rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4:** The effect of a first girl on desired fertility and the use of contraception

|dep. var:|=1 wants a|nother child|=1 using c|ontraceptives|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|frst-born girl|0.018**|0.023**|-0.008|-0.011**|
||(0.009)|(0.010)|(0.005)|(0.005)|
|age|0.004|0.003|0.010|0.011|
||(0.014)|(0.014)|(0.013)|(0.013)|
|age sq.|-0.000**|-0.000**|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|age at frst birth|0.018***|0.019***|0.002|0.001|
||(0.002)|(0.002)|(0.001)|(0.001)|
|eduyrs|-0.003**|-0.003**|0.005***|0.005***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|husband age|0.003|0.003|0.002|0.002|
||(0.003)|(0.003)|(0.002)|(0.002)|
|husband age sq.|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|husband eduyrs|0.004***|0.004***|0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|
|age at frst marriage|0.004***|0.004**|-0.002*|-0.002|
||(0.002)|(0.002)|(0.001)|(0.001)|
|frst–born dead||0.109***<br>(0.014)||-0.032***<br>(0.009)|
|frst-born girl x||-0.014||0.015|
|frst-born dead||(0.023)||(0.017)|
|Observations|17460|17460|17587|17587|
|Pseudo R2|0.277|0.281|0.222|0.223|

_Notes._ Probit estimates, marginal effects reported. Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights. Includes six region dummies, urban dummy, birth year fixed effects, ethnicity and religion, ethnic and region-specific time trends, # children ever born, and a wealth index.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels.

1).<sup>30</sup> As expected, the effect is larger for women with a surviving first-born daughter (column 2), and equivalent to a 3.6 percent effect (compared to the mean for women with a first-born son).

Columns (3–4) of table 4 show the results for the use of contraception. Women with a first-born girl are 0 _._ 8 percentage points less likely to use any method of contraception, although the coefficient is not statistically significant. Column (2) estimates the same effect for women with a surviving daughter and, as expected, the effect on the use of contraception is larger ( _−_ 1 _._ 1 percentage points, equivalent to 6.6 percent effect), now significant at the 5% level. This is an important effect considering that only a small fraction of women are currently using contraceptives.

Table 5 shows the estimated effects of the birth of two consecutive daughters on fertility (realized and desired) and contraceptive use for the subsample of women with at least two children ever born. As expected, the percent effects are larger: compared to women with two first born sons, women with two first born daughters have 2.6 percent more children, and are 8.6 percent more (less) likely to desire to have more children (to use contraceptives).

> 30The regressions for desired fertility and contraceptive use also control for the number of children ever born.

20
14 Next, we present the model’s theoretical underpinnings. In a first version, following Maddala and Nelson (1974), we assume that both residuals ε1,t and ε2,t are stationary processes, independently and normally distributed with variance σ<sup>2</sup> 1<sup>and σ2</sup> 2<sup>respectively.</sup> Under these regularity assumptions, the transformed variable ε1,t - ε2,t is normally distributed with a variance equal to σ<sup>2</sup> = σ<sup>2</sup> 1<sup>+σ2</sup> 2<sup>.</sup> Hence, the reduced variable (ε1,t - ε2,t)/σ follows a _N(0,1)_ . The probability that the observation _qt_ belongs to the demand regime, denoted πt(d), can then be computed as the corresponding _N(0,1)_ fractile: _' '_ where _ht_ =  _x_ 2, _t_  2  _x_ 1, _t_  1  /  , and  _x_ denotes the cumulative distribution function of the (s) _N(0,1)_ . Symmetrically, the probability of obtaining the supply regime, denoted πt , is defined by _P_  _St_ < _Dt_  = 1   _ht_ 8

# **3.3 Data**

The quarterly dataset from 1998 to 2011 was compiled from different sources. Data regarding credit to private sector came from the banking survey published in CBE’s monthly report. Total deposits, banks’ reserves with the CBE (that were used to construct the lending capacity) and bank lending rates also come from the same source. Industrial production comes from the Central Agency for Public Mobilization and Statistics (CAPMAS) deflated by the CPI; and the stock market index ( _EGX30_ ) as reported by the Egyptian Stock Market. EGX 30 Index is a market capitalization-weighted index. Finally, lending rates were taken from CBE reports while T-Bill rates were gathered from Ministry of Finance reports<sup>9</sup> .

All nominal variables were deflated by the consumer price index. To partially control for endogeneity bias, the models use the lagged values for industrial production, bank deposits, bank reserves and the stock market index.

