rafmacalaba/datause-encoder-data huggingface dataset:
manufacturing goods are subject to zero‐MFN rates, less than a quarter of agricultural imports benefit from duty free treatment. At the same time, nearly 40 percent of agricultural imports are subject to MFN rates over 10 percent (see Figure 5), compared to less than one‐tenth of manufacturing imports.

Figure 5: MFN rates vary significantly across the three economic sectors (value of imports) <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>MFN = 0 MFN < 5% MFN [5%,10%] MFN (10%,15%] MFN > 15% Missing<br>Agriculture Natural Resources Manufacturing<br>Share of 2015 sector imports<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database Also, a higher share of tariff lines is subject to higher MFN rates in agriculture, compared to manufacturing and natural resources (see Figure 6). Nearly two‐fifths of agricultural tariff lines and about one‐fifth of manufacturing tariff lines are subject to MFN rates over 15 percent.

Figure 6: MFN rates vary significantly across the three economic sectors (tariff lines) <!-- Start of picture text --> 45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>MFN = 0 MFN < 5% MFN [5%,10%] MFN (10%,15%] MFN > 15% Missing<br>Agriculture Natural Resources Manufacturing<br>Share of 2015 sector tariff lines<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database 8
having been established by the current owner. Some 83 % of managers cited the desire to operate in one’s state of birth was one of the major reasons for the choice of the current plant location.

Depending on its line of business, the typical plant would have been in operation at the time of the survey for between 12 years (leather goods) and 34 years (fabricated metals). During this interval it would have grown to several times of its start up size in terms of employment-ranging from just under twice the initial size in textiles to more than four times in drugs and pharmaceuticals. In spite of the seemingly high average age there seems to have been substantial turnover in the population from which our sample has been drawn. An indication of this is that a quarter of the full sample and nearly a third of those in the garments industry have been in business only for five years or less.

Plant capital intensity in terms of the book value of equipment per worker ranges from a little over US $ 2000 in fabricated metals to about US $ 5600 in the drugs and pharmaceuticals industry. This corresponds to a range in annual value added per worker from US $ 2737 in fabricated metals, to US$ 7492 in drugs and pharmaceuticals.<sup>13</sup> We define value added per worker as annual sales revenue less the cost of materials and energy. The average worker earned US $ 1087 over the full sample at the average official exchange rate of the rupee for 1999, but this also varied considerably by industry from lows of under US $ 800 in the leather goods industry and in fabricated metals to a high of US $ 1578 in machine tools.

# **(a)   Business environment indices: labor regulation**

The World Bank Investment Climate Survey data on India includes observations on a wide range of indicators on what we refer here as the business environment. These indicators can be classified into four broad groups: a) macro-economic stabilization; b) quality of governance including the performance of law and contract enforcement institutions; c) regulation of trade, industry, and factor markets; and d) quality of physical

> 13 All monetary figures relating to the survey are expressed in US dollars at the annual average official exchange rate of the Indian rupee for year 1999.

9
# **5. CONCLUSION**

This paper poses the following question: can recurrent productivity shocks exacerbate failures in rural credit markets? We develop a stylized theoretical model that suggests a positive answer to this question, and the predictions of which, are also confirmed with data from a framed field experiment simulating a repeated input credit market. We find consistent evidence from descriptive, econometric, and non parametric analysis that productivity shocks such as weather-induced rainfall shocks can affect the repayment behavior of borrowers in rural credit markets, thereby increasing the risks of default and leading to further failures in markets. The findings are fairly robust to the existence of credit information exchange mechanisms (such as credit bureaus) increasingly promoted as a pathway towards broader financial inclusion. The implications of these findings are important as climate change and its negative effects are increasingly undermining agricultural production, especially in poor communities with low resilience. Specifically, our results suggest that climate change could also significantly undermine efforts to develop inclusive financial solutions such as credit markets for poor farmers. The findings offer additional arguments for the importance of insurance products tailored to the needs and conditions of the rural poor.

