rafmacalaba/datause-encoder-data huggingface dataset:
|||**Table 34: IV Est**|**imation (9)**|||||
|---|---|---|---|---|---|---|---|
||No. of HH members e<br>Agriculture|ngaged:<br>Industry|Commerce|Service||Public sector|HH Monthly income|
|D|0.098 (0.671)|0.057 (0.062)|0.109 (0.185)|0.321|(0.151) **|0.950 (0.371) ***|0.290 (0.223)|
|lnHH size|0.765 (0.209) ***|-0.002 (0.019)|0.022 (0.058)|0.059|(0.047)|0.276 (0.116) **|0.395 (0.068) ***|
|Adult share|1.145 (0.439) ***|0.046 (0.040)|-0.032 (0.121)|0.029|(0.099)|0.316 (0.242)|1.323 (0.142) ***|
|HH head sex: Male|-0.530 (0.252) **|-0.007 (0.023)|0.005 (0.070)|0.065|(0.057)|-0.049 (0.139)|0.005 (0.080)|
|HH head education: Elementary|0.168 (0.181)|0.021 (0.017)|0.068 (0.050)|0.027|(0.041)|0.224 (0.100) **|0.026 (0.058)|
|HH head education: Secondary|-0.484 (0.295) *|0.000 (0.027)|0.288 (0.081) ***|0.021|(0.066)|0.619 (0.163) ***|0.074 (0.096)|
|HH head education: University|-1.344 (0.598) **|0.002 (0.055)|-0.075 (0.165)|-0.077|(0.134)|2.583 (0.330) ***|0.907 (0.187) ***|
|Appliance ownership: Refrigerator|0.149 (0.274)|0.001 (0.025)|-0.036 (0.075)|0.049|(0.062)|0.083 (0.151)|-0.012 (0.089)|
|Appliance ownership: Color TV|0.145 (0.272)|0.017 (0.025)|0.053 (0.075)|0.020|(0.061)|0.138 (0.150)|0.046 (0.090)|
|Appliance ownership: Radio|-0.192 (0.168)|0.015 (0.016)|0.000 (0.046)|-0.026|(0.038)|-0.272 (0.093) ***|0.048 (0.054)|
|Appliance ownership: Gas stove|0.295 (0.283)|0.007 (0.026)|0.019 (0.078)|0.057|(0.064)|0.258 (0.156) *|0.018 (0.095)|
|Appliance ownership: Washing||||||||
|machine|-0.088 (0.197)|0.001 (0.018)|0.008 (0.054)|0.000|(0.044)|0.123 (0.109)|0.153 (0.063) **|
|Gas use for cooking|-0.216 (0.190)|0.007 (0.018)|0.128 (0.052) **|0.112|(0.043) ***|0.283 (0.105) ***|0.285 (0.061) ***|
|Power use for lighting|-0.561 (0.289) **|0.007 (0.027)|0.049 (0.080)|0.084|(0.065)|0.134 (0.159)|-0.099 (0.093)|
|Tap water for cooking|0.053 (0.183)|0.022 (0.017)|0.027 (0.050)|-0.008|(0.041)|0.142 (0.101)|0.118 (0.059) **|
|Constant|-3.426 (1.002)***|-4.714(0.092)***|-4.780 (0.276)***|-5.106|(0.225)***|-6.021(0.553)***|4.180 (0.331)***|
|Obs.|911|911|911|911||911|859|
|R-squared|0.051|0.006|0.032|||0.081|0.159|
|F-stat|3.15|0.59|1.94|1.19||8.00|11.02|
|Sargan overidentifying restriction test|0.54|1.06|0.06|1.00||0.23|0.35|
|Hausmanexogeneity test|23.07***|1.07|15.32 ***|8.51|***|28.80***|13.83***|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

53
TABLE 3: Firm-level elasticity of substitution across products

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|log<br>�<br>ˆ_p_<br>ˆ ̃_p_<br>�|0.0842|0.2014|0.1996|0.3136|0.1712|0.1996|
||(0.0039)|(0.0045)|(0.0048)|(0.1060)|(0.0040)|(0.0048)|
|_ζ_|0.9157|0.7985|0.8003|0.6863|0.4368|0.4721|
|Obs|1292329|794376|766804|1292329|794376|766804|
|K-PF|.|.|.|27.284|17.950|15.868|
|Estimator|OLS|OLS|OLS|2SLS|2SLS|2SLS|
|HS digits|4|8|8|4|8|8|
|Product-time FE|Y|Y|Y|Y|Y|Y|
|Buyer-time FE|Y|Y|Y|Y|Y|Y|
|Buyer-product FE|||Y|||Y|