> 8 For further details, see appendix 1-4

> 9 To check whether our series are stationary or not, we ran the Augmented Dickey-Fuller test and found they all suffer from a unit root problem. After taking the first difference and running the ADF, we found our variables stationary. Finally, we conducted the Johansen’s test to determine whether our series are co-integrated or not. We found that they form a cointegrated vector, allowing us to estimate the model in levels.
Russia to import from China and India; (iii) third-market export competition, and (iv) indirect trade impacts.

In this study, we began with a representation of the world in 2005. We then projected forward to 2020, to take into account the rapidly-increasing importance of China and India in the world economy. We also took into account liberalization and reductions in trading costs that increased India’s interaction with the world economy. For our analysis, we used a special version of the Global Trade Analysis Project (GTAP) model designed to allow for extensive exportoriented manufacturing where Chinese and Indian exporters have access to imported intermediate inputs duty-free. In addition to examining the consequences of high export growth from China and India, we considered two scenarios under which the quality and variety of exports from these two giants improve as they grow.

Russia benefits substantially from high growth in China and India. Real incomes in Russia rise by $US 8.4 billion at 2004 prices when growth alone is considered. When we took into account increases in product variety and improvements in quality of exports from India and China these gains increased substantially. Because of uncertainty about the closeness of substitution between varieties, we considered two possible levels of preference for variety in the expansion of exports from China and India. The gains to Russia were $US 14.7 billion in the strong preference-for-variety scenario and $US 10.3 billion in the central scenario using the elasticity estimates preferred by Hummels and Klenow (2005).

We found that this increase in growth rates of China and India increases their exports to and imports from Russia. The overwhelming majority of the welfare gains—between $US 8.7 billion and $US 12.6 billion—accrued through improvements in Russia’s terms of trade. These terms-of-trade gains were primarily associated with increases in world prices of energy products. The increment to growth that we considered raised Russia’s energy export prices between 1.7 and 6.1 percent, while export prices for manufactured goods and services declined up to 5.8 percent. The prices of most agricultural prices changed somewhere between -2.8 to 1.8 percent, with downward pressure on these prices coming from increased productivity growth in China and India, and from the low income elasticities of demand for these products, and upward pressure coming from labor being pulled by Rybcznski effects out of agriculture in China and India into more capital and skill-intensive activities.

18
Avila, Joseph A. and Bradley W. Whitehead (1993): “What is Environmental Strategy?,” _The Mckinsey Quarterly_ 4, p. 53-68 Bahar, Heymi, Jagoda Egeland, and Ronald Steenblik (2013): “Domestic Incentive Measures for Renewable Energy with Possible Trade Implications,” _OECD Trade and Environment Paper_ 2013/01, OECD Publishing Beckenstein, Alan R. and H. Landis Gabel (1985): “The Economics of Antitrust Compliance,” _Southern Economic Journal_ 52, p. 673-692 Beltramello, Andrea, Koen De Backer and Laurent Moussiegt (2012): “The Export Performance of Countries within Global Value Chains (GVCs),” _OECD Science, Technology and Industry Working Papers_ 2012/02, OECD Publishing Blais, Ann-Renée and Bernard Sinclair-Desgagné (2002): “Leadership in 3S’s: Skills, Styles, Situations,” CIRANO Burgundy Report, Montréal, available online at http://neumann.hec.ca/chaireing/art/Leader_engl.pdf DCED Green Growth Working Group (June 2012): “Green Value Chains to promote Green Growth,” available at - <u>www.Enterprise Development.org</u> David, Maia and Bernard Sinclair-Desgagné (2005): “Environmental Regulation and the Eco-Industry,” _Journal of Regulatory Economics_ 28, p. 141-155 De Backer, Koen and Norihiko Yamano (2012): “International Comparative Evidence on Global Value Chains,” OECD Science, Technology and Industry Working Paper 2012/3, Paris <u>http://dx.doi.org/10.1787/5k9bb2vcwv5j-en</u> De Marchi, Valentina (June 2011): “Greening Global Value Chains: The Role of Lead Firms in Fostering Environmental Innovations,” unpublished Doctoral Dissertation, University of Padua Durand, Aurélia and Bernard Sinclair-Desgagné (2012): “The Growing Business of Mitigating Ecological Footprints,” Working Paper available at <u>http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2043092</u> EBI Report 2020 (2011): _The U.S. Environmental Industry & Global Market_ , Environmental Business International, 3269 pages Gabel, H. Landis and Bernard Sinclair-Desgagné (2000): “Corporate Responses to Environmental Concerns,” in _Principles of Environmental and Resource Economics. A Guide for Students and Decision Makers_ (Henk Folmer, H. Landis Gabel, and Hans Opschoor, eds.), Edward Elgar (2<sup>nd</sup> edition), p. 265-279 Gabel, H. Landis and Bernard Sinclair-Desgagné (1997): “Environmental Auditing in Management Systems and Public Policy,” _Journal of Environmental Economics and Management_ 33, 331-346 Glachant, Matthieu (2013): “Greening Global Value Chains: Innovation and the International Diffusion of technologies and Knowledge,” paper presented at the Annual Green Growth Knowledge Platform (GGKP) conference, Paris, 4-5 April 2013 Guide, V. Daniel R. and Luk Van Wassenhove (2009): “The Evolution of Closed-Loop Supply Chain Research,” _Operations Research_ 57(1), p. 10-21 Gimpel, Jean (1975): _La révolution industrielle du Moyen-Âge_ , Éditions du Seuil Holmstrom, Bengt and Paul R. Milgrom (1991): “Multi-Task Principal-Agent Analysis: Incentive Contract, Asset Ownership, and Job Design,” _Journal of Law, Economics, and Organization_ 98(6), p. 1119-1158 Johnstone, Nick, editor (2007): _Environmental Policy and Corporate Behavior_ , Edward Elgar Publishing 21
Table <u>8: Trade, Legal Origins and Bureaucratic Quality, 5-year averages</u> Dep. Var.: log of average trade flow

