2008). That begs the question then of why there are two neighborhoods of seemingly noninteracting firms found in one city. The answer must be that firms benefit more generally from the urban scale.

In Table 8 we address this issue for household income data. All columns control for citywide ln PD. Local density and overall measures are correlated, so if the local density measures matter that will reduce the elasticity (0.59 in col 1) of ln city PD. This is the case except in column 4 where the local density measure is insignificant. While the coefficients on ln city PD decline when we include key local measures, they are still very large, indicating large marginal returns to overall city density. Columns 2-5 each experiment with a different measure of local economic density. Although we have argued that a simple PD measure works as well or better than anything else for a citywide measure, that does not mean at the local level it is the best measure. In column 2 the local measure is ln (local PD); in 3 it is ln (local PPD); in column 4 it is ln (own cell population), and in 5 it is the simple de la Roca-Puga measure (A in eq. 3). The ln (own cell population) has an insignificant effect. In all other columns, local density measures have strong significant effects, with little to choose between them in terms of magnitudes. Magnitudes are all about 0.14, much smaller than the elasticity of about 0.45 for overall city density. R-squared’s are very similar across columns 2, 3 and 5, although the column with ln local PPD does have the highest R-squared. Horseraces suggest ln local PPD with a coefficient (s.e.) of 0.135 (0.071) dominates ln local PD (0.038 (0.068)) and ln local PPD with a coefficient of 0.141 (0.058) dominates ln local A (0.030 (0.053)).

In summary, for a household, the overall density measure for the city has strong effects on incomes, but also the local area around the household has strong effects as well. Households benefit from both dense overall cities and dense 11 x 11 km square neighborhoods.

# **5.2.3 The core versus fringe of cities**

Finally, in this section, we examine the role of the core versus fringe of cities. Do people benefit from overall city PD or really just the PD of the core? Do people living in the fringe benefit differently from overall or local density effects? We look at this in Table 9. Columns 1 and 2 are very important. Column 1 shows that, while people in the fringe of 25