**Notes:** The estimates come from estimating Equation (10). Time is monthly frequency. Price indices are constructed by recovering the residuals when estimating _ε_ and the estimates of _ε_ . The first three columns are OLS estimates of _ζ_ . The last three columns are 2SLS of estimates of _ζ_ using weighted averages of both bilateral and seller-level instruments across sellers. Bilateral instruments correspond to Equation (17), and seller-level instruments correspond to Equation (16). Each column corresponds to a different combination of fixed effects and HS codes. Columns (1)-(2) and (4)-(5) correspond to our preferred specification when estimating _ε_ for 4-digit and 8-digit HS codes. Additionally, in columns (3) and (6), we also include buyer-by-product fixed effects. The first row reports the estimates associated with changes in log relative unit values. Standard errors are clustered at the buyer’s district level and are reported in parentheses below each estimate. The third row reports the implied value for _ζ_ , which is 1 minus the estimate on the first row. The fourth row reports the number of observations. The fifth row reports the Kleibergen-Paap F statistic from the first stage. The sixth row denotes whether the estimates are obtained through OLS or 2SLS. The seventh row reports the HS code.

of complementarity across products compared to suppliers.<sup>27</sup> In columns (5) and (6), similar values for this elasticity hold when we define a product as an 8-digit HS code, and after the inclusion of buyer-by-product fixed effects. Finally, first-stage F-stats are high, which reflects the statistical relevance of our weighted averaged instruments.

Unlike the elasticity of substitution across suppliers within a product category, there have been previous attempts in the literature to estimate the elasticity of substitution across products or industries. In particular, other work has estimated a wide range of values for parameters akin to _ζ_ depending on the aggregation of the industry and the research question. Our elasticity is close to Boehm et al. (2019), who estimate an elasticity across HS-10 products that lies between 0 _._ 42 − 0 _._ 62 for non-Japanese affiliates and 0 _._ 20 for Japanese affiliates. Atalay (2017) finds an estimate of around 0 _._ 10 for 30 aggregated industries using US data.

27This is consistent with the macroeconomics literature (Houthakker, 1955; Bachmann et al., 2022; Lagos, 2006).

29
core component of social capital (Coleman, 1994; Putnam, 2001) and the absence of trust severely hampers transactions between actors (Fehr, 2009).

Trust is formed between individuals through networks and institutions (Ostrom, 2000). In networks, the repeated nature of social interaction allows individuals to examine each other’s behaviors. If these repeated interactions send a positive (negative) signal, trust is enhanced (diminished). In institutions, rules are established to punish or reward behaviors, and a common understanding of these rules between individuals can foster trust. However, when rules are not clear in institutions, a lack of common expectations can decrease trust.

We view the information treatment as an intervention aimed to enhance network formation as parents are expected to participate in group discussions with other parents and teachers, and share their views and experiences. In the information sessions, parents learn about what teachers are teaching in school and how the learning objectives align with children’s development. This means that in theory, the information sessions give parents an opportunity to receive repeated positive signals about teachers. Thus, we hypothesize an enhancement of trust between parents and teachers from the information intervention.

In contrast, we view the double grant treatment as an intervention aimed to strengthen rules in institutions (in this case, schools) by giving parents more financial authority over school resources. Given the flexibility in how these funds can be allocated, the double grant intervention can create “an incomplete social contract” (Ostrom, 2000), whereby parents and teachers may not share common expectations about how these funds should be distributed. Thus, we hypothesize that the institutional context surrounding the double grant intervention can lead to a decline in trust between teachers and parents.

To test these theoretical predictions, we estimate the effect of the two experiments on trust. Trust is widely measured by asking survey respondents whether they trust others.<sup>9</sup> Following the survey literature on measuring trust in economics (Fehr, 2009; Glaeser, Laibson, Scheinkman, & Soutter, 2000), we directly asked parent and teachers about their trust in each other. Specifically, we asked parents, "do you think that most teachers can be trusted?" and asked teachers, "do you

> 9The most frequently used measure of trust is based on the American General Social Survey (GSS) and the World Values Survey (WVS), which asks, “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” Miller and Mitamura (2003) demonstrates that this wording measures both beliefs about the trustworthiness of others as well as preferences towards taking social risks. We follow the recommendation by Miller and Mitamura (2003) and remove the risk preference aspect of the question.