||(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|(5)<br>OLS|(6)<br>IV|
|---|---|---|---|---|---|---|
|Years in Average|19|85-1989|19|90-1994|19|95-1999|
|Different Legal Origin|-0.04|-0.20|-0.11|-0.19|-0.15|-0.19|
||(-0.50)|(-2.33)|(-1.72)|(-2.78)|(-3.10)|(-3.51)|
|Bureaucratic Quality|0.45|0.81|0.43|0.58|0.32|0.44|
||(20.65)|(13.55)|(20.30)|(11.78)|(14.82)|(7.49)|
|log distance|-1.25|-1.20|-1.12|-1.09|-1.12|-1.10|
||(-27.42)|(-24.82)|(-29.94)|(-27.33)|(-38.88)|(-37.26)|
|log product of GDP|1.16|1.02|1.16|1.11|1.05|1.00|
||(62.27)|(37.21)|(73.58)|(50.92)|(78.48)|(42.41)|
|log product of land area|-0.27|-0.22|-0.24|-0.23|-0.13|-0.11|
||(-20.06)|(-15.27)|(-20.89)|(-17.43)|(-13.36)|(-7.30)|
|no. of landlocked|-0.08|-0.16|-0.29|-0.31|-0.41|-0.43|
||(-1.01)|(-1.97)|(-4.97)|(-5.31)|(-9.22)|(-9.70)|
|no. of islands|-0.05|-0.36|-0.14|-0.22|-0.21|-0.28|
||(-0.76)|(-4.32)|(-2.32)|(-3.40)|(-4.28)|(-4.96)|
|common border|0.91|1.08|1.12|1.21|1.22|1.26|
||(4.76)|(5.21)|(6.52)|(6.83)|(7.70)|(7.75)|
|common language|0.46|0.35|0.52|0.48|0.53|0.51|
||(5.34)|(3.91)|(7.18)|(6.59)|(8.73)|(8.12)|
|currency union|1.96|1.54|2.23|2.18|1.69|1.72|
||(6.83)|(4.98)|(8.25)|(7.98)|(6.03)|(6.11)|
|Observations|5353|5353|5627|5627|7014|7014|
|R^2|0.62|0.61|0.71|0.71|0.74|0.74|
|First stage R^2||0.38||0.44||0.52|
|% increase in trade due to<br>1s.d. institution|121%|318%|99%|154%|58%|88%|

Notes: t-values in parentheses. Standard errors are calculated taking into account country-pair clusters. A constant is not reported. Each column reports a regression performed on one cross-section of 5-year averages. The averages are taken over all _available_ years in the 5-year sub-sample, and the procedure takes into account missing year observations. IV estimators use the sum of absolute latitude as an instrument for the sum of bureaucratic quality for both countries in a trading pair. The sum of bureaucratic quality ranges from 0 to 8 with means 4.21, 4.58, 4.85, with standard deviations 1.77, 1.62, 1.42, in years 1985-89, 1990-94, 1995-99, respectively. The percent increase in trade due to 1s.d. institution (property rights) is calculated as follows: exp(coef*sd)-1.