Though this experiment was conducted in a very specific context in Nigeria, the results suggest a relevant consideration for researchers and development practitioners focused on inclusive financial mechanisms to reach smallholder farmers. Further studies using larger and more representative samples, in various other contexts, to better understand the linkages between produc40
**Table 9: Firm Investment amount and the Intensity and Purpose of Mobile Money Use**

|**Dependent variable: Log of Purchase of Fixed Assets**<br>**(amount in USD)**||**O**|**LS**||
|---|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|% of total labor cost paid using mobile money (0 if not<br>adopted or no payment)|0.045***||||
||(0.013)||||
|% of raw material cost paid using mobile money (0 if not<br>adopted or no payment)||0.065***<br>(0.020)|||
|% of utility bill paid using mobile money (0 if not adopted<br>or no payment)|||0.030***||
||||(0.012)||
|% of annual sales from customer payments using mobile<br>money (0 if not adopted or||||0.055***<br>(0.016)|
|Log of size|1.150***|1.180***|1.177***|1.251***|
||(0.276)|(0.280)|(0.283)|(0.272)|
|Top manager experience in sector (years)|-0.032|-0.035|-0.029|-0.034|
||(0.022)|(0.022)|(0.022)|(0.022)|
|Log of sales per worker (outliers removed)|0.064|0.151|0.053|0.132|
||(0.125)|(0.135)|(0.136)|(0.140)|
|Log of age of firm|-0.418|-0.396|-0.385|-0.288|
||(0.410)|(0.425)|(0.427)|(0.427)|
|Firm is part of a larger firm Y:1 N:0|-0.263|-0.406|-0.525|-0.384|
||(0.893)|(0.900)|(0.871)|(0.869)|
|Exports 10% or more of sales Y:1 N:0|-0.029|0.122|0.144|-0.020|
||(0.825)|(0.857)|(0.858)|(0.819)|
|Foreign ownership Y:1 N:0|0.332|0.525|0.649|0.561|
||(0.859)|(0.931)|(0.961)|(0.933)|
|Firm with unfavorable credit record Y:1 N:0|-0.041|-0.183|-0.067|-0.111|
||(0.490)|(0.466)|(0.502)|(0.467)|
|Establishment has a line of credit or loan at this time Y:1<br>N:0|1.487***|1.543***|1.705***|1.776***|
||(0.545)|(0.562)|(0.555)|(0.549)|
|Firms use email to interact with clients/suppliers Y:1 N:0|0.765|0.916*|0.963*|1.027*|
||(0.508)|(0.512)|(0.533)|(0.541)|
|Establishment has checking or savings account at this time<br>Y:1 N:0|-0.271|-0.348|-0.171|-0.617|
||(0.609)|(0.535)|(0.633)|(0.523)|
|Log of mean distance - bank (km)|-2.103***|-2.268***|-2.361***|-2.362***|
||(0.672)|(0.691)|(0.688)|(0.670)|

32
|**Table 5.**Heterogeneous_Pantawid_program impacts on schooling|and work by ho|usehold compos|ition|||||
|---|---|---|---|---|---|---|---|
||Education|Work past|<br>12 months|Mut|uallyexclus|<br>ive combina|<br>tions|
||||Work for|||In school|Neither in|
||Attends<br>regularly|Any work|pay, outside<br>own houshold|In school<br>only|In work<br>only|and in<br>work|school nor<br>in work|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Panel A: Heterogeneous effects on core sample (10-14), by older||||||||
|siblings||||||||
|No siblings aged 15-17|0.040|0.033|0.019|-0.004|-0.015|0.048|-0.029|
||(0.033)|(0.042)|(0.037)|(0.043)|(0.018)|(0.037)|(0.020)|
|One or more siblings aged 15-17|0.134***|0.067**|0.085***|-0.030|0.003|0.063*|-0.037|
||(0.036)|(0.033)|(0.032)|(0.040)|(0.014)|(0.033)|(0.024)|
|Number of observations:||||||||
|P-value F-test (impact no siblings = impact siblings)|0.005|0.617|0.151|0.716|0.491|0.409|0.246|
|Number of observations|1,193|1,212|1,212|1,212|1,212|1,212|1,212|
|Mean in control group, no siblings|0.758|0.211|0.091|0.712|0.040|0.171|0.077|
|Mean in treatment group, siblings|0.888|0.247|0.148|0.707|0.034|0.213|0.046|
|Mean in control group, one or more siblings|0.841|0.190|0.091|0.743|0.036|0.154|0.067|
|Mean in treatment group, one or more siblings|0.868|0.231|0.122|0.732|0.020|0.210|0.037|
|Panel B: Heterogeneous effects on core sample (10-14), by||||||||
|eligible siblings||||||||
|No enrolled siblings aged 6-14|0.264*|-0.075|-0.077|0.275**|-0.154|0.079|-0.200*|
||(0.135)|(0.103)|(0.092)|(0.128)|(0.098)|(0.063)|(0.117)|
|One or two enrolled siblings aged 6-14|0.066**|0.068**|0.071**|-0.065*|0.003|0.064**|-0.003|
||(0.031)|(0.033)|(0.028)|(0.033)|(0.014)|(0.032)|(0.017)|
|Three or more enrolled siblings aged 6-14|0.044*|0.027|0.051*|-0.016|0.014|0.013|-0.010|
||(0.026)|(0.047)|(0.031)|(0.046)|(0.009)|(0.047)|(0.011)|
|Number of observations:||||||||
|P-value F-test (impact 0 siblings = impact 1 or 2 siblings)|0.147|0.187|0.117|0.011|0.115|0.841|0.094|
|P-value F-test (impact 0 siblings = impact 3+ siblings)|0.107|0.349|0.196|0.031|0.094|0.399|0.104|
|P-value F-test (impact 1 or 2 siblings = 3+ impact siblings)|0.568|0.450|0.626|0.367|0.501|0.339|0.709|
|Number of observations|1,264|1,264|1,264|1,264|1,264|1,264|1,264|
|Mean in control group, no enrolled siblings|0.296|0.280|0.200|0.260|0.240|0.040|0.460|
|Mean in treatment group, enrolled siblings|0.571|0.217|0.130|0.522|0.087|0.130|0.261|
|Mean in control group, one or two enrolled siblings|0.801|0.193|0.084|0.749|0.032|0.161|0.058|
|Mean in treatment group, one or two enrolled siblings|0.870|0.239|0.144|0.706|0.034|0.206|0.055|
|Mean in control group, three or more enrolled siblings|0.881|0.196|0.078|0.787|0.004|0.191|0.017|
|Mean in treatment group, three or more enrolled siblings|0.917|0.247|0.139|0.743|0.017|0.229|0.010|