19
higher values for s: for any given value of σ, a higher s (say, from s0 to s1 > s0) is associated with a higher value of y (say, from y0 to y1 > y0).

While technological progress in the automotive industry in and of itself cannot fully offset the adverse impacts of labor reallocation, it does go some way towards providing a partial countervailing force. Furthermore, from the point of view of a government that inherits a previously-established autoindustry, technological change will make all the difference: Policies that help reduce σ or increase s will determine the overall impact on output and welfare.

**Figure 1. Value-Added Per Worker and Labor Misallocation**

<!-- Start of picture text --> y<br>1<br>y1<br>y0 s1 > s0<br>s0<br>0 σ<br>σ0<br><!-- End of picture text -->

# **VI. Model Limitations and Possible Extensions for Future Research**

While highly stylized, the model discussed in this paper sheds light on the risks and costs of fomenting structural change by fiat, through the use of highly distortionary policy choices. The results of the model are also consistent with the empirical evidence presented in IDB (2010) and the papers in Araujo _et al_ (2016) on the role of resource misallocation in constraining Latin America’s economic growth prospects. Nonetheless, the analysis in this paper could usefully be extended in different directions, which are not pursued here but rather left as a possible future line of research.

One such extension is the introduction of a time dimension. As it is, the model is presented in a static, one-period setup. An intertemporal setup would allow for a more refined discussion of consumption behavior as well as for the possible consideration of positive externalities – such as learning effects or technology transfer – from manufacturing vehicles domestically. Such spillovers could only be appropriately analyzed in a dynamic context, in which they could (at least partially) compensate over time for the adverse effects from import substitution discussed in the previous section.

The model could also potentially be extended to incorporate spatial dimensions. This would involve modeling “regions” at different development levels within a country. In this case, policy distortions could take the form of fiscal incentives (such as tax breaks) designed to attract vehicle manufacturing 13
The remaining section is then to know if the current strategy to invest massively in rural roads is sustainable in the short and medium term.

# **4. How much to invest in roads to ensure rural growth? The public policy perspective**

As stated in the previous section, mechanization in Uganda is not widespread because average plot size is small and consolidation does not take place (on top of a lack of investments). The GOU adopted an ambitious investment plan for rural roads. However, regarding investment in roads to ensure rural growth, constructing or maintaining roads in areas with high agriculture potential is a recommended policy. For the time being, there is strong disconnect between the funds allocations to maintain rural roads and agriculture potential despite official discourses.

In March 2008, a _Ten-Year District, Urban and Community Access Roads Investment Plan_ (DUCARIP) with a corresponding financing plan was announced. In the upcoming ten years, GOU has committed itself to invest a total of 1,594 billion Shillings or USD862 million, of which 953 billion Shillings for district roads for ten years from fiscal year 2008/09 to fiscal year 2017/2018. Over the medium term, 2008/09 to 2012/13, the estimated shortfall in financing the medium-term investment plan for both DUCARIP and MTEF is around 365.5 billion Shillings (USD197.7 million equivalents; see Table 11 for details).

**Table 11:   Financing Plan of the Medium Term Expenditure (in billion Shillings)**

|**Expenditure**<br>**estimates**|**2008/09**|**2009/10**|**2010/11**|**2011/12**|**2012/13**|**Total**|
|---|---|---|---|---|---|---|
|DUCARIP projection|125.5|141.2|156.9|174.2|171.7|769.5|
|MTEF projections|55.8|75.8|90.8|90.8|90.8|404|
|Shortfall|69.7|65.4|66.1|83.4|80.9|365.5|

Source: MOFPED MTEF Ceiling FY 2007/08 – FY2012/13.

32
demand more independent work, problem solving, creativity, and leadership skills – all socio-emotional and higher-order cognitive skills – than do non-exporters (DiGropello 2010) (Figure 5).