43
At this point a word is in order about the independent variables that we use. Most are derived from the 2010 VF survey, but we are able to match the areas covered in that survey with household data from the 2009 Socio-economic Survey, and, for rural areas, the 2009 Community Survey. Some of the variables reflect the supply side, including measures of initial injections of capital into the Village Fund, as well as whether the VF encourages, or requires, borrowers to first make deposits; Table 11 provides some summary statistics. The supply of VF credit may also be affected by the characteristics of the VF committee – the educational attainment and professional experience of committee members, the extent to which they feel a need for further training, the method by which officers are chosen, the degree of commitment to equity in lending, and the willingness to borrow from banks to extend VF lending.

The remaining variables pick up demand-side influences, including the size and remoteness of the village or ward, and characteristics of households, including their income, education, gender mix, and propensity to borrow. The complete list of variables that we use is given in Table 11. This is a subset of a much larger list, which we winnowed down by excluding variables that consistently had little or no explanatory power, or had many missing values, or were closely correlated with one of the included variables.

# _Financial or Social Intermediaries?_

Before examining the determinants of VF performance, we first need to ask whether VFs act as proto-banks, or merely as conduits for allocating government funds to members of the public.

Our theoretical conclusions were predicated on the assumption that VFs have a fixed initial quantity of funds ( ) available to lend. In practice, a soundly-run Village Fund can easily borrow a million baht from the BAAC or GSP, at a fairly modest interest rate (3.8%), effectively doubling the amount of funds that they can lend to villagers. In 2010, 24% of VFs undertook such borrowing for on-lending. On the other hand, 72% of VF committees reported that they could safely lend at least an additional million baht to their members. In other words, despite apparently strong effective demand for VF loans, most VFs do not choose to borrow to on-lend.

If VFs were profit maximizers, this behavior would be puzzling. But altruistic VFs are likely to be reluctant to take on more debt. This is because borrowing, for on-lending, exposes VF committee members to personal liability without much commensurate upside (in the form of profit, or honoraria, or larger loans). The VF committees also have limited formal ability to compel repayment. The implication here is that Village Funds will not grow much beyond the initial injection of working capital. Since their comparative 19
In Figure 4, we compare the evolution of the capital intensity of manufacturing in Ethiopia, Vietnam and the United States. Overall manufacturing capital intensity in Ethiopia and Vietnam look similar, with comparable levels and growth trajectories in capital-labor ratios from 2006-2017. The key distinction is that Ethiopia’s capital-labor ratio in manufacturing is four times the capital intensity for its economy as a whole capital (Diao et al, 2021); in Vietnam, the two were roughly equal. In US manufacturing, capital intensity is actually lower than economywide capital intensity. In short, the prevailing capital intensity of manufacturing does not play to Ethiopia’s comparative advantage in relatively low cost and low skilled labor.

Might certain sectors of manufacturing be less capital-intensive? The mass produced readymade garment industry is the least capital-intensive industry within manufacturing, and it still employs large numbers of workers across the globe.<sup>7</sup> In 2019, the ready-made garment industry in the Asia and the Pacific region employed an estimated 65 million garment sector workers or 75 percent of all readymade garment workers worldwide (ILO 2020). More than half of these workers are in China and Vietnam, two countries where wages are rising. To get a sense for technological changes in the readymade garment industry, Figure 4 also examines trends in capital-labor ratios in the apparel industry in Ethiopia, Vietnam and the United States; we include the US as a benchmark for where the industry might be headed.

In 2017, capital-labor ratios in the apparel industries of Ethiopia and Vietnam are a fraction of what they are for total manufacturing. (Contrary to popular perception, this pattern does not hold for textiles or leather.) Moreover, in Ethiopia and Vietnam, capital-labor ratios in apparel are not rising the way they are in total manufacturing. There is an uptick in apparel manufacturing’s capital intensity in Vietnam between 2015 and 2017 but the overall level remains at less than one-third of total manufacturing’s capital intensity. In the US economy, although capital intensity in apparel far exceeds that in Ethiopia or Vietnam (as shown by the difference in the vertical axis), capital-labor ratios increased substantially in the apparel industry between 2000 and 2010 but then leveled off. While the apparel products produced in the United States are likely not comparable to those produced in Ethiopia and Vietnam, the leveling of capital intensity in the apparel industry is indicative of a stall in technological change in this industry. One reason for this appears to stem from the difficulty associated

> 7 Authors’ calculations based on firm-level data for Ethiopia, Tanzania and Vietnam, the NBER productivity database and KLEMS.