Note. Estimates of heterogeneous program impact on work by children aged 10 to 14 from eligible households. Only municipality and child age fixed effects are included. Standard errors are clustered at the village level. *** p<0.01, ** p<0.05, * p<0.1 53
**Table 2.6 Share of total bank loans outstanding used for infrastructure financing**

||Infrastructure loans as<br>% total bank loans /a|Infrastructure loans as<br>% total bank loans /a|Total outstanding loans to<br>infrastructure sectors (US$ millions) /b|
|---|---|---|---|
|Country|2005|2006|2006|
|Benin|18|12|123.6|
|Burkina Faso|8|10|84.5|
|Cameroon|—|—|—|
|Cape Verde|24|45|107.6|
|Chad|0|0|0.0|
|Congo, Dem. Rep.|8|—|5.8|
|Côte d’Ivoire|16|15|334.7|
|Ethiopia|6|7|247.9|
|Ghana|6|8|177.9|
|Kenya|7|9|574.5|
|Lesotho|2|43|20.9|
|Madagascar|3|3|67.6|
|Malawi|5|9|17.4|
|Mozambique|6|6|60.7|
|Namibia|3|4|117.1|
|Niger|20|20|66.5|
|Nigeria|11|12|2,443.6|
|Rwanda|10|8|25.6|
|Senegal|12|12|286.0|
|South Africa|2|3|6,274.9|
|Sudan|8|9|5.2|
|Tanzania|8|—|93.0|
|Uganda|7|8|74.9|
|Zambia|—|7|72.9|
|Chile|10|11|7,213|
|Malaysia|4|3|5,023.8|

Source: National and regional central banks and finance ministries.

a. Data for 2005–06 or most recently available consecutive two years.

b. Data for end-2006 with the exception of: Democratic Republic of the Congo (end-2003), Madagascar (end-2004), Namibia (end-June 2005), Tanzania (end-2005), Ghana (June 2006), South Africa (end-September 2006), Zambia (May 2007), and Chad and Malaysia (March 2006). — = Not available.

Differences in the categorization of economic sectors by central banks in several countries make it difficult to rank specific infrastructure sectors by receipts of local bank lending. Despite the limited local bank lending data by infrastructure sector, certain trends can be identified. The “transport, communication, and storage” sector, although quite broad, can be identified as the recipient of the largest amount of total local bank loans outstanding in 2006 (or the most recent year) for the 23 African focus countries that compile and report these data (table 2.7). The category accounted for just over $8.3 billion, or just under three-quarters of the $11.3 billion in total loans outstanding for infrastructure purposes. Of this amount, $232.5 million was allocated to the narrower “transport” category (by Madagascar, 18
7

# **A.   Empirical Strategy**

To empirically test the relationship between exchange rates and exports, we exploit the variation across countries and over time to capture the marginal impact of real exchange rate changes on growth of export volumes, conditional on time/country fixed effects and other controls. The regression framework is based on the approach in the cross-country literature discussed in the Introduction, notably Freund and Pierola (2012) and Eichengreen and Gupta (2013).