Although exporters demand more of every skill type than non-exporters, with a particular demand for higher-order cognitive and socio-emotional skills, the priority ranking for skill demands do not differ by export-orientation. In the three-country LAC sample, firms that sell to local markets, national markets, or international markets all value socio-emotional skills as the most important skill set, ranking it 20 percentage points higher than general (cognitive) skills (Bassi et al. 2012). Technical skills were ranked the lowest, prioritized by only 16 percent of firms, regardless of their market. Indonesian and Filipino exporting and non-exporting employers prioritize the same skills sets. In Indonesia, exporters and non-exporters most demand thinking skills among their managers/professionals and basic skills among their skilled workers. Behavioral skills are ranked second most important for all three occupation groups (Di Gropello 2011). Filipino exporting and non-exporting employers prioritize (in order of descending importance) independent work, communications, teamwork, and problem solving, though the problem-solving ranks second for exporting firms (Figure 5).

The Philippines study is the only data point in our sample that examines the gaps identified by exporter and non-exporting companies. The greatest skills gaps identified by exporters are initiative, time management, leadership, and problem-solving. Non-exporters perceive the greatest skills gaps in these, areas, as well, but they add creativity to the list (diGropello 2010).

# **4.6  Innovator v. traditional firms**

The Russian and Macedonian<sup>48</sup> studies, only reporting skills gaps perceived by innovator and traditional firms, demonstrate that innovator firms perceive a more severe shortage of higher-order cognitive and technical skills than traditional firms. Russian innovator firms note that managers most lack decision-making skills, followed by leadership while unskilled workers most lack conscientiousness and problem-solving skills, followed by professional skills (Table 7, left column). Traditional Russian firms name the same skills gaps as the innovator firms for both managers and workers (Vasiliev et al. 2013).

Though the top ranked skills gaps do not differ by firm modernity, Russian innovator firms identify larger skills gaps than traditional firms. Innovator firms identify greater gaps across all skills, but the gaps are particularly large in decision-making, and problem-solving (Table 7, right column). Other

> 48 In the Russian sample, modern firms are defined as those that have their own website. In the Macedonian sample, modern firms are those that have recently invested in new technology.

22
water pollution intensity across China's provinces since 1987. The experience of regulation has been more mixed: Higher air- and water-pollution levies have significantly reduced pollution intensity, but real effective air levies have actually fallen in all five focal provinces since 1987 and the water levies have fallen in Beijing and Shanghai.

The results have been equally mixed. In Guangdong, where increases in the water levy have reinforced the impact of reform (including a decline in the share of COD-intensive sectors), there has been a very sharp decline in water pollution intensity. In Beijing, by contrast, the effective air pollution levy has fallen substantially while the share of S02-and TSP-intensive sectors has risen. These changes have partly counteracted the impact of the reforms, and industrial S02- and TSP-intensities have declined modestly while production scale has risen rapidly.

What would happen in the future if the reforms continued but regulation wasn't tightened? To explore this possibility, we have combined our econometrically-estimated intensity equations for COD, S02 and TSP with projected changes in pollution intensity factors (sector, scale, ownership), while holding air and water pollution levies at 1993 levels for each province. This is equivalen **t** o projecting the environmental consequences of continued economic reform alone, with no contribution from stricter regulation. We term this the 'win-win' scenario because economic reform is already presumed to be beneficial on other grounds. Its impact on pollution intensity provides a net environmental gain without any cost explicitly incurred for pollution control.

# **Tightening Regulation: Two Scenarios**

Tightening regulation will provide explicit incentives to reduce pollution intensity, which will complemen **t** he impact of economic reform. To explore the implications of different strategies, we have developed two scenarios: In the first, air and water pollution levies are both increased by 5% annually. This leads to a fourfold increase in the real levy by 2020. A second scenario increases levies by 10% annually, leading to a thirteenfold increase by 2020.

# **Water Pollution**

<!-- Start of picture text --> Industrial COD Loads in China<br>- .........<br>a .0<br>.0 Reform<br>a .... Levy 5X<br>. L....-|-- Levy 10%<br>2.0<br>0.0 I ,<br>1988 1994 2000 2010 2020<br><!-- End of picture text -->

Comparative forecasts for total - ......... COD load are presented for China a .0 as a whole and by city in Figure 24. The projected impact of continued **a ....** reform alone (the 'win-win' . scenario) is remarkable. For China, 2.0 total COD emissions from industry 0.0 **I** , are projected to rise only modestly, 1988 1994 **2000** 2010 2020 from 8.3 million tons annually to 9.8 Figure **24: COD Loads in Three Scenarios** million tons, during a period of