23
reveals the respondents’ ethnic background. It is important to note, however, that we do not assume that being a native speaker of any language signifies advantage or disadvantage; we simply use language as a proxy to identify the different ethnic groups in each country.

Five censuses in the IPUMS-I database include this information, in slightly different forms: “mother tongue” in Bolivia (2001), Peru (2007) and Sierra Leone (2004); “indigenouslanguage speaker” in Mexico (2010); and “primary language” in Senegal (2002).<sup>17</sup> Focusing on these countries, we use language to stratify respondents in each country into two groups. The native speakers of a minority language are categorized as the “minority group”, whereas the native speakers of the official and/or the most common language are categorized as the “reference group” (Table 1).<sup>18</sup> What we refer to as the “minority group” does not necessarily represent the groups officially classified as minorities in the sampled countries, nor does it imply that these groups make up less than a certain percentage of their country’s population.<sup>19</sup> In Bolivia, individuals who identify Quechua, Aymara or another indigenous language as their mother tongue are classified as members of the minority group. Similarly, the minority group in Peru includes individuals who identify Quechua as their mother tongue.<sup>20</sup> In Mexico, the census does not explicitly state the respondents’ mother tongue, but it identifies whether or not they speak an indigenous language. We interpret this information as an indicator of indigenous mother tongue and use it as a proxy for ethnic background.<sup>21</sup> In all three of these

> 17 Although several other country censuses in the IPUMS-I database included linguistic variables, these countries were excluded from the final sample because country specialists indicated that language is not a good proxy for ethnicity in their specific contexts.

18 The identification of the reference and minority languages as proxies for ethnicity was supplemented through consultations with specialists in each country.

19 In Bolivia, for example, the official classification of minorities cuts across multiple languages (including Spanish and indigenous languages) as well as ancestral roots.

20 In Peru (2007), we limit the analysis to individuals who speak Spanish and Quechua as their mother tongue, since Quechua is by far the largest indigenous group in the country. However, using a broader “indigenous mother tongue” category, which encompasses all native languages including Quechua, does not change the results.

21 Admittedly, this distinction may mistakenly classify those members of the reference group who learned an indigenous language, but who otherwise have no ethnic relation to indigenous groups in Mexico, as a member of the minority group (e.g., those who speak an indigenous language for professional reasons or through bilingual education). Although our data is not conducive to identifying these cases, we expect these situations to comprise a 10
region to the dollar. However, as domestic markets in the GCC region develop, this trend may change in the medium term. Other than Malaysia and the GCC region, sukuk are expanding as a financing tool in markets such as Indonesia, Pakistan, and Turkey. Recently, a number of traditionally conventional markets, such as Hong Kong, China; Luxembourg; South Africa; and the United Kingdom have issued their first sukuk. Other markets may follow suit.

Figure 1. Sukuk Issuance <!-- Start of picture text --> 160 147<br> 140 124<br> 120<br>93<br> 100<br> 80<br>56<br> 60 47<br>37<br> 40<br> 20<br> - 2008 2009 2010 2011 2012 2013<br>Malaysia 27 35 45 67 110 86<br>GCC countries 8 8 6 20 25 26<br>Rest of the World 1 4 5 6 12 12<br>$ billion<br><!-- End of picture text --> _Source:_ Islamic Finance Information Service (http://www.islamicfinanceservice.com) as of April 15, 2014. _Note:_ GCC = Gulf Cooperation Council.

Figure 2. Sukuk Market by Country of Obligor <!-- Start of picture text --> Rest of the<br>Indonesia Pakistan Turkey World<br>5% 1% 2% 2%<br>GCC<br>countries<br>Kuwait<br>1%<br>Bahrain<br>2%<br>Qatar<br>4%<br>United Arab<br>Emirates Malaysia<br>8% 61%<br>Saudi Arabia<br>14%<br><!-- End of picture text --> _Source:_ Islamic Finance Information Service database (http://www.islamicfinanceservice.com) as of April 15, 2014.

_Note:_ GCC = Gulf Cooperation Council.

4