We use a panel framework, where annual real export growth is expressed as a function of annual real exchange rate growth. We obtain export data from various sources<sup>3</sup> (total exports, goods plus services, from OECD-WTO TiVA, merchandise and manufacturing exports from UNCOMTRADE) and data on Real Effective Exchange Rates from the IMF Information Notice System (INS).<sup>4</sup> To control for country specific differences, we include cross section specific fixed effects. Similarly, we include time specific fixed effects to control for global demand and any other time specific factor that might influence the results (e.g. changes in trade restrictiveness, the slowdown in global trade). The regression specification is as follows: where  _Expit_ denotes real (total, merchandise or manufacturing) export growth of country _i_ at time _t_ ,  _REERit_ is the growth in the real effective exchange rate for country _i_ at time _t_ ,<sup></sup> _i_<sup>and</sup> _t_<sup>are country</sup> and time fixed effects, respectively. The coefficient _β_ captures the effect of a depreciation on export growth. We also include in the regression a number of controls, _Controlit_ , commonly used in the literature. Specifically, we control for initial conditions by including lagged GDP and use exportweighted GDP of the country’s main trading partners which gives a sense of the demand for the

> 3 In order to calculate annual real exports growth time series, we transform nominal USD exports annual time series to local currency using the nominal exchange rates and deflate by Consumers Price Indices.

> 4 For countries where real effective exchange rate is not available in INS, we use data from JP Morgan.
We estimate excess mortality rates using all registered deaths since 2015 that are provided by the government’s office of birth and death registry. Using the pre-pandemic number of deaths for 31 provinces over 16 quarters, we predict the number of expected deaths during the pandemic. Excess deaths are the difference between the linear prediction of deaths and their actual number during the COVID-19 quarters. We do not distinguish COVID-19 mortality risks by gender or age because the province-level excess death rates for men and women are nearly perfectly correlated during the past 8 years, and we believe that mortality risks of all ages are relevant for household decisions.

The justification for measuring the pandemic shock to labor markets with excess deaths is that, even in the absence of lockdowns, families weigh the risks of infection against the benefits of market work or sending their children to school. Whereas some workers can work from home and stay employed, some may have to quit their jobs or leave the labor force. For example, low-skilled service workers who cannot work remotely and for whom the benefits of staying at home exceed going to work and risking infection, may leave employment. This is particularly important for women with school-age children, who, given the Islamic Republic of Iran’s patriarchal gender norms, are most likely to stay home to take care of children and deal with the extra household chores imposed by the pandemic. We assume that the excess death rate is a good measure of the perceived risks that affect family decisions regarding going to work and sending their children to school. This approach helps us pick up additional exogenous variation in the intensity of the pandemic shock across provinces and over time, which is not captured if exposure to the pandemic is defined by either the year (or quarter) and province dummy variables alone.

5
# **1.4  Unilateral Trade Liberalization**

The standard arguments in favor of trade liberalization are that it expands the small domestic market, provides access to foreign direct investment, creates greater competition, facilitates technology transfer, creates marketing networks, and provides much-needed managerial and technical skills. It is also argued that these changes lead to higher economic growth and reduced poverty.

In Bangladesh, trade liberalization programs and associated economic reforms during the 1980s and the 1990s significantly liberalized its external trade and foreign exchange regimes. Specific measures included the following. Import procedures were simplified and the number of tariff bands was dramatically reduced. In 1992 the highest customs duty rate was 350 percent. It was reduced to 37.5 percent in 2000. The unweighted average tariff rate declined from 88.6 percent in 1991 to 22.2 percent in 1999 while the import-weighted average tariff rate declined from 42.1 percent to 14.7 percent over the same period (World Bank, 1999; WTO, 2000). It is, however, important to note the presence of a number of para-tariffs – infrastructure development surcharge, supplementary duties, regulatory duties, VAT exemptions for specified domestic products, and license fees – which can substantially increase the level of protection. For example, including these para-tariffs raised the average tariff rate from 22.2 percent to 32 percent in 1999.