-- rapid industrial growth. In three of the five cities - Shanghai, Guangzhou and Shenyang

_25_
Policy Research Working Paper 9735

# **Abstract**

This study documents the labor market outcomes and timeuse patterns of women in urban Bangladesh. Using survey data collected in 2018 in low-income neighborhoods of Dhaka, the paper finds that women with children aged 0–5 years have lower likelihood of labor market participation, lower likelihood of working, and lower likelihood of being an earner, compared to women with no children and women with children aged 6 years or older. While this motherhood penalty affects all mothers, those who have young children but have no access to childcare support face the largest penalty. Time-use patterns confirm these findings, indicating that mothers of young children with no access to childcare spend less time on market work, more time on unpaid work, and less time on leisure or other activities. In addition, they are more likely to perform childcare as a secondary activity along with other paid and unpaid work, which may have implications for their productivity and the quality of care provided to children. The paper proposes entry points to ease the double burden of paid and unpaid care work on mothers in urban areas, where the availability and affordability of formal childcare services is low, and community-based or other informal care arrangements are not common.

This paper is a product of the Social Sustainability and Inclusion 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 etas@worldbank.org and tahmed9@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
grow at the still formidable rate as that of India.<sup>12</sup> Some African countries<sup>13</sup> had per capita growth rates below 3 percent in 2000-09. Their future per capita growth rates are assumed to be 3.91 percent - the average rate for high growth African economies in 2000-09.

**Table 3: Average annual real GDP per capita growth rates in Sub-Saharan Africa**

|**Country/Region**|**1980-89**|**1990-99**|**2000-09**|
|---|---|---|---|
|**I**|**II**|**III**|**IV**|
|**AFRICA**||||
|Burkina Faso|1.32|2.59|2.87|
|Botswana|7.15|3.09|2.75|
|Côte d’Ivoire|-3.21|0.47|-0.69|
|Cameroon|1.40|-1.54|0.65|
|Ethiopia|-0.91|1.00|5.78|
|Ghana|-0.51|1.65|3.09|
|Kenya|0.28|-0.74|1.57|
|Madagascar|-1.96|-1.44|1.39|
|Mozambique|-1.96|2.74|4.68|
|Malawi|-1.87|2.17|1.58|
|Nigeria|-1.83|-0.11|3.82|
|Rwanda|-1.50|-2.12|5.33|
|Senegal|-0.31|0.04|1.51|
|Tanzania|-0.15|-0.17|4.08|
|Uganda|-0.41|3.71|4.16|
|Rest of SSA|1.00|-0.20|4.17|
|South Africa|-1.02|-0.12|2.62|
|Zambia|-2.02|-2.26|2.66|
|Zimbabwe|-0.48|0.80|-8.01|
|**AGGREGATE REGIONS**||||
|Sub-Saharan Africa|-0.35|0.25|0.73|
|East Asia and Pacific|5.88|7.18|8.14|
|Low Income South Asia|1.59|2.14|3.90|
|Middle Income South Asia|3.03|3.49|5.45|

Source: Authors’ estimates from World Bank (2013e) The low growth baseline examines the implications of a future where Africa did not maintain the recent high growth and reverted to the poor performance of 1980-99. All countries experience their average annual per capita growth from 1980-99, with the exception of the high income countries China, Russia, OPEC, and the countries of SSA that had net contractions over the period. The per capita growth of high income countries has been lower in the new millennium compared to the past, and there is consensus that the growth is unlikely to return to those rates. So the 2000-09 growth rates are considered for high income countries, the same as in the ‘high growth’ baseline. China experiences the same growth rate as India for the reasons described for the high income countries. Russia experienced contractions in the 1990s that left it with a noticeably smaller economy between 1980 and 1999. So the growth rate for the 1980s is considered. For similar reasons, OPEC’s growth rate is considered to be the rate from the 1990s. The African countries that experienced net contractions between 1980 and 1999<sup>14</sup>

> 12 The issue of China’s future growth is treated extensively in World Bank and DRC (2013).

> 13 Burkina Faso, Botswana, Cote d’Ivoire, Cameroon, Ghana, Kenya, Madagascar, Malawi, Nigeria, South Africa, Zimbabwe, and Zambia.

14 Côte d’Ivoire, Cameroon, Ethiopia, Kenya, Madagascar, Malawi, Rwanda, Senegal, South Africa, and Zambia. 13