There has also been a significant reduction of the number of commodities under quantitative restrictions (QRs). In 1987 the number of commodities under the four-digit code subject to QRs was 550, which declined to 124 under the Import Policy Order (IPO) of 19972002 of which only 28 were trade related. In IPO 2003-06, trade-related QRs have all been abolished (barring 3 for which WTO waivers exist). In addition, there have also been moves towards a more market-determined exchange rate regime. The Taka (the domestic currency) has 4
<!-- Start of picture text --> 1<br>HE Atarky<br>HE In-Kind Transfer<br>(HG vsP (Asset Replacement)<br>0.8 +) [7] VSP (Insurance Subsidy)<br>0.6<br>0.4<br>020 |<br>25 yrs 50 yrs 75 yrs 100 yrs<br><!-- End of picture text -->

<!-- Start of picture text --> 0.25<br>HR Atarky<br>HE In-Kind Transfer<br>(HG vsP (Asset Replacement)<br>0.2 +) [7] VSP (Insurance Subsidy)<br>0.15<br>01<br>0.050 i i i i<br>25 yrs 50 yrs 75 yrs 100 yrs<br><!-- End of picture text -->
Mazumder (2008) and (2011)). )). Let be the years of schooling of sibling j in family i. It can be expressed as: where is a family component which is common to all siblings in family i and is the individual specific component for sibling j which captures j‘s deviation from the family component. Assuming these two components are independent, the variance of can be expressed as the sum of variances of the family and individual components as: The sibling correlation in education then can be expressed as: The sibling correlation depicts the share of variance of years of education that can be attributed to common family background effects. Thus sibling correlation can be thought of as a summary statistic measuring the importance of common family and community effects which includes anything and everything shared by the siblings. It is useful to distinguish among different types of family and community factors that are commonly experienced by siblings. The family level variables include observable factors such as parental education and occupation as well as unobserved factors such as common genetic traits, parental aspirations, child rearing ability and style, cultural inheritances and interaction among siblings. The community effects include factors such as school availability and quality as well as peer effects within the neighborhood. Though sibling correlation captures most of the family background influences, it does not capture all of them. For instance, genetic traits not shared by siblings, differential treatment of siblings and time dependent changes in family and neighborhood factors will show up in the individual component of outcome variance, though they might be part of family background. As a result, the estimate of sibling correlations can be taken as a _lower bound estimate_ of the total influence of the common family background on children‘s education outcome (for a discussion on this point, see Bjorklund and Salvanes (2010)).

# **Intergenerational Correlation (IGC)**

It is instructive to look at the difference between sibling correlations and intergenerational correlations as measures of intergenerational persistence in economic outcomes. The standard 8
<!-- Start of picture text --> Face-to-face interview, traditional paper-and-pencil<br>Respondent Enumerator Data entry<br>Paper Database<br>Face-to-face interview using phone, laptop or tablet computer with<br>electronic form<br>Respondent Enumerator ZA GPRS<br>Server/ Database<br>Direct responses to phone using SMS, USSD, IVR or electronic form<br>Server/ Database<br>Interview via phone with enumerator<br>Server/ Database<br><!-- End of picture text -->
**_Appendix Figure D3. Apparel export growth (year-to-year) decomposition for Kenya_**

<!-- Start of picture text --> Kenya<br>23.0<br>20 17.1 14.8 17.515.8 14.116.8<br>7.7 3.5 6.5 8.6 8.3 5.2 5.5 3.4 3.9 7.6 3.9 5.0<br>0.2 0.0 0.3 0.1 0.2 0.4<br>0<br>-0.1-0.2 -2.5 -0.0 -0.7 -1.7 -2.8 -0.1 -3.6 -0.1 -1.4<br>-7.6<br>-10.0 -9.6-9.7<br>-11.4<br>-13.8<br>-20<br>-26.0<br>-36.0<br>-40<br>Agg. Export Growth Continuing Exporters New Exporters Exiting Exporters<br>2006--2007 2007--2008 2008--2009 2009--2010 2010--2011 2011--2012 2012--2013 2013--2014 2014--2015 2015--2016 2016--2017<br>Export Growth<br><!-- End of picture text -->

**_Appendix Figure D4. Apparel export growth (year-to-year) decomposition for Madagascar_**

<!-- Start of picture text --> Madagascar<br>53.8<br>50 47.0<br>34.326.1 32.2 30.735.6 37.229.4 30.828.8<br>9.5 9.1 10.9 9.7 7.0<br>0.1 0.1 0.3 0.6 0.1 2.6 0.0 0.7 2.2<br>0<br>-1.3 -2.8 -2.4 -2.5 -5.7 -1.9 -0.2 -0.1<br>-25.4 -28.5<br>-50<br>-64.5-61.9 -60.9<br>-100<br>-108.8<br>-134.1<br>-150<br>Agg. Export Growth Continuing Exporters New Exporters Exiting Exporters<br>2007--2008 2008--2009 2009--2010 2010--2011 2011--2012 2012--2013 2013--2014 2014--2015 2015--2016 2016--2017<br>Export Growth<br><!-- End of picture text --> 13